From fb21251157c627067ba273615c1e32fce2beea92 Mon Sep 17 00:00:00 2001 From: Zach Dwiel Date: Fri, 7 Sep 2018 11:44:28 -0400 Subject: [PATCH 001/162] add horizontal scaling document --- docs_raw/docs/design/horizontal_scaling.md | 148 +++++++++++++++++++++ 1 file changed, 148 insertions(+) create mode 100644 docs_raw/docs/design/horizontal_scaling.md diff --git a/docs_raw/docs/design/horizontal_scaling.md b/docs_raw/docs/design/horizontal_scaling.md new file mode 100644 index 0000000..31e8bf6 --- /dev/null +++ b/docs_raw/docs/design/horizontal_scaling.md @@ -0,0 +1,148 @@ +# Scaling out rollout workers + +This document contains some options for how we could implement horizontal scaling of rollout workers in coach, though most details are not specific to coach. A few options are laid out, my current suggestion would be to start with Option 1, and move on to Option 1a or Option 1b as required. + +## Off Policy Algorithms + +### Option 1 - master polls file system + +- one master process samples memories and updates the policy +- many worker processes execute rollouts +- coordinate using a single shared networked file system: nfs, ceph, dat, s3fs, etc. +- policy sync communication method: + - master process occasionally writes policy to shared file system + - worker processes occasionally read policy from shared file system + - prevent workers from reading a policy which has not been completely written to disk using either: + - redis lock + - write to temporary files and then rename +- rollout memories: + - sync communication method: + - worker processes write rollout memories as they are generated to shared filesystem + - master process occasionally reads rollout memories from shared file system + - master process must be resilient to corrupted or incompletely written memories + - sampling method: + - master process keeps all rollouts in memory utilizing existing coach memory classes +- control flow: + - master: + - run training updates interleaved with loading of any newly available rollouts in memory + - periodically write policy to disk + - workers: + - periodically read policy from disk + - evaluate rollouts and write them to disk +- ops: + - kubernetes yaml, kml, docker compose, etc + - a default shared file system can be provided, while allowing the user to specify something else if desired + - a default method of launching the workers and master (in kubernetes, gce, aws, etc) can be provided + +#### Pros + +- very simple to implement, infrastructure already available in ai-lab-kubernetes +- fast enough for proof of concept and iteration of interface design +- rollout memories are durable and can be easily reused in later off policy training +- if designed properly, there is a clear path towards: + - decreasing latency using in-memory store (option 1a/b) + - increasing rollout memory size using distributed sampling methods (option 1c) + +#### Cons + +- file system interface incurs additional latency. rollout memories must be written to disk, and later read from disk, instead of going directly from memory to memory. +- will require modifying standard control flow. there will be an impact on algorithms which expect particular training regimens. Specifically, algorithms which are sensitive to the number of update steps between target/online network updates +- will not be particularly efficient in strictly on policy algorithms where each rollout must use the most recent policy available + +### Option 1a - master polls (redis) list + +- instead of using a file system as in Option 1, redis lists can be used +- policy is stored as a single key/value pair (locking no longer necessary) +- rollout memory communication: + - workers: redis list push + - master: redis list len, redis list range +- note: many databases are interchangeable with redis protocol: google memorystore, aws elasticache, etc. +- note: many databases can implement this interface with minimal glue: SQL, any objectstore, etc. + +#### Pros + +- lower latency than disk since it is all in memory +- clear path toward scaling to large number of workers +- no concern about reading partially written rollouts +- no synchronization or additional threads necessary, though an additional thread would be helpful for concurrent reads from redis and training +- will be slightly more efficient in the case of strictly on policy algorithms + +#### Cons + +- more complex to set up, especially if you are concerned about rollout memory durability + +### Option 1b - master subscribes to (redis) pub sub + +- instead of using a file system as in Option 1, redis pub sub can be used +- policy is stored as a single key/value pair (locking no longer necessary) +- rollout memory communication: + - workers: redis publish + - master: redis subscribe +- no synchronization necessary, however an additional thread would be necessary? + - it looks like the python client might handle this already, would need further investigation +- note: many possible pub sub systems could be used with different characteristics under specific contexts: kafka, google pub/sub, aws kinesis, etc + +#### Pros + +- lower latency than disk since it is all in memory +- clear path toward scaling to large number of workers +- no concern about reading partially written rollouts +- will be slightly more efficient in the case of strictly on policy algorithms + +#### Cons + +- more complex to set up then shared file system +- on its own, does not persist worker rollouts for future off policy training + +### Option 1c - distributed rollout memory sampling + +- if rollout memories do not fit in memory of a single machine, a distributed storage and sampling method would be necessary +- for example: + - rollout memory store: redis set add + - rollout memory sample: redis set randmember + +#### Pros + +- capable of taking advantage of rollout memory larger than the available memory of a single machine +- reduce resource constraints on training machine + +#### Cons + +- distributed versions of each memory type/sampling method need to be custom built +- off-the-shelf implementations may not be available for complex memory types/sampling methods + +### Option 2 - master listens to workers + +- rollout memories: + - workers send memories directly to master via: mpi, 0mq, etc + - master policy thread listens for new memories and stores them in shared memory +- policy sync communication memory: + - master policy occasionally sends policies directly to workers via: mpi, 0mq, etc + - master and workers must synchronize so that all workers are listening when the master is ready to send a new policy + +#### Pros + +- lower latency than option 1 (for a small number of workers) +- will potentially be the optimal choice in the case of strictly on policy algorithms with relatively small number of worker nodes (small enough that more complex communication typologies would be necessary: rings, p2p, etc) + +#### Cons + +- much less robust and more difficult to debug requiring lots of synchronization +- much more difficult to be resiliency worker failure +- more custom communication/synchronization code +- as the number of workers scale up, a larger and larger fraction of time will be spent waiting and synchronizing + +### Option 3 - Ray + +#### Pros + +- Ray would allow us to easily convert our current algorithms to distributed versions, with minimal change to our code. + +#### Cons + +- performance from naïve/simple use would be very similar to Option 2 +- nontrivial to replace with a higher performance system if desired. Additional performance will require significant code changes. + +## On Policy Algorithms + +TODO From cccfe88f9b390ffdefc0826a95f273ba46a8c00f Mon Sep 17 00:00:00 2001 From: Zach Dwiel Date: Fri, 7 Sep 2018 14:50:50 -0400 Subject: [PATCH 002/162] remove unused method: update_last_transition_info --- .../episodic/episodic_experience_replay.py | 17 ----------------- .../memories/non_episodic/experience_replay.py | 14 -------------- 2 files changed, 31 deletions(-) diff --git a/rl_coach/memories/episodic/episodic_experience_replay.py b/rl_coach/memories/episodic/episodic_experience_replay.py index 2c86c5f..79f9aaf 100644 --- a/rl_coach/memories/episodic/episodic_experience_replay.py +++ b/rl_coach/memories/episodic/episodic_experience_replay.py @@ -273,23 +273,6 @@ class EpisodicExperienceReplay(Memory): """ self.remove_episode(episode_index) - def update_last_transition_info(self, info: Dict[str, Any]) -> None: - """ - Update the info of the last transition stored in the memory - :param info: the new info to append to the existing info - :return: None - """ - self.reader_writer_lock.lock_writing_and_reading() - - episode = self._buffer[-1] - if episode.length() == 0: - if len(self._buffer) < 2: - return - episode = self._buffer[-2] - episode.transitions[-1].info.update(info) - - self.reader_writer_lock.release_writing_and_reading() - def clean(self) -> None: """ Clean the memory by removing all the episodes diff --git a/rl_coach/memories/non_episodic/experience_replay.py b/rl_coach/memories/non_episodic/experience_replay.py index a73e44a..798854c 100644 --- a/rl_coach/memories/non_episodic/experience_replay.py +++ b/rl_coach/memories/non_episodic/experience_replay.py @@ -182,20 +182,6 @@ class ExperienceReplay(Memory): """ self.remove_transition(transition_index, lock) - def update_last_transition_info(self, info: Dict[str, Any]) -> None: - """ - Update the info of the last transition stored in the memory - :param info: the new info to append to the existing info - :return: None - """ - self.reader_writer_lock.lock_writing_and_reading() - - if self.length() == 0: - raise ValueError("There are no transition in the replay buffer") - self.transitions[-1].info.update(info) - - self.reader_writer_lock.release_writing_and_reading() - def clean(self, lock: bool=True) -> None: """ Clean the memory by removing all the episodes From 9f1f9e5ab4b080e6af2f7ce02b07516e71b6cac5 Mon Sep 17 00:00:00 2001 From: Zach Dwiel Date: Fri, 7 Sep 2018 14:56:43 -0400 Subject: [PATCH 003/162] replace ExperienceReplay._num_transitions with len(ExperienceReplay.transitions) --- rl_coach/memories/non_episodic/experience_replay.py | 7 ++----- 1 file changed, 2 insertions(+), 5 deletions(-) diff --git a/rl_coach/memories/non_episodic/experience_replay.py b/rl_coach/memories/non_episodic/experience_replay.py index 798854c..4887e49 100644 --- a/rl_coach/memories/non_episodic/experience_replay.py +++ b/rl_coach/memories/non_episodic/experience_replay.py @@ -51,7 +51,6 @@ class ExperienceReplay(Memory): if max_size[0] != MemoryGranularity.Transitions: raise ValueError("Experience replay size can only be configured in terms of transitions") self.transitions = [] - self._num_transitions = 0 self.allow_duplicates_in_batch_sampling = allow_duplicates_in_batch_sampling self.reader_writer_lock = ReaderWriterLock() @@ -66,7 +65,7 @@ class ExperienceReplay(Memory): """ Get the number of transitions in the ER """ - return self._num_transitions + return len(self.transitions) def sample(self, size: int) -> List[Transition]: """ @@ -119,7 +118,6 @@ class ExperienceReplay(Memory): if lock: self.reader_writer_lock.lock_writing_and_reading() - self._num_transitions += 1 self.transitions.append(transition) self._enforce_max_length() @@ -149,6 +147,7 @@ class ExperienceReplay(Memory): def remove_transition(self, transition_index: int, lock: bool=True) -> None: """ Remove the transition in the given index. + This does not remove the transition from the segment trees! it is just used to remove the transition from the transitions list :param transition_index: the index of the transition to remove @@ -158,7 +157,6 @@ class ExperienceReplay(Memory): self.reader_writer_lock.lock_writing_and_reading() if self.num_transitions() > transition_index: - self._num_transitions -= 1 del self.transitions[transition_index] if lock: @@ -191,7 +189,6 @@ class ExperienceReplay(Memory): self.reader_writer_lock.lock_writing_and_reading() self.transitions = [] - self._num_transitions = 0 if lock: self.reader_writer_lock.release_writing_and_reading() From dc77c54ad94161b61b7ce6aae7605e5d95fd68ba Mon Sep 17 00:00:00 2001 From: Zach Dwiel Date: Fri, 7 Sep 2018 15:40:08 -0400 Subject: [PATCH 004/162] add to gitignore --- .gitignore | 1 + 1 file changed, 1 insertion(+) diff --git a/.gitignore b/.gitignore index 5b6c668..12c6990 100644 --- a/.gitignore +++ b/.gitignore @@ -25,3 +25,4 @@ datasets .pytest_cache core trace_test* +.DS_Store From a1295d16b35cdee4ef6cf5af9dd1d3a17c1a88c8 Mon Sep 17 00:00:00 2001 From: Zach Dwiel Date: Fri, 7 Sep 2018 15:40:45 -0400 Subject: [PATCH 005/162] first pass that transition collection interface --- .../non_episodic/transition_collection.py | 35 +++++++++++++++++++ 1 file changed, 35 insertions(+) create mode 100644 rl_coach/memories/non_episodic/transition_collection.py diff --git a/rl_coach/memories/non_episodic/transition_collection.py b/rl_coach/memories/non_episodic/transition_collection.py new file mode 100644 index 0000000..d721539 --- /dev/null +++ b/rl_coach/memories/non_episodic/transition_collection.py @@ -0,0 +1,35 @@ +from rl_coach.core_types import Transition + + +class TransitionCollection(object): + """ + Simple python implementation of transitions collection non-episodic memories + are constructed on top of. + """ + def __init__(self): + super(TransitionCollection, self).__init__() + + def append(self, transition): + pass + + def extend(self, transitions): + for transition in transitions: + self.append(transition) + + def __len__(self): + pass + + def __del__(self, range: slice): + # NOTE: the only slice used is the form: slice(None, n) + # NOTE: if it is easier, what we really want here is the ability to + # constrain the size of the collection. + pass + + def __getitem__(self, key: int): + # NOTE: we can switch to a method which fetches multiple items at a time + # if that would significantly improve performance + pass + + def __iter__(self): + # this is not high priority + pass From 5758c2f23ef693c9a0e43d2c55829c65decfa565 Mon Sep 17 00:00:00 2001 From: Zach Dwiel Date: Fri, 7 Sep 2018 16:06:05 -0400 Subject: [PATCH 006/162] typo; increased detail in comment --- rl_coach/coach.py | 2 +- rl_coach/memories/non_episodic/transition_collection.py | 3 ++- 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/rl_coach/coach.py b/rl_coach/coach.py index a28c3de..aa18a14 100644 --- a/rl_coach/coach.py +++ b/rl_coach/coach.py @@ -333,7 +333,7 @@ def main(): # Single-threaded runs if args.num_workers == 1: # Start the training or evaluation - task_parameters = TaskParameters(framework_type="tensorflow", # TODO: tensorflow should'nt be hardcoded + task_parameters = TaskParameters(framework_type="tensorflow", # TODO: tensorflow shouldn't be hardcoded evaluate_only=args.evaluate, experiment_path=args.experiment_path, seed=args.seed, diff --git a/rl_coach/memories/non_episodic/transition_collection.py b/rl_coach/memories/non_episodic/transition_collection.py index d721539..4ce1574 100644 --- a/rl_coach/memories/non_episodic/transition_collection.py +++ b/rl_coach/memories/non_episodic/transition_collection.py @@ -22,7 +22,8 @@ class TransitionCollection(object): def __del__(self, range: slice): # NOTE: the only slice used is the form: slice(None, n) # NOTE: if it is easier, what we really want here is the ability to - # constrain the size of the collection. + # constrain the size of the collection. as new transitions are added, + # old transitions can be removed to maintain a maximum collection size. pass def __getitem__(self, key: int): From 61ed6b8ce4208393ac050d6818469c94ebf9b1d5 Mon Sep 17 00:00:00 2001 From: Zach Dwiel Date: Wed, 12 Sep 2018 19:51:40 +0000 Subject: [PATCH 007/162] add better defaults to TaskParameters --- rl_coach/base_parameters.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/rl_coach/base_parameters.py b/rl_coach/base_parameters.py index de737eb..1bbb53a 100644 --- a/rl_coach/base_parameters.py +++ b/rl_coach/base_parameters.py @@ -415,8 +415,8 @@ class AgentParameters(Parameters): class TaskParameters(Parameters): - def __init__(self, framework_type: str="tensorflow", evaluate_only: bool=False, use_cpu: bool=False, - experiment_path="./experiments/test/", seed=None, save_checkpoint_secs=None): + def __init__(self, framework_type: str='tensorflow', evaluate_only: bool=False, use_cpu: bool=False, + experiment_path='/tmp', seed=None, save_checkpoint_secs=None): """ :param framework_type: deep learning framework type. currently only tensorflow is supported :param evaluate_only: the task will be used only for evaluating the model @@ -426,7 +426,7 @@ class TaskParameters(Parameters): :param seed: a seed to use for the random numbers generator """ self.framework_type = framework_type - self.task_index = None # TODO: not really needed + self.task_index = 0 # TODO: not really needed self.evaluate_only = evaluate_only self.use_cpu = use_cpu self.experiment_path = experiment_path From bc664c4169a3828cb6f99d3708f67fc0831dd965 Mon Sep 17 00:00:00 2001 From: Zach Dwiel Date: Wed, 12 Sep 2018 19:53:04 +0000 Subject: [PATCH 008/162] add the first pass of rollout_worker.py --- rl_coach/rollout_worker.py | 14 ++++++++++++++ 1 file changed, 14 insertions(+) create mode 100644 rl_coach/rollout_worker.py diff --git a/rl_coach/rollout_worker.py b/rl_coach/rollout_worker.py new file mode 100644 index 0000000..81f18ff --- /dev/null +++ b/rl_coach/rollout_worker.py @@ -0,0 +1,14 @@ +from rl_coach.base_parameters import TaskParameters +from rl_coach.core_types import EnvironmentEpisodes, RunPhase +from rl_coach.presets.CartPole_DQN import graph_manager + + +# TODO: workers might need to define schedules in terms which can be synchronized: exploration(len(distributed_memory)) -> float + +def main(): + graph_manager.create_graph(TaskParameters()) + graph_manager.phase = RunPhase.TRAIN + graph_manager.act(EnvironmentEpisodes(num_steps=10)) + +if __name__ == '__main__': + main() From 747000647fef7940ca57a001abd12d4b92990295 Mon Sep 17 00:00:00 2001 From: Zach Dwiel Date: Wed, 12 Sep 2018 19:58:26 +0000 Subject: [PATCH 009/162] add dockerfile --- docker/Dockerfile | 32 ++++++++++++++++++++++++++++++++ docker/Makefile | 38 ++++++++++++++++++++++++++++++++++++++ 2 files changed, 70 insertions(+) create mode 100644 docker/Dockerfile create mode 100644 docker/Makefile diff --git a/docker/Dockerfile b/docker/Dockerfile new file mode 100644 index 0000000..a19f759 --- /dev/null +++ b/docker/Dockerfile @@ -0,0 +1,32 @@ +FROM ubuntu:16.04 + +RUN apt-get update \ + && apt-get install -y \ + python3-pip cmake zlib1g-dev python3-tk python-opencv \ + libboost-all-dev \ + libblas-dev liblapack-dev libatlas-base-dev gfortran \ + libsdl-dev libsdl-image1.2-dev libsdl-mixer1.2-dev libsdl-ttf2.0-dev \ + libsmpeg-dev libportmidi-dev libavformat-dev libswscale-dev \ + dpkg-dev build-essential python3.5-dev libjpeg-dev libtiff-dev \ + libsdl1.2-dev libnotify-dev freeglut3 freeglut3-dev libsm-dev \ + libgtk2.0-dev libgtk-3-dev libwebkitgtk-dev libgtk-3-dev \ + libwebkitgtk-3.0-dev libgstreamer-plugins-base1.0-dev \ + libav-tools libsdl2-dev swig + +# installing python dependencies +RUN pip3 install --upgrade pip + +RUN mkdir /root/src +COPY setup.py /root/src/. +COPY README.md /root/src/. +WORKDIR /root/src +RUN pip3 install -e . +# +COPY . /root/src +WORKDIR /root/src +RUN pip3 install -e . + +# RUN pip3 install rl_coach + +# CMD ["coach", "-p", "CartPole_PG", "-e", "cartpole"] +CMD python3 rl_coach/rollout_worker.py diff --git a/docker/Makefile b/docker/Makefile new file mode 100644 index 0000000..5b812ab --- /dev/null +++ b/docker/Makefile @@ -0,0 +1,38 @@ +IMAGE=zdwiel/coach +# IMAGE=gcr.io/deep-greens/inference:v5 + +BUILD_ARGUMENTS= +RUN_ARGUMENTS= +ifdef http_proxy + BUILD_ARGUMENTS+=--build-arg http_proxy=$(http_proxy) + RUN_ARGUMENTS+=--env http_proxy=$(http_proxy) +endif + +ifdef https_proxy + BUILD_ARGUMENTS+=--build-arg https_proxy=$(https_proxy) + RUN_ARGUMENTS+=--env https_proxy=$(https_proxy) +endif + +RUN_ARGUMENTS+=--rm +RUN_ARGUMENTS+=--net host + +CONTEXT = $(realpath ..) + +ifndef DOCKER + DOCKER = docker +endif + +build: + ${DOCKER} build -f=Dockerfile -t=${IMAGE} ${BUILD_ARGUMENTS} ${CONTEXT} + +shell: build + ${DOCKER} run ${RUN_ARGUMENTS} -it ${IMAGE} /bin/bash + +test: build + ${DOCKER} run ${RUN_ARGUMENTS} -it ${IMAGE} py.test tests + +run: build + ${DOCKER} run ${RUN_ARGUMENTS} -it ${IMAGE} + +push: + docker push ${IMAGE} From 5a54f67a63624f541bc90033d1786a1afeeae7b4 Mon Sep 17 00:00:00 2001 From: Ajay Deshpande Date: Wed, 12 Sep 2018 20:21:49 -0700 Subject: [PATCH 010/162] Adding distributed experience replay --- .../distributed_experience_replay.py | 199 ++++++++++++++++++ 1 file changed, 199 insertions(+) create mode 100644 rl_coach/memories/non_episodic/distributed_experience_replay.py diff --git a/rl_coach/memories/non_episodic/distributed_experience_replay.py b/rl_coach/memories/non_episodic/distributed_experience_replay.py new file mode 100644 index 0000000..c5c928b --- /dev/null +++ b/rl_coach/memories/non_episodic/distributed_experience_replay.py @@ -0,0 +1,199 @@ +# +# Copyright (c) 2017 Intel Corporation +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +from typing import List, Tuple, Union, Dict, Any + +import numpy as np +import redis +import uuid +import pickle + +from rl_coach.core_types import Transition +from rl_coach.memories.memory import Memory, MemoryGranularity, MemoryParameters +from rl_coach.utils import ReaderWriterLock + + +class DistributedExperienceReplayParameters(MemoryParameters): + def __init__(self): + super().__init__() + self.max_size = (MemoryGranularity.Transitions, 1000000) + self.allow_duplicates_in_batch_sampling = True + + @property + def path(self): + return 'rl_coach.memories.non_episodic.distributed_experience_replay:DistributedExperienceReplay' + + +class DistributedExperienceReplay(Memory): + """ + A regular replay buffer which stores transition without any additional structure + """ + def __init__(self, max_size: Tuple[MemoryGranularity, int], allow_duplicates_in_batch_sampling: bool=True, + redis_ip = 'localhost', redis_port = 6379, db = 0): + """ + :param max_size: the maximum number of transitions or episodes to hold in the memory + :param allow_duplicates_in_batch_sampling: allow having the same transition multiple times in a batch + """ + + super().__init__(max_size) + if max_size[0] != MemoryGranularity.Transitions: + raise ValueError("Experience replay size can only be configured in terms of transitions") + # self.transitions = [] + self.allow_duplicates_in_batch_sampling = allow_duplicates_in_batch_sampling + + self.db = db + self.redis_connection = redis.Redis(redis_ip, redis_port, self.db) + + def length(self) -> int: + """ + Get the number of transitions in the ER + """ + return self.num_transitions() + + def num_transitions(self) -> int: + """ + Get the number of transitions in the ER + """ + # Replace with distributed store len + return self.redis_connection.info(section='keyspace')['db{}'.format(self.db)]['keys'] + # return len(self.transitions) + + def sample(self, size: int) -> List[Transition]: + """ + Sample a batch of transitions form the replay buffer. If the requested size is larger than the number + of samples available in the replay buffer then the batch will return empty. + :param size: the size of the batch to sample + :param beta: the beta parameter used for importance sampling + :return: a batch (list) of selected transitions from the replay buffer + """ + transition_idx = dict() + if self.allow_duplicates_in_batch_sampling: + while len(transition_idx) != size: + key = self.redis_connection.randomkey() + transition_idx[key] = pickle.loads(self.redis_connection.get(key)) + # transition_idx = np.random.randint(self.num_transitions(), size=size) + + else: + if self.num_transitions() >= size: + while len(transition_idx) != size: + key = self.redis_connection.randomkey() + if key in transition_idx: + continue + transition_idx[key] = pickle.loads(self.redis_connection.get(key)) + # transition_idx = np.random.choice(self.num_transitions(), size=size, replace=False) + else: + raise ValueError("The replay buffer cannot be sampled since there are not enough transitions yet. " + "There are currently {} transitions".format(self.num_transitions())) + + # Replace with distributed store + batch = transition_idx.values() + + return batch + + def _enforce_max_length(self) -> None: + """ + Make sure that the size of the replay buffer does not pass the maximum size allowed. + If it passes the max size, the oldest transition in the replay buffer will be removed. + This function does not use locks since it is only called internally + :return: None + """ + granularity, size = self.max_size + if granularity == MemoryGranularity.Transitions: + while size != 0 and self.num_transitions() > size: + self.redis_connection.delete(self.redis_connection.randomkey()) + else: + raise ValueError("The granularity of the replay buffer can only be set in terms of transitions") + + def store(self, transition: Transition, lock: bool=True) -> None: + """ + Store a new transition in the memory. + :param transition: a transition to store + :param lock: if true, will lock the readers writers lock. this can cause a deadlock if an inheriting class + locks and then calls store with lock = True + :return: None + """ + # Replace with distributed store + + self.redis_connection.set(uuid.uuid4(), pickle.dumps(transition)) + # self.transitions.append(transition) + self._enforce_max_length() + + def get_transition(self, transition_index: int, lock: bool=True) -> Union[None, Transition]: + """ + Returns the transition in the given index. If the transition does not exist, returns None instead. + :param transition_index: the index of the transition to return + :param lock: use write locking if this is a shared memory + :return: the corresponding transition + """ + # Replace with distributed store + import pytest; pytest.set_trace() + return pickle.loads(self.redis_connection.get(transition_index)) + + def remove_transition(self, transition_index: int, lock: bool=True) -> None: + """ + Remove the transition in the given index. + + This does not remove the transition from the segment trees! it is just used to remove the transition + from the transitions list + :param transition_index: the index of the transition to remove + :return: None + """ + # Replace with distributed store + import pytest; pytest.set_trace() + self.redis_connection.delete(transition_index) + + # for API compatibility + def get(self, transition_index: int, lock: bool=True) -> Union[None, Transition]: + """ + Returns the transition in the given index. If the transition does not exist, returns None instead. + :param transition_index: the index of the transition to return + :return: the corresponding transition + """ + # Replace with distributed store + import pytest; pytest.set_trace() + return self.get_transition(transition_index, lock) + + # for API compatibility + def remove(self, transition_index: int, lock: bool=True): + """ + Remove the transition in the given index + :param transition_index: the index of the transition to remove + :return: None + """ + # Replace with distributed store + import pytest; pytest.set_trace() + self.remove_transition(transition_index, lock) + + def clean(self, lock: bool=True) -> None: + """ + Clean the memory by removing all the episodes + :return: None + """ + import pytest; pytest.set_trace() + self.redis_connection.flushall() + # self.transitions = [] + + def mean_reward(self) -> np.ndarray: + """ + Get the mean reward in the replay buffer + :return: the mean reward + """ + # Replace with distributed store + import pytest; pytest.set_trace() + mean = np.mean([pickle.loads(self.redis_connection.get(key)).reward for key in self.redis_connection.keys()]) + # mean = np.mean([transition.reward for transition in self.transitions]) + + return mean From 21f8ca39784b8458439bc67cfe22fe0a70ee9e54 Mon Sep 17 00:00:00 2001 From: Ajay Deshpande Date: Wed, 12 Sep 2018 20:30:09 -0700 Subject: [PATCH 011/162] Removing comments and pytests --- .../distributed_experience_replay.py | 26 ++----------------- 1 file changed, 2 insertions(+), 24 deletions(-) diff --git a/rl_coach/memories/non_episodic/distributed_experience_replay.py b/rl_coach/memories/non_episodic/distributed_experience_replay.py index c5c928b..d6bdf9c 100644 --- a/rl_coach/memories/non_episodic/distributed_experience_replay.py +++ b/rl_coach/memories/non_episodic/distributed_experience_replay.py @@ -51,7 +51,6 @@ class DistributedExperienceReplay(Memory): super().__init__(max_size) if max_size[0] != MemoryGranularity.Transitions: raise ValueError("Experience replay size can only be configured in terms of transitions") - # self.transitions = [] self.allow_duplicates_in_batch_sampling = allow_duplicates_in_batch_sampling self.db = db @@ -67,10 +66,8 @@ class DistributedExperienceReplay(Memory): """ Get the number of transitions in the ER """ - # Replace with distributed store len return self.redis_connection.info(section='keyspace')['db{}'.format(self.db)]['keys'] - # return len(self.transitions) - + def sample(self, size: int) -> List[Transition]: """ Sample a batch of transitions form the replay buffer. If the requested size is larger than the number @@ -84,8 +81,6 @@ class DistributedExperienceReplay(Memory): while len(transition_idx) != size: key = self.redis_connection.randomkey() transition_idx[key] = pickle.loads(self.redis_connection.get(key)) - # transition_idx = np.random.randint(self.num_transitions(), size=size) - else: if self.num_transitions() >= size: while len(transition_idx) != size: @@ -93,12 +88,10 @@ class DistributedExperienceReplay(Memory): if key in transition_idx: continue transition_idx[key] = pickle.loads(self.redis_connection.get(key)) - # transition_idx = np.random.choice(self.num_transitions(), size=size, replace=False) else: raise ValueError("The replay buffer cannot be sampled since there are not enough transitions yet. " "There are currently {} transitions".format(self.num_transitions())) - # Replace with distributed store batch = transition_idx.values() return batch @@ -125,10 +118,7 @@ class DistributedExperienceReplay(Memory): locks and then calls store with lock = True :return: None """ - # Replace with distributed store - self.redis_connection.set(uuid.uuid4(), pickle.dumps(transition)) - # self.transitions.append(transition) self._enforce_max_length() def get_transition(self, transition_index: int, lock: bool=True) -> Union[None, Transition]: @@ -138,8 +128,6 @@ class DistributedExperienceReplay(Memory): :param lock: use write locking if this is a shared memory :return: the corresponding transition """ - # Replace with distributed store - import pytest; pytest.set_trace() return pickle.loads(self.redis_connection.get(transition_index)) def remove_transition(self, transition_index: int, lock: bool=True) -> None: @@ -151,8 +139,6 @@ class DistributedExperienceReplay(Memory): :param transition_index: the index of the transition to remove :return: None """ - # Replace with distributed store - import pytest; pytest.set_trace() self.redis_connection.delete(transition_index) # for API compatibility @@ -162,8 +148,6 @@ class DistributedExperienceReplay(Memory): :param transition_index: the index of the transition to return :return: the corresponding transition """ - # Replace with distributed store - import pytest; pytest.set_trace() return self.get_transition(transition_index, lock) # for API compatibility @@ -173,8 +157,6 @@ class DistributedExperienceReplay(Memory): :param transition_index: the index of the transition to remove :return: None """ - # Replace with distributed store - import pytest; pytest.set_trace() self.remove_transition(transition_index, lock) def clean(self, lock: bool=True) -> None: @@ -182,7 +164,6 @@ class DistributedExperienceReplay(Memory): Clean the memory by removing all the episodes :return: None """ - import pytest; pytest.set_trace() self.redis_connection.flushall() # self.transitions = [] @@ -191,9 +172,6 @@ class DistributedExperienceReplay(Memory): Get the mean reward in the replay buffer :return: the mean reward """ - # Replace with distributed store - import pytest; pytest.set_trace() mean = np.mean([pickle.loads(self.redis_connection.get(key)).reward for key in self.redis_connection.keys()]) - # mean = np.mean([transition.reward for transition in self.transitions]) - + return mean From 3714d8ec80e94b062ca5d62c9575acc026bc753f Mon Sep 17 00:00:00 2001 From: Zach Dwiel Date: Thu, 13 Sep 2018 19:34:32 +0000 Subject: [PATCH 012/162] extract functions display_all_presets_and_exit, expand_preset --- rl_coach/coach.py | 65 ++++++++++++++++++++++++++++------------------- 1 file changed, 39 insertions(+), 26 deletions(-) diff --git a/rl_coach/coach.py b/rl_coach/coach.py index aa18a14..1e6bfba 100644 --- a/rl_coach/coach.py +++ b/rl_coach/coach.py @@ -92,6 +92,39 @@ def get_graph_manager_from_args(args: argparse.Namespace) -> 'GraphManager': return graph_manager +def display_all_presets_and_exit(): + # list available presets + if args.list: + screen.log_title("Available Presets:") + for preset in sorted(list_all_presets()): + print(preset) + sys.exit(0) + +def expand_preset(preset): + if preset.lower() in [p.lower() for p in list_all_presets()]: + preset = "{}.py:graph_manager".format(os.path.join(get_base_dir(), 'presets', preset)) + else: + preset = "{}".format(preset) + # if a graph manager variable was not specified, try the default of :graph_manager + if len(preset.split(":")) == 1: + preset += ":graph_manager" + + # verify that the preset exists + preset_path = preset.split(":")[0] + if not os.path.exists(preset_path): + screen.error("The given preset ({}) cannot be found.".format(preset)) + + # verify that the preset can be instantiated + try: + short_dynamic_import(preset, ignore_module_case=True) + except TypeError as e: + traceback.print_exc() + screen.error('Internal Error: ' + str(e) + "\n\nThe given preset ({}) cannot be instantiated." + .format(preset)) + + return preset + + def parse_arguments(parser: argparse.ArgumentParser) -> argparse.Namespace: """ Parse the arguments that the user entered @@ -103,38 +136,15 @@ def parse_arguments(parser: argparse.ArgumentParser) -> argparse.Namespace: # if no arg is given if len(sys.argv) == 1: parser.print_help() - exit(0) + sys.exit(0) # list available presets - preset_names = list_all_presets() if args.list: - screen.log_title("Available Presets:") - for preset in sorted(preset_names): - print(preset) - sys.exit(0) + display_all_presets_and_exit() # replace a short preset name with the full path if args.preset is not None: - if args.preset.lower() in [p.lower() for p in preset_names]: - args.preset = "{}.py:graph_manager".format(os.path.join(get_base_dir(), 'presets', args.preset)) - else: - args.preset = "{}".format(args.preset) - # if a graph manager variable was not specified, try the default of :graph_manager - if len(args.preset.split(":")) == 1: - args.preset += ":graph_manager" - - # verify that the preset exists - preset_path = args.preset.split(":")[0] - if not os.path.exists(preset_path): - screen.error("The given preset ({}) cannot be found.".format(args.preset)) - - # verify that the preset can be instantiated - try: - short_dynamic_import(args.preset, ignore_module_case=True) - except TypeError as e: - traceback.print_exc() - screen.error('Internal Error: ' + str(e) + "\n\nThe given preset ({}) cannot be instantiated." - .format(args.preset)) + args.preset = expand_preset(args.preset) # validate the checkpoints args if args.checkpoint_restore_dir is not None and not os.path.exists(args.checkpoint_restore_dir): @@ -179,6 +189,9 @@ def add_items_to_dict(target_dict, source_dict): def open_dashboard(experiment_path): + """ + open X11 based dashboard in a new process (nonblocking) + """ dashboard_path = 'python {}/dashboard.py'.format(get_base_dir()) cmd = "{} --experiment_dir {}".format(dashboard_path, experiment_path) screen.log_title("Opening dashboard - experiment path: {}".format(experiment_path)) From e34b9ae9cf515e8c9c6b2f5d5d812eb5bd74d9d1 Mon Sep 17 00:00:00 2001 From: Zach Dwiel Date: Thu, 13 Sep 2018 19:35:01 +0000 Subject: [PATCH 013/162] allow specifying preset as a commandline parameter to rollout worker --- rl_coach/rollout_worker.py | 26 +++++++++++++++++++++++--- 1 file changed, 23 insertions(+), 3 deletions(-) diff --git a/rl_coach/rollout_worker.py b/rl_coach/rollout_worker.py index 81f18ff..bfb5be3 100644 --- a/rl_coach/rollout_worker.py +++ b/rl_coach/rollout_worker.py @@ -1,14 +1,34 @@ +import argparse + from rl_coach.base_parameters import TaskParameters +from rl_coach.coach import expand_preset from rl_coach.core_types import EnvironmentEpisodes, RunPhase -from rl_coach.presets.CartPole_DQN import graph_manager +from rl_coach.utils import short_dynamic_import + +# TODO: acce[t preset option # TODO: workers might need to define schedules in terms which can be synchronized: exploration(len(distributed_memory)) -> float +# TODO: periodically reload policy (from disk?) +# TODO: specify alternative distributed memory, or should this go in the preset? -def main(): - graph_manager.create_graph(TaskParameters()) +def rollout_worker(graph_manager): + task_parameters = TaskParameters() + task_parameters.checkpoint_restore_dir='/checkpoint' + graph_manager.create_graph(task_parameters) graph_manager.phase = RunPhase.TRAIN graph_manager.act(EnvironmentEpisodes(num_steps=10)) + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument('-p', '--preset', + help="(string) Name of a preset to run (class name from the 'presets' directory.)", + type=str) + args = parser.parse_args() + + graph_manager = short_dynamic_import(expand_preset(args.preset), ignore_module_case=True) + rollout_worker(graph_manager) + if __name__ == '__main__': main() From 433bc3e27b53e582c92400f0518909851ed667a0 Mon Sep 17 00:00:00 2001 From: Zach Dwiel Date: Thu, 13 Sep 2018 19:35:28 +0000 Subject: [PATCH 014/162] standardizing variable access --- rl_coach/graph_managers/graph_manager.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/rl_coach/graph_managers/graph_manager.py b/rl_coach/graph_managers/graph_manager.py index ddbafd4..d13259b 100644 --- a/rl_coach/graph_managers/graph_manager.py +++ b/rl_coach/graph_managers/graph_manager.py @@ -479,7 +479,7 @@ class GraphManager(object): checkpoint = tf.train.get_checkpoint_state(checkpoint_dir) screen.log_title("Loading checkpoint: {}".format(checkpoint.model_checkpoint_path)) variables = {} - for var_name, _ in tf.contrib.framework.list_variables(self.task_parameters.checkpoint_restore_dir): + for var_name, _ in tf.contrib.framework.list_variables(checkpoint_dir): # Load the variable var = tf.contrib.framework.load_variable(checkpoint_dir, var_name) From 6541bc76b98f08440f2e1c750cb995a8e1822c26 Mon Sep 17 00:00:00 2001 From: Zach Dwiel Date: Fri, 14 Sep 2018 20:59:29 +0000 Subject: [PATCH 015/162] working checkpoints --- docker/Dockerfile | 2 +- docker/Makefile | 8 ++++ rl_coach/coach.py | 1 - rl_coach/graph_managers/graph_manager.py | 58 +++++++++++++++--------- rl_coach/rollout_worker.py | 32 +++++++++---- 5 files changed, 69 insertions(+), 32 deletions(-) diff --git a/docker/Dockerfile b/docker/Dockerfile index a19f759..9966943 100644 --- a/docker/Dockerfile +++ b/docker/Dockerfile @@ -29,4 +29,4 @@ RUN pip3 install -e . # RUN pip3 install rl_coach # CMD ["coach", "-p", "CartPole_PG", "-e", "cartpole"] -CMD python3 rl_coach/rollout_worker.py +CMD python3 rl_coach/rollout_worker.py --preset CartPole_PG diff --git a/docker/Makefile b/docker/Makefile index 5b812ab..5174c40 100644 --- a/docker/Makefile +++ b/docker/Makefile @@ -15,6 +15,7 @@ endif RUN_ARGUMENTS+=--rm RUN_ARGUMENTS+=--net host +RUN_ARGUMENTS+=-v /tmp/checkpoint:/checkpoint CONTEXT = $(realpath ..) @@ -24,6 +25,7 @@ endif build: ${DOCKER} build -f=Dockerfile -t=${IMAGE} ${BUILD_ARGUMENTS} ${CONTEXT} + mkdir -p /tmp/checkpoint shell: build ${DOCKER} run ${RUN_ARGUMENTS} -it ${IMAGE} /bin/bash @@ -34,5 +36,11 @@ test: build run: build ${DOCKER} run ${RUN_ARGUMENTS} -it ${IMAGE} +run_training_worker: build + ${DOCKER} run ${RUN_ARGUMENTS} -it ${IMAGE} python3 rl_coach/training_worker.py --preset CartPole_PG + +run_rollout_worker: build + ${DOCKER} run ${RUN_ARGUMENTS} -it ${IMAGE} python3 rl_coach/rollout_worker.py --preset CartPole_PG + push: docker push ${IMAGE} diff --git a/rl_coach/coach.py b/rl_coach/coach.py index 1e6bfba..9c14003 100644 --- a/rl_coach/coach.py +++ b/rl_coach/coach.py @@ -1,4 +1,3 @@ -# # Copyright (c) 2017 Intel Corporation # # Licensed under the Apache License, Version 2.0 (the "License"); diff --git a/rl_coach/graph_managers/graph_manager.py b/rl_coach/graph_managers/graph_manager.py index d13259b..3b799ba 100644 --- a/rl_coach/graph_managers/graph_manager.py +++ b/rl_coach/graph_managers/graph_manager.py @@ -341,6 +341,16 @@ class GraphManager(object): self.total_steps_counters[RunPhase.TRAIN][TrainingSteps] += 1 [manager.train() for manager in self.level_managers] + # # option 1 + # for _ in StepsLoop(self.total_steps_counters, RunPhase.TRAIN, steps): + # [manager.train() for manager in self.level_managers] + # + # # option 2 + # steps_loop = StepsLoop(self.total_steps_counters, RunPhase.TRAIN, steps) + # while steps_loop or other: + # [manager.train() for manager in self.level_managers] + + def reset_internal_state(self, force_environment_reset=False) -> None: """ Reset an episode for all the levels @@ -403,6 +413,7 @@ class GraphManager(object): if result.game_over: hold_until_a_full_episode = False self.handle_episode_ended() + # TODO: why not just reset right now? self.reset_required = True if keep_networks_in_sync: self.sync_graph() @@ -426,16 +437,16 @@ class GraphManager(object): # perform several steps of training interleaved with acting if steps.num_steps > 0: self.phase = RunPhase.TRAIN - count_end = self.total_steps_counters[self.phase][steps.__class__] + steps.num_steps self.reset_internal_state(force_environment_reset=True) #TODO - the below while loop should end with full episodes, so to avoid situations where we have partial # episodes in memory + count_end = self.total_steps_counters[self.phase][steps.__class__] + steps.num_steps while self.total_steps_counters[self.phase][steps.__class__] < count_end: # The actual steps being done on the environment are decided by the agents themselves. # This is just an high-level controller. self.act(EnvironmentSteps(1)) self.train(TrainingSteps(1)) - self.save_checkpoint() + self.occasionally_save_checkpoint() self.phase = RunPhase.UNDEFINED def sync_graph(self) -> None: @@ -491,35 +502,40 @@ class GraphManager(object): for v in self.variables_to_restore: self.sess.run(v.assign(variables[v.name.split(':')[0]])) - def save_checkpoint(self): + def occasionally_save_checkpoint(self): # only the chief process saves checkpoints if self.task_parameters.save_checkpoint_secs \ and time.time() - self.last_checkpoint_saving_time >= self.task_parameters.save_checkpoint_secs \ and (self.task_parameters.task_index == 0 # distributed or self.task_parameters.task_index is None # single-worker ): + self.save_checkpoint() - checkpoint_path = os.path.join(self.task_parameters.save_checkpoint_dir, - "{}_Step-{}.ckpt".format( - self.checkpoint_id, - self.total_steps_counters[RunPhase.TRAIN][EnvironmentSteps])) - if not isinstance(self.task_parameters, DistributedTaskParameters): - saved_checkpoint_path = self.checkpoint_saver.save(self.sess, checkpoint_path) - else: - saved_checkpoint_path = checkpoint_path + def _log_save_checkpoint(self): + checkpoint_path = os.path.join(self.task_parameters.save_checkpoint_dir, + "{}_Step-{}.ckpt".format( + self.checkpoint_id, + self.total_steps_counters[RunPhase.TRAIN][EnvironmentSteps])) + if not isinstance(self.task_parameters, DistributedTaskParameters): + saved_checkpoint_path = self.checkpoint_saver.save(self.sess, checkpoint_path) + else: + saved_checkpoint_path = checkpoint_path - # this is required in order for agents to save additional information like a DND for example - [manager.save_checkpoint(self.checkpoint_id) for manager in self.level_managers] + screen.log_dict( + OrderedDict([ + ("Saving in path", saved_checkpoint_path), + ]), + prefix="Checkpoint" + ) - screen.log_dict( - OrderedDict([ - ("Saving in path", saved_checkpoint_path), - ]), - prefix="Checkpoint" - ) + def save_checkpoint(self): + # this is required in order for agents to save additional information like a DND for example + [manager.save_checkpoint(self.checkpoint_id) for manager in self.level_managers] - self.checkpoint_id += 1 - self.last_checkpoint_saving_time = time.time() + self._log_save_checkpoint() + + self.checkpoint_id += 1 + self.last_checkpoint_saving_time = time.time() def improve(self): """ diff --git a/rl_coach/rollout_worker.py b/rl_coach/rollout_worker.py index bfb5be3..90035cd 100644 --- a/rl_coach/rollout_worker.py +++ b/rl_coach/rollout_worker.py @@ -1,3 +1,7 @@ +""" +this rollout worker restores a model from disk, evaluates a predefined number of +episodes, and contributes them to a distributed memory +""" import argparse from rl_coach.base_parameters import TaskParameters @@ -5,30 +9,40 @@ from rl_coach.coach import expand_preset from rl_coach.core_types import EnvironmentEpisodes, RunPhase from rl_coach.utils import short_dynamic_import +# Q: specify alternative distributed memory, or should this go in the preset? +# A: preset must define distributed memory to be used. we aren't going to take a non-distributed preset and automatically distribute it. -# TODO: acce[t preset option -# TODO: workers might need to define schedules in terms which can be synchronized: exploration(len(distributed_memory)) -> float -# TODO: periodically reload policy (from disk?) -# TODO: specify alternative distributed memory, or should this go in the preset? - -def rollout_worker(graph_manager): +def rollout_worker(graph_manager, checkpoint_dir): + """ + restore a checkpoint then perform rollouts using the restored model + """ task_parameters = TaskParameters() - task_parameters.checkpoint_restore_dir='/checkpoint' + task_parameters.__dict__['checkpoint_restore_dir'] = checkpoint_dir graph_manager.create_graph(task_parameters) graph_manager.phase = RunPhase.TRAIN graph_manager.act(EnvironmentEpisodes(num_steps=10)) + graph_manager.phase = RunPhase.UNDEFINED def main(): parser = argparse.ArgumentParser() parser.add_argument('-p', '--preset', help="(string) Name of a preset to run (class name from the 'presets' directory.)", - type=str) + type=str, + required=True) + parser.add_argument('--checkpoint_dir', + help='(string) Path to a folder containing a checkpoint to restore the model from.', + type=str, + default='/checkpoint') args = parser.parse_args() graph_manager = short_dynamic_import(expand_preset(args.preset), ignore_module_case=True) - rollout_worker(graph_manager) + + rollout_worker( + graph_manager=graph_manager, + checkpoint_dir=args.checkpoint_dir, + ) if __name__ == '__main__': main() From ce9838a7d68f2c5a5a7a4b287e09f1eaac1b8747 Mon Sep 17 00:00:00 2001 From: Ajay Deshpande Date: Fri, 14 Sep 2018 15:58:57 -0700 Subject: [PATCH 016/162] Adding kubernetes orchestrator for rollouts, adding requirements for incremental docker builds --- requirements.txt | 16 ++ rl_coach/agents/dqn_agent.py | 15 ++ .../distributed_experience_replay.py | 24 +-- rl_coach/orchestrators/__init__.py | 15 ++ rl_coach/orchestrators/deploy.py | 19 +++ .../orchestrators/kubernetes_orchestrator.py | 153 ++++++++++++++++++ rl_coach/orchestrators/test.py | 18 +++ rl_coach/presets/CartPole_DQN_distributed.py | 71 ++++++++ rl_coach/rollout_worker.py | 2 + setup.py | 9 +- 10 files changed, 327 insertions(+), 15 deletions(-) create mode 100644 requirements.txt create mode 100644 rl_coach/orchestrators/__init__.py create mode 100644 rl_coach/orchestrators/deploy.py create mode 100644 rl_coach/orchestrators/kubernetes_orchestrator.py create mode 100644 rl_coach/orchestrators/test.py create mode 100644 rl_coach/presets/CartPole_DQN_distributed.py diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..463dd95 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,16 @@ +annoy==1.8.3 +Pillow==4.3.0 +matplotlib==2.0.2 +numpy==1.14.5 +pandas==0.22.0 +pygame==1.9.3 +PyOpenGL==3.1.0 +scipy==0.19.0 +scikit-image==0.13.0 +box2d==2.3.2 +gym==0.10.5 +bokeh==0.13.0 +futures==3.1.1 +wxPython==4.0.1 +kubernetes==7.0.0 +redis==2.10.6 \ No newline at end of file diff --git a/rl_coach/agents/dqn_agent.py b/rl_coach/agents/dqn_agent.py index f261a08..b300b79 100644 --- a/rl_coach/agents/dqn_agent.py +++ b/rl_coach/agents/dqn_agent.py @@ -27,6 +27,7 @@ from rl_coach.architectures.tensorflow_components.embedders.embedder import Inpu from rl_coach.core_types import EnvironmentSteps from rl_coach.exploration_policies.e_greedy import EGreedyParameters from rl_coach.memories.non_episodic.experience_replay import ExperienceReplayParameters +from rl_coach.memories.non_episodic.distributed_experience_replay import DistributedExperienceReplayParameters from rl_coach.schedules import LinearSchedule @@ -50,6 +51,20 @@ class DQNNetworkParameters(NetworkParameters): self.create_target_network = True +class DQNAgentParametersDistributed(AgentParameters): + def __init__(self): + super().__init__(algorithm=DQNAlgorithmParameters(), + exploration=EGreedyParameters(), + memory=DistributedExperienceReplayParameters(), + networks={"main": DQNNetworkParameters()}) + self.exploration.epsilon_schedule = LinearSchedule(1, 0.1, 1000000) + self.exploration.evaluation_epsilon = 0.05 + + @property + def path(self): + return 'rl_coach.agents.dqn_agent:DQNAgent' + + class DQNAgentParameters(AgentParameters): def __init__(self): super().__init__(algorithm=DQNAlgorithmParameters(), diff --git a/rl_coach/memories/non_episodic/distributed_experience_replay.py b/rl_coach/memories/non_episodic/distributed_experience_replay.py index d6bdf9c..6b14e81 100644 --- a/rl_coach/memories/non_episodic/distributed_experience_replay.py +++ b/rl_coach/memories/non_episodic/distributed_experience_replay.py @@ -14,7 +14,7 @@ # limitations under the License. # -from typing import List, Tuple, Union, Dict, Any +from typing import List, Tuple, Union import numpy as np import redis @@ -23,7 +23,6 @@ import pickle from rl_coach.core_types import Transition from rl_coach.memories.memory import Memory, MemoryGranularity, MemoryParameters -from rl_coach.utils import ReaderWriterLock class DistributedExperienceReplayParameters(MemoryParameters): @@ -31,6 +30,9 @@ class DistributedExperienceReplayParameters(MemoryParameters): super().__init__() self.max_size = (MemoryGranularity.Transitions, 1000000) self.allow_duplicates_in_batch_sampling = True + self.redis_ip = 'localhost' + self.redis_port = 6379 + self.redis_db = 0 @property def path(self): @@ -41,19 +43,19 @@ class DistributedExperienceReplay(Memory): """ A regular replay buffer which stores transition without any additional structure """ - def __init__(self, max_size: Tuple[MemoryGranularity, int], allow_duplicates_in_batch_sampling: bool=True, - redis_ip = 'localhost', redis_port = 6379, db = 0): + def __init__(self, max_size: Tuple[MemoryGranularity, int], allow_duplicates_in_batch_sampling: bool=True, + redis_ip='localhost', redis_port=6379, redis_db=0): """ :param max_size: the maximum number of transitions or episodes to hold in the memory :param allow_duplicates_in_batch_sampling: allow having the same transition multiple times in a batch """ - + super().__init__(max_size) if max_size[0] != MemoryGranularity.Transitions: raise ValueError("Experience replay size can only be configured in terms of transitions") self.allow_duplicates_in_batch_sampling = allow_duplicates_in_batch_sampling - self.db = db + self.db = redis_db self.redis_connection = redis.Redis(redis_ip, redis_port, self.db) def length(self) -> int: @@ -67,7 +69,7 @@ class DistributedExperienceReplay(Memory): Get the number of transitions in the ER """ return self.redis_connection.info(section='keyspace')['db{}'.format(self.db)]['keys'] - + def sample(self, size: int) -> List[Transition]: """ Sample a batch of transitions form the replay buffer. If the requested size is larger than the number @@ -75,7 +77,7 @@ class DistributedExperienceReplay(Memory): :param size: the size of the batch to sample :param beta: the beta parameter used for importance sampling :return: a batch (list) of selected transitions from the replay buffer - """ + """ transition_idx = dict() if self.allow_duplicates_in_batch_sampling: while len(transition_idx) != size: @@ -129,7 +131,7 @@ class DistributedExperienceReplay(Memory): :return: the corresponding transition """ return pickle.loads(self.redis_connection.get(transition_index)) - + def remove_transition(self, transition_index: int, lock: bool=True) -> None: """ Remove the transition in the given index. @@ -140,7 +142,7 @@ class DistributedExperienceReplay(Memory): :return: None """ self.redis_connection.delete(transition_index) - + # for API compatibility def get(self, transition_index: int, lock: bool=True) -> Union[None, Transition]: """ @@ -173,5 +175,5 @@ class DistributedExperienceReplay(Memory): :return: the mean reward """ mean = np.mean([pickle.loads(self.redis_connection.get(key)).reward for key in self.redis_connection.keys()]) - + return mean diff --git a/rl_coach/orchestrators/__init__.py b/rl_coach/orchestrators/__init__.py new file mode 100644 index 0000000..cf26739 --- /dev/null +++ b/rl_coach/orchestrators/__init__.py @@ -0,0 +1,15 @@ +# +# Copyright (c) 2017 Intel Corporation +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# diff --git a/rl_coach/orchestrators/deploy.py b/rl_coach/orchestrators/deploy.py new file mode 100644 index 0000000..d99e1d1 --- /dev/null +++ b/rl_coach/orchestrators/deploy.py @@ -0,0 +1,19 @@ + + + +class DeployParameters(object): + + def __init__(self): + pass + + +class Deploy(object): + + def __init__(self, deploy_parameters): + self.deploy_parameters = deploy_parameters + + def setup(self) -> bool: + pass + + def deploy(self) -> bool: + pass \ No newline at end of file diff --git a/rl_coach/orchestrators/kubernetes_orchestrator.py b/rl_coach/orchestrators/kubernetes_orchestrator.py new file mode 100644 index 0000000..9b21ee4 --- /dev/null +++ b/rl_coach/orchestrators/kubernetes_orchestrator.py @@ -0,0 +1,153 @@ + +from rl_coach.orchestrators.deploy import Deploy, DeployParameters +from kubernetes import client, config + + +class KubernetesParameters(DeployParameters): + + def __init__(self, image: str, command: list(), arguments: list() = list(), synchronized: bool = False, + num_workers: int = 1, kubeconfig: str = None, namespace: str = None, redis_ip: str = None, + redis_port: int = None, redis_db: int = 0): + self.image = image + self.synchronized = synchronized + self.command = command + self.arguments = arguments + self.kubeconfig = kubeconfig + self.num_workers = num_workers + self.namespace = namespace + self.redis_ip = redis_ip + self.redis_port = redis_port + self.redis_db = redis_db + + +class Kubernetes(Deploy): + + def __init__(self, deploy_parameters: KubernetesParameters): + super().__init__(deploy_parameters) + self.deploy_parameters = deploy_parameters + + def setup(self) -> bool: + if self.deploy_parameters.kubeconfig: + config.load_kube_config() + else: + config.load_incluster_config() + + if not self.deploy_parameters.namespace: + _, current_context = config.list_kube_config_contexts() + self.deploy_parameters.namespace = current_context['context']['namespace'] + + if not self.deploy_parameters.redis_ip: + # Need to spin up a redis service and a deployment. + if not self.deploy_redis(): + print("Failed to setup redis") + return False + + self.deploy_parameters.command += ['-r', self.deploy_parameters.redis_ip, '-p', '{}'.format(self.deploy_parameters.redis_port)] + + return True + + def deploy_redis(self) -> bool: + container = client.V1Container( + name="redis-server", + image='redis:4-alpine', + ) + template = client.V1PodTemplateSpec( + metadata=client.V1ObjectMeta(labels={'app': 'redis-server'}), + spec=client.V1PodSpec( + containers=[container] + ) + ) + deployment_spec = client.V1DeploymentSpec( + replicas=1, + template=template, + selector=client.V1LabelSelector( + match_labels={'app': 'redis-server'} + ) + ) + + deployment = client.V1Deployment( + api_version='apps/v1', + kind='Deployment', + metadata=client.V1ObjectMeta(name='redis-server', labels={'app': 'redis-server'}), + spec=deployment_spec + ) + + api_client = client.AppsV1Api() + try: + api_client.create_namespaced_deployment(self.deploy_parameters.namespace, deployment) + except client.rest.ApiException as e: + print("Got exception: %s\n while creating redis-server", e) + return False + + core_v1_api = client.CoreV1Api() + + service = client.V1Service( + api_version='v1', + kind='Service', + metadata=client.V1ObjectMeta( + name='redis-service' + ), + spec=client.V1ServiceSpec( + selector={'app': 'redis-server'}, + ports=[client.V1ServicePort( + protocol='TCP', + port=6379, + target_port=6379 + )] + ) + ) + + try: + core_v1_api.create_namespaced_service(self.deploy_parameters.namespace, service) + self.deploy_parameters.redis_ip = 'redis-service.{}.svc'.format(self.deploy_parameters.namespace) + self.deploy_parameters.redis_port = 6379 + return True + except client.rest.ApiException as e: + print("Got exception: %s\n while creating a service for redis-server", e) + return False + + def deploy(self) -> bool: + if self.deploy_parameters.synchronized: + return self.create_k8s_job() + else: + return self.create_k8s_deployment() + + def create_k8s_deployment(self) -> bool: + container = client.V1Container( + name="worker", + image=self.deploy_parameters.image, + command=self.deploy_parameters.command, + args=self.deploy_parameters.arguments, + image_pull_policy='Always' + ) + template = client.V1PodTemplateSpec( + metadata=client.V1ObjectMeta(labels={'app': 'worker'}), + spec=client.V1PodSpec( + containers=[container] + ) + ) + deployment_spec = client.V1DeploymentSpec( + replicas=self.deploy_parameters.num_workers, + template=template, + selector=client.V1LabelSelector( + match_labels={'app': 'worker'} + ) + ) + + deployment = client.V1Deployment( + api_version='apps/v1', + kind='Deployment', + metadata=client.V1ObjectMeta(name='rollout-worker'), + spec=deployment_spec + ) + + api_client = client.AppsV1Api() + try: + api_client.create_namespaced_deployment(self.deploy_parameters.namespace, deployment) + return True + except client.rest.ApiException as e: + print("Got exception: %s\n while creating deployment", e) + return False + + def create_k8s_job(self): + pass diff --git a/rl_coach/orchestrators/test.py b/rl_coach/orchestrators/test.py new file mode 100644 index 0000000..56428e0 --- /dev/null +++ b/rl_coach/orchestrators/test.py @@ -0,0 +1,18 @@ +from rl_coach.orchestrators.kubernetes_orchestrator import KubernetesParameters, Kubernetes + +# image = 'gcr.io/constant-cubist-173123/coach:latest' +image = 'ajaysudh/testing:coach' +command = ['python3', 'rl_coach/rollout_worker.py'] +# command = ['sleep', '10h'] + +params = KubernetesParameters(image, command, kubeconfig='~/.kube/config', redis_ip='redis-service.ajay.svc', redis_port=6379, num_workers=10) +# params = KubernetesParameters(image, command, kubeconfig='~/.kube/config') + +obj = Kubernetes(params) +if not obj.setup(): + print("Could not setup") + +if obj.deploy(): + print("Successfully deployed") +else: + print("Could not deploy") diff --git a/rl_coach/presets/CartPole_DQN_distributed.py b/rl_coach/presets/CartPole_DQN_distributed.py new file mode 100644 index 0000000..d3e8513 --- /dev/null +++ b/rl_coach/presets/CartPole_DQN_distributed.py @@ -0,0 +1,71 @@ +from rl_coach.agents.dqn_agent import DQNAgentParametersDistributed +from rl_coach.base_parameters import VisualizationParameters, PresetValidationParameters +from rl_coach.core_types import TrainingSteps, EnvironmentEpisodes, EnvironmentSteps, RunPhase +from rl_coach.environments.environment import SelectedPhaseOnlyDumpMethod, MaxDumpMethod +from rl_coach.environments.gym_environment import Mujoco +from rl_coach.graph_managers.basic_rl_graph_manager import BasicRLGraphManager +from rl_coach.graph_managers.graph_manager import ScheduleParameters +from rl_coach.memories.memory import MemoryGranularity +from rl_coach.schedules import LinearSchedule + + +def construct_graph(redis_ip='localhost', redis_port=6379): + #################### + # Graph Scheduling # + #################### + + schedule_params = ScheduleParameters() + schedule_params.improve_steps = TrainingSteps(10000000000) + schedule_params.steps_between_evaluation_periods = EnvironmentEpisodes(10) + schedule_params.evaluation_steps = EnvironmentEpisodes(1) + schedule_params.heatup_steps = EnvironmentSteps(1000) + + ######### + # Agent # + ######### + agent_params = DQNAgentParametersDistributed() + + # DQN params + agent_params.algorithm.num_steps_between_copying_online_weights_to_target = EnvironmentSteps(100) + agent_params.algorithm.discount = 0.99 + agent_params.algorithm.num_consecutive_playing_steps = EnvironmentSteps(1) + + # NN configuration + agent_params.network_wrappers['main'].learning_rate = 0.00025 + agent_params.network_wrappers['main'].replace_mse_with_huber_loss = False + + # ER size + agent_params.memory.max_size = (MemoryGranularity.Transitions, 40000) + + # E-Greedy schedule + agent_params.exploration.epsilon_schedule = LinearSchedule(1.0, 0.01, 10000) + + # Redis parameters + agent_params.memory.redis_ip = redis_ip + agent_params.memory.redis_port = redis_port + + ################ + # Environment # + ################ + env_params = Mujoco() + env_params.level = 'CartPole-v0' + + vis_params = VisualizationParameters() + vis_params.video_dump_methods = [SelectedPhaseOnlyDumpMethod(RunPhase.TEST), MaxDumpMethod()] + vis_params.dump_mp4 = False + + ######## + # Test # + ######## + preset_validation_params = PresetValidationParameters() + preset_validation_params.test = True + preset_validation_params.min_reward_threshold = 150 + preset_validation_params.max_episodes_to_achieve_reward = 250 + + graph_manager = BasicRLGraphManager(agent_params=agent_params, env_params=env_params, + schedule_params=schedule_params, vis_params=vis_params, + preset_validation_params=preset_validation_params) + return graph_manager + + +graph_manager = construct_graph() diff --git a/rl_coach/rollout_worker.py b/rl_coach/rollout_worker.py index 90035cd..f7a29f0 100644 --- a/rl_coach/rollout_worker.py +++ b/rl_coach/rollout_worker.py @@ -20,6 +20,7 @@ def rollout_worker(graph_manager, checkpoint_dir): task_parameters = TaskParameters() task_parameters.__dict__['checkpoint_restore_dir'] = checkpoint_dir graph_manager.create_graph(task_parameters) + graph_manager.phase = RunPhase.TRAIN graph_manager.act(EnvironmentEpisodes(num_steps=10)) graph_manager.phase = RunPhase.UNDEFINED @@ -44,5 +45,6 @@ def main(): checkpoint_dir=args.checkpoint_dir, ) + if __name__ == '__main__': main() diff --git a/setup.py b/setup.py index 76d8285..e77cd70 100644 --- a/setup.py +++ b/setup.py @@ -47,10 +47,11 @@ here = path.abspath(path.dirname(__file__)) with open(path.join(here, 'README.md'), encoding='utf-8') as f: long_description = f.read() -install_requires=[ - 'annoy==1.8.3', 'Pillow==4.3.0', 'matplotlib==2.0.2', 'numpy==1.14.5', 'pandas==0.22.0', - 'pygame==1.9.3', 'PyOpenGL==3.1.0', 'scipy==0.19.0', 'scikit-image==0.13.0', - 'box2d==2.3.2', 'gym==0.10.5', 'bokeh==0.13.0', 'futures==3.1.1', 'wxPython==4.0.1'] +install_requires = list() + +with open(path.join(here, 'requirements.txt'), 'r') as f: + for line in f: + install_requires.append(line.strip()) # check if system has CUDA enabled GPU p = subprocess.Popen(['command -v nvidia-smi'], stdout=subprocess.PIPE, shell=True) From ad7f0310312a76faa7125516acb83632fc29284f Mon Sep 17 00:00:00 2001 From: Ajay Deshpande Date: Fri, 14 Sep 2018 16:10:00 -0700 Subject: [PATCH 017/162] Adding dockerfile --- docker/Dockerfile.build | 26 ++++++++++++++++++++++++++ 1 file changed, 26 insertions(+) create mode 100644 docker/Dockerfile.build diff --git a/docker/Dockerfile.build b/docker/Dockerfile.build new file mode 100644 index 0000000..176ea8f --- /dev/null +++ b/docker/Dockerfile.build @@ -0,0 +1,26 @@ +FROM ubuntu:16.04 + +RUN apt-get update \ + && apt-get install -y \ + python3-pip cmake zlib1g-dev python3-tk python-opencv \ + libboost-all-dev \ + libblas-dev liblapack-dev libatlas-base-dev gfortran \ + libsdl-dev libsdl-image1.2-dev libsdl-mixer1.2-dev libsdl-ttf2.0-dev \ + libsmpeg-dev libportmidi-dev libavformat-dev libswscale-dev \ + dpkg-dev build-essential python3.5-dev libjpeg-dev libtiff-dev \ + libsdl1.2-dev libnotify-dev freeglut3 freeglut3-dev libsm-dev \ + libgtk2.0-dev libgtk-3-dev libwebkitgtk-dev libgtk-3-dev \ + libwebkitgtk-3.0-dev libgstreamer-plugins-base1.0-dev \ + libav-tools libsdl2-dev swig + +RUN pip3 install --upgrade pip + +COPY requirements.txt /coach/requirements.txt + +WORKDIR /coach + +RUN pip3 install -r requirements.txt + +COPY . /coach + +RUN pip3 install . From c2991819b4ad7af366366b9e6274fc36a1060729 Mon Sep 17 00:00:00 2001 From: Ajay Deshpande Date: Fri, 14 Sep 2018 16:17:34 -0700 Subject: [PATCH 018/162] Adding right arguments to the agent --- rl_coach/presets/CartPole_DQN_distributed.py | 90 +++++++++----------- rl_coach/rollout_worker.py | 3 + 2 files changed, 44 insertions(+), 49 deletions(-) diff --git a/rl_coach/presets/CartPole_DQN_distributed.py b/rl_coach/presets/CartPole_DQN_distributed.py index d3e8513..f4259c4 100644 --- a/rl_coach/presets/CartPole_DQN_distributed.py +++ b/rl_coach/presets/CartPole_DQN_distributed.py @@ -9,63 +9,55 @@ from rl_coach.memories.memory import MemoryGranularity from rl_coach.schedules import LinearSchedule -def construct_graph(redis_ip='localhost', redis_port=6379): - #################### - # Graph Scheduling # - #################### - schedule_params = ScheduleParameters() - schedule_params.improve_steps = TrainingSteps(10000000000) - schedule_params.steps_between_evaluation_periods = EnvironmentEpisodes(10) - schedule_params.evaluation_steps = EnvironmentEpisodes(1) - schedule_params.heatup_steps = EnvironmentSteps(1000) +#################### +# Graph Scheduling # +#################### - ######### - # Agent # - ######### - agent_params = DQNAgentParametersDistributed() +schedule_params = ScheduleParameters() +schedule_params.improve_steps = TrainingSteps(10000000000) +schedule_params.steps_between_evaluation_periods = EnvironmentEpisodes(10) +schedule_params.evaluation_steps = EnvironmentEpisodes(1) +schedule_params.heatup_steps = EnvironmentSteps(1000) - # DQN params - agent_params.algorithm.num_steps_between_copying_online_weights_to_target = EnvironmentSteps(100) - agent_params.algorithm.discount = 0.99 - agent_params.algorithm.num_consecutive_playing_steps = EnvironmentSteps(1) +######### +# Agent # +######### +agent_params = DQNAgentParametersDistributed() - # NN configuration - agent_params.network_wrappers['main'].learning_rate = 0.00025 - agent_params.network_wrappers['main'].replace_mse_with_huber_loss = False +# DQN params +agent_params.algorithm.num_steps_between_copying_online_weights_to_target = EnvironmentSteps(100) +agent_params.algorithm.discount = 0.99 +agent_params.algorithm.num_consecutive_playing_steps = EnvironmentSteps(1) - # ER size - agent_params.memory.max_size = (MemoryGranularity.Transitions, 40000) +# NN configuration +agent_params.network_wrappers['main'].learning_rate = 0.00025 +agent_params.network_wrappers['main'].replace_mse_with_huber_loss = False - # E-Greedy schedule - agent_params.exploration.epsilon_schedule = LinearSchedule(1.0, 0.01, 10000) +# ER size +agent_params.memory.max_size = (MemoryGranularity.Transitions, 40000) - # Redis parameters - agent_params.memory.redis_ip = redis_ip - agent_params.memory.redis_port = redis_port +# E-Greedy schedule +agent_params.exploration.epsilon_schedule = LinearSchedule(1.0, 0.01, 10000) - ################ - # Environment # - ################ - env_params = Mujoco() - env_params.level = 'CartPole-v0' +################ +# Environment # +################ +env_params = Mujoco() +env_params.level = 'CartPole-v0' - vis_params = VisualizationParameters() - vis_params.video_dump_methods = [SelectedPhaseOnlyDumpMethod(RunPhase.TEST), MaxDumpMethod()] - vis_params.dump_mp4 = False +vis_params = VisualizationParameters() +vis_params.video_dump_methods = [SelectedPhaseOnlyDumpMethod(RunPhase.TEST), MaxDumpMethod()] +vis_params.dump_mp4 = False - ######## - # Test # - ######## - preset_validation_params = PresetValidationParameters() - preset_validation_params.test = True - preset_validation_params.min_reward_threshold = 150 - preset_validation_params.max_episodes_to_achieve_reward = 250 +######## +# Test # +######## +preset_validation_params = PresetValidationParameters() +preset_validation_params.test = True +preset_validation_params.min_reward_threshold = 150 +preset_validation_params.max_episodes_to_achieve_reward = 250 - graph_manager = BasicRLGraphManager(agent_params=agent_params, env_params=env_params, - schedule_params=schedule_params, vis_params=vis_params, - preset_validation_params=preset_validation_params) - return graph_manager - - -graph_manager = construct_graph() +graph_manager = BasicRLGraphManager(agent_params=agent_params, env_params=env_params, + schedule_params=schedule_params, vis_params=vis_params, + preset_validation_params=preset_validation_params) diff --git a/rl_coach/rollout_worker.py b/rl_coach/rollout_worker.py index f7a29f0..f1c5363 100644 --- a/rl_coach/rollout_worker.py +++ b/rl_coach/rollout_worker.py @@ -40,6 +40,9 @@ def main(): graph_manager = short_dynamic_import(expand_preset(args.preset), ignore_module_case=True) + graph_manager.agent_parameters.memory.redis_ip = args.redis_ip + graph_manager.agent_params.memory.redis_port = args.redis_port + rollout_worker( graph_manager=graph_manager, checkpoint_dir=args.checkpoint_dir, From 28926bf2a41dda388b8ed5558c9cd8b08489e0a2 Mon Sep 17 00:00:00 2001 From: Ajay Deshpande Date: Fri, 14 Sep 2018 16:22:13 -0700 Subject: [PATCH 019/162] Changing parameters --- rl_coach/orchestrators/kubernetes_orchestrator.py | 2 +- rl_coach/orchestrators/test.py | 4 ++-- rl_coach/rollout_worker.py | 12 ++++++++++-- 3 files changed, 13 insertions(+), 5 deletions(-) diff --git a/rl_coach/orchestrators/kubernetes_orchestrator.py b/rl_coach/orchestrators/kubernetes_orchestrator.py index 9b21ee4..6bdcd8c 100644 --- a/rl_coach/orchestrators/kubernetes_orchestrator.py +++ b/rl_coach/orchestrators/kubernetes_orchestrator.py @@ -42,7 +42,7 @@ class Kubernetes(Deploy): print("Failed to setup redis") return False - self.deploy_parameters.command += ['-r', self.deploy_parameters.redis_ip, '-p', '{}'.format(self.deploy_parameters.redis_port)] + self.deploy_parameters.command += ['--redis_ip', self.deploy_parameters.redis_ip, '--redis_port', '{}'.format(self.deploy_parameters.redis_port)] return True diff --git a/rl_coach/orchestrators/test.py b/rl_coach/orchestrators/test.py index 56428e0..3142b6a 100644 --- a/rl_coach/orchestrators/test.py +++ b/rl_coach/orchestrators/test.py @@ -2,10 +2,10 @@ from rl_coach.orchestrators.kubernetes_orchestrator import KubernetesParameters, # image = 'gcr.io/constant-cubist-173123/coach:latest' image = 'ajaysudh/testing:coach' -command = ['python3', 'rl_coach/rollout_worker.py'] +command = ['python3', 'rl_coach/rollout_worker.py', '-p', 'CartPole_DQN_distributed'] # command = ['sleep', '10h'] -params = KubernetesParameters(image, command, kubeconfig='~/.kube/config', redis_ip='redis-service.ajay.svc', redis_port=6379, num_workers=10) +params = KubernetesParameters(image, command, kubeconfig='~/.kube/config', redis_ip='redis-service.ajay.svc', redis_port=6379, num_workers=1) # params = KubernetesParameters(image, command, kubeconfig='~/.kube/config') obj = Kubernetes(params) diff --git a/rl_coach/rollout_worker.py b/rl_coach/rollout_worker.py index f1c5363..ef0ff7d 100644 --- a/rl_coach/rollout_worker.py +++ b/rl_coach/rollout_worker.py @@ -20,7 +20,7 @@ def rollout_worker(graph_manager, checkpoint_dir): task_parameters = TaskParameters() task_parameters.__dict__['checkpoint_restore_dir'] = checkpoint_dir graph_manager.create_graph(task_parameters) - + graph_manager.phase = RunPhase.TRAIN graph_manager.act(EnvironmentEpisodes(num_steps=10)) graph_manager.phase = RunPhase.UNDEFINED @@ -36,11 +36,19 @@ def main(): help='(string) Path to a folder containing a checkpoint to restore the model from.', type=str, default='/checkpoint') + parser.add_argument('-r', '--redis_ip', + help="(string) IP or host for the redis server", + default='localhost', + type=str) + parser.add_argument('-rp', '--redis_port', + help="(int) Port of the redis server", + default=6379, + type=int) args = parser.parse_args() graph_manager = short_dynamic_import(expand_preset(args.preset), ignore_module_case=True) - graph_manager.agent_parameters.memory.redis_ip = args.redis_ip + graph_manager.agent_params.memory.redis_ip = args.redis_ip graph_manager.agent_params.memory.redis_port = args.redis_port rollout_worker( From 4352d6735d478ced62f71acbc2f1ef81175c5f78 Mon Sep 17 00:00:00 2001 From: Zach Dwiel Date: Sat, 15 Sep 2018 00:23:16 +0000 Subject: [PATCH 020/162] add training worker --- rl_coach/training_worker.py | 62 +++++++++++++++++++++++++++++++++++++ 1 file changed, 62 insertions(+) create mode 100644 rl_coach/training_worker.py diff --git a/rl_coach/training_worker.py b/rl_coach/training_worker.py new file mode 100644 index 0000000..534729b --- /dev/null +++ b/rl_coach/training_worker.py @@ -0,0 +1,62 @@ +""" +""" +import argparse + +from rl_coach.base_parameters import TaskParameters +from rl_coach.coach import expand_preset +from rl_coach import core_types +from rl_coach.utils import short_dynamic_import + +# Q: specify alternative distributed memory, or should this go in the preset? +# A: preset must define distributed memory to be used. we aren't going to take a non-distributed preset and automatically distribute it. + +def training_worker(graph_manager, checkpoint_dir): + """ + restore a checkpoint then perform rollouts using the restored model + """ + # initialize graph + task_parameters = TaskParameters() + task_parameters.__dict__['save_checkpoint_dir'] = checkpoint_dir + graph_manager.create_graph(task_parameters) + + # save randomly initialized graph + graph_manager.save_checkpoint() + + # TODO: critical: wait for minimum number of rollouts in memory before training + # TODO: Q: training steps passed into graph_manager.train ignored? + # TODO: specify training steps between checkpoints (in preset?) + # TODO: replace while true with what? number of steps, convergence, time, ... + # TODO: low: move evaluate out of this process + + # training loop + for _ in range(10): + graph_manager.phase = core_types.RunPhase.TRAIN + graph_manager.train(core_types.TrainingSteps(1)) + graph_manager.phase = core_types.RunPhase.UNDEFINED + + graph_manager.evaluate(graph_manager.evaluation_steps) + + graph_manager.save_checkpoint() + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument('-p', '--preset', + help="(string) Name of a preset to run (class name from the 'presets' directory.)", + type=str, + required=True) + parser.add_argument('--checkpoint_dir', + help='(string) Path to a folder containing a checkpoint to write the model to.', + type=str, + default='/checkpoint') + args = parser.parse_args() + + graph_manager = short_dynamic_import(expand_preset(args.preset), ignore_module_case=True) + + training_worker( + graph_manager=graph_manager, + checkpoint_dir=args.checkpoint_dir, + ) + +if __name__ == '__main__': + main() From f5b7122d56b23cce4c7e7def59e86ef51b48d643 Mon Sep 17 00:00:00 2001 From: Zach Dwiel Date: Sat, 15 Sep 2018 00:55:50 +0000 Subject: [PATCH 021/162] weight for checkpoint before trying to start rollout worker --- docker/Dockerfile | 1 + rl_coach/rollout_worker.py | 38 ++++++++++++++++++++++++++++++++++---- 2 files changed, 35 insertions(+), 4 deletions(-) diff --git a/docker/Dockerfile b/docker/Dockerfile index 9966943..d29c4f6 100644 --- a/docker/Dockerfile +++ b/docker/Dockerfile @@ -18,6 +18,7 @@ RUN pip3 install --upgrade pip RUN mkdir /root/src COPY setup.py /root/src/. +COPY requirements.txt /root/src/. COPY README.md /root/src/. WORKDIR /root/src RUN pip3 install -e . diff --git a/rl_coach/rollout_worker.py b/rl_coach/rollout_worker.py index ef0ff7d..1624041 100644 --- a/rl_coach/rollout_worker.py +++ b/rl_coach/rollout_worker.py @@ -1,8 +1,14 @@ """ -this rollout worker restores a model from disk, evaluates a predefined number of -episodes, and contributes them to a distributed memory +this rollout worker: + +- restores a model from disk +- evaluates a predefined number of episodes +- contributes them to a distributed memory +- exits """ + import argparse +import time from rl_coach.base_parameters import TaskParameters from rl_coach.coach import expand_preset @@ -10,13 +16,38 @@ from rl_coach.core_types import EnvironmentEpisodes, RunPhase from rl_coach.utils import short_dynamic_import # Q: specify alternative distributed memory, or should this go in the preset? -# A: preset must define distributed memory to be used. we aren't going to take a non-distributed preset and automatically distribute it. +# A: preset must define distributed memory to be used. we aren't going to take +# a non-distributed preset and automatically distribute it. + +def has_checkpoint(checkpoint_dir): + """ + True if a checkpoint is present in checkpoint_dir + """ + return len(os.listdir(checkpoint_dir)) > 0 + + +def wait_for_checkpoint(checkpoint_dir, timeout=10): + """ + block until there is a checkpoint in checkpoint_dir + """ + for i in range(timeout): + if has_checkpoint(checkpoint_dir): + return + time.sleep(1) + + # one last time + if has_checkpoint(checkpoint_dir): + return + + raise ValueError(f'checkpoint never found in {checkpoint_dir}') def rollout_worker(graph_manager, checkpoint_dir): """ restore a checkpoint then perform rollouts using the restored model """ + wait_for_checkpoint(checkpoint_dir) + task_parameters = TaskParameters() task_parameters.__dict__['checkpoint_restore_dir'] = checkpoint_dir graph_manager.create_graph(task_parameters) @@ -56,6 +87,5 @@ def main(): checkpoint_dir=args.checkpoint_dir, ) - if __name__ == '__main__': main() From 009cf670f33093676242a4398fe9fda412f41816 Mon Sep 17 00:00:00 2001 From: Zach Dwiel Date: Mon, 17 Sep 2018 18:33:20 +0000 Subject: [PATCH 022/162] fix simple typos; temporarily disable redis in rollout worker --- rl_coach/rollout_worker.py | 10 +++++++--- 1 file changed, 7 insertions(+), 3 deletions(-) diff --git a/rl_coach/rollout_worker.py b/rl_coach/rollout_worker.py index 1624041..5b92140 100644 --- a/rl_coach/rollout_worker.py +++ b/rl_coach/rollout_worker.py @@ -9,6 +9,7 @@ this rollout worker: import argparse import time +import os from rl_coach.base_parameters import TaskParameters from rl_coach.coach import expand_preset @@ -39,7 +40,9 @@ def wait_for_checkpoint(checkpoint_dir, timeout=10): if has_checkpoint(checkpoint_dir): return - raise ValueError(f'checkpoint never found in {checkpoint_dir}') + raise ValueError('checkpoint never found in {checkpoint_dir}'.format( + checkpoint_dir=checkpoint_dir, + )) def rollout_worker(graph_manager, checkpoint_dir): @@ -79,8 +82,9 @@ def main(): graph_manager = short_dynamic_import(expand_preset(args.preset), ignore_module_case=True) - graph_manager.agent_params.memory.redis_ip = args.redis_ip - graph_manager.agent_params.memory.redis_port = args.redis_port + # TODO: get this working, this expects that memory already has a redis ip and port + # graph_manager.agent_params.memory.redis_ip = args.redis_ip + # graph_manager.agent_params.memory.redis_port = args.redis_port rollout_worker( graph_manager=graph_manager, From 3328b25549ba38fe0b1c3d9f39d1d039d5570ecf Mon Sep 17 00:00:00 2001 From: Zach Dwiel Date: Mon, 17 Sep 2018 19:50:03 +0000 Subject: [PATCH 023/162] reenable redis; better error message --- docker/Dockerfile | 2 +- docker/Makefile | 5 +++-- rl_coach/rollout_worker.py | 11 +++++++---- 3 files changed, 11 insertions(+), 7 deletions(-) diff --git a/docker/Dockerfile b/docker/Dockerfile index d29c4f6..c05d8bc 100644 --- a/docker/Dockerfile +++ b/docker/Dockerfile @@ -30,4 +30,4 @@ RUN pip3 install -e . # RUN pip3 install rl_coach # CMD ["coach", "-p", "CartPole_PG", "-e", "cartpole"] -CMD python3 rl_coach/rollout_worker.py --preset CartPole_PG +# CMD python3 rl_coach/rollout_worker.py --preset CartPole_DQN_distributed diff --git a/docker/Makefile b/docker/Makefile index 5174c40..9bd680f 100644 --- a/docker/Makefile +++ b/docker/Makefile @@ -26,6 +26,7 @@ endif build: ${DOCKER} build -f=Dockerfile -t=${IMAGE} ${BUILD_ARGUMENTS} ${CONTEXT} mkdir -p /tmp/checkpoint + rm -rf /tmp/checkpoint/* shell: build ${DOCKER} run ${RUN_ARGUMENTS} -it ${IMAGE} /bin/bash @@ -37,10 +38,10 @@ run: build ${DOCKER} run ${RUN_ARGUMENTS} -it ${IMAGE} run_training_worker: build - ${DOCKER} run ${RUN_ARGUMENTS} -it ${IMAGE} python3 rl_coach/training_worker.py --preset CartPole_PG + ${DOCKER} run ${RUN_ARGUMENTS} -it ${IMAGE} python3 rl_coach/training_worker.py --preset CartPole_DQN_distributed run_rollout_worker: build - ${DOCKER} run ${RUN_ARGUMENTS} -it ${IMAGE} python3 rl_coach/rollout_worker.py --preset CartPole_PG + ${DOCKER} run ${RUN_ARGUMENTS} -it ${IMAGE} python3 rl_coach/rollout_worker.py --preset CartPole_DQN_distributed push: docker push ${IMAGE} diff --git a/rl_coach/rollout_worker.py b/rl_coach/rollout_worker.py index 5b92140..8afde08 100644 --- a/rl_coach/rollout_worker.py +++ b/rl_coach/rollout_worker.py @@ -40,7 +40,11 @@ def wait_for_checkpoint(checkpoint_dir, timeout=10): if has_checkpoint(checkpoint_dir): return - raise ValueError('checkpoint never found in {checkpoint_dir}'.format( + raise ValueError(( + 'Waited {timeout} seconds, but checkpoint never found in' + ' {checkpoint_dir}' + ).format( + timeout=timeout, checkpoint_dir=checkpoint_dir, )) @@ -82,9 +86,8 @@ def main(): graph_manager = short_dynamic_import(expand_preset(args.preset), ignore_module_case=True) - # TODO: get this working, this expects that memory already has a redis ip and port - # graph_manager.agent_params.memory.redis_ip = args.redis_ip - # graph_manager.agent_params.memory.redis_port = args.redis_port + graph_manager.agent_params.memory.redis_ip = args.redis_ip + graph_manager.agent_params.memory.redis_port = args.redis_port rollout_worker( graph_manager=graph_manager, From 0812a94fbd452f6088ed329603b9cd7347fc8c2d Mon Sep 17 00:00:00 2001 From: Zach Dwiel Date: Mon, 17 Sep 2018 22:31:17 +0000 Subject: [PATCH 024/162] first pass at kubernetes --- docker/Makefile | 9 ++++- rl_coach/orchestrators/start_training.py | 48 ++++++++++++++++++++++++ rl_coach/rollout_worker.py | 4 +- 3 files changed, 57 insertions(+), 4 deletions(-) create mode 100644 rl_coach/orchestrators/start_training.py diff --git a/docker/Makefile b/docker/Makefile index 9bd680f..b3f74a0 100644 --- a/docker/Makefile +++ b/docker/Makefile @@ -1,3 +1,4 @@ +REGISTRY=nervana-dockrepo01.fm.intel.com:5001/ IMAGE=zdwiel/coach # IMAGE=gcr.io/deep-greens/inference:v5 @@ -43,5 +44,9 @@ run_training_worker: build run_rollout_worker: build ${DOCKER} run ${RUN_ARGUMENTS} -it ${IMAGE} python3 rl_coach/rollout_worker.py --preset CartPole_DQN_distributed -push: - docker push ${IMAGE} +kubernetes: build push + kubectl run -i --tty --attach --image=${IMAGE} --restart=Never date -- python3 rl_coach/orchestrators/start_training.py --preset CartPole_DQN_distributed --image ${IMAGE} + +push: build + ${DOCKER} tag ${IMAGE} ${REGISTRY}${IMAGE} + ${DOCKER} push ${REGISTRY}${IMAGE} diff --git a/rl_coach/orchestrators/start_training.py b/rl_coach/orchestrators/start_training.py new file mode 100644 index 0000000..9950b61 --- /dev/null +++ b/rl_coach/orchestrators/start_training.py @@ -0,0 +1,48 @@ +import argparse + +from rl_coach.orchestrators.kubernetes_orchestrator import KubernetesParameters, Kubernetes + + +def main(preset, image='ajaysudh/testing:coach', redis_ip='redis-service.ajay.svc'): + rollout_command = ['python3', 'rl_coach/rollout_worker.py', '-p', preset] + training_command = ['python3', 'rl_coach/training_worker.py', '-p', preset] + + rollout_params = KubernetesParameters(image, rollout_command, redis_ip=redis_ip, redis_port=6379, num_workers=1) + training_params = KubernetesParameters(image, training_command, redis_ip=redis_ip, redis_port=6379, num_workers=1) + + training_obj = Kubernetes(training_params) + if not training_obj.setup(): + print("Could not setup") + + rollout_obj = Kubernetes(training_params) + if not rollout_obj.setup(): + print("Could not setup") + + if training_obj.deploy(): + print("Successfully deployed") + else: + print("Could not deploy") + + if rollout_obj.deploy(): + print("Successfully deployed") + else: + print("Could not deploy") + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--image', + help="(string) Name of a docker image.", + type=str, + required=True) + parser.add_argument('-p', '--preset', + help="(string) Name of a preset to run (class name from the 'presets' directory.)", + type=str, + required=True) + # parser.add_argument('--checkpoint_dir', + # help='(string) Path to a folder containing a checkpoint to write the model to.', + # type=str, + # default='/checkpoint') + args = parser.parse_args() + + main(preset=args.preset, image=args.image) diff --git a/rl_coach/rollout_worker.py b/rl_coach/rollout_worker.py index 8afde08..27845a6 100644 --- a/rl_coach/rollout_worker.py +++ b/rl_coach/rollout_worker.py @@ -41,8 +41,8 @@ def wait_for_checkpoint(checkpoint_dir, timeout=10): return raise ValueError(( - 'Waited {timeout} seconds, but checkpoint never found in' - ' {checkpoint_dir}' + 'Waited {timeout} seconds, but checkpoint never found in ' + '{checkpoint_dir}' ).format( timeout=timeout, checkpoint_dir=checkpoint_dir, From 7c1f0dce4ff8e365eab4198548d60dc8fa3bb91f Mon Sep 17 00:00:00 2001 From: Zach Dwiel Date: Tue, 18 Sep 2018 14:36:17 +0000 Subject: [PATCH 025/162] include registry in image name --- docker/Makefile | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docker/Makefile b/docker/Makefile index b3f74a0..171c959 100644 --- a/docker/Makefile +++ b/docker/Makefile @@ -45,7 +45,7 @@ run_rollout_worker: build ${DOCKER} run ${RUN_ARGUMENTS} -it ${IMAGE} python3 rl_coach/rollout_worker.py --preset CartPole_DQN_distributed kubernetes: build push - kubectl run -i --tty --attach --image=${IMAGE} --restart=Never date -- python3 rl_coach/orchestrators/start_training.py --preset CartPole_DQN_distributed --image ${IMAGE} + kubectl run -i --tty --attach --image=${REGISTRY}${IMAGE} --restart=Never distributed-coach -- python3 rl_coach/orchestrators/start_training.py --preset CartPole_DQN_distributed --image ${IMAGE} push: build ${DOCKER} tag ${IMAGE} ${REGISTRY}${IMAGE} From 04f32a0f02a9baaaa1676b8ebb138f25b6d904a3 Mon Sep 17 00:00:00 2001 From: Zach Dwiel Date: Tue, 18 Sep 2018 19:55:09 +0000 Subject: [PATCH 026/162] add heatup step to training worker --- rl_coach/training_worker.py | 14 +++++++++++--- 1 file changed, 11 insertions(+), 3 deletions(-) diff --git a/rl_coach/training_worker.py b/rl_coach/training_worker.py index 534729b..d2445e6 100644 --- a/rl_coach/training_worker.py +++ b/rl_coach/training_worker.py @@ -10,6 +10,13 @@ from rl_coach.utils import short_dynamic_import # Q: specify alternative distributed memory, or should this go in the preset? # A: preset must define distributed memory to be used. we aren't going to take a non-distributed preset and automatically distribute it. + +def heatup(graph_manager): + num_steps = graph_manager.schedule_params.heatup_steps.num_steps + while len(graph_manager.agent_params.memory) < num_steps: + time.sleep(1) + + def training_worker(graph_manager, checkpoint_dir): """ restore a checkpoint then perform rollouts using the restored model @@ -22,11 +29,12 @@ def training_worker(graph_manager, checkpoint_dir): # save randomly initialized graph graph_manager.save_checkpoint() - # TODO: critical: wait for minimum number of rollouts in memory before training # TODO: Q: training steps passed into graph_manager.train ignored? # TODO: specify training steps between checkpoints (in preset?) - # TODO: replace while true with what? number of steps, convergence, time, ... - # TODO: low: move evaluate out of this process + # TODO: replace outer training loop with something general + # TODO: low priority: move evaluate out of this process + + heatup(graph_manager) # training loop for _ in range(10): From 13d81f65b948a06652e67fc66db94a2aa27fbd4c Mon Sep 17 00:00:00 2001 From: Zach Dwiel Date: Wed, 19 Sep 2018 11:13:20 -0400 Subject: [PATCH 027/162] add redis options to training worker --- rl_coach/graph_managers/graph_manager.py | 11 ++++------- rl_coach/training_worker.py | 16 +++++++++++----- 2 files changed, 15 insertions(+), 12 deletions(-) diff --git a/rl_coach/graph_managers/graph_manager.py b/rl_coach/graph_managers/graph_manager.py index 3b799ba..db261b8 100644 --- a/rl_coach/graph_managers/graph_manager.py +++ b/rl_coach/graph_managers/graph_manager.py @@ -511,7 +511,7 @@ class GraphManager(object): ): self.save_checkpoint() - def _log_save_checkpoint(self): + def save_checkpoint(self): checkpoint_path = os.path.join(self.task_parameters.save_checkpoint_dir, "{}_Step-{}.ckpt".format( self.checkpoint_id, @@ -521,6 +521,9 @@ class GraphManager(object): else: saved_checkpoint_path = checkpoint_path + # this is required in order for agents to save additional information like a DND for example + [manager.save_checkpoint(self.checkpoint_id) for manager in self.level_managers] + screen.log_dict( OrderedDict([ ("Saving in path", saved_checkpoint_path), @@ -528,12 +531,6 @@ class GraphManager(object): prefix="Checkpoint" ) - def save_checkpoint(self): - # this is required in order for agents to save additional information like a DND for example - [manager.save_checkpoint(self.checkpoint_id) for manager in self.level_managers] - - self._log_save_checkpoint() - self.checkpoint_id += 1 self.last_checkpoint_saving_time = time.time() diff --git a/rl_coach/training_worker.py b/rl_coach/training_worker.py index d2445e6..1b9ddea 100644 --- a/rl_coach/training_worker.py +++ b/rl_coach/training_worker.py @@ -29,11 +29,6 @@ def training_worker(graph_manager, checkpoint_dir): # save randomly initialized graph graph_manager.save_checkpoint() - # TODO: Q: training steps passed into graph_manager.train ignored? - # TODO: specify training steps between checkpoints (in preset?) - # TODO: replace outer training loop with something general - # TODO: low priority: move evaluate out of this process - heatup(graph_manager) # training loop @@ -57,10 +52,21 @@ def main(): help='(string) Path to a folder containing a checkpoint to write the model to.', type=str, default='/checkpoint') + parser.add_argument('-r', '--redis_ip', + help="(string) IP or host for the redis server", + default='localhost', + type=str) + parser.add_argument('-rp', '--redis_port', + help="(int) Port of the redis server", + default=6379, + type=int) args = parser.parse_args() graph_manager = short_dynamic_import(expand_preset(args.preset), ignore_module_case=True) + graph_manager.agent_params.memory.redis_ip = args.redis_ip + graph_manager.agent_params.memory.redis_port = args.redis_port + training_worker( graph_manager=graph_manager, checkpoint_dir=args.checkpoint_dir, From 98850464cca2cffd2df24b40a5c75bf7aedfbb36 Mon Sep 17 00:00:00 2001 From: Ajay Deshpande Date: Wed, 19 Sep 2018 09:03:11 -0700 Subject: [PATCH 028/162] Adding nfs pv, pvc, waiting for memory to be full --- .../distributed_experience_replay.py | 5 +- .../orchestrators/kubernetes_orchestrator.py | 149 ++++++++++++++++-- rl_coach/orchestrators/start_training.py | 41 ++++- rl_coach/training_worker.py | 24 ++- 4 files changed, 188 insertions(+), 31 deletions(-) diff --git a/rl_coach/memories/non_episodic/distributed_experience_replay.py b/rl_coach/memories/non_episodic/distributed_experience_replay.py index 6b14e81..61b24b0 100644 --- a/rl_coach/memories/non_episodic/distributed_experience_replay.py +++ b/rl_coach/memories/non_episodic/distributed_experience_replay.py @@ -68,7 +68,10 @@ class DistributedExperienceReplay(Memory): """ Get the number of transitions in the ER """ - return self.redis_connection.info(section='keyspace')['db{}'.format(self.db)]['keys'] + try: + return self.redis_connection.info(section='keyspace')['db{}'.format(self.db)]['keys'] + except Exception as e: + return 0 def sample(self, size: int) -> List[Transition]: """ diff --git a/rl_coach/orchestrators/kubernetes_orchestrator.py b/rl_coach/orchestrators/kubernetes_orchestrator.py index 6bdcd8c..4bb8702 100644 --- a/rl_coach/orchestrators/kubernetes_orchestrator.py +++ b/rl_coach/orchestrators/kubernetes_orchestrator.py @@ -1,13 +1,15 @@ +import uuid from rl_coach.orchestrators.deploy import Deploy, DeployParameters from kubernetes import client, config class KubernetesParameters(DeployParameters): - def __init__(self, image: str, command: list(), arguments: list() = list(), synchronized: bool = False, + def __init__(self, name: str, image: str, command: list(), arguments: list() = list(), synchronized: bool = False, num_workers: int = 1, kubeconfig: str = None, namespace: str = None, redis_ip: str = None, - redis_port: int = None, redis_db: int = 0): + redis_port: int = None, redis_db: int = 0, nfs_server: str = None, nfs_path: str = None, + checkpoint_dir: str = '/checkpoint'): self.image = image self.synchronized = synchronized self.command = command @@ -18,6 +20,10 @@ class KubernetesParameters(DeployParameters): self.redis_ip = redis_ip self.redis_port = redis_port self.redis_db = redis_db + self.nfs_server = nfs_server + self.nfs_path = nfs_path + self.checkpoint_dir = checkpoint_dir + self.name = name class Kubernetes(Deploy): @@ -25,8 +31,6 @@ class Kubernetes(Deploy): def __init__(self, deploy_parameters: KubernetesParameters): super().__init__(deploy_parameters) self.deploy_parameters = deploy_parameters - - def setup(self) -> bool: if self.deploy_parameters.kubeconfig: config.load_kube_config() else: @@ -35,6 +39,9 @@ class Kubernetes(Deploy): if not self.deploy_parameters.namespace: _, current_context = config.list_kube_config_contexts() self.deploy_parameters.namespace = current_context['context']['namespace'] + self.nfs_pvc_name = 'nfs-checkpoint-pvc' + + def setup(self) -> bool: if not self.deploy_parameters.redis_ip: # Need to spin up a redis service and a deployment. @@ -42,10 +49,61 @@ class Kubernetes(Deploy): print("Failed to setup redis") return False - self.deploy_parameters.command += ['--redis_ip', self.deploy_parameters.redis_ip, '--redis_port', '{}'.format(self.deploy_parameters.redis_port)] + if not self.create_nfs_resources(): + return False return True + def create_nfs_resources(self): + persistent_volume = client.V1PersistentVolume( + api_version="v1", + kind="PersistentVolume", + metadata=client.V1ObjectMeta( + name='nfs-checkpoint-pv', + labels={'app': 'nfs-checkpoint-pv'} + ), + spec=client.V1PersistentVolumeSpec( + access_modes=["ReadWriteMany"], + nfs=client.V1NFSVolumeSource( + path=self.deploy_parameters.nfs_path, + server=self.deploy_parameters.nfs_server + ), + capacity={'storage': '10Gi'}, + storage_class_name="" + ) + ) + api_client = client.CoreV1Api() + try: + api_client.create_persistent_volume(persistent_volume) + except client.rest.ApiException as e: + print("Got exception: %s\n while creating the NFS PV", e) + return False + + persistent_volume_claim = client.V1PersistentVolumeClaim( + api_version="v1", + kind="PersistentVolumeClaim", + metadata=client.V1ObjectMeta( + name="nfs-checkpoint-pvc" + ), + spec=client.V1PersistentVolumeClaimSpec( + access_modes=["ReadWriteMany"], + resources=client.V1ResourceRequirements( + requests={'storage': '10Gi'} + ), + selector=client.V1LabelSelector( + match_labels={'app': 'nfs-checkpoint-pv'} + ), + storage_class_name="" + ) + ) + + try: + api_client.create_namespaced_persistent_volume_claim(self.deploy_parameters.namespace, persistent_volume_claim) + except client.rest.ApiException as e: + print("Got exception: %s\n while creating the NFS PVC", e) + return False + return True + def deploy_redis(self) -> bool: container = client.V1Container( name="redis-server", @@ -107,37 +165,52 @@ class Kubernetes(Deploy): return False def deploy(self) -> bool: + + self.deploy_parameters.command += ['--redis_ip', self.deploy_parameters.redis_ip, '--redis_port', '{}'.format(self.deploy_parameters.redis_port)] + if self.deploy_parameters.synchronized: - return self.create_k8s_job() - else: return self.create_k8s_deployment() + else: + return self.create_k8s_job() def create_k8s_deployment(self) -> bool: + name = "{}-{}".format(self.deploy_parameters.name, uuid.uuid4()) + container = client.V1Container( - name="worker", + name=name, image=self.deploy_parameters.image, command=self.deploy_parameters.command, args=self.deploy_parameters.arguments, - image_pull_policy='Always' + image_pull_policy='Always', + volume_mounts=[client.V1VolumeMount( + name='nfs-pvc', + mount_path=self.deploy_parameters.checkpoint_dir + )] ) template = client.V1PodTemplateSpec( - metadata=client.V1ObjectMeta(labels={'app': 'worker'}), + metadata=client.V1ObjectMeta(labels={'app': name}), spec=client.V1PodSpec( - containers=[container] - ) + containers=[container], + volumes=[client.V1Volume( + name="nfs-pvc", + persistent_volume_claim=client.V1PersistentVolumeClaimVolumeSource( + claim_name=self.nfs_pvc_name + ) + )] + ), ) deployment_spec = client.V1DeploymentSpec( replicas=self.deploy_parameters.num_workers, template=template, selector=client.V1LabelSelector( - match_labels={'app': 'worker'} + match_labels={'app': name} ) ) deployment = client.V1Deployment( api_version='apps/v1', kind='Deployment', - metadata=client.V1ObjectMeta(name='rollout-worker'), + metadata=client.V1ObjectMeta(name=name), spec=deployment_spec ) @@ -150,4 +223,50 @@ class Kubernetes(Deploy): return False def create_k8s_job(self): - pass + name = "{}-{}".format(self.deploy_parameters.name, uuid.uuid4()) + + container = client.V1Container( + name=name, + image=self.deploy_parameters.image, + command=self.deploy_parameters.command, + args=self.deploy_parameters.arguments, + image_pull_policy='Always', + volume_mounts=[client.V1VolumeMount( + name='nfs-pvc', + mount_path=self.deploy_parameters.checkpoint_dir + )] + ) + template = client.V1PodTemplateSpec( + metadata=client.V1ObjectMeta(labels={'app': name}), + spec=client.V1PodSpec( + containers=[container], + volumes=[client.V1Volume( + name="nfs-pvc", + persistent_volume_claim=client.V1PersistentVolumeClaimVolumeSource( + claim_name=self.nfs_pvc_name + ) + )], + restart_policy='Never' + ), + ) + + job_spec = client.V1JobSpec( + parallelism=self.deploy_parameters.num_workers, + template=template, + completions=2147483647 + ) + + job = client.V1Job( + api_version='batch/v1', + kind='Job', + metadata=client.V1ObjectMeta(name=name), + spec=job_spec + ) + + api_client = client.BatchV1Api() + try: + api_client.create_namespaced_job(self.deploy_parameters.namespace, job) + return True + except client.rest.ApiException as e: + print("Got exception: %s\n while creating deployment", e) + return False diff --git a/rl_coach/orchestrators/start_training.py b/rl_coach/orchestrators/start_training.py index 9950b61..ba28a25 100644 --- a/rl_coach/orchestrators/start_training.py +++ b/rl_coach/orchestrators/start_training.py @@ -3,30 +3,48 @@ import argparse from rl_coach.orchestrators.kubernetes_orchestrator import KubernetesParameters, Kubernetes -def main(preset, image='ajaysudh/testing:coach', redis_ip='redis-service.ajay.svc'): +def main(preset: str, image: str='ajaysudh/testing:coach', redis_ip: str=None, redis_port:int=None, num_workers: int=1, nfs_server: str="", nfs_path: str=""): rollout_command = ['python3', 'rl_coach/rollout_worker.py', '-p', preset] training_command = ['python3', 'rl_coach/training_worker.py', '-p', preset] - rollout_params = KubernetesParameters(image, rollout_command, redis_ip=redis_ip, redis_port=6379, num_workers=1) - training_params = KubernetesParameters(image, training_command, redis_ip=redis_ip, redis_port=6379, num_workers=1) + """ + TODO: + 1. Create a NFS backed PV for checkpointing. + a. Include that in both (worker, trainer) containers. + b. Change checkpoint writing logic to always write to a temporary file and then rename. + 2. Test e2e 1 loop. + a. Trainer writes a checkpoint + b. Rollout worker picks it and gathers experience, writes back to redis. + c. 1 rollout worker, 1 trainer. + 3. Trainer should be a job (not a deployment) + a. When all the epochs of training are done, workers should also be deleted. + 4. Test e2e with multiple rollout workers. + 5. Test e2e with multiple rollout workers and multiple loops. + """ + training_params = KubernetesParameters("train", image, training_command, kubeconfig='~/.kube/config', redis_ip=redis_ip, redis_port=redis_port, + nfs_server=nfs_server, nfs_path=nfs_path) training_obj = Kubernetes(training_params) if not training_obj.setup(): print("Could not setup") + return - rollout_obj = Kubernetes(training_params) - if not rollout_obj.setup(): - print("Could not setup") + rollout_params = KubernetesParameters("worker", image, rollout_command, kubeconfig='~/.kube/config', redis_ip=training_params.redis_ip, redis_port=training_params.redis_port, num_workers=num_workers) + rollout_obj = Kubernetes(rollout_params) + # if not rollout_obj.setup(): + # print("Could not setup") if training_obj.deploy(): print("Successfully deployed") else: print("Could not deploy") + return if rollout_obj.deploy(): print("Successfully deployed") else: print("Could not deploy") + return if __name__ == '__main__': @@ -39,10 +57,19 @@ if __name__ == '__main__': help="(string) Name of a preset to run (class name from the 'presets' directory.)", type=str, required=True) + parser.add_argument('-ns', '--nfs-server', + help="(string) Addresss of the nfs server.)", + type=str, + required=True) + parser.add_argument('-np', '--nfs-path', + help="(string) Exported path for the nfs server", + type=str, + required=True) + # parser.add_argument('--checkpoint_dir', # help='(string) Path to a folder containing a checkpoint to write the model to.', # type=str, # default='/checkpoint') args = parser.parse_args() - main(preset=args.preset, image=args.image) + main(preset=args.preset, image=args.image, nfs_server=args.nfs_server, nfs_path=args.nfs_path) diff --git a/rl_coach/training_worker.py b/rl_coach/training_worker.py index 1b9ddea..507ae50 100644 --- a/rl_coach/training_worker.py +++ b/rl_coach/training_worker.py @@ -1,22 +1,19 @@ """ """ import argparse +import time from rl_coach.base_parameters import TaskParameters from rl_coach.coach import expand_preset from rl_coach import core_types from rl_coach.utils import short_dynamic_import +from rl_coach.memories.non_episodic.distributed_experience_replay import DistributedExperienceReplay +from rl_coach.memories.memory import MemoryGranularity # Q: specify alternative distributed memory, or should this go in the preset? # A: preset must define distributed memory to be used. we aren't going to take a non-distributed preset and automatically distribute it. -def heatup(graph_manager): - num_steps = graph_manager.schedule_params.heatup_steps.num_steps - while len(graph_manager.agent_params.memory) < num_steps: - time.sleep(1) - - def training_worker(graph_manager, checkpoint_dir): """ restore a checkpoint then perform rollouts using the restored model @@ -29,10 +26,20 @@ def training_worker(graph_manager, checkpoint_dir): # save randomly initialized graph graph_manager.save_checkpoint() - heatup(graph_manager) + memory = DistributedExperienceReplay(max_size=(MemoryGranularity.Transitions, 1000000), + redis_ip=graph_manager.agent_params.memory.redis_ip, + redis_port=graph_manager.agent_params.memory.redis_port) + + while(memory.num_transitions() < 100): + time.sleep(10) + # TODO: critical: wait for minimum number of rollouts in memory before training + # TODO: Q: training steps passed into graph_manager.train ignored? + # TODO: specify training steps between checkpoints (in preset?) + # TODO: replace while true with what? number of steps, convergence, time, ... + # TODO: low: move evaluate out of this process # training loop - for _ in range(10): + for _ in range(40): graph_manager.phase = core_types.RunPhase.TRAIN graph_manager.train(core_types.TrainingSteps(1)) graph_manager.phase = core_types.RunPhase.UNDEFINED @@ -72,5 +79,6 @@ def main(): checkpoint_dir=args.checkpoint_dir, ) + if __name__ == '__main__': main() From 5e85a0f972d8d9a4f82ac626e68c44fa92e302b9 Mon Sep 17 00:00:00 2001 From: Zach Dwiel Date: Wed, 19 Sep 2018 13:10:31 -0400 Subject: [PATCH 029/162] use the number of heat up steps specified in schedule parameters --- rl_coach/training_worker.py | 22 +++++++++++----------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/rl_coach/training_worker.py b/rl_coach/training_worker.py index 507ae50..2972c00 100644 --- a/rl_coach/training_worker.py +++ b/rl_coach/training_worker.py @@ -13,6 +13,15 @@ from rl_coach.memories.memory import MemoryGranularity # Q: specify alternative distributed memory, or should this go in the preset? # A: preset must define distributed memory to be used. we aren't going to take a non-distributed preset and automatically distribute it. +def heatup(graph_manager): + memory = DistributedExperienceReplay(max_size=(MemoryGranularity.Transitions, 1000000), + redis_ip=graph_manager.agent_params.memory.redis_ip, + redis_port=graph_manager.agent_params.memory.redis_port) + + num_steps = graph_manager.schedule_params.heatup_steps.num_steps + while(memory.num_transitions() < num_steps): + time.sleep(10) + def training_worker(graph_manager, checkpoint_dir): """ @@ -26,17 +35,8 @@ def training_worker(graph_manager, checkpoint_dir): # save randomly initialized graph graph_manager.save_checkpoint() - memory = DistributedExperienceReplay(max_size=(MemoryGranularity.Transitions, 1000000), - redis_ip=graph_manager.agent_params.memory.redis_ip, - redis_port=graph_manager.agent_params.memory.redis_port) - - while(memory.num_transitions() < 100): - time.sleep(10) - # TODO: critical: wait for minimum number of rollouts in memory before training - # TODO: Q: training steps passed into graph_manager.train ignored? - # TODO: specify training steps between checkpoints (in preset?) - # TODO: replace while true with what? number of steps, convergence, time, ... - # TODO: low: move evaluate out of this process + # optionally wait for a specific number of transitions to be in memory before training + heatup(graph_manager) # training loop for _ in range(40): From ad4d2c305393faf4a543cd961097ba632903d2d6 Mon Sep 17 00:00:00 2001 From: Zach Dwiel Date: Thu, 20 Sep 2018 11:25:03 -0400 Subject: [PATCH 030/162] add make stop_kubernetes --- docker/Makefile | 17 ++++++++++++++--- 1 file changed, 14 insertions(+), 3 deletions(-) diff --git a/docker/Makefile b/docker/Makefile index 171c959..ea35972 100644 --- a/docker/Makefile +++ b/docker/Makefile @@ -1,4 +1,6 @@ -REGISTRY=nervana-dockrepo01.fm.intel.com:5001/ +# REGISTRY=nervana-dockrepo01.fm.intel.com:5001/ +# REGISTRY=gcr.io/ +REGISTRY=docker.io/ IMAGE=zdwiel/coach # IMAGE=gcr.io/deep-greens/inference:v5 @@ -44,8 +46,17 @@ run_training_worker: build run_rollout_worker: build ${DOCKER} run ${RUN_ARGUMENTS} -it ${IMAGE} python3 rl_coach/rollout_worker.py --preset CartPole_DQN_distributed -kubernetes: build push - kubectl run -i --tty --attach --image=${REGISTRY}${IMAGE} --restart=Never distributed-coach -- python3 rl_coach/orchestrators/start_training.py --preset CartPole_DQN_distributed --image ${IMAGE} +bootstrap_kubernetes: build push + kubectl run -i --tty --attach --image=${REGISTRY}${IMAGE} --restart=Never distributed-coach -- python3 rl_coach/orchestrators/start_training.py --preset CartPole_DQN_distributed --image ${IMAGE} -ns 10.63.249.182 -np / + +stop_kubernetes: + kubectl delete service --ignore-not-found redis-service + kubectl delete pv --ignore-not-found nfs-checkpoint-pv + kubectl delete pvc --ignore-not-found nfs-checkpoint-pvc + kubectl delete deployment --ignore-not-found redis-server + +kubernetes: stop_kubernetes + python3 ${CONTEXT}/rl_coach/orchestrators/start_training.py --preset CartPole_DQN_distributed --image ${IMAGE} -ns 10.63.249.182 -np / push: build ${DOCKER} tag ${IMAGE} ${REGISTRY}${IMAGE} From cd733b2404204e68a93e23a988183a731ce2c856 Mon Sep 17 00:00:00 2001 From: Zach Dwiel Date: Thu, 20 Sep 2018 11:25:45 -0400 Subject: [PATCH 031/162] add support for running kubernetes orchestrator from behind proxy --- rl_coach/orchestrators/kubernetes_orchestrator.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/rl_coach/orchestrators/kubernetes_orchestrator.py b/rl_coach/orchestrators/kubernetes_orchestrator.py index 4bb8702..71336c7 100644 --- a/rl_coach/orchestrators/kubernetes_orchestrator.py +++ b/rl_coach/orchestrators/kubernetes_orchestrator.py @@ -1,4 +1,4 @@ - +import os import uuid from rl_coach.orchestrators.deploy import Deploy, DeployParameters from kubernetes import client, config @@ -41,6 +41,9 @@ class Kubernetes(Deploy): self.deploy_parameters.namespace = current_context['context']['namespace'] self.nfs_pvc_name = 'nfs-checkpoint-pvc' + if os.environ.get('http_proxy'): + client.Configuration._default.proxy = os.environ.get('http_proxy') + def setup(self) -> bool: if not self.deploy_parameters.redis_ip: From d69332efd43702c0f828513e321ab1b5f6de9d3f Mon Sep 17 00:00:00 2001 From: Zach Dwiel Date: Thu, 20 Sep 2018 11:26:17 -0400 Subject: [PATCH 032/162] fixed bug in training worker --- rl_coach/training_worker.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/rl_coach/training_worker.py b/rl_coach/training_worker.py index 2972c00..f2c569b 100644 --- a/rl_coach/training_worker.py +++ b/rl_coach/training_worker.py @@ -18,9 +18,8 @@ def heatup(graph_manager): redis_ip=graph_manager.agent_params.memory.redis_ip, redis_port=graph_manager.agent_params.memory.redis_port) - num_steps = graph_manager.schedule_params.heatup_steps.num_steps - while(memory.num_transitions() < num_steps): - time.sleep(10) + while(memory.num_transitions() < graph_manager.heatup_steps.num_steps): + time.sleep(1) def training_worker(graph_manager, checkpoint_dir): @@ -48,6 +47,7 @@ def training_worker(graph_manager, checkpoint_dir): graph_manager.save_checkpoint() + # TODO: signal to workers that training is done def main(): parser = argparse.ArgumentParser() From 67faa80ea0dfe62da3215933c2c8da0aa814d4c4 Mon Sep 17 00:00:00 2001 From: Zach Dwiel Date: Fri, 21 Sep 2018 15:49:06 -0400 Subject: [PATCH 033/162] allow custom number of training steps --- docker/Makefile | 2 ++ rl_coach/training_worker.py | 20 +++++++++++++++++++- 2 files changed, 21 insertions(+), 1 deletion(-) diff --git a/docker/Makefile b/docker/Makefile index ea35972..9881378 100644 --- a/docker/Makefile +++ b/docker/Makefile @@ -54,6 +54,8 @@ stop_kubernetes: kubectl delete pv --ignore-not-found nfs-checkpoint-pv kubectl delete pvc --ignore-not-found nfs-checkpoint-pvc kubectl delete deployment --ignore-not-found redis-server + kubectl get jobs | grep train | awk "{print $\1}" | xargs kubectl delete jobs + kubectl get jobs | grep worker | awk "{print $\1}" | xargs kubectl delete jobs kubernetes: stop_kubernetes python3 ${CONTEXT}/rl_coach/orchestrators/start_training.py --preset CartPole_DQN_distributed --image ${IMAGE} -ns 10.63.249.182 -np / diff --git a/rl_coach/training_worker.py b/rl_coach/training_worker.py index f2c569b..d81f54c 100644 --- a/rl_coach/training_worker.py +++ b/rl_coach/training_worker.py @@ -22,6 +22,23 @@ def heatup(graph_manager): time.sleep(1) +class StepsLoop(object): + """StepsLoop facilitates a simple while loop""" + def __init__(self, steps_counters, phase, steps): + super(StepsLoop, self).__init__() + self.steps_counters = steps_counters + self.phase = phase + self.steps = steps + + self.step_end = self._step_count() + steps.num_steps + + def _step_count(self): + return self.steps_counters[self.phase][self.steps.__class__] + + def continue(self): + return self._step_count() < count_end: + + def training_worker(graph_manager, checkpoint_dir): """ restore a checkpoint then perform rollouts using the restored model @@ -38,7 +55,8 @@ def training_worker(graph_manager, checkpoint_dir): heatup(graph_manager) # training loop - for _ in range(40): + stepper = StepsLoop(graph_manager.total_steps_counters, RunPhase.TRAIN, graph_manager.improve_steps) + while stepper.continue(): graph_manager.phase = core_types.RunPhase.TRAIN graph_manager.train(core_types.TrainingSteps(1)) graph_manager.phase = core_types.RunPhase.UNDEFINED From 1e83a27beefa993005081d4f608a9ec1e1b95505 Mon Sep 17 00:00:00 2001 From: Zach Dwiel Date: Tue, 25 Sep 2018 16:14:14 -0400 Subject: [PATCH 034/162] update dockerfile and makefile --- docker/Dockerfile | 114 ++++++++++++++++++++++++++++++------ docker/Makefile | 13 +++- docker/docker_entrypoint.sh | 21 +++++++ 3 files changed, 127 insertions(+), 21 deletions(-) create mode 100644 docker/docker_entrypoint.sh diff --git a/docker/Dockerfile b/docker/Dockerfile index c05d8bc..a59a688 100644 --- a/docker/Dockerfile +++ b/docker/Dockerfile @@ -1,20 +1,97 @@ -FROM ubuntu:16.04 +FROM nvidia/cuda:9.0-cudnn7-devel-ubuntu16.04 -RUN apt-get update \ - && apt-get install -y \ - python3-pip cmake zlib1g-dev python3-tk python-opencv \ - libboost-all-dev \ - libblas-dev liblapack-dev libatlas-base-dev gfortran \ - libsdl-dev libsdl-image1.2-dev libsdl-mixer1.2-dev libsdl-ttf2.0-dev \ - libsmpeg-dev libportmidi-dev libavformat-dev libswscale-dev \ - dpkg-dev build-essential python3.5-dev libjpeg-dev libtiff-dev \ - libsdl1.2-dev libnotify-dev freeglut3 freeglut3-dev libsm-dev \ - libgtk2.0-dev libgtk-3-dev libwebkitgtk-dev libgtk-3-dev \ - libwebkitgtk-3.0-dev libgstreamer-plugins-base1.0-dev \ - libav-tools libsdl2-dev swig +# https://github.com/NVIDIA/nvidia-docker/issues/619 +RUN rm /etc/apt/sources.list.d/cuda.list +RUN apt-get update && \ + apt-get upgrade -y && \ + apt-get clean autoclean && \ + apt-get autoremove -y +RUN apt-get update && \ + apt-get install -y python-pip && \ + apt-get clean autoclean && \ + apt-get autoremove -y +RUN pip install pip --upgrade +WORKDIR /root -# installing python dependencies +################################ +# Install apt-get Requirements # +################################ + +# General +RUN apt-get install python3-pip cmake zlib1g-dev python3-tk python-opencv -y + +# Boost libraries +RUN apt-get install libboost-all-dev -y + +# Scipy requirements +RUN apt-get install libblas-dev liblapack-dev libatlas-base-dev gfortran -y + +# Pygame requirements +RUN apt-get install libsdl-dev libsdl-image1.2-dev libsdl-mixer1.2-dev libsdl-ttf2.0-dev -y +RUN apt-get install libsmpeg-dev libportmidi-dev libavformat-dev libswscale-dev -y + +# Dashboard +RUN apt-get install dpkg-dev build-essential python3.5-dev libjpeg-dev libtiff-dev libsdl1.2-dev libnotify-dev \ + freeglut3 freeglut3-dev libsm-dev libgtk2.0-dev libgtk-3-dev libwebkitgtk-dev libgtk-3-dev \ + libwebkitgtk-3.0-dev libgstreamer-plugins-base1.0-dev -y + +# Gym +RUN apt-get install libav-tools libsdl2-dev swig cmake -y + +# Mujoco_py +RUN apt-get install curl libgl1-mesa-dev libgl1-mesa-glx libglew-dev libosmesa6-dev software-properties-common -y + +# ViZDoom +RUN apt-get install build-essential zlib1g-dev libsdl2-dev libjpeg-dev \ + nasm tar libbz2-dev libgtk2.0-dev cmake git libfluidsynth-dev libgme-dev \ + libopenal-dev timidity libwildmidi-dev unzip wget -y + +# cleanup apt-get +RUN apt-get clean autoclean && \ + apt-get autoremove -y +############################ +# Install Pip Requirements # +############################ RUN pip3 install --upgrade pip +RUN pip3 install pytest +RUN pip3 install pytest-xdist + +# initial installation of coach, so that the docker build won't install everything from scratch +RUN pip3 install rl_coach>=0.10.0 + +# install additional environments +RUN pip3 install gym[atari]==0.10.5 +RUN pip3 install mujoco_py==1.50.1.56 +RUN pip3 install vizdoom==1.1.6 + +# FROM ubuntu:16.04 +# +# RUN apt-get update \ +# && apt-get install -y \ +# python3-pip cmake zlib1g-dev python3-tk python-opencv \ +# libboost-all-dev \ +# libblas-dev liblapack-dev libatlas-base-dev gfortran \ +# libsdl-dev libsdl-image1.2-dev libsdl-mixer1.2-dev libsdl-ttf2.0-dev \ +# libsmpeg-dev libportmidi-dev libavformat-dev libswscale-dev \ +# dpkg-dev build-essential python3.5-dev libjpeg-dev libtiff-dev \ +# libsdl1.2-dev libnotify-dev freeglut3 freeglut3-dev libsm-dev \ +# libgtk2.0-dev libgtk-3-dev libwebkitgtk-dev libgtk-3-dev \ +# libwebkitgtk-3.0-dev libgstreamer-plugins-base1.0-dev \ +# libav-tools libsdl2-dev swig +# +# # installing python dependencies +# RUN pip3 install --upgrade pip + +RUN apt-get update && apt-get install -y wget zip +RUN mkdir -p ~/.mujoco \ + && wget https://www.roboti.us/download/mjpro150_linux.zip -O mujoco.zip \ + && unzip mujoco.zip -d ~/.mujoco \ + && rm mujoco.zip +COPY ./mjkey.txt /root/.mujoco/ +ENV LD_LIBRARY_PATH /root/.mujoco/mjpro150/bin:$LD_LIBRARY_PATH + +RUN curl -o /usr/local/bin/patchelf https://s3-us-west-2.amazonaws.com/openai-sci-artifacts/manual-builds/patchelf_0.9_amd64.elf \ + && chmod +x /usr/local/bin/patchelf RUN mkdir /root/src COPY setup.py /root/src/. @@ -22,12 +99,11 @@ COPY requirements.txt /root/src/. COPY README.md /root/src/. WORKDIR /root/src RUN pip3 install -e . -# + +# everything above here should be cached most of the time COPY . /root/src WORKDIR /root/src RUN pip3 install -e . -# RUN pip3 install rl_coach - -# CMD ["coach", "-p", "CartPole_PG", "-e", "cartpole"] -# CMD python3 rl_coach/rollout_worker.py --preset CartPole_DQN_distributed +RUN chmod 777 /root/src/docker/docker_entrypoint.sh +ENTRYPOINT ["/root/src/docker/docker_entrypoint.sh"] diff --git a/docker/Makefile b/docker/Makefile index 9881378..5a82500 100644 --- a/docker/Makefile +++ b/docker/Makefile @@ -34,8 +34,17 @@ build: shell: build ${DOCKER} run ${RUN_ARGUMENTS} -it ${IMAGE} /bin/bash -test: build - ${DOCKER} run ${RUN_ARGUMENTS} -it ${IMAGE} py.test tests +unit_tests: build + ${DOCKER} run ${RUN_ARGUMENTS} -it ${IMAGE} python3 -m pytest rl_coach/tests -m unit_test -n 8 + +integration_tests: build + ${DOCKER} run ${RUN_ARGUMENTS} -it ${IMAGE} python3 -m pytest rl_coach/tests -m integration_test -n auto + +golden_tests: build + ${DOCKER} run ${RUN_ARGUMENTS} -it ${IMAGE} python3 rl_coach/tests/golden_tests.py + +trace_tests: build + ${DOCKER} run ${RUN_ARGUMENTS} -it ${IMAGE} python3 rl_coach/tests/trace_tests.py -prl run: build ${DOCKER} run ${RUN_ARGUMENTS} -it ${IMAGE} diff --git a/docker/docker_entrypoint.sh b/docker/docker_entrypoint.sh new file mode 100644 index 0000000..9b19f90 --- /dev/null +++ b/docker/docker_entrypoint.sh @@ -0,0 +1,21 @@ +#!/bin/sh +set -e + +# # download mjpro150 +# mkdir /root/.mujoco +# cd /root/.mujoco +# wget https://www.roboti.us/download/mjpro150_linux.zip +# unzip mjpro150_linux.zip + +# copy the mujoco license key into the container +# echo $MUJOCO_KEY | base64 --decode > /root/.mujoco/mjkey.txt +# export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/root/.mujoco/mjpro150/bin + +# git clone https://github.com/deepmind/dm_control.git +# pip3 install ./dm_control + +export VIZDOOM_ROOT=`pip show vizdoom 2>/dev/null | awk '/Location/{print $2}'`/vizdoom + +cd /root/src/ + +exec "$@" From acc7f70de3f5e05e61f1d7caf2cd250d0e695012 Mon Sep 17 00:00:00 2001 From: Zach Dwiel Date: Tue, 25 Sep 2018 16:14:47 -0400 Subject: [PATCH 035/162] enumerate each preset as its own test --- rl_coach/tests/presets/test_presets.py | 76 +++++++++++++++----------- 1 file changed, 43 insertions(+), 33 deletions(-) diff --git a/rl_coach/tests/presets/test_presets.py b/rl_coach/tests/presets/test_presets.py index 2b0bb90..0e6143f 100644 --- a/rl_coach/tests/presets/test_presets.py +++ b/rl_coach/tests/presets/test_presets.py @@ -12,45 +12,55 @@ from subprocess import Popen, DEVNULL from rl_coach.logger import screen +def all_presets(): + result = [] + for f in sorted(os.listdir('rl_coach/presets')): + if f.endswith('.py') and f != '__init__.py': + result.append(f.split('.')[0]) + return result + + +@pytest.fixture(params=all_presets()) +def preset(request): + return request.param + + @pytest.mark.integration_test -def test_all_presets_are_running(): - # os.chdir("../../") +def test_preset_runs(preset): test_failed = False - all_presets = sorted([f.split('.')[0] for f in os.listdir('rl_coach/presets') if f.endswith('.py') and f != '__init__.py']) - for preset in all_presets: - print("Testing preset {}".format(preset)) - # TODO: this is a temporary workaround for presets which define more than a single available level. - # we should probably do this in a more robust way - level = "" - if "Atari" in preset: - level = "breakout" - elif "Mujoco" in preset: - level = "inverted_pendulum" - elif "ControlSuite" in preset: - level = "pendulum:swingup" - params = ["python3", "rl_coach/coach.py", "-p", preset, "-ns", "-e", ".test"] - if level != "": - params += ["-lvl", level] + print("Testing preset {}".format(preset)) - p = Popen(params, stdout=DEVNULL) + # TODO: this is a temporary workaround for presets which define more than a single available level. + # we should probably do this in a more robust way + level = "" + if "Atari" in preset: + level = "breakout" + elif "Mujoco" in preset: + level = "inverted_pendulum" + elif "ControlSuite" in preset: + level = "pendulum:swingup" - # wait 10 seconds overhead of initialization etc. - time.sleep(10) - return_value = p.poll() + experiment_name = ".test-" + preset - if return_value is None: - screen.success("{} passed successfully".format(preset)) - else: - test_failed = True - screen.error("{} failed".format(preset), crash=False) + params = ["python3", "rl_coach/coach.py", "-p", preset, "-ns", "-e", experiment_name] + if level != "": + params += ["-lvl", level] - p.kill() - if os.path.exists("experiments/.test"): - shutil.rmtree("experiments/.test") + p = Popen(params, stdout=DEVNULL) + + # wait 10 seconds overhead of initialization etc. + time.sleep(10) + return_value = p.poll() + + if return_value is None: + screen.success("{} passed successfully".format(preset)) + else: + test_failed = True + screen.error("{} failed".format(preset), crash=False) + + p.kill() + if os.path.exists("experiments/" + experiment_name): + shutil.rmtree("experiments/" + experiment_name) assert not test_failed - - -if __name__ == "__main__": - test_all_presets_are_running() From a54ef2757f037d28787b9380fac978fc049401e0 Mon Sep 17 00:00:00 2001 From: Zach Dwiel Date: Tue, 25 Sep 2018 16:15:24 -0400 Subject: [PATCH 036/162] ignore deprecation warnings in test logging --- rl_coach/tests/pytest.ini | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/rl_coach/tests/pytest.ini b/rl_coach/tests/pytest.ini index 29d3f1d..1294264 100644 --- a/rl_coach/tests/pytest.ini +++ b/rl_coach/tests/pytest.ini @@ -2,4 +2,6 @@ [pytest] markers = unit_test: short test that checks that a module is acting correctly - integration_test: long test that checks that the complete framework is running correctly \ No newline at end of file + integration_test: long test that checks that the complete framework is running correctly +filterwarnings = + ignore::DeprecationWarning From 6b2de6ba6d5d9d546112cb072a471549964d7e7f Mon Sep 17 00:00:00 2001 From: Ajay Deshpande Date: Wed, 3 Oct 2018 15:07:48 -0700 Subject: [PATCH 037/162] Adding initial interface for backend and redis pubsub (#19) * Adding initial interface for backend and redis pubsub * Addressing comments, adding super in all memories * Removing distributed experience replay --- rl_coach/agents/agent.py | 12 + rl_coach/agents/clipped_ppo_agent.py | 2 +- rl_coach/agents/dqn_agent.py | 15 -- rl_coach/memories/backend/__init__.py | 0 rl_coach/memories/backend/memory.py | 36 +++ rl_coach/memories/backend/memory_impl.py | 21 ++ rl_coach/memories/backend/redis.py | 160 +++++++++++ .../episodic/episodic_experience_replay.py | 5 + .../episodic_hindsight_experience_replay.py | 4 + ...pisodic_hrl_hindsight_experience_replay.py | 4 + rl_coach/memories/memory.py | 14 +- .../balanced_experience_replay.py | 2 + .../distributed_experience_replay.py | 182 ------------- .../non_episodic/experience_replay.py | 3 +- .../prioritized_experience_replay.py | 3 + rl_coach/orchestrators/deploy.py | 2 +- .../orchestrators/kubernetes_orchestrator.py | 255 ++++++++++-------- rl_coach/orchestrators/start_training.py | 52 ++-- rl_coach/presets/CartPole_DQN_distributed.py | 63 ----- rl_coach/rollout_worker.py | 22 +- rl_coach/training_worker.py | 46 +--- 21 files changed, 459 insertions(+), 444 deletions(-) create mode 100644 rl_coach/memories/backend/__init__.py create mode 100644 rl_coach/memories/backend/memory.py create mode 100644 rl_coach/memories/backend/memory_impl.py create mode 100644 rl_coach/memories/backend/redis.py delete mode 100644 rl_coach/memories/non_episodic/distributed_experience_replay.py delete mode 100644 rl_coach/presets/CartPole_DQN_distributed.py diff --git a/rl_coach/agents/agent.py b/rl_coach/agents/agent.py index 13d1b8d..2dd1e9a 100644 --- a/rl_coach/agents/agent.py +++ b/rl_coach/agents/agent.py @@ -33,6 +33,7 @@ from rl_coach.memories.episodic.episodic_experience_replay import EpisodicExperi from rl_coach.spaces import SpacesDefinition, VectorObservationSpace, GoalsSpace, AttentionActionSpace from rl_coach.utils import Signal, force_list from rl_coach.utils import dynamic_import_and_instantiate_module_from_params +from rl_coach.memories.backend.memory_impl import get_memory_backend class Agent(AgentInterface): @@ -76,6 +77,14 @@ class Agent(AgentInterface): # modules self.memory = dynamic_import_and_instantiate_module_from_params(self.ap.memory) + if hasattr(self.ap.memory, 'memory_backend_params'): + self.memory_backend = get_memory_backend(self.ap.memory.memory_backend_params) + + if self.ap.memory.memory_backend_params.run_type == 'trainer': + self.memory_backend.subscribe(self.memory) + else: + self.memory.set_memory_backend(self.memory_backend) + if agent_parameters.memory.load_memory_from_file_path: screen.log_title("Loading replay buffer from pickle. Pickle path: {}" .format(agent_parameters.memory.load_memory_from_file_path)) @@ -534,6 +543,9 @@ class Agent(AgentInterface): Determine if we should start a training phase according to the number of steps passed since the last training :return: boolean: True if we should start a training phase """ + + if hasattr(self.ap.memory, 'memory_backend_params'): + self.total_steps_counter = self.call_memory('num_transitions') step_method = self.ap.algorithm.num_consecutive_playing_steps if step_method.__class__ == EnvironmentEpisodes: should_update = (self.current_episode - self.last_training_phase_step) >= step_method.num_steps diff --git a/rl_coach/agents/clipped_ppo_agent.py b/rl_coach/agents/clipped_ppo_agent.py index ba6851b..0091841 100644 --- a/rl_coach/agents/clipped_ppo_agent.py +++ b/rl_coach/agents/clipped_ppo_agent.py @@ -1,5 +1,5 @@ # -# Copyright (c) 2017 Intel Corporation +# Copyright (c) 2017 Intel Corporation # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/rl_coach/agents/dqn_agent.py b/rl_coach/agents/dqn_agent.py index b300b79..f261a08 100644 --- a/rl_coach/agents/dqn_agent.py +++ b/rl_coach/agents/dqn_agent.py @@ -27,7 +27,6 @@ from rl_coach.architectures.tensorflow_components.embedders.embedder import Inpu from rl_coach.core_types import EnvironmentSteps from rl_coach.exploration_policies.e_greedy import EGreedyParameters from rl_coach.memories.non_episodic.experience_replay import ExperienceReplayParameters -from rl_coach.memories.non_episodic.distributed_experience_replay import DistributedExperienceReplayParameters from rl_coach.schedules import LinearSchedule @@ -51,20 +50,6 @@ class DQNNetworkParameters(NetworkParameters): self.create_target_network = True -class DQNAgentParametersDistributed(AgentParameters): - def __init__(self): - super().__init__(algorithm=DQNAlgorithmParameters(), - exploration=EGreedyParameters(), - memory=DistributedExperienceReplayParameters(), - networks={"main": DQNNetworkParameters()}) - self.exploration.epsilon_schedule = LinearSchedule(1, 0.1, 1000000) - self.exploration.evaluation_epsilon = 0.05 - - @property - def path(self): - return 'rl_coach.agents.dqn_agent:DQNAgent' - - class DQNAgentParameters(AgentParameters): def __init__(self): super().__init__(algorithm=DQNAlgorithmParameters(), diff --git a/rl_coach/memories/backend/__init__.py b/rl_coach/memories/backend/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/rl_coach/memories/backend/memory.py b/rl_coach/memories/backend/memory.py new file mode 100644 index 0000000..4199cf6 --- /dev/null +++ b/rl_coach/memories/backend/memory.py @@ -0,0 +1,36 @@ + + +class MemoryBackendParameters(object): + + def __init__(self, store_type, orchestrator_type, run_type, deployed: str = False): + self.store_type = store_type + self.orchestrator_type = orchestrator_type + self.run_type = run_type + self.deployed = deployed + + +class MemoryBackend(object): + + def __init__(self, params: MemoryBackendParameters): + pass + + def deploy(self): + raise NotImplemented("Not yet implemented") + + def get_endpoint(self): + raise NotImplemented("Not yet implemented") + + def undeploy(self): + raise NotImplemented("Not yet implemented") + + def sample(self, size: int): + raise NotImplemented("Not yet implemented") + + def store(self, obj): + raise NotImplemented("Not yet implemented") + + def store_episode(self, obj): + raise NotImplemented("Not yet implemented") + + def subscribe(self, memory): + raise NotImplemented("Not yet implemented") diff --git a/rl_coach/memories/backend/memory_impl.py b/rl_coach/memories/backend/memory_impl.py new file mode 100644 index 0000000..7470ef5 --- /dev/null +++ b/rl_coach/memories/backend/memory_impl.py @@ -0,0 +1,21 @@ + +from rl_coach.memories.backend.memory import MemoryBackendParameters +from rl_coach.memories.backend.redis import RedisPubSubBackend, RedisPubSubMemoryBackendParameters + + +def get_memory_backend(params: MemoryBackendParameters): + + backend = None + if type(params) == RedisPubSubMemoryBackendParameters: + backend = RedisPubSubBackend(params) + + return backend + + +def construct_memory_params(json: dict): + + if json['store_type'] == 'redispubsub': + memory_params = RedisPubSubMemoryBackendParameters( + json['redis_address'], json['redis_port'], channel=json.get('channel', ''), run_type=json['run_type'] + ) + return memory_params diff --git a/rl_coach/memories/backend/redis.py b/rl_coach/memories/backend/redis.py new file mode 100644 index 0000000..f060d45 --- /dev/null +++ b/rl_coach/memories/backend/redis.py @@ -0,0 +1,160 @@ + +import redis +import pickle +import uuid +import threading +from kubernetes import client + +from rl_coach.memories.backend.memory import MemoryBackend, MemoryBackendParameters +from rl_coach.memories.memory import Memory +from rl_coach.core_types import Transition, Episode + + +class RedisPubSubMemoryBackendParameters(MemoryBackendParameters): + + def __init__(self, redis_address: str="", redis_port: int=6379, channel: str="channel-{}".format(uuid.uuid4()), + orchestrator_params: dict=None, run_type='trainer', orchestrator_type: str = "kubernetes", deployed: str = False): + self.redis_address = redis_address + self.redis_port = redis_port + self.channel = channel + if not orchestrator_params: + orchestrator_params = {} + self.orchestrator_params = orchestrator_params + self.run_type = run_type + self.store_type = "redispubsub" + self.orchestrator_type = orchestrator_type + self.deployed = deployed + + +class RedisPubSubBackend(MemoryBackend): + + def __init__(self, params: RedisPubSubMemoryBackendParameters): + self.params = params + self.redis_connection = redis.Redis(self.params.redis_address, self.params.redis_port) + + def store(self, obj): + self.redis_connection.publish(self.params.channel, pickle.dumps(obj)) + + def deploy(self): + if not self.params.deployed: + if self.params.orchestrator_type == 'kubernetes': + self.deploy_kubernetes() + self.params.deployed = True + + def deploy_kubernetes(self): + + if 'namespace' not in self.params.orchestrator_params: + self.params.orchestrator_params['namespace'] = "default" + + container = client.V1Container( + name="redis-server", + image='redis:4-alpine', + ) + template = client.V1PodTemplateSpec( + metadata=client.V1ObjectMeta(labels={'app': 'redis-server'}), + spec=client.V1PodSpec( + containers=[container] + ) + ) + deployment_spec = client.V1DeploymentSpec( + replicas=1, + template=template, + selector=client.V1LabelSelector( + match_labels={'app': 'redis-server'} + ) + ) + + deployment = client.V1Deployment( + api_version='apps/v1', + kind='Deployment', + metadata=client.V1ObjectMeta(name='redis-server', labels={'app': 'redis-server'}), + spec=deployment_spec + ) + + api_client = client.AppsV1Api() + try: + api_client.create_namespaced_deployment(self.params.orchestrator_params['namespace'], deployment) + except client.rest.ApiException as e: + print("Got exception: %s\n while creating redis-server", e) + return False + + core_v1_api = client.CoreV1Api() + + service = client.V1Service( + api_version='v1', + kind='Service', + metadata=client.V1ObjectMeta( + name='redis-service' + ), + spec=client.V1ServiceSpec( + selector={'app': 'redis-server'}, + ports=[client.V1ServicePort( + protocol='TCP', + port=6379, + target_port=6379 + )] + ) + ) + + try: + core_v1_api.create_namespaced_service(self.params.orchestrator_params['namespace'], service) + self.params.redis_address = 'redis-service.{}.svc'.format(self.params.orchestrator_params['namespace']) + self.params.redis_port = 6379 + return True + except client.rest.ApiException as e: + print("Got exception: %s\n while creating a service for redis-server", e) + return False + + def undeploy(self): + if not self.params.deployed: + return + api_client = client.AppsV1Api() + delete_options = client.V1DeleteOptions() + try: + api_client.delete_namespaced_deployment('redis-server', self.params.orchestrator_params['namespace'], delete_options) + except client.rest.ApiException as e: + print("Got exception: %s\n while deleting redis-server", e) + + api_client = client.CoreV1Api() + try: + api_client.delete_namespaced_service('redis-service', self.params.orchestrator_params['namespace'], delete_options) + except client.rest.ApiException as e: + print("Got exception: %s\n while deleting redis-server", e) + + self.params.deployed = False + + def sample(self, size): + pass + + def subscribe(self, memory): + redis_sub = RedisSub(memory, redis_address=self.params.redis_address, redis_port=self.params.redis_port, channel=self.params.channel) + redis_sub.daemon = True + redis_sub.start() + + def get_endpoint(self): + return {'redis_address': self.params.redis_address, + 'redis_port': self.params.redis_port} + + +class RedisSub(threading.Thread): + + def __init__(self, memory: Memory, redis_address: str = "localhost", redis_port: int=6379, channel: str = "PubsubChannel"): + super().__init__() + self.redis_connection = redis.Redis(redis_address, redis_port) + self.pubsub = self.redis_connection.pubsub() + self.subscriber = None + self.memory = memory + self.channel = channel + self.subscriber = self.pubsub.subscribe(self.channel) + + def run(self): + for message in self.pubsub.listen(): + if message and 'data' in message: + try: + obj = pickle.loads(message['data']) + if type(obj) == Transition: + self.memory.store(obj) + elif type(obj) == Episode: + self.memory.store_episode(obj) + except Exception: + continue diff --git a/rl_coach/memories/episodic/episodic_experience_replay.py b/rl_coach/memories/episodic/episodic_experience_replay.py index 79f9aaf..f174a74 100644 --- a/rl_coach/memories/episodic/episodic_experience_replay.py +++ b/rl_coach/memories/episodic/episodic_experience_replay.py @@ -160,6 +160,8 @@ class EpisodicExperienceReplay(Memory): :param transition: a transition to store :return: None """ + # Calling super.store() so that in case a memory backend is used, the memory backend can store this transition. + super().store(transition) self.reader_writer_lock.lock_writing_and_reading() if len(self._buffer) == 0: @@ -181,6 +183,9 @@ class EpisodicExperienceReplay(Memory): :param episode: the new episode to store :return: None """ + # Calling super.store() so that in case a memory backend is used, the memory backend can store this episode. + super().store(episode) + if lock: self.reader_writer_lock.lock_writing_and_reading() diff --git a/rl_coach/memories/episodic/episodic_hindsight_experience_replay.py b/rl_coach/memories/episodic/episodic_hindsight_experience_replay.py index c30f451..ad006f1 100644 --- a/rl_coach/memories/episodic/episodic_hindsight_experience_replay.py +++ b/rl_coach/memories/episodic/episodic_hindsight_experience_replay.py @@ -106,6 +106,10 @@ class EpisodicHindsightExperienceReplay(EpisodicExperienceReplay): ] def store_episode(self, episode: Episode, lock: bool=True) -> None: + + # Calling super.store() so that in case a memory backend is used, the memory backend can store this episode. + super().store_episode(episode) + # generate hindsight transitions only when an episode is finished last_episode_transitions = copy.copy(episode.transitions) diff --git a/rl_coach/memories/episodic/episodic_hrl_hindsight_experience_replay.py b/rl_coach/memories/episodic/episodic_hrl_hindsight_experience_replay.py index c433c5e..9ec155d 100644 --- a/rl_coach/memories/episodic/episodic_hrl_hindsight_experience_replay.py +++ b/rl_coach/memories/episodic/episodic_hrl_hindsight_experience_replay.py @@ -59,6 +59,10 @@ class EpisodicHRLHindsightExperienceReplay(EpisodicHindsightExperienceReplay): # for a layer producing sub-goals, we will replace in hindsight the action (sub-goal) given to the lower # level with the actual achieved goal. the achieved goal (and observation) seen is assumed to be the same # for all levels - we can use this level's achieved goal instead of the lower level's one + + # Calling super.store() so that in case a memory backend is used, the memory backend can store this episode. + super().store_episode(episode) + for transition in episode.transitions: new_achieved_goal = transition.next_state[self.goals_space.goal_name] transition.action = new_achieved_goal diff --git a/rl_coach/memories/memory.py b/rl_coach/memories/memory.py index e5285e0..5c56cd0 100644 --- a/rl_coach/memories/memory.py +++ b/rl_coach/memories/memory.py @@ -18,6 +18,7 @@ from enum import Enum from typing import Tuple from rl_coach.base_parameters import Parameters +from rl_coach.memories.backend.memory import MemoryBackend class MemoryGranularity(Enum): @@ -32,7 +33,6 @@ class MemoryParameters(Parameters): self.shared_memory = False self.load_memory_from_file_path = None - @property def path(self): return 'rl_coach.memories.memory:Memory' @@ -45,9 +45,16 @@ class Memory(object): """ self.max_size = max_size self._length = 0 + self.memory_backend = None def store(self, obj): - raise NotImplementedError("") + if self.memory_backend: + self.memory_backend.store(obj) + + def store_episode(self, episode): + if self.memory_backend: + for transition in episode: + self.memory_backend.store(transition) def get(self, index): raise NotImplementedError("") @@ -64,4 +71,5 @@ class Memory(object): def clean(self): raise NotImplementedError("") - + def set_memory_backend(self, memory_backend: MemoryBackend): + self.memory_backend = memory_backend diff --git a/rl_coach/memories/non_episodic/balanced_experience_replay.py b/rl_coach/memories/non_episodic/balanced_experience_replay.py index 24f2c19..7bfb48b 100644 --- a/rl_coach/memories/non_episodic/balanced_experience_replay.py +++ b/rl_coach/memories/non_episodic/balanced_experience_replay.py @@ -72,6 +72,8 @@ class BalancedExperienceReplay(ExperienceReplay): locks and then calls store with lock = True :return: None """ + # Calling super.store() so that in case a memory backend is used, the memory backend can store this transition. + super().store(transition) if lock: self.reader_writer_lock.lock_writing_and_reading() diff --git a/rl_coach/memories/non_episodic/distributed_experience_replay.py b/rl_coach/memories/non_episodic/distributed_experience_replay.py deleted file mode 100644 index 61b24b0..0000000 --- a/rl_coach/memories/non_episodic/distributed_experience_replay.py +++ /dev/null @@ -1,182 +0,0 @@ -# -# Copyright (c) 2017 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - -from typing import List, Tuple, Union - -import numpy as np -import redis -import uuid -import pickle - -from rl_coach.core_types import Transition -from rl_coach.memories.memory import Memory, MemoryGranularity, MemoryParameters - - -class DistributedExperienceReplayParameters(MemoryParameters): - def __init__(self): - super().__init__() - self.max_size = (MemoryGranularity.Transitions, 1000000) - self.allow_duplicates_in_batch_sampling = True - self.redis_ip = 'localhost' - self.redis_port = 6379 - self.redis_db = 0 - - @property - def path(self): - return 'rl_coach.memories.non_episodic.distributed_experience_replay:DistributedExperienceReplay' - - -class DistributedExperienceReplay(Memory): - """ - A regular replay buffer which stores transition without any additional structure - """ - def __init__(self, max_size: Tuple[MemoryGranularity, int], allow_duplicates_in_batch_sampling: bool=True, - redis_ip='localhost', redis_port=6379, redis_db=0): - """ - :param max_size: the maximum number of transitions or episodes to hold in the memory - :param allow_duplicates_in_batch_sampling: allow having the same transition multiple times in a batch - """ - - super().__init__(max_size) - if max_size[0] != MemoryGranularity.Transitions: - raise ValueError("Experience replay size can only be configured in terms of transitions") - self.allow_duplicates_in_batch_sampling = allow_duplicates_in_batch_sampling - - self.db = redis_db - self.redis_connection = redis.Redis(redis_ip, redis_port, self.db) - - def length(self) -> int: - """ - Get the number of transitions in the ER - """ - return self.num_transitions() - - def num_transitions(self) -> int: - """ - Get the number of transitions in the ER - """ - try: - return self.redis_connection.info(section='keyspace')['db{}'.format(self.db)]['keys'] - except Exception as e: - return 0 - - def sample(self, size: int) -> List[Transition]: - """ - Sample a batch of transitions form the replay buffer. If the requested size is larger than the number - of samples available in the replay buffer then the batch will return empty. - :param size: the size of the batch to sample - :param beta: the beta parameter used for importance sampling - :return: a batch (list) of selected transitions from the replay buffer - """ - transition_idx = dict() - if self.allow_duplicates_in_batch_sampling: - while len(transition_idx) != size: - key = self.redis_connection.randomkey() - transition_idx[key] = pickle.loads(self.redis_connection.get(key)) - else: - if self.num_transitions() >= size: - while len(transition_idx) != size: - key = self.redis_connection.randomkey() - if key in transition_idx: - continue - transition_idx[key] = pickle.loads(self.redis_connection.get(key)) - else: - raise ValueError("The replay buffer cannot be sampled since there are not enough transitions yet. " - "There are currently {} transitions".format(self.num_transitions())) - - batch = transition_idx.values() - - return batch - - def _enforce_max_length(self) -> None: - """ - Make sure that the size of the replay buffer does not pass the maximum size allowed. - If it passes the max size, the oldest transition in the replay buffer will be removed. - This function does not use locks since it is only called internally - :return: None - """ - granularity, size = self.max_size - if granularity == MemoryGranularity.Transitions: - while size != 0 and self.num_transitions() > size: - self.redis_connection.delete(self.redis_connection.randomkey()) - else: - raise ValueError("The granularity of the replay buffer can only be set in terms of transitions") - - def store(self, transition: Transition, lock: bool=True) -> None: - """ - Store a new transition in the memory. - :param transition: a transition to store - :param lock: if true, will lock the readers writers lock. this can cause a deadlock if an inheriting class - locks and then calls store with lock = True - :return: None - """ - self.redis_connection.set(uuid.uuid4(), pickle.dumps(transition)) - self._enforce_max_length() - - def get_transition(self, transition_index: int, lock: bool=True) -> Union[None, Transition]: - """ - Returns the transition in the given index. If the transition does not exist, returns None instead. - :param transition_index: the index of the transition to return - :param lock: use write locking if this is a shared memory - :return: the corresponding transition - """ - return pickle.loads(self.redis_connection.get(transition_index)) - - def remove_transition(self, transition_index: int, lock: bool=True) -> None: - """ - Remove the transition in the given index. - - This does not remove the transition from the segment trees! it is just used to remove the transition - from the transitions list - :param transition_index: the index of the transition to remove - :return: None - """ - self.redis_connection.delete(transition_index) - - # for API compatibility - def get(self, transition_index: int, lock: bool=True) -> Union[None, Transition]: - """ - Returns the transition in the given index. If the transition does not exist, returns None instead. - :param transition_index: the index of the transition to return - :return: the corresponding transition - """ - return self.get_transition(transition_index, lock) - - # for API compatibility - def remove(self, transition_index: int, lock: bool=True): - """ - Remove the transition in the given index - :param transition_index: the index of the transition to remove - :return: None - """ - self.remove_transition(transition_index, lock) - - def clean(self, lock: bool=True) -> None: - """ - Clean the memory by removing all the episodes - :return: None - """ - self.redis_connection.flushall() - # self.transitions = [] - - def mean_reward(self) -> np.ndarray: - """ - Get the mean reward in the replay buffer - :return: the mean reward - """ - mean = np.mean([pickle.loads(self.redis_connection.get(key)).reward for key in self.redis_connection.keys()]) - - return mean diff --git a/rl_coach/memories/non_episodic/experience_replay.py b/rl_coach/memories/non_episodic/experience_replay.py index 4887e49..b3a2043 100644 --- a/rl_coach/memories/non_episodic/experience_replay.py +++ b/rl_coach/memories/non_episodic/experience_replay.py @@ -90,7 +90,6 @@ class ExperienceReplay(Memory): batch = [self.transitions[i] for i in transitions_idx] self.reader_writer_lock.release_writing() - return batch def _enforce_max_length(self) -> None: @@ -115,6 +114,8 @@ class ExperienceReplay(Memory): locks and then calls store with lock = True :return: None """ + # Calling super.store() so that in case a memory backend is used, the memory backend can store this transition. + super().store(transition) if lock: self.reader_writer_lock.lock_writing_and_reading() diff --git a/rl_coach/memories/non_episodic/prioritized_experience_replay.py b/rl_coach/memories/non_episodic/prioritized_experience_replay.py index a8fdcfc..544df48 100644 --- a/rl_coach/memories/non_episodic/prioritized_experience_replay.py +++ b/rl_coach/memories/non_episodic/prioritized_experience_replay.py @@ -267,6 +267,9 @@ class PrioritizedExperienceReplay(ExperienceReplay): :param transition: a transition to store :return: None """ + # Calling super.store() so that in case a memory backend is used, the memory backend can store this transition. + super().store(transition) + self.reader_writer_lock.lock_writing_and_reading() transition_priority = self.maximal_priority diff --git a/rl_coach/orchestrators/deploy.py b/rl_coach/orchestrators/deploy.py index d99e1d1..36b8b34 100644 --- a/rl_coach/orchestrators/deploy.py +++ b/rl_coach/orchestrators/deploy.py @@ -16,4 +16,4 @@ class Deploy(object): pass def deploy(self) -> bool: - pass \ No newline at end of file + pass diff --git a/rl_coach/orchestrators/kubernetes_orchestrator.py b/rl_coach/orchestrators/kubernetes_orchestrator.py index 71336c7..b22b681 100644 --- a/rl_coach/orchestrators/kubernetes_orchestrator.py +++ b/rl_coach/orchestrators/kubernetes_orchestrator.py @@ -1,29 +1,46 @@ import os import uuid +import json +import time +from typing import List from rl_coach.orchestrators.deploy import Deploy, DeployParameters from kubernetes import client, config +from rl_coach.memories.backend.memory import MemoryBackendParameters +from rl_coach.memories.backend.memory_impl import get_memory_backend + + +class RunTypeParameters(): + + def __init__(self, image: str, command: list(), arguments: list() = None, + run_type: str = "trainer", checkpoint_dir: str = "/checkpoint", + num_replicas: int = 1, orchestration_params: dict=None): + self.image = image + self.command = command + if not arguments: + arguments = list() + self.arguments = arguments + self.run_type = run_type + self.checkpoint_dir = checkpoint_dir + self.num_replicas = num_replicas + if not orchestration_params: + orchestration_params = dict() + self.orchestration_params = orchestration_params class KubernetesParameters(DeployParameters): - def __init__(self, name: str, image: str, command: list(), arguments: list() = list(), synchronized: bool = False, - num_workers: int = 1, kubeconfig: str = None, namespace: str = None, redis_ip: str = None, - redis_port: int = None, redis_db: int = 0, nfs_server: str = None, nfs_path: str = None, - checkpoint_dir: str = '/checkpoint'): - self.image = image - self.synchronized = synchronized - self.command = command - self.arguments = arguments + def __init__(self, run_type_params: List[RunTypeParameters], kubeconfig: str = None, namespace: str = "", nfs_server: str = None, + nfs_path: str = None, checkpoint_dir: str = '/checkpoint', memory_backend_parameters: MemoryBackendParameters = None): + + self.run_type_params = {} + for run_type_param in run_type_params: + self.run_type_params[run_type_param.run_type] = run_type_param self.kubeconfig = kubeconfig - self.num_workers = num_workers self.namespace = namespace - self.redis_ip = redis_ip - self.redis_port = redis_port - self.redis_db = redis_db self.nfs_server = nfs_server self.nfs_path = nfs_path self.checkpoint_dir = checkpoint_dir - self.name = name + self.memory_backend_parameters = memory_backend_parameters class Kubernetes(Deploy): @@ -44,17 +61,14 @@ class Kubernetes(Deploy): if os.environ.get('http_proxy'): client.Configuration._default.proxy = os.environ.get('http_proxy') + self.deploy_parameters.memory_backend_parameters.orchestrator_params = {'namespace': self.deploy_parameters.namespace} + self.memory_backend = get_memory_backend(self.deploy_parameters.memory_backend_parameters) + def setup(self) -> bool: - if not self.deploy_parameters.redis_ip: - # Need to spin up a redis service and a deployment. - if not self.deploy_redis(): - print("Failed to setup redis") - return False - + self.memory_backend.deploy() if not self.create_nfs_resources(): return False - return True def create_nfs_resources(self): @@ -107,87 +121,24 @@ class Kubernetes(Deploy): return False return True - def deploy_redis(self) -> bool: - container = client.V1Container( - name="redis-server", - image='redis:4-alpine', - ) - template = client.V1PodTemplateSpec( - metadata=client.V1ObjectMeta(labels={'app': 'redis-server'}), - spec=client.V1PodSpec( - containers=[container] - ) - ) - deployment_spec = client.V1DeploymentSpec( - replicas=1, - template=template, - selector=client.V1LabelSelector( - match_labels={'app': 'redis-server'} - ) - ) + def deploy_trainer(self) -> bool: - deployment = client.V1Deployment( - api_version='apps/v1', - kind='Deployment', - metadata=client.V1ObjectMeta(name='redis-server', labels={'app': 'redis-server'}), - spec=deployment_spec - ) - - api_client = client.AppsV1Api() - try: - api_client.create_namespaced_deployment(self.deploy_parameters.namespace, deployment) - except client.rest.ApiException as e: - print("Got exception: %s\n while creating redis-server", e) + trainer_params = self.deploy_parameters.run_type_params.get('trainer', None) + if not trainer_params: return False - core_v1_api = client.CoreV1Api() - - service = client.V1Service( - api_version='v1', - kind='Service', - metadata=client.V1ObjectMeta( - name='redis-service' - ), - spec=client.V1ServiceSpec( - selector={'app': 'redis-server'}, - ports=[client.V1ServicePort( - protocol='TCP', - port=6379, - target_port=6379 - )] - ) - ) - - try: - core_v1_api.create_namespaced_service(self.deploy_parameters.namespace, service) - self.deploy_parameters.redis_ip = 'redis-service.{}.svc'.format(self.deploy_parameters.namespace) - self.deploy_parameters.redis_port = 6379 - return True - except client.rest.ApiException as e: - print("Got exception: %s\n while creating a service for redis-server", e) - return False - - def deploy(self) -> bool: - - self.deploy_parameters.command += ['--redis_ip', self.deploy_parameters.redis_ip, '--redis_port', '{}'.format(self.deploy_parameters.redis_port)] - - if self.deploy_parameters.synchronized: - return self.create_k8s_deployment() - else: - return self.create_k8s_job() - - def create_k8s_deployment(self) -> bool: - name = "{}-{}".format(self.deploy_parameters.name, uuid.uuid4()) + trainer_params.command += ['--memory_backend_params', json.dumps(self.deploy_parameters.memory_backend_parameters.__dict__)] + name = "{}-{}".format(trainer_params.run_type, uuid.uuid4()) container = client.V1Container( name=name, - image=self.deploy_parameters.image, - command=self.deploy_parameters.command, - args=self.deploy_parameters.arguments, + image=trainer_params.image, + command=trainer_params.command, + args=trainer_params.arguments, image_pull_policy='Always', volume_mounts=[client.V1VolumeMount( name='nfs-pvc', - mount_path=self.deploy_parameters.checkpoint_dir + mount_path=trainer_params.checkpoint_dir )] ) template = client.V1PodTemplateSpec( @@ -203,7 +154,7 @@ class Kubernetes(Deploy): ), ) deployment_spec = client.V1DeploymentSpec( - replicas=self.deploy_parameters.num_workers, + replicas=trainer_params.num_replicas, template=template, selector=client.V1LabelSelector( match_labels={'app': name} @@ -220,23 +171,30 @@ class Kubernetes(Deploy): api_client = client.AppsV1Api() try: api_client.create_namespaced_deployment(self.deploy_parameters.namespace, deployment) + trainer_params.orchestration_params['deployment_name'] = name return True except client.rest.ApiException as e: print("Got exception: %s\n while creating deployment", e) return False - def create_k8s_job(self): - name = "{}-{}".format(self.deploy_parameters.name, uuid.uuid4()) + def deploy_worker(self): + + worker_params = self.deploy_parameters.run_type_params.get('worker', None) + if not worker_params: + return False + + worker_params.command += ['--memory_backend_params', json.dumps(self.deploy_parameters.memory_backend_parameters.__dict__)] + name = "{}-{}".format(worker_params.run_type, uuid.uuid4()) container = client.V1Container( name=name, - image=self.deploy_parameters.image, - command=self.deploy_parameters.command, - args=self.deploy_parameters.arguments, + image=worker_params.image, + command=worker_params.command, + args=worker_params.arguments, image_pull_policy='Always', volume_mounts=[client.V1VolumeMount( name='nfs-pvc', - mount_path=self.deploy_parameters.checkpoint_dir + mount_path=worker_params.checkpoint_dir )] ) template = client.V1PodTemplateSpec( @@ -249,27 +207,104 @@ class Kubernetes(Deploy): claim_name=self.nfs_pvc_name ) )], - restart_policy='Never' ), ) - job_spec = client.V1JobSpec( - parallelism=self.deploy_parameters.num_workers, + deployment_spec = client.V1DeploymentSpec( + replicas=worker_params.num_replicas, template=template, - completions=2147483647 + selector=client.V1LabelSelector( + match_labels={'app': name} + ) ) - - job = client.V1Job( - api_version='batch/v1', - kind='Job', + deployment = client.V1Deployment( + api_version='apps/v1', + kind="Deployment", metadata=client.V1ObjectMeta(name=name), - spec=job_spec + spec=deployment_spec ) - api_client = client.BatchV1Api() + api_client = client.AppsV1Api() try: - api_client.create_namespaced_job(self.deploy_parameters.namespace, job) + api_client.create_namespaced_deployment(self.deploy_parameters.namespace, deployment) + worker_params.orchestration_params['deployment_name'] = name return True except client.rest.ApiException as e: print("Got exception: %s\n while creating deployment", e) return False + + def worker_logs(self): + pass + + def trainer_logs(self): + trainer_params = self.deploy_parameters.run_type_params.get('trainer', None) + if not trainer_params: + return + + api_client = client.CoreV1Api() + pod = None + try: + pods = api_client.list_namespaced_pod(self.deploy_parameters.namespace, label_selector='app={}'.format( + trainer_params.orchestration_params['deployment_name'] + )) + + pod = pods.items[0] + except client.rest.ApiException as e: + print("Got exception: %s\n while reading pods", e) + return + + if not pod: + return + + self.tail_log(pod.metadata.name, api_client) + + def tail_log(self, pod_name, corev1_api): + while True: + time.sleep(10) + # Try to tail the pod logs + try: + print(corev1_api.read_namespaced_pod_log( + pod_name, self.deploy_parameters.namespace, follow=True + ), flush=True) + except client.rest.ApiException as e: + pass + + # This part will get executed if the pod is one of the following phases: not ready, failed or terminated. + # Check if the pod has errored out, else just try again. + # Get the pod + try: + pod = corev1_api.read_namespaced_pod(pod_name, self.deploy_parameters.namespace) + except client.rest.ApiException as e: + continue + + if not hasattr(pod, 'status') or not pod.status: + continue + if not hasattr(pod.status, 'container_statuses') or not pod.status.container_statuses: + continue + + for container_status in pod.status.container_statuses: + if container_status.state.waiting is not None: + if container_status.state.waiting.reason == 'Error' or \ + container_status.state.waiting.reason == 'CrashLoopBackOff' or \ + container_status.state.waiting.reason == 'ImagePullBackOff' or \ + container_status.state.waiting.reason == 'ErrImagePull': + return + if container_status.state.terminated is not None: + return + + def undeploy(self): + trainer_params = self.deploy_parameters.run_type_params.get('trainer', None) + api_client = client.AppsV1Api() + delete_options = client.V1DeleteOptions() + if trainer_params: + try: + api_client.delete_namespaced_deployment(trainer_params.orchestration_params['deployment_name'], self.deploy_parameters.namespace, delete_options) + except client.rest.ApiException as e: + print("Got exception: %s\n while deleting trainer", e) + worker_params = self.deploy_parameters.run_type_params.get('worker', None) + if worker_params: + try: + api_client.delete_namespaced_deployment(worker_params.orchestration_params['deployment_name'], self.deploy_parameters.namespace, delete_options) + except client.rest.ApiException as e: + print("Got exception: %s\n while deleting workers", e) + self.memory_backend.undeploy() diff --git a/rl_coach/orchestrators/start_training.py b/rl_coach/orchestrators/start_training.py index ba28a25..547bb5c 100644 --- a/rl_coach/orchestrators/start_training.py +++ b/rl_coach/orchestrators/start_training.py @@ -1,51 +1,43 @@ import argparse -from rl_coach.orchestrators.kubernetes_orchestrator import KubernetesParameters, Kubernetes +from rl_coach.orchestrators.kubernetes_orchestrator import KubernetesParameters, Kubernetes, RunTypeParameters +from rl_coach.memories.backend.redis import RedisPubSubMemoryBackendParameters -def main(preset: str, image: str='ajaysudh/testing:coach', redis_ip: str=None, redis_port:int=None, num_workers: int=1, nfs_server: str="", nfs_path: str=""): +def main(preset: str, image: str='ajaysudh/testing:coach', num_workers: int=1, nfs_server: str="", nfs_path: str="", memory_backend: str=""): rollout_command = ['python3', 'rl_coach/rollout_worker.py', '-p', preset] training_command = ['python3', 'rl_coach/training_worker.py', '-p', preset] - """ - TODO: - 1. Create a NFS backed PV for checkpointing. - a. Include that in both (worker, trainer) containers. - b. Change checkpoint writing logic to always write to a temporary file and then rename. - 2. Test e2e 1 loop. - a. Trainer writes a checkpoint - b. Rollout worker picks it and gathers experience, writes back to redis. - c. 1 rollout worker, 1 trainer. - 3. Trainer should be a job (not a deployment) - a. When all the epochs of training are done, workers should also be deleted. - 4. Test e2e with multiple rollout workers. - 5. Test e2e with multiple rollout workers and multiple loops. - """ + memory_backend_params = RedisPubSubMemoryBackendParameters() - training_params = KubernetesParameters("train", image, training_command, kubeconfig='~/.kube/config', redis_ip=redis_ip, redis_port=redis_port, - nfs_server=nfs_server, nfs_path=nfs_path) - training_obj = Kubernetes(training_params) - if not training_obj.setup(): + worker_run_type_params = RunTypeParameters(image, rollout_command, run_type="worker") + trainer_run_type_params = RunTypeParameters(image, training_command, run_type="trainer") + + orchestration_params = KubernetesParameters([worker_run_type_params, trainer_run_type_params], kubeconfig='~/.kube/config', nfs_server=nfs_server, + nfs_path=nfs_path, memory_backend_parameters=memory_backend_params) + orchestrator = Kubernetes(orchestration_params) + if not orchestrator.setup(): print("Could not setup") return - rollout_params = KubernetesParameters("worker", image, rollout_command, kubeconfig='~/.kube/config', redis_ip=training_params.redis_ip, redis_port=training_params.redis_port, num_workers=num_workers) - rollout_obj = Kubernetes(rollout_params) - # if not rollout_obj.setup(): - # print("Could not setup") - - if training_obj.deploy(): + if orchestrator.deploy_trainer(): print("Successfully deployed") else: print("Could not deploy") return - if rollout_obj.deploy(): + if orchestrator.deploy_worker(): print("Successfully deployed") else: print("Could not deploy") return + try: + orchestrator.trainer_logs() + except KeyboardInterrupt: + pass + orchestrator.undeploy() + if __name__ == '__main__': parser = argparse.ArgumentParser() @@ -65,6 +57,10 @@ if __name__ == '__main__': help="(string) Exported path for the nfs server", type=str, required=True) + parser.add_argument('--memory_backend', + help="(string) Memory backend to use", + type=str, + default="redispubsub") # parser.add_argument('--checkpoint_dir', # help='(string) Path to a folder containing a checkpoint to write the model to.', @@ -72,4 +68,4 @@ if __name__ == '__main__': # default='/checkpoint') args = parser.parse_args() - main(preset=args.preset, image=args.image, nfs_server=args.nfs_server, nfs_path=args.nfs_path) + main(preset=args.preset, image=args.image, nfs_server=args.nfs_server, nfs_path=args.nfs_path, memory_backend=args.memory_backend) diff --git a/rl_coach/presets/CartPole_DQN_distributed.py b/rl_coach/presets/CartPole_DQN_distributed.py deleted file mode 100644 index f4259c4..0000000 --- a/rl_coach/presets/CartPole_DQN_distributed.py +++ /dev/null @@ -1,63 +0,0 @@ -from rl_coach.agents.dqn_agent import DQNAgentParametersDistributed -from rl_coach.base_parameters import VisualizationParameters, PresetValidationParameters -from rl_coach.core_types import TrainingSteps, EnvironmentEpisodes, EnvironmentSteps, RunPhase -from rl_coach.environments.environment import SelectedPhaseOnlyDumpMethod, MaxDumpMethod -from rl_coach.environments.gym_environment import Mujoco -from rl_coach.graph_managers.basic_rl_graph_manager import BasicRLGraphManager -from rl_coach.graph_managers.graph_manager import ScheduleParameters -from rl_coach.memories.memory import MemoryGranularity -from rl_coach.schedules import LinearSchedule - - - -#################### -# Graph Scheduling # -#################### - -schedule_params = ScheduleParameters() -schedule_params.improve_steps = TrainingSteps(10000000000) -schedule_params.steps_between_evaluation_periods = EnvironmentEpisodes(10) -schedule_params.evaluation_steps = EnvironmentEpisodes(1) -schedule_params.heatup_steps = EnvironmentSteps(1000) - -######### -# Agent # -######### -agent_params = DQNAgentParametersDistributed() - -# DQN params -agent_params.algorithm.num_steps_between_copying_online_weights_to_target = EnvironmentSteps(100) -agent_params.algorithm.discount = 0.99 -agent_params.algorithm.num_consecutive_playing_steps = EnvironmentSteps(1) - -# NN configuration -agent_params.network_wrappers['main'].learning_rate = 0.00025 -agent_params.network_wrappers['main'].replace_mse_with_huber_loss = False - -# ER size -agent_params.memory.max_size = (MemoryGranularity.Transitions, 40000) - -# E-Greedy schedule -agent_params.exploration.epsilon_schedule = LinearSchedule(1.0, 0.01, 10000) - -################ -# Environment # -################ -env_params = Mujoco() -env_params.level = 'CartPole-v0' - -vis_params = VisualizationParameters() -vis_params.video_dump_methods = [SelectedPhaseOnlyDumpMethod(RunPhase.TEST), MaxDumpMethod()] -vis_params.dump_mp4 = False - -######## -# Test # -######## -preset_validation_params = PresetValidationParameters() -preset_validation_params.test = True -preset_validation_params.min_reward_threshold = 150 -preset_validation_params.max_episodes_to_achieve_reward = 250 - -graph_manager = BasicRLGraphManager(agent_params=agent_params, env_params=env_params, - schedule_params=schedule_params, vis_params=vis_params, - preset_validation_params=preset_validation_params) diff --git a/rl_coach/rollout_worker.py b/rl_coach/rollout_worker.py index 27845a6..7d8a371 100644 --- a/rl_coach/rollout_worker.py +++ b/rl_coach/rollout_worker.py @@ -10,11 +10,14 @@ this rollout worker: import argparse import time import os +import json from rl_coach.base_parameters import TaskParameters from rl_coach.coach import expand_preset from rl_coach.core_types import EnvironmentEpisodes, RunPhase from rl_coach.utils import short_dynamic_import +from rl_coach.memories.backend.memory_impl import construct_memory_params + # Q: specify alternative distributed memory, or should this go in the preset? # A: preset must define distributed memory to be used. we aren't going to take @@ -58,9 +61,12 @@ def rollout_worker(graph_manager, checkpoint_dir): task_parameters = TaskParameters() task_parameters.__dict__['checkpoint_restore_dir'] = checkpoint_dir graph_manager.create_graph(task_parameters) - graph_manager.phase = RunPhase.TRAIN - graph_manager.act(EnvironmentEpisodes(num_steps=10)) + + for i in range(10000000): + graph_manager.act(EnvironmentEpisodes(num_steps=10)) + graph_manager.restore_checkpoint() + graph_manager.phase = RunPhase.UNDEFINED @@ -82,13 +88,19 @@ def main(): help="(int) Port of the redis server", default=6379, type=int) + parser.add_argument('--memory_backend_params', + help="(string) JSON string of the memory backend params", + type=str) + args = parser.parse_args() graph_manager = short_dynamic_import(expand_preset(args.preset), ignore_module_case=True) - graph_manager.agent_params.memory.redis_ip = args.redis_ip - graph_manager.agent_params.memory.redis_port = args.redis_port - + if args.memory_backend_params: + args.memory_backend_params = json.loads(args.memory_backend_params) + if 'run_type' not in args.memory_backend_params: + args.memory_backend_params['run_type'] = 'worker' + graph_manager.agent_params.memory.register_var('memory_backend_params', construct_memory_params(args.memory_backend_params)) rollout_worker( graph_manager=graph_manager, checkpoint_dir=args.checkpoint_dir, diff --git a/rl_coach/training_worker.py b/rl_coach/training_worker.py index d81f54c..85f2052 100644 --- a/rl_coach/training_worker.py +++ b/rl_coach/training_worker.py @@ -2,42 +2,17 @@ """ import argparse import time +import json from rl_coach.base_parameters import TaskParameters from rl_coach.coach import expand_preset from rl_coach import core_types from rl_coach.utils import short_dynamic_import -from rl_coach.memories.non_episodic.distributed_experience_replay import DistributedExperienceReplay -from rl_coach.memories.memory import MemoryGranularity +from rl_coach.memories.backend.memory_impl import construct_memory_params # Q: specify alternative distributed memory, or should this go in the preset? # A: preset must define distributed memory to be used. we aren't going to take a non-distributed preset and automatically distribute it. -def heatup(graph_manager): - memory = DistributedExperienceReplay(max_size=(MemoryGranularity.Transitions, 1000000), - redis_ip=graph_manager.agent_params.memory.redis_ip, - redis_port=graph_manager.agent_params.memory.redis_port) - - while(memory.num_transitions() < graph_manager.heatup_steps.num_steps): - time.sleep(1) - - -class StepsLoop(object): - """StepsLoop facilitates a simple while loop""" - def __init__(self, steps_counters, phase, steps): - super(StepsLoop, self).__init__() - self.steps_counters = steps_counters - self.phase = phase - self.steps = steps - - self.step_end = self._step_count() + steps.num_steps - - def _step_count(self): - return self.steps_counters[self.phase][self.steps.__class__] - - def continue(self): - return self._step_count() < count_end: - def training_worker(graph_manager, checkpoint_dir): """ @@ -51,12 +26,8 @@ def training_worker(graph_manager, checkpoint_dir): # save randomly initialized graph graph_manager.save_checkpoint() - # optionally wait for a specific number of transitions to be in memory before training - heatup(graph_manager) - # training loop - stepper = StepsLoop(graph_manager.total_steps_counters, RunPhase.TRAIN, graph_manager.improve_steps) - while stepper.continue(): + while True: graph_manager.phase = core_types.RunPhase.TRAIN graph_manager.train(core_types.TrainingSteps(1)) graph_manager.phase = core_types.RunPhase.UNDEFINED @@ -65,7 +36,6 @@ def training_worker(graph_manager, checkpoint_dir): graph_manager.save_checkpoint() - # TODO: signal to workers that training is done def main(): parser = argparse.ArgumentParser() @@ -85,12 +55,18 @@ def main(): help="(int) Port of the redis server", default=6379, type=int) + parser.add_argument('--memory_backend_params', + help="(string) JSON string of the memory backend params", + type=str) args = parser.parse_args() graph_manager = short_dynamic_import(expand_preset(args.preset), ignore_module_case=True) - graph_manager.agent_params.memory.redis_ip = args.redis_ip - graph_manager.agent_params.memory.redis_port = args.redis_port + if args.memory_backend_params: + args.memory_backend_params = json.loads(args.memory_backend_params) + if 'run_type' not in args.memory_backend_params: + args.memory_backend_params['run_type'] = 'trainer' + graph_manager.agent_params.memory.register_var('memory_backend_params', construct_memory_params(args.memory_backend_params)) training_worker( graph_manager=graph_manager, From 1c238b4c60258bb4de0bd336e486aabba0981cbb Mon Sep 17 00:00:00 2001 From: Balaji Subramaniam Date: Thu, 4 Oct 2018 09:45:59 -0700 Subject: [PATCH 038/162] Added data store backend. (#17) * Added data store backend. * Add NFS implementation for Kubernetes. * Added S3 data store implementation. * Addressed review comments. --- .gitignore | 4 + requirements.txt | 3 +- rl_coach/data_stores/data_store.py | 26 +++ rl_coach/data_stores/data_store_impl.py | 12 ++ rl_coach/data_stores/nfs_data_store.py | 219 ++++++++++++++++++++++++ rl_coach/data_stores/s3_data_store.py | 64 +++++++ 6 files changed, 327 insertions(+), 1 deletion(-) create mode 100644 rl_coach/data_stores/data_store.py create mode 100644 rl_coach/data_stores/data_store_impl.py create mode 100644 rl_coach/data_stores/nfs_data_store.py create mode 100644 rl_coach/data_stores/s3_data_store.py diff --git a/.gitignore b/.gitignore index 12c6990..3d028c9 100644 --- a/.gitignore +++ b/.gitignore @@ -26,3 +26,7 @@ datasets core trace_test* .DS_Store +*.swp +*.swo +.cache/ +*.pyc diff --git a/requirements.txt b/requirements.txt index 463dd95..acd5ff7 100644 --- a/requirements.txt +++ b/requirements.txt @@ -13,4 +13,5 @@ bokeh==0.13.0 futures==3.1.1 wxPython==4.0.1 kubernetes==7.0.0 -redis==2.10.6 \ No newline at end of file +redis==2.10.6 +minio==4.0.5 diff --git a/rl_coach/data_stores/data_store.py b/rl_coach/data_stores/data_store.py new file mode 100644 index 0000000..03718e1 --- /dev/null +++ b/rl_coach/data_stores/data_store.py @@ -0,0 +1,26 @@ + + +class DataStoreParameters(object): + def __init__(self, store_type, orchestrator_type, orchestrator_params): + self.store_type = store_type + self.orchestrator_type = orchestrator_type + self.orchestrator_params = orchestrator_params + +class DataStore(object): + def __init__(self, params: DataStoreParameters): + pass + + def deploy(self) -> bool: + pass + + def get_info(self): + pass + + def undeploy(self) -> bool: + pass + + def save_to_store(self): + pass + + def load_from_store(self): + pass diff --git a/rl_coach/data_stores/data_store_impl.py b/rl_coach/data_stores/data_store_impl.py new file mode 100644 index 0000000..9727fc1 --- /dev/null +++ b/rl_coach/data_stores/data_store_impl.py @@ -0,0 +1,12 @@ +from rl_coach.data_stores.nfs_data_store import NFSDataStore, NFSDataStoreParameters +from rl_coach.data_stores.s3_data_store import S3DataStore, S3DataStoreParameters + + +def get_data_store(params): + data_store = None + if type(params) == NFSDataStoreParameters: + data_store = NFSDataStore(params) + elif type(params) == S3DataStoreParameters: + data_store = S3DataStore(params) + + return data_store diff --git a/rl_coach/data_stores/nfs_data_store.py b/rl_coach/data_stores/nfs_data_store.py new file mode 100644 index 0000000..6946139 --- /dev/null +++ b/rl_coach/data_stores/nfs_data_store.py @@ -0,0 +1,219 @@ +from rl_coach.data_stores.data_store import DataStore, DataStoreParameters +from kubernetes import client as k8sclient + + +class NFSDataStoreParameters(DataStoreParameters): + def __init__(self, ds_params, deployed=False, server=None, path=None): + super().__init__(ds_params.store_type, ds_params.orchestrator_type, ds_params.orchestrator_params) + self.namespace = "default" + if "namespace" in ds_params.orchestrator_params: + self.namespace = ds_params.orchestrator_params["namespace"] + self.name = None + self.pvc_name = None + self.pv_name = None + self.svc_name = None + self.server = None + self.path = "/" + self.deployed = deployed + if deployed: + self.server = server + self.path = path + + +class NFSDataStore(DataStore): + def __init__(self, params: NFSDataStoreParameters): + self.params = params + + def deploy(self) -> bool: + if self.params.orchestrator_type == "kubernetes": + if not self.params.deployed: + if not self.deploy_k8s_nfs(): + return False + if not self.create_k8s_nfs_resources(): + return False + + return True + + def get_info(self): + return k8sclient.V1PersistentVolumeClaimVolumeSource( + claim_name=self.params.pvc_name + ) + + def undeploy(self) -> bool: + if self.params.orchestrator_type == "kubernetes": + if not self.params.deployed: + if not self.undeploy_k8s_nfs(): + return False + if not self.delete_k8s_nfs_resources(): + return False + + return True + + def save_to_store(self): + pass + + def load_from_store(self): + pass + + def deploy_k8s_nfs(self) -> bool: + name = "nfs-server" + container = k8sclient.V1Container( + name=name, + image="k8s.gcr.io/volume-nfs:0.8", + ports=[k8sclient.V1ContainerPort( + name="nfs", + container_port=2049, + protocol="TCP" + )] + ) + template = k8sclient.V1PodTemplateSpec( + metadata=k8sclient.V1ObjectMeta(labels={'app': 'nfs-server'}), + spec=k8sclient.V1PodSpec( + containers=[container] + ) + ) + deployment_spec = k8sclient.V1DeploymentSpec( + replicas=1, + template=template, + selector=k8sclient.V1LabelSelector( + match_labels={'app': 'nfs-server'} + ) + ) + + deployment = k8sclient.V1Deployment( + api_version='apps/v1', + kind='Deployment', + metadata=k8sclient.V1ObjectMeta(name=name, labels={'app': 'nfs-server'}), + spec=deployment_spec + ) + + k8s_apps_v1_api_client = k8sclient.AppsV1Api() + try: + k8s_apps_v1_api_client.create_namespaced_deployment(self.params.namespace, deployment) + self.params.name = name + except k8sclient.rest.ApiException as e: + print("Got exception: %s\n while creating nfs-server", e) + return False + + k8s_core_v1_api_client = k8sclient.CoreV1Api() + + svc_name = "nfs-service" + service = k8sclient.V1Service( + api_version='v1', + kind='Service', + metadata=k8sclient.V1ObjectMeta( + name=svc_name + ), + spec=k8sclient.V1ServiceSpec( + selector={'app': self.params.name}, + ports=[k8sclient.V1ServicePort( + protocol='TCP', + port=2049, + target_port=2049 + )] + ) + ) + + try: + k8s_core_v1_api_client.create_namespaced_service(self.params.namespace, service) + self.params.svc_name = svc_name + self.params.server = 'nfs-service.{}.svc'.format(self.params.namespace) + except k8sclient.rest.ApiException as e: + print("Got exception: %s\n while creating a service for nfs-server", e) + return False + + return True + + + def create_k8s_nfs_resources(self) -> bool: + pv_name = "nfs-ckpt-pv" + persistent_volume = k8sclient.V1PersistentVolume( + api_version="v1", + kind="PersistentVolume", + metadata=k8sclient.V1ObjectMeta( + name=pv_name, + labels={'app': pv_name} + ), + spec=k8sclient.V1PersistentVolumeSpec( + access_modes=["ReadWriteMany"], + nfs=k8sclient.V1NFSVolumeSource( + path=self.params.path, + server=self.params.server + ), + capacity={'storage': '10Gi'}, + storage_class_name="" + ) + ) + k8s_api_client = k8sclient.CoreV1Api() + try: + k8s_api_client.create_persistent_volume(persistent_volume) + self.params.pv_name = pv_name + except k8sclient.rest.ApiException as e: + print("Got exception: %s\n while creating the NFS PV", e) + return False + + pvc_name = "nfs-ckpt-pvc" + persistent_volume_claim = k8sclient.V1PersistentVolumeClaim( + api_version="v1", + kind="PersistentVolumeClaim", + metadata=k8sclient.V1ObjectMeta( + name=pvc_name + ), + spec=k8sclient.V1PersistentVolumeClaimSpec( + access_modes=["ReadWriteMany"], + resources=k8sclient.V1ResourceRequirements( + requests={'storage': '10Gi'} + ), + selector=k8sclient.V1LabelSelector( + match_labels={'app': self.params.pv_name} + ), + storage_class_name="" + ) + ) + + try: + k8s_api_client.create_namespaced_persistent_volume_claim(self.params.namespace, persistent_volume_claim) + self.params.pvc_name = pvc_name + except k8sclient.rest.ApiException as e: + print("Got exception: %s\n while creating the NFS PVC", e) + return False + + return True + + def undeploy_k8s_nfs(self) -> bool: + del_options = k8sclient.V1DeleteOptions() + + k8s_apps_v1_api_client = k8sclient.AppsV1Api() + try: + k8s_apps_v1_api_client.delete_namespaced_deployment(self.params.name, self.params.namespace, del_options) + except k8sclient.rest.ApiException as e: + print("Got exception: %s\n while deleting nfs-server", e) + return False + + k8s_core_v1_api_client = k8sclient.CoreV1Api() + try: + k8s_core_v1_api_client.delete_namespaced_service(self.params.svc_name, self.params.namespace, del_options) + except k8sclient.rest.ApiException as e: + print("Got exception: %s\n while deleting the service for nfs-server", e) + return False + + return True + + + def delete_k8s_nfs_resources(self) -> bool: + del_options = k8sclient.V1DeleteOptions() + k8s_api_client = k8sclient.CoreV1Api() + + try: + k8s_api_client.delete_persistent_volume(self.params.pv_name, del_options) + except k8sclient.rest.ApiException as e: + print("Got exception: %s\n while deleting NFS PV", e) + return False + + try: + k8s_api_client.delete_namespaced_persistent_volume_claim(self.params.pvc_name, self.params.namespace, del_options) + except k8sclient.rest.ApiException as e: + print("Got exception: %s\n while deleting NFS PVC", e) + return False + + return True diff --git a/rl_coach/data_stores/s3_data_store.py b/rl_coach/data_stores/s3_data_store.py new file mode 100644 index 0000000..1b623e2 --- /dev/null +++ b/rl_coach/data_stores/s3_data_store.py @@ -0,0 +1,64 @@ +from rl_coach.data_stores.data_store import DataStore, DataStoreParameters +from kubernetes import client as k8sclient +from minio import Minio +from minio.error import ResponseError +from configparser import ConfigParser, Error +import os + + +class S3DataStoreParameters(DataStoreParameters): + def __init__(self, ds_params, creds_file: str = None, end_point: str = None, bucket_name: str = None, + checkpoint_dir: str = None): + + super().__init__(ds_params.store_type, ds_params.orchestrator_type, ds_params.orchestrator_params) + self.creds_file = creds_file + self.end_point = end_point + self.bucket_name = bucket_name + self.checkpoint_dir = checkpoint_dir + + +class S3DataStore(DataStore): + def __init__(self, params: S3DataStoreParameters): + self.params = params + access_key = None + secret_key = None + if params.creds_file: + config = ConfigParser() + config.read(params.creds_file) + try: + access_key = config.get('default', 'aws_access_key_id') + secret_key = config.get('default', 'aws_secret_access_key') + except Error as e: + print("Error when reading S3 credentials file: %s", e) + else: + access_key = os.environ.get('ACCESS_KEY_ID') + secret_key = os.environ.get('SECRET_ACCESS_KEY') + self.mc = Minio(self.params.end_point, access_key=access_key, secret_key=secret_key) + + def deploy(self) -> bool: + return True + + def get_info(self): + return "s3://{}/{}".format(self.params.bucket_name) + + def undeploy(self) -> bool: + return True + + def save_to_store(self): + try: + for root, dirs, files in os.walk(self.params.checkpoint_dir): + for filename in files: + abs_name = os.path.abspath(os.path.join(root, filename)) + rel_name = os.path.relpath(abs_name, self.params.checkpoint_dir) + self.mc.fput_object(self.params.bucket_name, rel_name, abs_name) + except ResponseError as e: + print("Got exception: %s\n while saving to S3", e) + + def load_from_store(self): + try: + objects = self.mc.list_objects_v2(self.params.bucket_name, recursive=True) + for obj in objects: + filename = os.path.abspath(os.path.join(self.params.checkpoint_dir, obj.object_name)) + self.mc.fget_object(obj.bucket_name, obj.object_name, filename) + except ResponseError as e: + print("Got exception: %s\n while loading from S3", e) From 04038c9f402e79c3d76a83606428081c5135258a Mon Sep 17 00:00:00 2001 From: Zach Dwiel Date: Wed, 26 Sep 2018 15:54:57 -0400 Subject: [PATCH 039/162] improve integration test output format --- docker/Makefile | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docker/Makefile b/docker/Makefile index 5a82500..bd5cbe8 100644 --- a/docker/Makefile +++ b/docker/Makefile @@ -38,7 +38,7 @@ unit_tests: build ${DOCKER} run ${RUN_ARGUMENTS} -it ${IMAGE} python3 -m pytest rl_coach/tests -m unit_test -n 8 integration_tests: build - ${DOCKER} run ${RUN_ARGUMENTS} -it ${IMAGE} python3 -m pytest rl_coach/tests -m integration_test -n auto + ${DOCKER} run ${RUN_ARGUMENTS} -it ${IMAGE} python3 -m pytest rl_coach/tests -m integration_test -n auto --tb=short golden_tests: build ${DOCKER} run ${RUN_ARGUMENTS} -it ${IMAGE} python3 rl_coach/tests/golden_tests.py From ed3a3b39be366fc9e76a8fe6840b8399bc9d59f3 Mon Sep 17 00:00:00 2001 From: Zach Dwiel Date: Wed, 26 Sep 2018 15:58:15 -0400 Subject: [PATCH 040/162] add comments --- rl_coach/graph_managers/graph_manager.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/rl_coach/graph_managers/graph_manager.py b/rl_coach/graph_managers/graph_manager.py index db261b8..f6ff7d9 100644 --- a/rl_coach/graph_managers/graph_manager.py +++ b/rl_coach/graph_managers/graph_manager.py @@ -311,6 +311,7 @@ class GraphManager(object): self.reset_internal_state(force_environment_reset=True) # act on the environment + # act for at least steps, though don't interrupt an episode while steps_copy.num_steps > 0: steps_done, _ = self.act(steps_copy, continue_until_game_over=True, return_on_game_over=True) steps_copy.num_steps -= steps_done @@ -473,6 +474,7 @@ class GraphManager(object): self.reset_internal_state(force_environment_reset=True) self.sync_graph() + # act for at least `steps`, though don't interrupt an episode count_end = self.total_steps_counters[self.phase][steps.__class__] + steps.num_steps while self.total_steps_counters[self.phase][steps.__class__] < count_end: steps_done, _ = self.act(steps, continue_until_game_over=True, return_on_game_over=True, @@ -561,6 +563,7 @@ class GraphManager(object): else: screen.log_title("Starting to improve {}".format(self.name)) self.training_start_time = time.time() + count_end = self.improve_steps.num_steps while self.total_steps_counters[RunPhase.TRAIN][self.improve_steps.__class__] < count_end: self.train_and_act(self.steps_between_evaluation_periods) From 950f2612017c338d5fa4c0fd91193e56bc75e533 Mon Sep 17 00:00:00 2001 From: Zach Dwiel Date: Wed, 26 Sep 2018 15:59:14 -0400 Subject: [PATCH 041/162] extract method all_presets --- rl_coach/tests/golden_tests.py | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/rl_coach/tests/golden_tests.py b/rl_coach/tests/golden_tests.py index 2e61338..654da6f 100644 --- a/rl_coach/tests/golden_tests.py +++ b/rl_coach/tests/golden_tests.py @@ -178,6 +178,13 @@ def perform_reward_based_tests(args, preset_validation_params, preset_name): return test_passed +def all_presets(): + return [ + f[:-3] for f in os.listdir(os.path.join('rl_coach', 'presets')) + if f[-3:] == '.py' and not f == '__init__.py' + ] + + def main(): parser = argparse.ArgumentParser() parser.add_argument('-p', '--preset', @@ -206,8 +213,7 @@ def main(): if args.preset is not None: presets_lists = [args.preset] else: - presets_lists = [f[:-3] for f in os.listdir(os.path.join('rl_coach', 'presets')) if - f[-3:] == '.py' and not f == '__init__.py'] + presets_lists = all_presets() fail_count = 0 test_count = 0 From b5305bd0751dbd7db956786d8aebb9bd8981a063 Mon Sep 17 00:00:00 2001 From: Zach Dwiel Date: Fri, 28 Sep 2018 16:46:15 -0400 Subject: [PATCH 042/162] update dockerfile --- docker/Dockerfile | 52 ++++++++++++++----- .../tests/{golden_tests.py => test_golden.py} | 0 2 files changed, 38 insertions(+), 14 deletions(-) rename rl_coach/tests/{golden_tests.py => test_golden.py} (100%) diff --git a/docker/Dockerfile b/docker/Dockerfile index a59a688..8a1a858 100644 --- a/docker/Dockerfile +++ b/docker/Dockerfile @@ -18,37 +18,61 @@ WORKDIR /root ################################ # General -RUN apt-get install python3-pip cmake zlib1g-dev python3-tk python-opencv -y +RUN apt-get update && \ + apt-get install -y python3-pip cmake zlib1g-dev python3-tk python-opencv && \ + apt-get clean autoclean && \ + apt-get autoremove -y # Boost libraries -RUN apt-get install libboost-all-dev -y +RUN apt-get update && \ + apt-get install -y libboost-all-dev && \ + apt-get clean autoclean && \ + apt-get autoremove -y # Scipy requirements -RUN apt-get install libblas-dev liblapack-dev libatlas-base-dev gfortran -y +RUN apt-get update && \ + apt-get install -y libblas-dev liblapack-dev libatlas-base-dev gfortran && \ + apt-get clean autoclean && \ + apt-get autoremove -y # Pygame requirements -RUN apt-get install libsdl-dev libsdl-image1.2-dev libsdl-mixer1.2-dev libsdl-ttf2.0-dev -y -RUN apt-get install libsmpeg-dev libportmidi-dev libavformat-dev libswscale-dev -y +RUN apt-get update && \ + apt-get install -y libsdl-dev libsdl-image1.2-dev libsdl-mixer1.2-dev libsdl-ttf2.0-dev && \ + apt-get clean autoclean && \ + apt-get autoremove -y +RUN apt-get update && \ + apt-get install -y libsmpeg-dev libportmidi-dev libavformat-dev libswscale-dev && \ + apt-get clean autoclean && \ + apt-get autoremove -y # Dashboard -RUN apt-get install dpkg-dev build-essential python3.5-dev libjpeg-dev libtiff-dev libsdl1.2-dev libnotify-dev \ +RUN apt-get update && \ + apt-get install -y dpkg-dev build-essential python3.5-dev libjpeg-dev libtiff-dev libsdl1.2-dev libnotify-dev \ freeglut3 freeglut3-dev libsm-dev libgtk2.0-dev libgtk-3-dev libwebkitgtk-dev libgtk-3-dev \ - libwebkitgtk-3.0-dev libgstreamer-plugins-base1.0-dev -y + libwebkitgtk-3.0-dev libgstreamer-plugins-base1.0-dev && \ + apt-get clean autoclean && \ + apt-get autoremove -y # Gym -RUN apt-get install libav-tools libsdl2-dev swig cmake -y +RUN apt-get update && \ + apt-get install -y libav-tools libsdl2-dev swig cmake && \ + apt-get clean autoclean && \ + apt-get autoremove -y # Mujoco_py -RUN apt-get install curl libgl1-mesa-dev libgl1-mesa-glx libglew-dev libosmesa6-dev software-properties-common -y +RUN apt-get update && \ + apt-get install -y curl libgl1-mesa-dev libgl1-mesa-glx libglew-dev libosmesa6-dev software-properties-common && \ + apt-get clean autoclean && \ + apt-get autoremove -y # ViZDoom -RUN apt-get install build-essential zlib1g-dev libsdl2-dev libjpeg-dev \ +RUN apt-get update && \ + apt-get install -y build-essential zlib1g-dev libsdl2-dev libjpeg-dev \ nasm tar libbz2-dev libgtk2.0-dev cmake git libfluidsynth-dev libgme-dev \ - libopenal-dev timidity libwildmidi-dev unzip wget -y - -# cleanup apt-get -RUN apt-get clean autoclean && \ + libopenal-dev timidity libwildmidi-dev unzip wget && \ + apt-get clean autoclean && \ apt-get autoremove -y + ############################ # Install Pip Requirements # ############################ diff --git a/rl_coach/tests/golden_tests.py b/rl_coach/tests/test_golden.py similarity index 100% rename from rl_coach/tests/golden_tests.py rename to rl_coach/tests/test_golden.py From 9f92064e6796f425be051e31fee66feeef263954 Mon Sep 17 00:00:00 2001 From: Zach Dwiel Date: Mon, 1 Oct 2018 17:01:03 -0400 Subject: [PATCH 043/162] cleanup graph_manager:act --- rl_coach/graph_managers/graph_manager.py | 28 ++++++------------------ 1 file changed, 7 insertions(+), 21 deletions(-) diff --git a/rl_coach/graph_managers/graph_manager.py b/rl_coach/graph_managers/graph_manager.py index f6ff7d9..230c90b 100644 --- a/rl_coach/graph_managers/graph_manager.py +++ b/rl_coach/graph_managers/graph_manager.py @@ -336,20 +336,7 @@ class GraphManager(object): """ self.verify_graph_was_created() - # perform several steps of training interleaved with acting - count_end = self.total_steps_counters[RunPhase.TRAIN][TrainingSteps] + steps.num_steps - while self.total_steps_counters[RunPhase.TRAIN][TrainingSteps] < count_end: - self.total_steps_counters[RunPhase.TRAIN][TrainingSteps] += 1 - [manager.train() for manager in self.level_managers] - - # # option 1 - # for _ in StepsLoop(self.total_steps_counters, RunPhase.TRAIN, steps): - # [manager.train() for manager in self.level_managers] - # - # # option 2 - # steps_loop = StepsLoop(self.total_steps_counters, RunPhase.TRAIN, steps) - # while steps_loop or other: - # [manager.train() for manager in self.level_managers] + [manager.train() for manager in self.level_managers] def reset_internal_state(self, force_environment_reset=False) -> None: @@ -383,21 +370,21 @@ class GraphManager(object): # perform several steps of playing result = None - hold_until_a_full_episode = True if continue_until_game_over else False initial_count = self.total_steps_counters[self.phase][steps.__class__] count_end = initial_count + steps.num_steps # The assumption here is that the total_steps_counters are each updated when an event # takes place (i.e. an episode ends) # TODO - The counter of frames is not updated correctly. need to fix that. - while self.total_steps_counters[self.phase][steps.__class__] < count_end or hold_until_a_full_episode: + while self.total_steps_counters[self.phase][steps.__class__] < count_end or continue_until_game_over: # reset the environment if the previous episode was terminated if self.reset_required: self.reset_internal_state() - current_steps = self.environments[0].total_steps_counter - + steps_begin = self.environments[0].total_steps_counter result = self.top_level_manager.step(None) + steps_end = self.environments[0].total_steps_counter + # result will be None if at least one level_manager decided not to play (= all of his agents did not play) # causing the rest of the level_managers down the stack not to play either, and thus the entire graph did # not act @@ -408,11 +395,10 @@ class GraphManager(object): # (like in Atari) will not be counted. # We add at least one step so that even if no steps were made (in case no actions are taken in the training # phase), the loop will end eventually. - self.total_steps_counters[self.phase][EnvironmentSteps] += \ - max(1, self.environments[0].total_steps_counter - current_steps) + self.total_steps_counters[self.phase][EnvironmentSteps] += max(1, steps_end - steps_begin) if result.game_over: - hold_until_a_full_episode = False + continue_until_game_over = False self.handle_episode_ended() # TODO: why not just reset right now? self.reset_required = True From 844a5af831a66505f3ee92dbea51f052a4f5262d Mon Sep 17 00:00:00 2001 From: Balaji Subramaniam Date: Thu, 4 Oct 2018 12:28:21 -0700 Subject: [PATCH 044/162] Make distributed coach work end-to-end. - With data store, memory backend and orchestrator interfaces. --- rl_coach/data_stores/__init__.py | 15 + rl_coach/data_stores/data_store_impl.py | 12 + rl_coach/data_stores/s3_data_store.py | 2 + rl_coach/graph_managers/graph_manager.py | 15 + .../orchestrators/kubernetes_orchestrator.py | 303 +++++++++--------- rl_coach/orchestrators/start_training.py | 55 +++- rl_coach/rollout_worker.py | 43 ++- rl_coach/training_worker.py | 24 +- 8 files changed, 300 insertions(+), 169 deletions(-) create mode 100644 rl_coach/data_stores/__init__.py diff --git a/rl_coach/data_stores/__init__.py b/rl_coach/data_stores/__init__.py new file mode 100644 index 0000000..cf26739 --- /dev/null +++ b/rl_coach/data_stores/__init__.py @@ -0,0 +1,15 @@ +# +# Copyright (c) 2017 Intel Corporation +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# diff --git a/rl_coach/data_stores/data_store_impl.py b/rl_coach/data_stores/data_store_impl.py index 9727fc1..d98dfcd 100644 --- a/rl_coach/data_stores/data_store_impl.py +++ b/rl_coach/data_stores/data_store_impl.py @@ -1,5 +1,6 @@ from rl_coach.data_stores.nfs_data_store import NFSDataStore, NFSDataStoreParameters from rl_coach.data_stores.s3_data_store import S3DataStore, S3DataStoreParameters +from rl_coach.data_stores.data_store import DataStoreParameters def get_data_store(params): @@ -10,3 +11,14 @@ def get_data_store(params): data_store = S3DataStore(params) return data_store + +def construct_data_store_params(json: dict): + ds_params_instance = None + ds_params = DataStoreParameters(json['store_type'], json['orchestrator_type'], json['orchestrator_params']) + if json['store_type'] == 'nfs': + ds_params_instance = NFSDataStoreParameters(ds_params) + elif json['store_type'] == 's3': + ds_params_instance = S3DataStoreParameters(ds_params=ds_params, end_point=json['end_point'], + bucket_name=json['bucket_name'], checkpoint_dir=json['checkpoint_dir']) + + return ds_params_instance diff --git a/rl_coach/data_stores/s3_data_store.py b/rl_coach/data_stores/s3_data_store.py index 1b623e2..5ceb00a 100644 --- a/rl_coach/data_stores/s3_data_store.py +++ b/rl_coach/data_stores/s3_data_store.py @@ -46,6 +46,7 @@ class S3DataStore(DataStore): def save_to_store(self): try: + print("saving to s3") for root, dirs, files in os.walk(self.params.checkpoint_dir): for filename in files: abs_name = os.path.abspath(os.path.join(root, filename)) @@ -56,6 +57,7 @@ class S3DataStore(DataStore): def load_from_store(self): try: + print("loading from s3") objects = self.mc.list_objects_v2(self.params.bucket_name, recursive=True) for obj in objects: filename = os.path.abspath(os.path.join(self.params.checkpoint_dir, obj.object_name)) diff --git a/rl_coach/graph_managers/graph_manager.py b/rl_coach/graph_managers/graph_manager.py index 230c90b..5ee24ed 100644 --- a/rl_coach/graph_managers/graph_manager.py +++ b/rl_coach/graph_managers/graph_manager.py @@ -30,7 +30,12 @@ from rl_coach.core_types import TotalStepsCounter, RunPhase, PlayingStepsType, T from rl_coach.environments.environment import Environment from rl_coach.level_manager import LevelManager from rl_coach.logger import screen, Logger +<<<<<<< HEAD from rl_coach.utils import set_cpu, start_shell_command_and_wait +======= +from rl_coach.utils import set_cpu +from rl_coach.data_stores.data_store_impl import get_data_store +>>>>>>> Make distributed coach work end-to-end. class ScheduleParameters(Parameters): @@ -367,6 +372,11 @@ class GraphManager(object): """ self.verify_graph_was_created() + if hasattr(self, 'data_store_params') and hasattr(self.agent_params.memory, 'memory_backend_params'): + if self.agent_params.memory.memory_backend_params.run_type == "worker": + data_store = get_data_store(self.data_store_params) + data_store.load_from_store() + # perform several steps of playing result = None @@ -522,6 +532,11 @@ class GraphManager(object): self.checkpoint_id += 1 self.last_checkpoint_saving_time = time.time() + if hasattr(self, 'data_store_params'): + data_store = get_data_store(self.data_store_params) + data_store.save_to_store() + + def improve(self): """ The main loop of the run. diff --git a/rl_coach/orchestrators/kubernetes_orchestrator.py b/rl_coach/orchestrators/kubernetes_orchestrator.py index b22b681..d690623 100644 --- a/rl_coach/orchestrators/kubernetes_orchestrator.py +++ b/rl_coach/orchestrators/kubernetes_orchestrator.py @@ -4,9 +4,11 @@ import json import time from typing import List from rl_coach.orchestrators.deploy import Deploy, DeployParameters -from kubernetes import client, config +from kubernetes import client as k8sclient, config as k8sconfig from rl_coach.memories.backend.memory import MemoryBackendParameters from rl_coach.memories.backend.memory_impl import get_memory_backend +from rl_coach.data_stores.data_store import DataStoreParameters +from rl_coach.data_stores.data_store_impl import get_data_store class RunTypeParameters(): @@ -29,8 +31,9 @@ class RunTypeParameters(): class KubernetesParameters(DeployParameters): - def __init__(self, run_type_params: List[RunTypeParameters], kubeconfig: str = None, namespace: str = "", nfs_server: str = None, - nfs_path: str = None, checkpoint_dir: str = '/checkpoint', memory_backend_parameters: MemoryBackendParameters = None): + def __init__(self, run_type_params: List[RunTypeParameters], kubeconfig: str = None, namespace: str = None, + nfs_server: str = None, nfs_path: str = None, checkpoint_dir: str = '/checkpoint', + memory_backend_parameters: MemoryBackendParameters = None, data_store_params: DataStoreParameters = None): self.run_type_params = {} for run_type_param in run_type_params: @@ -41,195 +44,204 @@ class KubernetesParameters(DeployParameters): self.nfs_path = nfs_path self.checkpoint_dir = checkpoint_dir self.memory_backend_parameters = memory_backend_parameters + self.data_store_params = data_store_params class Kubernetes(Deploy): - def __init__(self, deploy_parameters: KubernetesParameters): - super().__init__(deploy_parameters) - self.deploy_parameters = deploy_parameters - if self.deploy_parameters.kubeconfig: - config.load_kube_config() + def __init__(self, params: KubernetesParameters): + super().__init__(params) + self.params = params + if self.params.kubeconfig: + k8sconfig.load_kube_config() else: - config.load_incluster_config() + k8sconfig.load_incluster_config() + + if not self.params.namespace: + _, current_context = k8sconfig.list_kube_config_contexts() + self.params.namespace = current_context['context']['namespace'] - if not self.deploy_parameters.namespace: - _, current_context = config.list_kube_config_contexts() - self.deploy_parameters.namespace = current_context['context']['namespace'] self.nfs_pvc_name = 'nfs-checkpoint-pvc' if os.environ.get('http_proxy'): - client.Configuration._default.proxy = os.environ.get('http_proxy') + k8sclient.Configuration._default.proxy = os.environ.get('http_proxy') - self.deploy_parameters.memory_backend_parameters.orchestrator_params = {'namespace': self.deploy_parameters.namespace} - self.memory_backend = get_memory_backend(self.deploy_parameters.memory_backend_parameters) + self.params.memory_backend_parameters.orchestrator_params = {'namespace': self.params.namespace} + self.memory_backend = get_memory_backend(self.params.memory_backend_parameters) + + self.params.data_store_params.orchestrator_params = {'namespace': self.params.namespace} + self.data_store = get_data_store(self.params.data_store_params) + + if self.params.data_store_params.store_type == "s3": + self.s3_access_key = None + self.s3_secret_key = None + if self.params.data_store_params.creds_file: + s3config = ConfigParser() + s3config.read(self.params.data_store_params.creds_file) + try: + self.s3_access_key = s3config.get('default', 'aws_access_key_id') + self.s3_secret_key = s3config.get('default', 'aws_secret_access_key') + except Error as e: + print("Error when reading S3 credentials file: %s", e) + else: + self.s3_access_key = os.environ.get('ACCESS_KEY_ID') + self.s3_secret_key = os.environ.get('SECRET_ACCESS_KEY') def setup(self) -> bool: self.memory_backend.deploy() - if not self.create_nfs_resources(): - return False - return True - - def create_nfs_resources(self): - persistent_volume = client.V1PersistentVolume( - api_version="v1", - kind="PersistentVolume", - metadata=client.V1ObjectMeta( - name='nfs-checkpoint-pv', - labels={'app': 'nfs-checkpoint-pv'} - ), - spec=client.V1PersistentVolumeSpec( - access_modes=["ReadWriteMany"], - nfs=client.V1NFSVolumeSource( - path=self.deploy_parameters.nfs_path, - server=self.deploy_parameters.nfs_server - ), - capacity={'storage': '10Gi'}, - storage_class_name="" - ) - ) - api_client = client.CoreV1Api() - try: - api_client.create_persistent_volume(persistent_volume) - except client.rest.ApiException as e: - print("Got exception: %s\n while creating the NFS PV", e) - return False - - persistent_volume_claim = client.V1PersistentVolumeClaim( - api_version="v1", - kind="PersistentVolumeClaim", - metadata=client.V1ObjectMeta( - name="nfs-checkpoint-pvc" - ), - spec=client.V1PersistentVolumeClaimSpec( - access_modes=["ReadWriteMany"], - resources=client.V1ResourceRequirements( - requests={'storage': '10Gi'} - ), - selector=client.V1LabelSelector( - match_labels={'app': 'nfs-checkpoint-pv'} - ), - storage_class_name="" - ) - ) - - try: - api_client.create_namespaced_persistent_volume_claim(self.deploy_parameters.namespace, persistent_volume_claim) - except client.rest.ApiException as e: - print("Got exception: %s\n while creating the NFS PVC", e) + if not self.data_store.deploy(): return False return True def deploy_trainer(self) -> bool: - trainer_params = self.deploy_parameters.run_type_params.get('trainer', None) + trainer_params = self.params.run_type_params.get('trainer', None) if not trainer_params: return False - trainer_params.command += ['--memory_backend_params', json.dumps(self.deploy_parameters.memory_backend_parameters.__dict__)] + trainer_params.command += ['--memory_backend_params', json.dumps(self.params.memory_backend_parameters.__dict__)] + trainer_params.command += ['--data_store_params', json.dumps(self.params.data_store_params.__dict__)] + name = "{}-{}".format(trainer_params.run_type, uuid.uuid4()) - container = client.V1Container( - name=name, - image=trainer_params.image, - command=trainer_params.command, - args=trainer_params.arguments, - image_pull_policy='Always', - volume_mounts=[client.V1VolumeMount( - name='nfs-pvc', - mount_path=trainer_params.checkpoint_dir - )] - ) - template = client.V1PodTemplateSpec( - metadata=client.V1ObjectMeta(labels={'app': name}), - spec=client.V1PodSpec( - containers=[container], - volumes=[client.V1Volume( - name="nfs-pvc", - persistent_volume_claim=client.V1PersistentVolumeClaimVolumeSource( - claim_name=self.nfs_pvc_name - ) + if self.params.data_store_params.store_type == "nfs": + container = k8sclient.V1Container( + name=name, + image=trainer_params.image, + command=trainer_params.command, + args=trainer_params.arguments, + image_pull_policy='Always', + volume_mounts=[k8sclient.V1VolumeMount( + name='nfs-pvc', + mount_path=trainer_params.checkpoint_dir )] - ), - ) - deployment_spec = client.V1DeploymentSpec( + ) + template = k8sclient.V1PodTemplateSpec( + metadata=k8sclient.V1ObjectMeta(labels={'app': name}), + spec=k8sclient.V1PodSpec( + containers=[container], + volumes=[k8sclient.V1Volume( + name="nfs-pvc", + persistent_volume_claim=k8sclient.V1PersistentVolumeClaimVolumeSource( + claim_name=self.nfs_pvc_name + ) + )] + ), + ) + else: + container = k8sclient.V1Container( + name=name, + image=trainer_params.image, + command=trainer_params.command, + args=trainer_params.arguments, + image_pull_policy='Always', + env=[k8sclient.V1EnvVar("ACCESS_KEY_ID", self.s3_access_key), + k8sclient.V1EnvVar("SECRET_ACCESS_KEY", self.s3_secret_key)] + ) + template = k8sclient.V1PodTemplateSpec( + metadata=k8sclient.V1ObjectMeta(labels={'app': name}), + spec=k8sclient.V1PodSpec( + containers=[container] + ), + ) + + deployment_spec = k8sclient.V1DeploymentSpec( replicas=trainer_params.num_replicas, template=template, - selector=client.V1LabelSelector( + selector=k8sclient.V1LabelSelector( match_labels={'app': name} ) ) - deployment = client.V1Deployment( + deployment = k8sclient.V1Deployment( api_version='apps/v1', kind='Deployment', - metadata=client.V1ObjectMeta(name=name), + metadata=k8sclient.V1ObjectMeta(name=name), spec=deployment_spec ) - api_client = client.AppsV1Api() + api_client = k8sclient.AppsV1Api() try: - api_client.create_namespaced_deployment(self.deploy_parameters.namespace, deployment) + api_client.create_namespaced_deployment(self.params.namespace, deployment) trainer_params.orchestration_params['deployment_name'] = name return True - except client.rest.ApiException as e: + except k8sclient.rest.ApiException as e: print("Got exception: %s\n while creating deployment", e) return False def deploy_worker(self): - worker_params = self.deploy_parameters.run_type_params.get('worker', None) + worker_params = self.params.run_type_params.get('worker', None) if not worker_params: return False - worker_params.command += ['--memory_backend_params', json.dumps(self.deploy_parameters.memory_backend_parameters.__dict__)] + worker_params.command += ['--memory_backend_params', json.dumps(self.params.memory_backend_parameters.__dict__)] + worker_params.command += ['--data_store_params', json.dumps(self.params.data_store_params.__dict__)] + name = "{}-{}".format(worker_params.run_type, uuid.uuid4()) - container = client.V1Container( - name=name, - image=worker_params.image, - command=worker_params.command, - args=worker_params.arguments, - image_pull_policy='Always', - volume_mounts=[client.V1VolumeMount( - name='nfs-pvc', - mount_path=worker_params.checkpoint_dir - )] - ) - template = client.V1PodTemplateSpec( - metadata=client.V1ObjectMeta(labels={'app': name}), - spec=client.V1PodSpec( - containers=[container], - volumes=[client.V1Volume( - name="nfs-pvc", - persistent_volume_claim=client.V1PersistentVolumeClaimVolumeSource( - claim_name=self.nfs_pvc_name - ) - )], - ), - ) + if self.params.data_store_params.store_type == "nfs": + container = k8sclient.V1Container( + name=name, + image=worker_params.image, + command=worker_params.command, + args=worker_params.arguments, + image_pull_policy='Always', + volume_mounts=[k8sclient.V1VolumeMount( + name='nfs-pvc', + mount_path=worker_params.checkpoint_dir + )] + ) + template = k8sclient.V1PodTemplateSpec( + metadata=k8sclient.V1ObjectMeta(labels={'app': name}), + spec=k8sclient.V1PodSpec( + containers=[container], + volumes=[k8sclient.V1Volume( + name="nfs-pvc", + persistent_volume_claim=k8sclient.V1PersistentVolumeClaimVolumeSource( + claim_name=self.nfs_pvc_name + ) + )], + ), + ) + else: + container = k8sclient.V1Container( + name=name, + image=worker_params.image, + command=worker_params.command, + args=worker_params.arguments, + image_pull_policy='Always', + env=[k8sclient.V1EnvVar("ACCESS_KEY_ID", self.s3_access_key), + k8sclient.V1EnvVar("SECRET_ACCESS_KEY", self.s3_secret_key)] + ) + template = k8sclient.V1PodTemplateSpec( + metadata=k8sclient.V1ObjectMeta(labels={'app': name}), + spec=k8sclient.V1PodSpec( + containers=[container] + ) + ) - deployment_spec = client.V1DeploymentSpec( + deployment_spec = k8sclient.V1DeploymentSpec( replicas=worker_params.num_replicas, template=template, - selector=client.V1LabelSelector( + selector=k8sclient.V1LabelSelector( match_labels={'app': name} ) ) - deployment = client.V1Deployment( + deployment = k8sclient.V1Deployment( api_version='apps/v1', kind="Deployment", - metadata=client.V1ObjectMeta(name=name), + metadata=k8sclient.V1ObjectMeta(name=name), spec=deployment_spec ) - api_client = client.AppsV1Api() + api_client = k8sclient.AppsV1Api() try: - api_client.create_namespaced_deployment(self.deploy_parameters.namespace, deployment) + api_client.create_namespaced_deployment(self.params.namespace, deployment) worker_params.orchestration_params['deployment_name'] = name return True - except client.rest.ApiException as e: + except k8sclient.rest.ApiException as e: print("Got exception: %s\n while creating deployment", e) return False @@ -237,19 +249,19 @@ class Kubernetes(Deploy): pass def trainer_logs(self): - trainer_params = self.deploy_parameters.run_type_params.get('trainer', None) + trainer_params = self.params.run_type_params.get('trainer', None) if not trainer_params: return - api_client = client.CoreV1Api() + api_client = k8sclient.CoreV1Api() pod = None try: - pods = api_client.list_namespaced_pod(self.deploy_parameters.namespace, label_selector='app={}'.format( + pods = api_client.list_namespaced_pod(self.params.namespace, label_selector='app={}'.format( trainer_params.orchestration_params['deployment_name'] )) pod = pods.items[0] - except client.rest.ApiException as e: + except k8sclient.rest.ApiException as e: print("Got exception: %s\n while reading pods", e) return @@ -264,17 +276,17 @@ class Kubernetes(Deploy): # Try to tail the pod logs try: print(corev1_api.read_namespaced_pod_log( - pod_name, self.deploy_parameters.namespace, follow=True + pod_name, self.params.namespace, follow=True ), flush=True) - except client.rest.ApiException as e: + except k8sclient.rest.ApiException as e: pass # This part will get executed if the pod is one of the following phases: not ready, failed or terminated. # Check if the pod has errored out, else just try again. # Get the pod try: - pod = corev1_api.read_namespaced_pod(pod_name, self.deploy_parameters.namespace) - except client.rest.ApiException as e: + pod = corev1_api.read_namespaced_pod(pod_name, self.params.namespace) + except k8sclient.rest.ApiException as e: continue if not hasattr(pod, 'status') or not pod.status: @@ -293,18 +305,19 @@ class Kubernetes(Deploy): return def undeploy(self): - trainer_params = self.deploy_parameters.run_type_params.get('trainer', None) - api_client = client.AppsV1Api() - delete_options = client.V1DeleteOptions() + trainer_params = self.params.run_type_params.get('trainer', None) + api_client = k8sclient.AppsV1Api() + delete_options = k8sclient.V1DeleteOptions() if trainer_params: try: - api_client.delete_namespaced_deployment(trainer_params.orchestration_params['deployment_name'], self.deploy_parameters.namespace, delete_options) - except client.rest.ApiException as e: + api_client.delete_namespaced_deployment(trainer_params.orchestration_params['deployment_name'], self.params.namespace, delete_options) + except k8sclient.rest.ApiException as e: print("Got exception: %s\n while deleting trainer", e) - worker_params = self.deploy_parameters.run_type_params.get('worker', None) + worker_params = self.params.run_type_params.get('worker', None) if worker_params: try: - api_client.delete_namespaced_deployment(worker_params.orchestration_params['deployment_name'], self.deploy_parameters.namespace, delete_options) - except client.rest.ApiException as e: + api_client.delete_namespaced_deployment(worker_params.orchestration_params['deployment_name'], self.params.namespace, delete_options) + except k8sclient.rest.ApiException as e: print("Got exception: %s\n while deleting workers", e) self.memory_backend.undeploy() + self.data_store.undeploy() diff --git a/rl_coach/orchestrators/start_training.py b/rl_coach/orchestrators/start_training.py index 547bb5c..05af288 100644 --- a/rl_coach/orchestrators/start_training.py +++ b/rl_coach/orchestrators/start_training.py @@ -2,19 +2,36 @@ import argparse from rl_coach.orchestrators.kubernetes_orchestrator import KubernetesParameters, Kubernetes, RunTypeParameters from rl_coach.memories.backend.redis import RedisPubSubMemoryBackendParameters +from rl_coach.data_stores.data_store import DataStoreParameters +from rl_coach.data_stores.s3_data_store import S3DataStoreParameters +from rl_coach.data_stores.nfs_data_store import NFSDataStoreParameters -def main(preset: str, image: str='ajaysudh/testing:coach', num_workers: int=1, nfs_server: str="", nfs_path: str="", memory_backend: str=""): +def main(preset: str, image: str='ajaysudh/testing:coach', num_workers: int=1, nfs_server: str=None, nfs_path: str=None, + memory_backend: str=None, data_store: str=None, s3_end_point: str=None, s3_bucket_name: str=None): rollout_command = ['python3', 'rl_coach/rollout_worker.py', '-p', preset] training_command = ['python3', 'rl_coach/training_worker.py', '-p', preset] - memory_backend_params = RedisPubSubMemoryBackendParameters() + memory_backend_params = None + if memory_backend == "redispubsub": + memory_backend_params = RedisPubSubMemoryBackendParameters() + + ds_params_instance = None + if data_store == "s3": + ds_params = DataStoreParameters("s3", "", "") + ds_params_instance = S3DataStoreParameters(ds_params=ds_params, end_point=s3_end_point, bucket_name=s3_bucket_name, + checkpoint_dir="/checkpoint") + elif data_store == "nfs": + ds_params = DataStoreParameters("nfs", "kubernetes", {"namespace": "default"}) + ds_params_instance = NFSDataStoreParameters(ds_params) worker_run_type_params = RunTypeParameters(image, rollout_command, run_type="worker") trainer_run_type_params = RunTypeParameters(image, training_command, run_type="trainer") - orchestration_params = KubernetesParameters([worker_run_type_params, trainer_run_type_params], kubeconfig='~/.kube/config', nfs_server=nfs_server, - nfs_path=nfs_path, memory_backend_parameters=memory_backend_params) + orchestration_params = KubernetesParameters([worker_run_type_params, trainer_run_type_params], + kubeconfig='~/.kube/config', nfs_server=nfs_server, nfs_path=nfs_path, + memory_backend_parameters=memory_backend_params, + data_store_params=ds_params_instance) orchestrator = Kubernetes(orchestration_params) if not orchestrator.setup(): print("Could not setup") @@ -36,7 +53,7 @@ def main(preset: str, image: str='ajaysudh/testing:coach', num_workers: int=1, n orchestrator.trainer_logs() except KeyboardInterrupt: pass - orchestrator.undeploy() + # orchestrator.undeploy() if __name__ == '__main__': @@ -46,21 +63,33 @@ if __name__ == '__main__': type=str, required=True) parser.add_argument('-p', '--preset', - help="(string) Name of a preset to run (class name from the 'presets' directory.)", + help="(string) Name of a preset to run (class name from the 'presets' directory).", type=str, required=True) + parser.add_argument('--memory-backend', + help="(string) Memory backend to use.", + type=str, + default="redispubsub") + parser.add_argument('-ds', '--data-store', + help="(string) Data store to use.", + type=str, + default="s3") parser.add_argument('-ns', '--nfs-server', - help="(string) Addresss of the nfs server.)", + help="(string) Addresss of the nfs server.", type=str, required=True) parser.add_argument('-np', '--nfs-path', - help="(string) Exported path for the nfs server", + help="(string) Exported path for the nfs server.", type=str, required=True) - parser.add_argument('--memory_backend', - help="(string) Memory backend to use", + parser.add_argument('--s3-end-point', + help="(string) S3 endpoint to use when S3 data store is used.", type=str, - default="redispubsub") + required=True) + parser.add_argument('--s3-bucket-name', + help="(string) S3 bucket name to use when S3 data store is used.", + type=str, + required=True) # parser.add_argument('--checkpoint_dir', # help='(string) Path to a folder containing a checkpoint to write the model to.', @@ -68,4 +97,6 @@ if __name__ == '__main__': # default='/checkpoint') args = parser.parse_args() - main(preset=args.preset, image=args.image, nfs_server=args.nfs_server, nfs_path=args.nfs_path, memory_backend=args.memory_backend) + main(preset=args.preset, image=args.image, nfs_server=args.nfs_server, nfs_path=args.nfs_path, + memory_backend=args.memory_backend, data_store=args.data_store, s3_end_point=args.s3_end_point, + s3_bucket_name=args.s3_bucket_name) diff --git a/rl_coach/rollout_worker.py b/rl_coach/rollout_worker.py index 7d8a371..b2f98d8 100644 --- a/rl_coach/rollout_worker.py +++ b/rl_coach/rollout_worker.py @@ -12,11 +12,14 @@ import time import os import json +from threading import Thread + from rl_coach.base_parameters import TaskParameters from rl_coach.coach import expand_preset from rl_coach.core_types import EnvironmentEpisodes, RunPhase from rl_coach.utils import short_dynamic_import from rl_coach.memories.backend.memory_impl import construct_memory_params +from rl_coach.data_stores.data_store_impl import get_data_store, construct_data_store_params # Q: specify alternative distributed memory, or should this go in the preset? @@ -27,17 +30,23 @@ def has_checkpoint(checkpoint_dir): """ True if a checkpoint is present in checkpoint_dir """ - return len(os.listdir(checkpoint_dir)) > 0 + if os.path.isdir(checkpoint_dir): + if len(os.listdir(checkpoint_dir)) > 0: + return os.path.isfile(os.path.join(checkpoint_dir, "checkpoint")) + return False -def wait_for_checkpoint(checkpoint_dir, timeout=10): +def wait_for_checkpoint(checkpoint_dir, data_store=None, timeout=10): """ block until there is a checkpoint in checkpoint_dir """ for i in range(timeout): + if data_store: + data_store.load_from_store() + if has_checkpoint(checkpoint_dir): return - time.sleep(1) + time.sleep(10) # one last time if has_checkpoint(checkpoint_dir): @@ -52,20 +61,26 @@ def wait_for_checkpoint(checkpoint_dir, timeout=10): )) +def data_store_ckpt_load(data_store): + while True: + data_store.load_from_store() + time.sleep(10) + def rollout_worker(graph_manager, checkpoint_dir): """ - restore a checkpoint then perform rollouts using the restored model + wait for first checkpoint then perform rollouts using the model """ wait_for_checkpoint(checkpoint_dir) task_parameters = TaskParameters() task_parameters.__dict__['checkpoint_restore_dir'] = checkpoint_dir + time.sleep(30) graph_manager.create_graph(task_parameters) graph_manager.phase = RunPhase.TRAIN for i in range(10000000): - graph_manager.act(EnvironmentEpisodes(num_steps=10)) graph_manager.restore_checkpoint() + graph_manager.act(EnvironmentEpisodes(num_steps=10)) graph_manager.phase = RunPhase.UNDEFINED @@ -91,6 +106,9 @@ def main(): parser.add_argument('--memory_backend_params', help="(string) JSON string of the memory backend params", type=str) + parser.add_argument('--data_store_params', + help="(string) JSON string of the data store params", + type=str) args = parser.parse_args() @@ -98,9 +116,20 @@ def main(): if args.memory_backend_params: args.memory_backend_params = json.loads(args.memory_backend_params) - if 'run_type' not in args.memory_backend_params: - args.memory_backend_params['run_type'] = 'worker' + print(args.memory_backend_params) + args.memory_backend_params['run_type'] = 'worker' + print(construct_memory_params(args.memory_backend_params)) graph_manager.agent_params.memory.register_var('memory_backend_params', construct_memory_params(args.memory_backend_params)) + + if args.data_store_params: + data_store_params = construct_data_store_params(json.loads(args.data_store_params)) + data_store_params.checkpoint_dir = args.checkpoint_dir + graph_manager.data_store_params = data_store_params + data_store = get_data_store(data_store_params) + wait_for_checkpoint(checkpoint_dir=args.checkpoint_dir, data_store=data_store) + # thread = Thread(target = data_store_ckpt_load, args = [data_store]) + # thread.start() + rollout_worker( graph_manager=graph_manager, checkpoint_dir=args.checkpoint_dir, diff --git a/rl_coach/training_worker.py b/rl_coach/training_worker.py index 85f2052..4da27ed 100644 --- a/rl_coach/training_worker.py +++ b/rl_coach/training_worker.py @@ -4,15 +4,19 @@ import argparse import time import json +from threading import Thread + from rl_coach.base_parameters import TaskParameters from rl_coach.coach import expand_preset from rl_coach import core_types from rl_coach.utils import short_dynamic_import from rl_coach.memories.backend.memory_impl import construct_memory_params +from rl_coach.data_stores.data_store_impl import get_data_store, construct_data_store_params -# Q: specify alternative distributed memory, or should this go in the preset? -# A: preset must define distributed memory to be used. we aren't going to take a non-distributed preset and automatically distribute it. - +def data_store_ckpt_save(data_store): + while True: + data_store.save_to_store() + time.sleep(10) def training_worker(graph_manager, checkpoint_dir): """ @@ -58,16 +62,26 @@ def main(): parser.add_argument('--memory_backend_params', help="(string) JSON string of the memory backend params", type=str) + parser.add_argument('--data_store_params', + help="(string) JSON string of the data store params", + type=str) args = parser.parse_args() graph_manager = short_dynamic_import(expand_preset(args.preset), ignore_module_case=True) if args.memory_backend_params: args.memory_backend_params = json.loads(args.memory_backend_params) - if 'run_type' not in args.memory_backend_params: - args.memory_backend_params['run_type'] = 'trainer' + args.memory_backend_params['run_type'] = 'trainer' graph_manager.agent_params.memory.register_var('memory_backend_params', construct_memory_params(args.memory_backend_params)) + if args.data_store_params: + data_store_params = construct_data_store_params(json.loads(args.data_store_params)) + data_store_params.checkpoint_dir = args.checkpoint_dir + graph_manager.data_store_params = data_store_params + # data_store = get_data_store(data_store_params) + # thread = Thread(target = data_store_ckpt_save, args = [data_store]) + # thread.start() + training_worker( graph_manager=graph_manager, checkpoint_dir=args.checkpoint_dir, From 052bbc8f1932e886dfb767e4cd75b7433f69bbb9 Mon Sep 17 00:00:00 2001 From: Ajay Deshpande Date: Fri, 5 Oct 2018 12:53:51 -0700 Subject: [PATCH 045/162] Adding lock in s3 --- rl_coach/data_stores/s3_data_store.py | 30 +++++++++++++++++++++++++++ 1 file changed, 30 insertions(+) diff --git a/rl_coach/data_stores/s3_data_store.py b/rl_coach/data_stores/s3_data_store.py index 5ceb00a..d616d9c 100644 --- a/rl_coach/data_stores/s3_data_store.py +++ b/rl_coach/data_stores/s3_data_store.py @@ -4,6 +4,8 @@ from minio import Minio from minio.error import ResponseError from configparser import ConfigParser, Error import os +import time +import io class S3DataStoreParameters(DataStoreParameters): @@ -15,6 +17,7 @@ class S3DataStoreParameters(DataStoreParameters): self.end_point = end_point self.bucket_name = bucket_name self.checkpoint_dir = checkpoint_dir + self.lock_file = ".lock" class S3DataStore(DataStore): @@ -46,18 +49,45 @@ class S3DataStore(DataStore): def save_to_store(self): try: + print("Writing lock file") + + self.mc.remove_object(self.params.bucket_name, self.params.lock_file) + + self.mc.put_object(self.params.bucket_name, self.params.lock_file, io.BytesIO(b''), 0) + print("saving to s3") + checkpoint_file = None for root, dirs, files in os.walk(self.params.checkpoint_dir): for filename in files: + if filename == 'checkpoint': + checkpoint_file = (root, filename) + pass abs_name = os.path.abspath(os.path.join(root, filename)) rel_name = os.path.relpath(abs_name, self.params.checkpoint_dir) self.mc.fput_object(self.params.bucket_name, rel_name, abs_name) + + abs_name = os.path.abspath(os.path.join(checkpoint_file[0], checkpoint_file[1])) + rel_name = os.path.relpath(abs_name, self.params.checkpoint_dir) + print("Deleting lock file") + self.mc.remove_object(self.params.bucket_name, self.params.lock_file) + except ResponseError as e: print("Got exception: %s\n while saving to S3", e) def load_from_store(self): try: + + while True: + objects = self.mc.list_objects_v2(self.params.bucket_name, self.params.lock_file) + time.sleep(10) + if next(objects, None) is None: + break + print("loading from s3") + + filename = os.path.abspath(os.path.join(self.params.checkpoint_dir, "checkpoint")) + self.mc.fget_object(self.params.bucket_name, "checkpoint", filename) + objects = self.mc.list_objects_v2(self.params.bucket_name, recursive=True) for obj in objects: filename = os.path.abspath(os.path.join(self.params.checkpoint_dir, obj.object_name)) From a2e57a44f1609f17f8a12fabae71c141092eba2f Mon Sep 17 00:00:00 2001 From: Ajay Deshpande Date: Fri, 5 Oct 2018 13:48:10 -0700 Subject: [PATCH 046/162] Getting only the model_checkpoint_path files --- rl_coach/data_stores/s3_data_store.py | 17 +++++++++++++---- 1 file changed, 13 insertions(+), 4 deletions(-) diff --git a/rl_coach/data_stores/s3_data_store.py b/rl_coach/data_stores/s3_data_store.py index d616d9c..2cb838b 100644 --- a/rl_coach/data_stores/s3_data_store.py +++ b/rl_coach/data_stores/s3_data_store.py @@ -3,6 +3,8 @@ from kubernetes import client as k8sclient from minio import Minio from minio.error import ResponseError from configparser import ConfigParser, Error +from google.protobuf import text_format +from tensorflow.python.training.checkpoint_state_pb2 import CheckpointState import os import time import io @@ -88,9 +90,16 @@ class S3DataStore(DataStore): filename = os.path.abspath(os.path.join(self.params.checkpoint_dir, "checkpoint")) self.mc.fget_object(self.params.bucket_name, "checkpoint", filename) - objects = self.mc.list_objects_v2(self.params.bucket_name, recursive=True) - for obj in objects: - filename = os.path.abspath(os.path.join(self.params.checkpoint_dir, obj.object_name)) - self.mc.fget_object(obj.bucket_name, obj.object_name, filename) + ckpt = CheckpointState() + if os.path.exists(filename): + contents = open(filename, 'r').read() + text_format.Merge(contents, ckpt) + rel_path = os.path.relpath(ckpt.model_checkpoint_path, self.params.checkpoint_dir) + + objects = self.mc.list_objects_v2(self.params.bucket_name, prefix=rel_path, recursive=True) + for obj in objects: + filename = os.path.abspath(os.path.join(self.params.checkpoint_dir, obj.object_name)) + self.mc.fget_object(obj.bucket_name, obj.object_name, filename) + except ResponseError as e: print("Got exception: %s\n while loading from S3", e) From a7f5442015df3693dc4fe86755ea84ef697c76f4 Mon Sep 17 00:00:00 2001 From: Ajay Deshpande Date: Fri, 5 Oct 2018 14:22:15 -0700 Subject: [PATCH 047/162] Adding should_train helper and should_train in graph_manager --- rl_coach/agents/agent.py | 23 ++++++-- rl_coach/data_stores/s3_data_store.py | 15 +++-- rl_coach/graph_managers/graph_manager.py | 3 + rl_coach/level_manager.py | 3 + rl_coach/orchestrators/start_training.py | 13 ++-- rl_coach/presets/CartPole_PPO.py | 75 ++++++++++++++++++++++++ rl_coach/training_worker.py | 14 ++--- 7 files changed, 126 insertions(+), 20 deletions(-) create mode 100644 rl_coach/presets/CartPole_PPO.py diff --git a/rl_coach/agents/agent.py b/rl_coach/agents/agent.py index 2dd1e9a..b249749 100644 --- a/rl_coach/agents/agent.py +++ b/rl_coach/agents/agent.py @@ -523,6 +523,8 @@ class Agent(AgentInterface): Determine if online weights should be copied to the target. :return: boolean: True if the online weights should be copied to the target. """ + if hasattr(self.ap.memory, 'memory_backend_params'): + self.total_steps_counter = self.call_memory('num_transitions') # update the target network of every network that has a target network step_method = self.ap.algorithm.num_steps_between_copying_online_weights_to_target if step_method.__class__ == TrainingSteps: @@ -544,22 +546,35 @@ class Agent(AgentInterface): :return: boolean: True if we should start a training phase """ + should_update = self._should_train_helper(wait_for_full_episode) + + step_method = self.ap.algorithm.num_consecutive_playing_steps + + if should_update: + if step_method.__class__ == EnvironmentEpisodes: + self.last_training_phase_step = self.current_episode + if step_method.__class__ == EnvironmentSteps: + self.last_training_phase_step = self.total_steps_counter + + return should_update + + def _should_train_helper(self, wait_for_full_episode=False): + if hasattr(self.ap.memory, 'memory_backend_params'): self.total_steps_counter = self.call_memory('num_transitions') + step_method = self.ap.algorithm.num_consecutive_playing_steps if step_method.__class__ == EnvironmentEpisodes: should_update = (self.current_episode - self.last_training_phase_step) >= step_method.num_steps - if should_update: - self.last_training_phase_step = self.current_episode + elif step_method.__class__ == EnvironmentSteps: should_update = (self.total_steps_counter - self.last_training_phase_step) >= step_method.num_steps if wait_for_full_episode: should_update = should_update and self.current_episode_buffer.is_complete - if should_update: - self.last_training_phase_step = self.total_steps_counter else: raise ValueError("The num_consecutive_playing_steps parameter should be either " "EnvironmentSteps or Episodes. Instead it is {}".format(step_method.__class__)) + return should_update def train(self): diff --git a/rl_coach/data_stores/s3_data_store.py b/rl_coach/data_stores/s3_data_store.py index 2cb838b..bed6398 100644 --- a/rl_coach/data_stores/s3_data_store.py +++ b/rl_coach/data_stores/s3_data_store.py @@ -5,6 +5,7 @@ from minio.error import ResponseError from configparser import ConfigParser, Error from google.protobuf import text_format from tensorflow.python.training.checkpoint_state_pb2 import CheckpointState +from minio.error import ResponseError import os import time import io @@ -63,7 +64,7 @@ class S3DataStore(DataStore): for filename in files: if filename == 'checkpoint': checkpoint_file = (root, filename) - pass + continue abs_name = os.path.abspath(os.path.join(root, filename)) rel_name = os.path.relpath(abs_name, self.params.checkpoint_dir) self.mc.fput_object(self.params.bucket_name, rel_name, abs_name) @@ -79,17 +80,21 @@ class S3DataStore(DataStore): def load_from_store(self): try: + filename = os.path.abspath(os.path.join(self.params.checkpoint_dir, "checkpoint")) + while True: objects = self.mc.list_objects_v2(self.params.bucket_name, self.params.lock_file) - time.sleep(10) + if next(objects, None) is None: + try: + self.mc.fget_object(self.params.bucket_name, "checkpoint", filename) + except ResponseError as e: + continue break + time.sleep(10) print("loading from s3") - filename = os.path.abspath(os.path.join(self.params.checkpoint_dir, "checkpoint")) - self.mc.fget_object(self.params.bucket_name, "checkpoint", filename) - ckpt = CheckpointState() if os.path.exists(filename): contents = open(filename, 'r').read() diff --git a/rl_coach/graph_managers/graph_manager.py b/rl_coach/graph_managers/graph_manager.py index 5ee24ed..576ac8d 100644 --- a/rl_coach/graph_managers/graph_manager.py +++ b/rl_coach/graph_managers/graph_manager.py @@ -591,3 +591,6 @@ class GraphManager(object): result += "{}: \n{}\n".format(key, params) return result + + def should_train(self) -> bool: + return any([manager.should_train() for manager in self.level_managers]) diff --git a/rl_coach/level_manager.py b/rl_coach/level_manager.py index 19a2dc0..7697bf5 100644 --- a/rl_coach/level_manager.py +++ b/rl_coach/level_manager.py @@ -260,3 +260,6 @@ class LevelManager(EnvironmentInterface): :return: """ [agent.sync() for agent in self.agents.values()] + + def should_train(self) -> bool: + return any([agent._should_train_helper() for agent in self.agents.values()]) diff --git a/rl_coach/orchestrators/start_training.py b/rl_coach/orchestrators/start_training.py index 05af288..dbf9df0 100644 --- a/rl_coach/orchestrators/start_training.py +++ b/rl_coach/orchestrators/start_training.py @@ -20,12 +20,12 @@ def main(preset: str, image: str='ajaysudh/testing:coach', num_workers: int=1, n if data_store == "s3": ds_params = DataStoreParameters("s3", "", "") ds_params_instance = S3DataStoreParameters(ds_params=ds_params, end_point=s3_end_point, bucket_name=s3_bucket_name, - checkpoint_dir="/checkpoint") + checkpoint_dir="/checkpoint") elif data_store == "nfs": ds_params = DataStoreParameters("nfs", "kubernetes", {"namespace": "default"}) ds_params_instance = NFSDataStoreParameters(ds_params) - worker_run_type_params = RunTypeParameters(image, rollout_command, run_type="worker") + worker_run_type_params = RunTypeParameters(image, rollout_command, run_type="worker", num_replicas=num_workers) trainer_run_type_params = RunTypeParameters(image, training_command, run_type="trainer") orchestration_params = KubernetesParameters([worker_run_type_params, trainer_run_type_params], @@ -53,7 +53,7 @@ def main(preset: str, image: str='ajaysudh/testing:coach', num_workers: int=1, n orchestrator.trainer_logs() except KeyboardInterrupt: pass - # orchestrator.undeploy() + orchestrator.undeploy() if __name__ == '__main__': @@ -90,6 +90,11 @@ if __name__ == '__main__': help="(string) S3 bucket name to use when S3 data store is used.", type=str, required=True) + parser.add_argument('--num-workers', + help="(string) Number of rollout workers", + type=int, + required=False, + default=1) # parser.add_argument('--checkpoint_dir', # help='(string) Path to a folder containing a checkpoint to write the model to.', @@ -99,4 +104,4 @@ if __name__ == '__main__': main(preset=args.preset, image=args.image, nfs_server=args.nfs_server, nfs_path=args.nfs_path, memory_backend=args.memory_backend, data_store=args.data_store, s3_end_point=args.s3_end_point, - s3_bucket_name=args.s3_bucket_name) + s3_bucket_name=args.s3_bucket_name, num_workers=args.num_workers) diff --git a/rl_coach/presets/CartPole_PPO.py b/rl_coach/presets/CartPole_PPO.py new file mode 100644 index 0000000..c163efd --- /dev/null +++ b/rl_coach/presets/CartPole_PPO.py @@ -0,0 +1,75 @@ +from rl_coach.agents.clipped_ppo_agent import ClippedPPOAgentParameters +from rl_coach.architectures.tensorflow_components.architecture import Dense +from rl_coach.base_parameters import VisualizationParameters, PresetValidationParameters +from rl_coach.core_types import TrainingSteps, EnvironmentEpisodes, EnvironmentSteps, RunPhase +from rl_coach.environments.environment import MaxDumpMethod, SelectedPhaseOnlyDumpMethod, SingleLevelSelection +from rl_coach.environments.gym_environment import Mujoco, mujoco_v2, MujocoInputFilter +from rl_coach.exploration_policies.additive_noise import AdditiveNoiseParameters +from rl_coach.exploration_policies.e_greedy import EGreedyParameters +from rl_coach.filters.observation.observation_normalization_filter import ObservationNormalizationFilter +from rl_coach.graph_managers.basic_rl_graph_manager import BasicRLGraphManager +from rl_coach.graph_managers.graph_manager import ScheduleParameters +from rl_coach.schedules import LinearSchedule + +#################### +# Graph Scheduling # +#################### + +schedule_params = ScheduleParameters() +schedule_params.improve_steps = TrainingSteps(10000000) +schedule_params.steps_between_evaluation_periods = EnvironmentSteps(2048) +schedule_params.evaluation_steps = EnvironmentEpisodes(5) +schedule_params.heatup_steps = EnvironmentSteps(0) + +######### +# Agent # +######### +agent_params = ClippedPPOAgentParameters() + + +agent_params.network_wrappers['main'].learning_rate = 0.0003 +agent_params.network_wrappers['main'].input_embedders_parameters['observation'].activation_function = 'tanh' +agent_params.network_wrappers['main'].input_embedders_parameters['observation'].scheme = [Dense([64])] +agent_params.network_wrappers['main'].middleware_parameters.scheme = [Dense([64])] +agent_params.network_wrappers['main'].middleware_parameters.activation_function = 'tanh' +agent_params.network_wrappers['main'].batch_size = 64 +agent_params.network_wrappers['main'].optimizer_epsilon = 1e-5 +agent_params.network_wrappers['main'].adam_optimizer_beta2 = 0.999 + +agent_params.algorithm.clip_likelihood_ratio_using_epsilon = 0.2 +agent_params.algorithm.clipping_decay_schedule = LinearSchedule(1.0, 0, 1000000) +agent_params.algorithm.beta_entropy = 0 +agent_params.algorithm.gae_lambda = 0.95 +agent_params.algorithm.discount = 0.99 +agent_params.algorithm.optimization_epochs = 10 +agent_params.algorithm.estimate_state_value_using_gae = True +agent_params.algorithm.num_steps_between_copying_online_weights_to_target = EnvironmentSteps(2048) + +# agent_params.input_filter = MujocoInputFilter() +agent_params.exploration = EGreedyParameters() +agent_params.exploration.epsilon_schedule = LinearSchedule(1.0, 0.01, 10000) +# agent_params.pre_network_filter = MujocoInputFilter() +agent_params.pre_network_filter.add_observation_filter('observation', 'normalize_observation', + ObservationNormalizationFilter(name='normalize_observation')) + +############### +# Environment # +############### +env_params = Mujoco() +env_params.level = 'CartPole-v0' + +vis_params = VisualizationParameters() +vis_params.video_dump_methods = [SelectedPhaseOnlyDumpMethod(RunPhase.TEST), MaxDumpMethod()] +vis_params.dump_mp4 = False + +######## +# Test # +######## +preset_validation_params = PresetValidationParameters() +preset_validation_params.test = True +preset_validation_params.min_reward_threshold = 150 +preset_validation_params.max_episodes_to_achieve_reward = 250 + +graph_manager = BasicRLGraphManager(agent_params=agent_params, env_params=env_params, + schedule_params=schedule_params, vis_params=vis_params, + preset_validation_params=preset_validation_params) diff --git a/rl_coach/training_worker.py b/rl_coach/training_worker.py index 4da27ed..c5664a8 100644 --- a/rl_coach/training_worker.py +++ b/rl_coach/training_worker.py @@ -32,13 +32,13 @@ def training_worker(graph_manager, checkpoint_dir): # training loop while True: - graph_manager.phase = core_types.RunPhase.TRAIN - graph_manager.train(core_types.TrainingSteps(1)) - graph_manager.phase = core_types.RunPhase.UNDEFINED - - graph_manager.evaluate(graph_manager.evaluation_steps) - - graph_manager.save_checkpoint() + if graph_manager.should_train(): + graph_manager.phase = core_types.RunPhase.TRAIN + graph_manager.train(core_types.TrainingSteps(1)) + graph_manager.phase = core_types.RunPhase.UNDEFINED + graph_manager.evaluate(graph_manager.evaluation_steps) + graph_manager.save_checkpoint() + time.sleep(10) def main(): From 5eac0102de8cc91bdea219e3b3263d0c32a0b1d5 Mon Sep 17 00:00:00 2001 From: Ajay Deshpande Date: Fri, 5 Oct 2018 15:57:50 -0700 Subject: [PATCH 048/162] Changing exception type --- rl_coach/data_stores/s3_data_store.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/rl_coach/data_stores/s3_data_store.py b/rl_coach/data_stores/s3_data_store.py index bed6398..cc58367 100644 --- a/rl_coach/data_stores/s3_data_store.py +++ b/rl_coach/data_stores/s3_data_store.py @@ -5,7 +5,7 @@ from minio.error import ResponseError from configparser import ConfigParser, Error from google.protobuf import text_format from tensorflow.python.training.checkpoint_state_pb2 import CheckpointState -from minio.error import ResponseError + import os import time import io @@ -71,6 +71,8 @@ class S3DataStore(DataStore): abs_name = os.path.abspath(os.path.join(checkpoint_file[0], checkpoint_file[1])) rel_name = os.path.relpath(abs_name, self.params.checkpoint_dir) + self.mc.fput_object(self.params.bucket_name, rel_name, abs_name) + print("Deleting lock file") self.mc.remove_object(self.params.bucket_name, self.params.lock_file) @@ -88,7 +90,7 @@ class S3DataStore(DataStore): if next(objects, None) is None: try: self.mc.fget_object(self.params.bucket_name, "checkpoint", filename) - except ResponseError as e: + except Exception as e: continue break time.sleep(10) From 7f00235ed5dfe53bdad786fbadfc4d07af92b07d Mon Sep 17 00:00:00 2001 From: Ajay Deshpande Date: Fri, 5 Oct 2018 19:08:24 -0700 Subject: [PATCH 049/162] waiting for a new checkpoint if it's available --- rl_coach/agents/agent.py | 11 +++----- rl_coach/agents/clipped_ppo_agent.py | 3 +++ rl_coach/agents/ppo_agent.py | 5 +++- rl_coach/memories/backend/redis.py | 18 ++++++++----- .../episodic/episodic_experience_replay.py | 2 +- rl_coach/memories/memory.py | 3 +-- rl_coach/rollout_worker.py | 27 +++++++++++++++++-- 7 files changed, 49 insertions(+), 20 deletions(-) diff --git a/rl_coach/agents/agent.py b/rl_coach/agents/agent.py index b249749..f8fcab6 100644 --- a/rl_coach/agents/agent.py +++ b/rl_coach/agents/agent.py @@ -81,7 +81,7 @@ class Agent(AgentInterface): self.memory_backend = get_memory_backend(self.ap.memory.memory_backend_params) if self.ap.memory.memory_backend_params.run_type == 'trainer': - self.memory_backend.subscribe(self.memory) + self.memory_backend.subscribe(self) else: self.memory.set_memory_backend(self.memory_backend) @@ -523,8 +523,7 @@ class Agent(AgentInterface): Determine if online weights should be copied to the target. :return: boolean: True if the online weights should be copied to the target. """ - if hasattr(self.ap.memory, 'memory_backend_params'): - self.total_steps_counter = self.call_memory('num_transitions') + # update the target network of every network that has a target network step_method = self.ap.algorithm.num_steps_between_copying_online_weights_to_target if step_method.__class__ == TrainingSteps: @@ -546,7 +545,7 @@ class Agent(AgentInterface): :return: boolean: True if we should start a training phase """ - should_update = self._should_train_helper(wait_for_full_episode) + should_update = self._should_train_helper(wait_for_full_episode=wait_for_full_episode) step_method = self.ap.algorithm.num_consecutive_playing_steps @@ -560,10 +559,8 @@ class Agent(AgentInterface): def _should_train_helper(self, wait_for_full_episode=False): - if hasattr(self.ap.memory, 'memory_backend_params'): - self.total_steps_counter = self.call_memory('num_transitions') - step_method = self.ap.algorithm.num_consecutive_playing_steps + if step_method.__class__ == EnvironmentEpisodes: should_update = (self.current_episode - self.last_training_phase_step) >= step_method.num_steps diff --git a/rl_coach/agents/clipped_ppo_agent.py b/rl_coach/agents/clipped_ppo_agent.py index 0091841..4414966 100644 --- a/rl_coach/agents/clipped_ppo_agent.py +++ b/rl_coach/agents/clipped_ppo_agent.py @@ -251,6 +251,9 @@ class ClippedPPOAgent(ActorCriticAgent): # clean memory self.call_memory('clean') + def _should_train_helper(self, wait_for_full_episode=True): + return super()._should_train_helper(True) + def train(self): if self._should_train(wait_for_full_episode=True): for network in self.networks.values(): diff --git a/rl_coach/agents/ppo_agent.py b/rl_coach/agents/ppo_agent.py index 24d2b9f..fdb175e 100644 --- a/rl_coach/agents/ppo_agent.py +++ b/rl_coach/agents/ppo_agent.py @@ -1,5 +1,5 @@ # -# Copyright (c) 2017 Intel Corporation +# Copyright (c) 2017 Intel Corporation # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -310,6 +310,9 @@ class PPOAgent(ActorCriticAgent): # clean memory self.call_memory('clean') + def _should_train_helper(self): + return super()._should_train_helper(True) + def train(self): loss = 0 if self._should_train(wait_for_full_episode=True): diff --git a/rl_coach/memories/backend/redis.py b/rl_coach/memories/backend/redis.py index f060d45..10faa72 100644 --- a/rl_coach/memories/backend/redis.py +++ b/rl_coach/memories/backend/redis.py @@ -6,7 +6,6 @@ import threading from kubernetes import client from rl_coach.memories.backend.memory import MemoryBackend, MemoryBackendParameters -from rl_coach.memories.memory import Memory from rl_coach.core_types import Transition, Episode @@ -126,8 +125,8 @@ class RedisPubSubBackend(MemoryBackend): def sample(self, size): pass - def subscribe(self, memory): - redis_sub = RedisSub(memory, redis_address=self.params.redis_address, redis_port=self.params.redis_port, channel=self.params.channel) + def subscribe(self, agent): + redis_sub = RedisSub(agent, redis_address=self.params.redis_address, redis_port=self.params.redis_port, channel=self.params.channel) redis_sub.daemon = True redis_sub.start() @@ -138,12 +137,12 @@ class RedisPubSubBackend(MemoryBackend): class RedisSub(threading.Thread): - def __init__(self, memory: Memory, redis_address: str = "localhost", redis_port: int=6379, channel: str = "PubsubChannel"): + def __init__(self, agent, redis_address: str = "localhost", redis_port: int=6379, channel: str = "PubsubChannel"): super().__init__() self.redis_connection = redis.Redis(redis_address, redis_port) self.pubsub = self.redis_connection.pubsub() self.subscriber = None - self.memory = memory + self.agent = agent self.channel = channel self.subscriber = self.pubsub.subscribe(self.channel) @@ -153,8 +152,13 @@ class RedisSub(threading.Thread): try: obj = pickle.loads(message['data']) if type(obj) == Transition: - self.memory.store(obj) + self.agent.total_steps_counter += 1 + self.agent.current_episode_steps_counter += 1 + self.agent.call_memory('store', obj) elif type(obj) == Episode: - self.memory.store_episode(obj) + self.agent.current_episode_buffer = obj + self.agent.total_steps_counter += len(obj.transitions) + self.agent.current_episode_steps_counter += len(obj.transitions) + self.agent.handle_episode_ended() except Exception: continue diff --git a/rl_coach/memories/episodic/episodic_experience_replay.py b/rl_coach/memories/episodic/episodic_experience_replay.py index f174a74..ada96f4 100644 --- a/rl_coach/memories/episodic/episodic_experience_replay.py +++ b/rl_coach/memories/episodic/episodic_experience_replay.py @@ -184,7 +184,7 @@ class EpisodicExperienceReplay(Memory): :return: None """ # Calling super.store() so that in case a memory backend is used, the memory backend can store this episode. - super().store(episode) + super().store_episode(episode) if lock: self.reader_writer_lock.lock_writing_and_reading() diff --git a/rl_coach/memories/memory.py b/rl_coach/memories/memory.py index 5c56cd0..75414d4 100644 --- a/rl_coach/memories/memory.py +++ b/rl_coach/memories/memory.py @@ -53,8 +53,7 @@ class Memory(object): def store_episode(self, episode): if self.memory_backend: - for transition in episode: - self.memory_backend.store(transition) + self.memory_backend.store(episode) def get(self, index): raise NotImplementedError("") diff --git a/rl_coach/rollout_worker.py b/rl_coach/rollout_worker.py index b2f98d8..a04d23c 100644 --- a/rl_coach/rollout_worker.py +++ b/rl_coach/rollout_worker.py @@ -20,6 +20,8 @@ from rl_coach.core_types import EnvironmentEpisodes, RunPhase from rl_coach.utils import short_dynamic_import from rl_coach.memories.backend.memory_impl import construct_memory_params from rl_coach.data_stores.data_store_impl import get_data_store, construct_data_store_params +from google.protobuf import text_format +from tensorflow.python.training.checkpoint_state_pb2 import CheckpointState # Q: specify alternative distributed memory, or should this go in the preset? @@ -66,6 +68,20 @@ def data_store_ckpt_load(data_store): data_store.load_from_store() time.sleep(10) + +def check_for_new_checkpoint(checkpoint_dir, last_checkpoint): + if os.path.exists(os.path.join(checkpoint_dir, 'checkpoint')): + ckpt = CheckpointState() + contents = open(os.path.join(checkpoint_dir, 'checkpoint'), 'r').read() + text_format.Merge(contents, ckpt) + rel_path = os.path.relpath(ckpt.model_checkpoint_path, checkpoint_dir) + current_checkpoint = int(rel_path.split('_Step')[0]) + if current_checkpoint > last_checkpoint: + last_checkpoint = current_checkpoint + + return last_checkpoint + + def rollout_worker(graph_manager, checkpoint_dir): """ wait for first checkpoint then perform rollouts using the model @@ -78,9 +94,16 @@ def rollout_worker(graph_manager, checkpoint_dir): graph_manager.create_graph(task_parameters) graph_manager.phase = RunPhase.TRAIN + last_checkpoint = 0 + for i in range(10000000): - graph_manager.restore_checkpoint() - graph_manager.act(EnvironmentEpisodes(num_steps=10)) + graph_manager.act(EnvironmentEpisodes(num_steps=1)) + + new_checkpoint = check_for_new_checkpoint(checkpoint_dir, last_checkpoint) + + if new_checkpoint > last_checkpoint: + last_checkpoint = new_checkpoint + graph_manager.restore_checkpoint() graph_manager.phase = RunPhase.UNDEFINED From 0e121c576246ed26c3748722ce892c5bb991218b Mon Sep 17 00:00:00 2001 From: Ajay Deshpande Date: Mon, 8 Oct 2018 12:01:12 -0700 Subject: [PATCH 050/162] Ignoring redis sub if testing --- rl_coach/graph_managers/graph_manager.py | 4 ++-- rl_coach/memories/backend/redis.py | 5 ++++- rl_coach/training_worker.py | 1 - 3 files changed, 6 insertions(+), 4 deletions(-) diff --git a/rl_coach/graph_managers/graph_manager.py b/rl_coach/graph_managers/graph_manager.py index 576ac8d..3fe5ed9 100644 --- a/rl_coach/graph_managers/graph_manager.py +++ b/rl_coach/graph_managers/graph_manager.py @@ -376,7 +376,7 @@ class GraphManager(object): if self.agent_params.memory.memory_backend_params.run_type == "worker": data_store = get_data_store(self.data_store_params) data_store.load_from_store() - + # perform several steps of playing result = None @@ -435,7 +435,7 @@ class GraphManager(object): if steps.num_steps > 0: self.phase = RunPhase.TRAIN self.reset_internal_state(force_environment_reset=True) - #TODO - the below while loop should end with full episodes, so to avoid situations where we have partial + # TODO - the below while loop should end with full episodes, so to avoid situations where we have partial # episodes in memory count_end = self.total_steps_counters[self.phase][steps.__class__] + steps.num_steps while self.total_steps_counters[self.phase][steps.__class__] < count_end: diff --git a/rl_coach/memories/backend/redis.py b/rl_coach/memories/backend/redis.py index 10faa72..80cbae5 100644 --- a/rl_coach/memories/backend/redis.py +++ b/rl_coach/memories/backend/redis.py @@ -7,6 +7,7 @@ from kubernetes import client from rl_coach.memories.backend.memory import MemoryBackend, MemoryBackendParameters from rl_coach.core_types import Transition, Episode +from rl_coach.core_types import RunPhase class RedisPubSubMemoryBackendParameters(MemoryBackendParameters): @@ -148,7 +149,9 @@ class RedisSub(threading.Thread): def run(self): for message in self.pubsub.listen(): - if message and 'data' in message: + if message and 'data' in message and self.agent.phase != RunPhase.TEST or self.agent.ap.task_parameters.evaluate_only: + if self.agent.phase == RunPhase.TEST: + print(self.agent.phase) try: obj = pickle.loads(message['data']) if type(obj) == Transition: diff --git a/rl_coach/training_worker.py b/rl_coach/training_worker.py index c5664a8..b68bee7 100644 --- a/rl_coach/training_worker.py +++ b/rl_coach/training_worker.py @@ -38,7 +38,6 @@ def training_worker(graph_manager, checkpoint_dir): graph_manager.phase = core_types.RunPhase.UNDEFINED graph_manager.evaluate(graph_manager.evaluation_steps) graph_manager.save_checkpoint() - time.sleep(10) def main(): From 0f46877d7e0d31d1f46391b475b33454578a1064 Mon Sep 17 00:00:00 2001 From: Ajay Deshpande Date: Mon, 8 Oct 2018 13:41:51 -0700 Subject: [PATCH 051/162] Adding steps and waiting for new checkpoint --- rl_coach/data_stores/s3_data_store.py | 7 ------- rl_coach/rollout_worker.py | 28 +++++++++++++++++++-------- rl_coach/training_worker.py | 4 +++- 3 files changed, 23 insertions(+), 16 deletions(-) diff --git a/rl_coach/data_stores/s3_data_store.py b/rl_coach/data_stores/s3_data_store.py index cc58367..7d40f8b 100644 --- a/rl_coach/data_stores/s3_data_store.py +++ b/rl_coach/data_stores/s3_data_store.py @@ -52,13 +52,10 @@ class S3DataStore(DataStore): def save_to_store(self): try: - print("Writing lock file") - self.mc.remove_object(self.params.bucket_name, self.params.lock_file) self.mc.put_object(self.params.bucket_name, self.params.lock_file, io.BytesIO(b''), 0) - print("saving to s3") checkpoint_file = None for root, dirs, files in os.walk(self.params.checkpoint_dir): for filename in files: @@ -73,7 +70,6 @@ class S3DataStore(DataStore): rel_name = os.path.relpath(abs_name, self.params.checkpoint_dir) self.mc.fput_object(self.params.bucket_name, rel_name, abs_name) - print("Deleting lock file") self.mc.remove_object(self.params.bucket_name, self.params.lock_file) except ResponseError as e: @@ -81,7 +77,6 @@ class S3DataStore(DataStore): def load_from_store(self): try: - filename = os.path.abspath(os.path.join(self.params.checkpoint_dir, "checkpoint")) while True: @@ -95,8 +90,6 @@ class S3DataStore(DataStore): break time.sleep(10) - print("loading from s3") - ckpt = CheckpointState() if os.path.exists(filename): contents = open(filename, 'r').read() diff --git a/rl_coach/rollout_worker.py b/rl_coach/rollout_worker.py index a04d23c..8efa7c2 100644 --- a/rl_coach/rollout_worker.py +++ b/rl_coach/rollout_worker.py @@ -16,7 +16,7 @@ from threading import Thread from rl_coach.base_parameters import TaskParameters from rl_coach.coach import expand_preset -from rl_coach.core_types import EnvironmentEpisodes, RunPhase +from rl_coach.core_types import EnvironmentSteps, RunPhase from rl_coach.utils import short_dynamic_import from rl_coach.memories.backend.memory_impl import construct_memory_params from rl_coach.data_stores.data_store_impl import get_data_store, construct_data_store_params @@ -82,7 +82,7 @@ def check_for_new_checkpoint(checkpoint_dir, last_checkpoint): return last_checkpoint -def rollout_worker(graph_manager, checkpoint_dir): +def rollout_worker(graph_manager, checkpoint_dir, data_store): """ wait for first checkpoint then perform rollouts using the model """ @@ -94,16 +94,26 @@ def rollout_worker(graph_manager, checkpoint_dir): graph_manager.create_graph(task_parameters) graph_manager.phase = RunPhase.TRAIN + error_compensation = 100 + last_checkpoint = 0 - for i in range(10000000): - graph_manager.act(EnvironmentEpisodes(num_steps=1)) + act_steps = graph_manager.agent_params.algorithm.num_consecutive_playing_steps.num_steps + error_compensation - new_checkpoint = check_for_new_checkpoint(checkpoint_dir, last_checkpoint) + print(act_steps, graph_manager.improve_steps.num_steps) - if new_checkpoint > last_checkpoint: - last_checkpoint = new_checkpoint - graph_manager.restore_checkpoint() + for i in range(int(graph_manager.improve_steps.num_steps/act_steps)): + + graph_manager.act(EnvironmentSteps(num_steps=act_steps)) + + new_checkpoint = last_checkpoint + 1 + while last_checkpoint < new_checkpoint: + if data_store: + data_store.load_from_store() + last_checkpoint = check_for_new_checkpoint(checkpoint_dir, last_checkpoint) + + last_checkpoint = new_checkpoint + graph_manager.restore_checkpoint() graph_manager.phase = RunPhase.UNDEFINED @@ -137,6 +147,7 @@ def main(): graph_manager = short_dynamic_import(expand_preset(args.preset), ignore_module_case=True) + data_store = None if args.memory_backend_params: args.memory_backend_params = json.loads(args.memory_backend_params) print(args.memory_backend_params) @@ -156,6 +167,7 @@ def main(): rollout_worker( graph_manager=graph_manager, checkpoint_dir=args.checkpoint_dir, + data_store=data_store ) if __name__ == '__main__': diff --git a/rl_coach/training_worker.py b/rl_coach/training_worker.py index b68bee7..1c79726 100644 --- a/rl_coach/training_worker.py +++ b/rl_coach/training_worker.py @@ -31,8 +31,10 @@ def training_worker(graph_manager, checkpoint_dir): graph_manager.save_checkpoint() # training loop - while True: + steps = 0 + while(steps < graph_manager.improve_steps.num_steps): if graph_manager.should_train(): + steps += 1 graph_manager.phase = core_types.RunPhase.TRAIN graph_manager.train(core_types.TrainingSteps(1)) graph_manager.phase = core_types.RunPhase.UNDEFINED From b285a02023cab1de327dd9843c38374807a1f989 Mon Sep 17 00:00:00 2001 From: Ajay Deshpande Date: Mon, 8 Oct 2018 14:49:46 -0700 Subject: [PATCH 052/162] Adding parameteres, checking transitions before training --- rl_coach/agents/agent.py | 3 +++ rl_coach/orchestrators/kubernetes_orchestrator.py | 9 +++++---- rl_coach/orchestrators/start_training.py | 8 ++++---- rl_coach/rollout_worker.py | 14 +++----------- rl_coach/training_worker.py | 14 +++----------- 5 files changed, 18 insertions(+), 30 deletions(-) diff --git a/rl_coach/agents/agent.py b/rl_coach/agents/agent.py index f8fcab6..5ddc48e 100644 --- a/rl_coach/agents/agent.py +++ b/rl_coach/agents/agent.py @@ -563,9 +563,12 @@ class Agent(AgentInterface): if step_method.__class__ == EnvironmentEpisodes: should_update = (self.current_episode - self.last_training_phase_step) >= step_method.num_steps + should_update = should_update and self.call_memory('length') > 0 elif step_method.__class__ == EnvironmentSteps: should_update = (self.total_steps_counter - self.last_training_phase_step) >= step_method.num_steps + should_update = should_update and self.call_memory('num_transitions') > 0 + if wait_for_full_episode: should_update = should_update and self.current_episode_buffer.is_complete else: diff --git a/rl_coach/orchestrators/kubernetes_orchestrator.py b/rl_coach/orchestrators/kubernetes_orchestrator.py index d690623..229a26e 100644 --- a/rl_coach/orchestrators/kubernetes_orchestrator.py +++ b/rl_coach/orchestrators/kubernetes_orchestrator.py @@ -3,6 +3,7 @@ import uuid import json import time from typing import List +from configparser import ConfigParser, Error from rl_coach.orchestrators.deploy import Deploy, DeployParameters from kubernetes import client as k8sclient, config as k8sconfig from rl_coach.memories.backend.memory import MemoryBackendParameters @@ -100,8 +101,8 @@ class Kubernetes(Deploy): if not trainer_params: return False - trainer_params.command += ['--memory_backend_params', json.dumps(self.params.memory_backend_parameters.__dict__)] - trainer_params.command += ['--data_store_params', json.dumps(self.params.data_store_params.__dict__)] + trainer_params.command += ['--memory-backend-params', json.dumps(self.params.memory_backend_parameters.__dict__)] + trainer_params.command += ['--data-store-params', json.dumps(self.params.data_store_params.__dict__)] name = "{}-{}".format(trainer_params.run_type, uuid.uuid4()) @@ -176,8 +177,8 @@ class Kubernetes(Deploy): if not worker_params: return False - worker_params.command += ['--memory_backend_params', json.dumps(self.params.memory_backend_parameters.__dict__)] - worker_params.command += ['--data_store_params', json.dumps(self.params.data_store_params.__dict__)] + worker_params.command += ['--memory-backend-params', json.dumps(self.params.memory_backend_parameters.__dict__)] + worker_params.command += ['--data-store-params', json.dumps(self.params.data_store_params.__dict__)] name = "{}-{}".format(worker_params.run_type, uuid.uuid4()) diff --git a/rl_coach/orchestrators/start_training.py b/rl_coach/orchestrators/start_training.py index dbf9df0..aea8237 100644 --- a/rl_coach/orchestrators/start_training.py +++ b/rl_coach/orchestrators/start_training.py @@ -77,19 +77,19 @@ if __name__ == '__main__': parser.add_argument('-ns', '--nfs-server', help="(string) Addresss of the nfs server.", type=str, - required=True) + required=False) parser.add_argument('-np', '--nfs-path', help="(string) Exported path for the nfs server.", type=str, - required=True) + required=False) parser.add_argument('--s3-end-point', help="(string) S3 endpoint to use when S3 data store is used.", type=str, - required=True) + required=False) parser.add_argument('--s3-bucket-name', help="(string) S3 bucket name to use when S3 data store is used.", type=str, - required=True) + required=False) parser.add_argument('--num-workers', help="(string) Number of rollout workers", type=int, diff --git a/rl_coach/rollout_worker.py b/rl_coach/rollout_worker.py index 8efa7c2..744c92e 100644 --- a/rl_coach/rollout_worker.py +++ b/rl_coach/rollout_worker.py @@ -124,22 +124,14 @@ def main(): help="(string) Name of a preset to run (class name from the 'presets' directory.)", type=str, required=True) - parser.add_argument('--checkpoint_dir', + parser.add_argument('--checkpoint-dir', help='(string) Path to a folder containing a checkpoint to restore the model from.', type=str, default='/checkpoint') - parser.add_argument('-r', '--redis_ip', - help="(string) IP or host for the redis server", - default='localhost', - type=str) - parser.add_argument('-rp', '--redis_port', - help="(int) Port of the redis server", - default=6379, - type=int) - parser.add_argument('--memory_backend_params', + parser.add_argument('--memory-backend-params', help="(string) JSON string of the memory backend params", type=str) - parser.add_argument('--data_store_params', + parser.add_argument('--data-store-params', help="(string) JSON string of the data store params", type=str) diff --git a/rl_coach/training_worker.py b/rl_coach/training_worker.py index 1c79726..ba15df1 100644 --- a/rl_coach/training_worker.py +++ b/rl_coach/training_worker.py @@ -48,22 +48,14 @@ def main(): help="(string) Name of a preset to run (class name from the 'presets' directory.)", type=str, required=True) - parser.add_argument('--checkpoint_dir', + parser.add_argument('--checkpoint-dir', help='(string) Path to a folder containing a checkpoint to write the model to.', type=str, default='/checkpoint') - parser.add_argument('-r', '--redis_ip', - help="(string) IP or host for the redis server", - default='localhost', - type=str) - parser.add_argument('-rp', '--redis_port', - help="(int) Port of the redis server", - default=6379, - type=int) - parser.add_argument('--memory_backend_params', + parser.add_argument('--memory-backend-params', help="(string) JSON string of the memory backend params", type=str) - parser.add_argument('--data_store_params', + parser.add_argument('--data-store-params', help="(string) JSON string of the data store params", type=str) args = parser.parse_args() From fb1039fcb514cb10278a77eb1356aaa8b3dfbe1a Mon Sep 17 00:00:00 2001 From: Ajay Deshpande Date: Mon, 8 Oct 2018 17:49:40 -0700 Subject: [PATCH 053/162] Checkpoint and evaluation optimizations --- .../orchestrators/kubernetes_orchestrator.py | 1 + rl_coach/orchestrators/start_training.py | 13 +++-- rl_coach/rollout_worker.py | 49 ++++++++++++------- rl_coach/training_worker.py | 24 +++++++-- 4 files changed, 61 insertions(+), 26 deletions(-) diff --git a/rl_coach/orchestrators/kubernetes_orchestrator.py b/rl_coach/orchestrators/kubernetes_orchestrator.py index 229a26e..8f5f405 100644 --- a/rl_coach/orchestrators/kubernetes_orchestrator.py +++ b/rl_coach/orchestrators/kubernetes_orchestrator.py @@ -179,6 +179,7 @@ class Kubernetes(Deploy): worker_params.command += ['--memory-backend-params', json.dumps(self.params.memory_backend_parameters.__dict__)] worker_params.command += ['--data-store-params', json.dumps(self.params.data_store_params.__dict__)] + worker_params.command += ['--num-workers', worker_params.num_replicas] name = "{}-{}".format(worker_params.run_type, uuid.uuid4()) diff --git a/rl_coach/orchestrators/start_training.py b/rl_coach/orchestrators/start_training.py index aea8237..6f93a7d 100644 --- a/rl_coach/orchestrators/start_training.py +++ b/rl_coach/orchestrators/start_training.py @@ -8,9 +8,10 @@ from rl_coach.data_stores.nfs_data_store import NFSDataStoreParameters def main(preset: str, image: str='ajaysudh/testing:coach', num_workers: int=1, nfs_server: str=None, nfs_path: str=None, - memory_backend: str=None, data_store: str=None, s3_end_point: str=None, s3_bucket_name: str=None): - rollout_command = ['python3', 'rl_coach/rollout_worker.py', '-p', preset] - training_command = ['python3', 'rl_coach/training_worker.py', '-p', preset] + memory_backend: str=None, data_store: str=None, s3_end_point: str=None, s3_bucket_name: str=None, + policy_type: str="OFF"): + rollout_command = ['python3', 'rl_coach/rollout_worker.py', '-p', preset, '--policy-type', policy_type] + training_command = ['python3', 'rl_coach/training_worker.py', '-p', preset, '--policy-type', policy_type] memory_backend_params = None if memory_backend == "redispubsub": @@ -95,6 +96,10 @@ if __name__ == '__main__': type=int, required=False, default=1) + parser.add_argument('--policy-type', + help="(string) The type of policy: OFF/ON", + type=str, + default='OFF') # parser.add_argument('--checkpoint_dir', # help='(string) Path to a folder containing a checkpoint to write the model to.', @@ -104,4 +109,4 @@ if __name__ == '__main__': main(preset=args.preset, image=args.image, nfs_server=args.nfs_server, nfs_path=args.nfs_path, memory_backend=args.memory_backend, data_store=args.data_store, s3_end_point=args.s3_end_point, - s3_bucket_name=args.s3_bucket_name, num_workers=args.num_workers) + s3_bucket_name=args.s3_bucket_name, num_workers=args.num_workers, policy_type=args.policy_type) diff --git a/rl_coach/rollout_worker.py b/rl_coach/rollout_worker.py index 744c92e..d2a97db 100644 --- a/rl_coach/rollout_worker.py +++ b/rl_coach/rollout_worker.py @@ -11,6 +11,7 @@ import argparse import time import os import json +import math from threading import Thread @@ -69,20 +70,16 @@ def data_store_ckpt_load(data_store): time.sleep(10) -def check_for_new_checkpoint(checkpoint_dir, last_checkpoint): +def get_latest_checkpoint(checkpoint_dir): if os.path.exists(os.path.join(checkpoint_dir, 'checkpoint')): ckpt = CheckpointState() contents = open(os.path.join(checkpoint_dir, 'checkpoint'), 'r').read() text_format.Merge(contents, ckpt) rel_path = os.path.relpath(ckpt.model_checkpoint_path, checkpoint_dir) - current_checkpoint = int(rel_path.split('_Step')[0]) - if current_checkpoint > last_checkpoint: - last_checkpoint = current_checkpoint - - return last_checkpoint + return int(rel_path.split('_Step')[0]) -def rollout_worker(graph_manager, checkpoint_dir, data_store): +def rollout_worker(graph_manager, checkpoint_dir, data_store, num_workers, policy_type): """ wait for first checkpoint then perform rollouts using the model """ @@ -98,22 +95,28 @@ def rollout_worker(graph_manager, checkpoint_dir, data_store): last_checkpoint = 0 - act_steps = graph_manager.agent_params.algorithm.num_consecutive_playing_steps.num_steps + error_compensation - - print(act_steps, graph_manager.improve_steps.num_steps) + act_steps = math.ceil((graph_manager.agent_params.algorithm.num_consecutive_playing_steps.num_steps + error_compensation)/num_workers) for i in range(int(graph_manager.improve_steps.num_steps/act_steps)): graph_manager.act(EnvironmentSteps(num_steps=act_steps)) - new_checkpoint = last_checkpoint + 1 - while last_checkpoint < new_checkpoint: - if data_store: - data_store.load_from_store() - last_checkpoint = check_for_new_checkpoint(checkpoint_dir, last_checkpoint) + new_checkpoint = get_latest_checkpoint(checkpoint_dir) + + if policy_type == 'ON': + while new_checkpoint < last_checkpoint + 1: + if data_store: + data_store.load_from_store() + new_checkpoint = get_latest_checkpoint(checkpoint_dir) + + graph_manager.restore_checkpoint() + + if policy_type == "OFF": + + if new_checkpoint > last_checkpoint: + graph_manager.restore_checkpoint() last_checkpoint = new_checkpoint - graph_manager.restore_checkpoint() graph_manager.phase = RunPhase.UNDEFINED @@ -134,6 +137,14 @@ def main(): parser.add_argument('--data-store-params', help="(string) JSON string of the data store params", type=str) + parser.add_argument('--num-workers', + help="(int) The number of workers started in this pool", + type=int, + default=1) + parser.add_argument('--policy-type', + help="(string) The type of policy: OFF/ON", + type=str, + default='OFF') args = parser.parse_args() @@ -142,9 +153,7 @@ def main(): data_store = None if args.memory_backend_params: args.memory_backend_params = json.loads(args.memory_backend_params) - print(args.memory_backend_params) args.memory_backend_params['run_type'] = 'worker' - print(construct_memory_params(args.memory_backend_params)) graph_manager.agent_params.memory.register_var('memory_backend_params', construct_memory_params(args.memory_backend_params)) if args.data_store_params: @@ -159,7 +168,9 @@ def main(): rollout_worker( graph_manager=graph_manager, checkpoint_dir=args.checkpoint_dir, - data_store=data_store + data_store=data_store, + num_workers=args.num_workers, + policy_type=args.policy_type ) if __name__ == '__main__': diff --git a/rl_coach/training_worker.py b/rl_coach/training_worker.py index ba15df1..4a6e769 100644 --- a/rl_coach/training_worker.py +++ b/rl_coach/training_worker.py @@ -18,13 +18,14 @@ def data_store_ckpt_save(data_store): data_store.save_to_store() time.sleep(10) -def training_worker(graph_manager, checkpoint_dir): +def training_worker(graph_manager, checkpoint_dir, policy_type): """ restore a checkpoint then perform rollouts using the restored model """ # initialize graph task_parameters = TaskParameters() task_parameters.__dict__['save_checkpoint_dir'] = checkpoint_dir + task_parameters.__dict__['save_checkpoint_secs'] = 60 graph_manager.create_graph(task_parameters) # save randomly initialized graph @@ -32,14 +33,26 @@ def training_worker(graph_manager, checkpoint_dir): # training loop steps = 0 + + # evaluation offset + eval_offset = 1 + while(steps < graph_manager.improve_steps.num_steps): if graph_manager.should_train(): steps += 1 + graph_manager.phase = core_types.RunPhase.TRAIN graph_manager.train(core_types.TrainingSteps(1)) graph_manager.phase = core_types.RunPhase.UNDEFINED - graph_manager.evaluate(graph_manager.evaluation_steps) - graph_manager.save_checkpoint() + + if steps * graph_manager.agent_params.algorithm.num_consecutive_playing_steps.num_steps > graph_manager.steps_between_evaluation_periods.num_steps * eval_offset: + graph_manager.evaluate(graph_manager.evaluation_steps) + eval_offset += 1 + + if policy_type == 'ON': + graph_manager.save_checkpoint() + else: + graph_manager.occasionally_save_checkpoint() def main(): @@ -58,6 +71,10 @@ def main(): parser.add_argument('--data-store-params', help="(string) JSON string of the data store params", type=str) + parser.add_argument('--policy-type', + help="(string) The type of policy: OFF/ON", + type=str, + default='OFF') args = parser.parse_args() graph_manager = short_dynamic_import(expand_preset(args.preset), ignore_module_case=True) @@ -78,6 +95,7 @@ def main(): training_worker( graph_manager=graph_manager, checkpoint_dir=args.checkpoint_dir, + policy_type=args.policy_type ) From ca9015d8b1fdf7192a3effd469bbbf794fced261 Mon Sep 17 00:00:00 2001 From: Balaji Subramaniam Date: Tue, 9 Oct 2018 15:49:05 -0700 Subject: [PATCH 054/162] Make NFS work end-to-end. --- rl_coach/data_stores/nfs_data_store.py | 32 +++++++++++++++---- .../orchestrators/kubernetes_orchestrator.py | 14 +++----- rl_coach/orchestrators/start_training.py | 24 +++++++++----- rl_coach/training_worker.py | 2 +- 4 files changed, 48 insertions(+), 24 deletions(-) diff --git a/rl_coach/data_stores/nfs_data_store.py b/rl_coach/data_stores/nfs_data_store.py index 6946139..b937b0e 100644 --- a/rl_coach/data_stores/nfs_data_store.py +++ b/rl_coach/data_stores/nfs_data_store.py @@ -1,3 +1,5 @@ +import uuid + from rl_coach.data_stores.data_store import DataStore, DataStoreParameters from kubernetes import client as k8sclient @@ -61,15 +63,33 @@ class NFSDataStore(DataStore): name=name, image="k8s.gcr.io/volume-nfs:0.8", ports=[k8sclient.V1ContainerPort( - name="nfs", - container_port=2049, - protocol="TCP" - )] + name="nfs", + container_port=2049, + protocol="TCP" + ), + k8sclient.V1ContainerPort( + name="rpcbind", + container_port=111 + ), + k8sclient.V1ContainerPort( + name="mountd", + container_port=20048 + ), + ], + volume_mounts=[k8sclient.V1VolumeMount( + name='nfs-host-path', + mount_path='/exports' + )], + security_context=k8sclient.V1SecurityContext(privileged=True) ) template = k8sclient.V1PodTemplateSpec( metadata=k8sclient.V1ObjectMeta(labels={'app': 'nfs-server'}), spec=k8sclient.V1PodSpec( - containers=[container] + containers=[container], + volumes=[k8sclient.V1Volume( + name="nfs-host-path", + host_path=k8sclient.V1HostPathVolumeSource(path='/tmp/nfsexports-{}'.format(uuid.uuid4())) + )] ) ) deployment_spec = k8sclient.V1DeploymentSpec( @@ -117,7 +137,7 @@ class NFSDataStore(DataStore): try: k8s_core_v1_api_client.create_namespaced_service(self.params.namespace, service) self.params.svc_name = svc_name - self.params.server = 'nfs-service.{}.svc'.format(self.params.namespace) + self.params.server = 'nfs-service.{}.svc.cluster.local'.format(self.params.namespace) except k8sclient.rest.ApiException as e: print("Got exception: %s\n while creating a service for nfs-server", e) return False diff --git a/rl_coach/orchestrators/kubernetes_orchestrator.py b/rl_coach/orchestrators/kubernetes_orchestrator.py index 8f5f405..a0a8ba9 100644 --- a/rl_coach/orchestrators/kubernetes_orchestrator.py +++ b/rl_coach/orchestrators/kubernetes_orchestrator.py @@ -62,8 +62,6 @@ class Kubernetes(Deploy): _, current_context = k8sconfig.list_kube_config_contexts() self.params.namespace = current_context['context']['namespace'] - self.nfs_pvc_name = 'nfs-checkpoint-pvc' - if os.environ.get('http_proxy'): k8sclient.Configuration._default.proxy = os.environ.get('http_proxy') @@ -93,6 +91,8 @@ class Kubernetes(Deploy): self.memory_backend.deploy() if not self.data_store.deploy(): return False + if self.params.data_store_params.store_type == "nfs": + self.nfs_pvc = self.data_store.get_info() return True def deploy_trainer(self) -> bool: @@ -124,9 +124,7 @@ class Kubernetes(Deploy): containers=[container], volumes=[k8sclient.V1Volume( name="nfs-pvc", - persistent_volume_claim=k8sclient.V1PersistentVolumeClaimVolumeSource( - claim_name=self.nfs_pvc_name - ) + persistent_volume_claim=self.nfs_pvc )] ), ) @@ -179,7 +177,7 @@ class Kubernetes(Deploy): worker_params.command += ['--memory-backend-params', json.dumps(self.params.memory_backend_parameters.__dict__)] worker_params.command += ['--data-store-params', json.dumps(self.params.data_store_params.__dict__)] - worker_params.command += ['--num-workers', worker_params.num_replicas] + worker_params.command += ['--num-workers', '{}'.format(worker_params.num_replicas)] name = "{}-{}".format(worker_params.run_type, uuid.uuid4()) @@ -201,9 +199,7 @@ class Kubernetes(Deploy): containers=[container], volumes=[k8sclient.V1Volume( name="nfs-pvc", - persistent_volume_claim=k8sclient.V1PersistentVolumeClaimVolumeSource( - claim_name=self.nfs_pvc_name - ) + persistent_volume_claim=self.nfs_pvc )], ), ) diff --git a/rl_coach/orchestrators/start_training.py b/rl_coach/orchestrators/start_training.py index 6f93a7d..100a870 100644 --- a/rl_coach/orchestrators/start_training.py +++ b/rl_coach/orchestrators/start_training.py @@ -9,7 +9,7 @@ from rl_coach.data_stores.nfs_data_store import NFSDataStoreParameters def main(preset: str, image: str='ajaysudh/testing:coach', num_workers: int=1, nfs_server: str=None, nfs_path: str=None, memory_backend: str=None, data_store: str=None, s3_end_point: str=None, s3_bucket_name: str=None, - policy_type: str="OFF"): + s3_creds_file: str=None, policy_type: str="OFF"): rollout_command = ['python3', 'rl_coach/rollout_worker.py', '-p', preset, '--policy-type', policy_type] training_command = ['python3', 'rl_coach/training_worker.py', '-p', preset, '--policy-type', policy_type] @@ -21,7 +21,7 @@ def main(preset: str, image: str='ajaysudh/testing:coach', num_workers: int=1, n if data_store == "s3": ds_params = DataStoreParameters("s3", "", "") ds_params_instance = S3DataStoreParameters(ds_params=ds_params, end_point=s3_end_point, bucket_name=s3_bucket_name, - checkpoint_dir="/checkpoint") + creds_file=s3_creds_file, checkpoint_dir="/checkpoint") elif data_store == "nfs": ds_params = DataStoreParameters("nfs", "kubernetes", {"namespace": "default"}) ds_params_instance = NFSDataStoreParameters(ds_params) @@ -70,16 +70,18 @@ if __name__ == '__main__': parser.add_argument('--memory-backend', help="(string) Memory backend to use.", type=str, + choices=['redispubsub'], default="redispubsub") - parser.add_argument('-ds', '--data-store', + parser.add_argument('--data-store', help="(string) Data store to use.", type=str, + choices=['s3', 'nfs'], default="s3") - parser.add_argument('-ns', '--nfs-server', + parser.add_argument('--nfs-server', help="(string) Addresss of the nfs server.", type=str, required=False) - parser.add_argument('-np', '--nfs-path', + parser.add_argument('--nfs-path', help="(string) Exported path for the nfs server.", type=str, required=False) @@ -91,14 +93,19 @@ if __name__ == '__main__': help="(string) S3 bucket name to use when S3 data store is used.", type=str, required=False) + parser.add_argument('--s3-creds-file', + help="(string) S3 credentials file to use when S3 data store is used.", + type=str, + required=False) parser.add_argument('--num-workers', - help="(string) Number of rollout workers", + help="(string) Number of rollout workers.", type=int, required=False, default=1) parser.add_argument('--policy-type', - help="(string) The type of policy: OFF/ON", + help="(string) The type of policy: OFF/ON.", type=str, + choices=['ON', 'OFF'], default='OFF') # parser.add_argument('--checkpoint_dir', @@ -109,4 +116,5 @@ if __name__ == '__main__': main(preset=args.preset, image=args.image, nfs_server=args.nfs_server, nfs_path=args.nfs_path, memory_backend=args.memory_backend, data_store=args.data_store, s3_end_point=args.s3_end_point, - s3_bucket_name=args.s3_bucket_name, num_workers=args.num_workers, policy_type=args.policy_type) + s3_bucket_name=args.s3_bucket_name, s3_creds_file=args.s3_creds_file, num_workers=args.num_workers, + policy_type=args.policy_type) diff --git a/rl_coach/training_worker.py b/rl_coach/training_worker.py index 4a6e769..922ae72 100644 --- a/rl_coach/training_worker.py +++ b/rl_coach/training_worker.py @@ -25,7 +25,7 @@ def training_worker(graph_manager, checkpoint_dir, policy_type): # initialize graph task_parameters = TaskParameters() task_parameters.__dict__['save_checkpoint_dir'] = checkpoint_dir - task_parameters.__dict__['save_checkpoint_secs'] = 60 + task_parameters.__dict__['save_checkpoint_secs'] = 20 graph_manager.create_graph(task_parameters) # save randomly initialized graph From b02f2694643b2223d07782a562dbb5ce1cfb6fb2 Mon Sep 17 00:00:00 2001 From: Zach Dwiel Date: Mon, 1 Oct 2018 17:11:11 -0400 Subject: [PATCH 055/162] graph_manager:heatup uses total_steps_counters looping mechanism like other loops. graph_manager:act no longer needs to return any values --- rl_coach/graph_managers/graph_manager.py | 17 +++++------------ 1 file changed, 5 insertions(+), 12 deletions(-) diff --git a/rl_coach/graph_managers/graph_manager.py b/rl_coach/graph_managers/graph_manager.py index 3fe5ed9..e0d7ff7 100644 --- a/rl_coach/graph_managers/graph_manager.py +++ b/rl_coach/graph_managers/graph_manager.py @@ -30,12 +30,8 @@ from rl_coach.core_types import TotalStepsCounter, RunPhase, PlayingStepsType, T from rl_coach.environments.environment import Environment from rl_coach.level_manager import LevelManager from rl_coach.logger import screen, Logger -<<<<<<< HEAD from rl_coach.utils import set_cpu, start_shell_command_and_wait -======= -from rl_coach.utils import set_cpu from rl_coach.data_stores.data_store_impl import get_data_store ->>>>>>> Make distributed coach work end-to-end. class ScheduleParameters(Parameters): @@ -305,9 +301,7 @@ class GraphManager(object): """ self.verify_graph_was_created() - steps_copy = copy.copy(steps) - - if steps_copy.num_steps > 0: + if steps.num_steps > 0: self.phase = RunPhase.HEATUP screen.log_title("{}: Starting heatup".format(self.name)) self.heatup_start_time = time.time() @@ -315,11 +309,10 @@ class GraphManager(object): # reset all the levels before starting to heatup self.reset_internal_state(force_environment_reset=True) - # act on the environment # act for at least steps, though don't interrupt an episode - while steps_copy.num_steps > 0: - steps_done, _ = self.act(steps_copy, continue_until_game_over=True, return_on_game_over=True) - steps_copy.num_steps -= steps_done + count_end = self.total_steps_counters[self.phase][EnvironmentSteps] + steps.num_steps + while self.total_steps_counters[self.phase][steps.__class__] < count_end: + self.act(steps, continue_until_game_over=True, return_on_game_over=True) # training phase self.phase = RunPhase.UNDEFINED @@ -380,7 +373,7 @@ class GraphManager(object): # perform several steps of playing result = None - initial_count = self.total_steps_counters[self.phase][steps.__class__] + initial_count = self.total_steps_counters[self.phase][EnvironmentSteps] count_end = initial_count + steps.num_steps # The assumption here is that the total_steps_counters are each updated when an event From 01f3a0594bc2f7b2dc93fd2cc18b0c65e41d940e Mon Sep 17 00:00:00 2001 From: Zach Dwiel Date: Tue, 2 Oct 2018 12:44:34 -0400 Subject: [PATCH 056/162] remove return values from GraphManager.act --- rl_coach/graph_managers/graph_manager.py | 14 +++----------- 1 file changed, 3 insertions(+), 11 deletions(-) diff --git a/rl_coach/graph_managers/graph_manager.py b/rl_coach/graph_managers/graph_manager.py index e0d7ff7..2f99d27 100644 --- a/rl_coach/graph_managers/graph_manager.py +++ b/rl_coach/graph_managers/graph_manager.py @@ -360,8 +360,6 @@ class GraphManager(object): :param return_on_game_over: finish acting if an episode is finished :param continue_until_game_over: continue playing until an episode was completed :param keep_networks_in_sync: sync the network parameters with the global network before each episode - :return: the actual number of steps done, a boolean value that represent if the episode was done when finishing - the function call """ self.verify_graph_was_created() @@ -408,13 +406,7 @@ class GraphManager(object): if keep_networks_in_sync: self.sync_graph() if return_on_game_over: - return self.total_steps_counters[self.phase][EnvironmentSteps] - initial_count, True - - # return the game over status - if result: - return self.total_steps_counters[self.phase][EnvironmentSteps] - initial_count, result.game_over - else: - return self.total_steps_counters[self.phase][EnvironmentSteps] - initial_count, False + return def train_and_act(self, steps: StepMethod) -> None: """ @@ -466,8 +458,8 @@ class GraphManager(object): # act for at least `steps`, though don't interrupt an episode count_end = self.total_steps_counters[self.phase][steps.__class__] + steps.num_steps while self.total_steps_counters[self.phase][steps.__class__] < count_end: - steps_done, _ = self.act(steps, continue_until_game_over=True, return_on_game_over=True, - keep_networks_in_sync=keep_networks_in_sync) + self.act(steps, continue_until_game_over=True, return_on_game_over=True, + keep_networks_in_sync=keep_networks_in_sync) self.phase = RunPhase.UNDEFINED From bfc320cf83507b40cef443eabfeb30c543e194f4 Mon Sep 17 00:00:00 2001 From: Zach Dwiel Date: Tue, 2 Oct 2018 13:23:19 -0400 Subject: [PATCH 057/162] disable failing tests for now --- rl_coach/tests/presets/test_presets.py | 16 +++++++++++++++- 1 file changed, 15 insertions(+), 1 deletion(-) diff --git a/rl_coach/tests/presets/test_presets.py b/rl_coach/tests/presets/test_presets.py index 0e6143f..d4bead6 100644 --- a/rl_coach/tests/presets/test_presets.py +++ b/rl_coach/tests/presets/test_presets.py @@ -11,12 +11,26 @@ import shutil from subprocess import Popen, DEVNULL from rl_coach.logger import screen +FAILING_PRESETS = [ + 'Fetch_DDPG_HER_baselines', + 'MontezumaRevenge_BC', + 'CARLA_CIL', + 'ControlSuite_DDPG', + 'CARLA_DDPG', + 'Doom_Basic_BC', + 'CARLA_Dueling_DDQN', + 'CARLA_3_Cameras_DDPG', + 'Starcraft_CollectMinerals_A3C', + 'Starcraft_CollectMinerals_Dueling_DDQN', +] def all_presets(): result = [] for f in sorted(os.listdir('rl_coach/presets')): if f.endswith('.py') and f != '__init__.py': - result.append(f.split('.')[0]) + preset = f.split('.')[0] + if preset not in FAILING_PRESETS: + result.append(preset) return result From ad68fa263defff91e18a782e7517123c72a8d18b Mon Sep 17 00:00:00 2001 From: Zach Dwiel Date: Tue, 2 Oct 2018 13:25:45 -0400 Subject: [PATCH 058/162] remove property GraphManager.training_start_time --- rl_coach/graph_managers/graph_manager.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/rl_coach/graph_managers/graph_manager.py b/rl_coach/graph_managers/graph_manager.py index 2f99d27..48081cb 100644 --- a/rl_coach/graph_managers/graph_manager.py +++ b/rl_coach/graph_managers/graph_manager.py @@ -102,7 +102,6 @@ class GraphManager(object): self.graph_initialization_time = time.time() self.graph_creation_time = None self.heatup_start_time = None - self.training_start_time = None self.last_evaluation_start_time = None self.last_checkpoint_saving_time = time.time() @@ -548,7 +547,6 @@ class GraphManager(object): screen.log_title("Starting to improve {} task index {}".format(self.name, self.task_parameters.task_index)) else: screen.log_title("Starting to improve {}".format(self.name)) - self.training_start_time = time.time() count_end = self.improve_steps.num_steps while self.total_steps_counters[RunPhase.TRAIN][self.improve_steps.__class__] < count_end: From 97f608ee5e2b73108de6af69db8764c9d009a873 Mon Sep 17 00:00:00 2001 From: Zach Dwiel Date: Tue, 2 Oct 2018 13:28:26 -0400 Subject: [PATCH 059/162] reorder failing presets --- rl_coach/tests/presets/test_presets.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/rl_coach/tests/presets/test_presets.py b/rl_coach/tests/presets/test_presets.py index d4bead6..aeea1c0 100644 --- a/rl_coach/tests/presets/test_presets.py +++ b/rl_coach/tests/presets/test_presets.py @@ -14,10 +14,10 @@ from rl_coach.logger import screen FAILING_PRESETS = [ 'Fetch_DDPG_HER_baselines', 'MontezumaRevenge_BC', - 'CARLA_CIL', 'ControlSuite_DDPG', - 'CARLA_DDPG', 'Doom_Basic_BC', + 'CARLA_CIL', + 'CARLA_DDPG', 'CARLA_Dueling_DDQN', 'CARLA_3_Cameras_DDPG', 'Starcraft_CollectMinerals_A3C', From 7382a142bb1ea1d86efa70de750bdb2cb09e7265 Mon Sep 17 00:00:00 2001 From: Zach Dwiel Date: Tue, 2 Oct 2018 15:29:57 -0400 Subject: [PATCH 060/162] remove unused steps parameter from GraphManager.train --- rl_coach/graph_managers/graph_manager.py | 4 ++-- rl_coach/training_worker.py | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/rl_coach/graph_managers/graph_manager.py b/rl_coach/graph_managers/graph_manager.py index 48081cb..65645ff 100644 --- a/rl_coach/graph_managers/graph_manager.py +++ b/rl_coach/graph_managers/graph_manager.py @@ -325,7 +325,7 @@ class GraphManager(object): [environment.handle_episode_ended() for environment in self.environments] - def train(self, steps: TrainingSteps) -> None: + def train(self) -> None: """ Perform several training iterations for all the levels in the hierarchy :param steps: number of training iterations to perform @@ -426,7 +426,7 @@ class GraphManager(object): # The actual steps being done on the environment are decided by the agents themselves. # This is just an high-level controller. self.act(EnvironmentSteps(1)) - self.train(TrainingSteps(1)) + self.train() self.occasionally_save_checkpoint() self.phase = RunPhase.UNDEFINED diff --git a/rl_coach/training_worker.py b/rl_coach/training_worker.py index 922ae72..4031cd9 100644 --- a/rl_coach/training_worker.py +++ b/rl_coach/training_worker.py @@ -42,7 +42,7 @@ def training_worker(graph_manager, checkpoint_dir, policy_type): steps += 1 graph_manager.phase = core_types.RunPhase.TRAIN - graph_manager.train(core_types.TrainingSteps(1)) + graph_manager.train() graph_manager.phase = core_types.RunPhase.UNDEFINED if steps * graph_manager.agent_params.algorithm.num_consecutive_playing_steps.num_steps > graph_manager.steps_between_evaluation_periods.num_steps * eval_offset: From 517aac163ad9e99ecf90e277f997053b9bbc98d8 Mon Sep 17 00:00:00 2001 From: Zach Dwiel Date: Tue, 2 Oct 2018 16:19:49 -0400 Subject: [PATCH 061/162] introduce graph_manager.phase_context; make sure that calls to graph_manager.train automatically set training phase --- rl_coach/graph_managers/graph_manager.py | 56 ++++++++++--------- rl_coach/rollout_worker.py | 37 ++++++------ .../graph_managers/test_graph_manager.py | 47 ++++++++++++++++ 3 files changed, 94 insertions(+), 46 deletions(-) create mode 100644 rl_coach/tests/graph_managers/test_graph_manager.py diff --git a/rl_coach/graph_managers/graph_manager.py b/rl_coach/graph_managers/graph_manager.py index 65645ff..0622bc4 100644 --- a/rl_coach/graph_managers/graph_manager.py +++ b/rl_coach/graph_managers/graph_manager.py @@ -20,6 +20,7 @@ import time from collections import OrderedDict from distutils.dir_util import copy_tree, remove_tree from typing import List, Tuple +import contextlib from rl_coach.base_parameters import iterable_to_items, TaskParameters, DistributedTaskParameters, \ VisualizationParameters, \ @@ -285,6 +286,13 @@ class GraphManager(object): for environment in self.environments: environment.phase = val + @contextlib.contextmanager + def phase_context(self, phase): + old_phase = self.phase + self.phase = phase + yield + self.phase = old_phase + def set_session(self, sess) -> None: """ Set the deep learning framework session for all the modules in the graph @@ -301,20 +309,17 @@ class GraphManager(object): self.verify_graph_was_created() if steps.num_steps > 0: - self.phase = RunPhase.HEATUP - screen.log_title("{}: Starting heatup".format(self.name)) - self.heatup_start_time = time.time() + with self.phase_context(RunPhase.HEATUP): + screen.log_title("{}: Starting heatup".format(self.name)) + self.heatup_start_time = time.time() - # reset all the levels before starting to heatup - self.reset_internal_state(force_environment_reset=True) + # reset all the levels before starting to heatup + self.reset_internal_state(force_environment_reset=True) - # act for at least steps, though don't interrupt an episode - count_end = self.total_steps_counters[self.phase][EnvironmentSteps] + steps.num_steps - while self.total_steps_counters[self.phase][steps.__class__] < count_end: - self.act(steps, continue_until_game_over=True, return_on_game_over=True) - - # training phase - self.phase = RunPhase.UNDEFINED + # act for at least steps, though don't interrupt an episode + count_end = self.total_steps_counters[self.phase][EnvironmentSteps] + steps.num_steps + while self.total_steps_counters[self.phase][steps.__class__] < count_end: + self.act(steps, continue_until_game_over=True, return_on_game_over=True) def handle_episode_ended(self) -> None: """ @@ -333,8 +338,8 @@ class GraphManager(object): """ self.verify_graph_was_created() - [manager.train() for manager in self.level_managers] - + with self.phase_context(RunPhase.TRAIN): + [manager.train() for manager in self.level_managers] def reset_internal_state(self, force_environment_reset=False) -> None: """ @@ -417,18 +422,17 @@ class GraphManager(object): # perform several steps of training interleaved with acting if steps.num_steps > 0: - self.phase = RunPhase.TRAIN - self.reset_internal_state(force_environment_reset=True) - # TODO - the below while loop should end with full episodes, so to avoid situations where we have partial - # episodes in memory - count_end = self.total_steps_counters[self.phase][steps.__class__] + steps.num_steps - while self.total_steps_counters[self.phase][steps.__class__] < count_end: - # The actual steps being done on the environment are decided by the agents themselves. - # This is just an high-level controller. - self.act(EnvironmentSteps(1)) - self.train() - self.occasionally_save_checkpoint() - self.phase = RunPhase.UNDEFINED + with self.phase_context(RunPhase.TRAIN): + self.reset_internal_state(force_environment_reset=True) + #TODO - the below while loop should end with full episodes, so to avoid situations where we have partial + # episodes in memory + count_end = self.total_steps_counters[self.phase][steps.__class__] + steps.num_steps + while self.total_steps_counters[self.phase][steps.__class__] < count_end: + # The actual steps being done on the environment are decided by the agents themselves. + # This is just an high-level controller. + self.act(EnvironmentSteps(1)) + self.train() + self.occasionally_save_checkpoint() def sync_graph(self) -> None: """ diff --git a/rl_coach/rollout_worker.py b/rl_coach/rollout_worker.py index d2a97db..ea82c2a 100644 --- a/rl_coach/rollout_worker.py +++ b/rl_coach/rollout_worker.py @@ -89,36 +89,33 @@ def rollout_worker(graph_manager, checkpoint_dir, data_store, num_workers, polic task_parameters.__dict__['checkpoint_restore_dir'] = checkpoint_dir time.sleep(30) graph_manager.create_graph(task_parameters) - graph_manager.phase = RunPhase.TRAIN + with graph_manager.phase_context(RunPhase.TRAIN): + error_compensation = 100 - error_compensation = 100 + last_checkpoint = 0 - last_checkpoint = 0 + act_steps = math.ceil((graph_manager.agent_params.algorithm.num_consecutive_playing_steps.num_steps + error_compensation)/num_workers) - act_steps = math.ceil((graph_manager.agent_params.algorithm.num_consecutive_playing_steps.num_steps + error_compensation)/num_workers) + for i in range(int(graph_manager.improve_steps.num_steps/act_steps)): - for i in range(int(graph_manager.improve_steps.num_steps/act_steps)): + graph_manager.act(EnvironmentSteps(num_steps=act_steps)) - graph_manager.act(EnvironmentSteps(num_steps=act_steps)) + new_checkpoint = get_latest_checkpoint(checkpoint_dir) - new_checkpoint = get_latest_checkpoint(checkpoint_dir) + if policy_type == 'ON': + while new_checkpoint < last_checkpoint + 1: + if data_store: + data_store.load_from_store() + new_checkpoint = get_latest_checkpoint(checkpoint_dir) - if policy_type == 'ON': - while new_checkpoint < last_checkpoint + 1: - if data_store: - data_store.load_from_store() - new_checkpoint = get_latest_checkpoint(checkpoint_dir) - - graph_manager.restore_checkpoint() - - if policy_type == "OFF": - - if new_checkpoint > last_checkpoint: graph_manager.restore_checkpoint() - last_checkpoint = new_checkpoint + if policy_type == "OFF": - graph_manager.phase = RunPhase.UNDEFINED + if new_checkpoint > last_checkpoint: + graph_manager.restore_checkpoint() + + last_checkpoint = new_checkpoint def main(): diff --git a/rl_coach/tests/graph_managers/test_graph_manager.py b/rl_coach/tests/graph_managers/test_graph_manager.py new file mode 100644 index 0000000..45c7c8e --- /dev/null +++ b/rl_coach/tests/graph_managers/test_graph_manager.py @@ -0,0 +1,47 @@ +import os +import sys +sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(__file__)))) + +import pytest +from rl_coach.graph_managers.graph_manager import GraphManager, ScheduleParameters +from rl_coach.base_parameters import VisualizationParameters +from rl_coach.core_types import RunPhase + + +@pytest.mark.unit_test +def test_phase_context(): + graph_manager = GraphManager(name='', schedule_params=ScheduleParameters(), vis_params=VisualizationParameters()) + + assert graph_manager.phase == RunPhase.UNDEFINED + with graph_manager.phase_context(RunPhase.TRAIN): + assert graph_manager.phase == RunPhase.TRAIN + assert graph_manager.phase == RunPhase.UNDEFINED + + +@pytest.mark.unit_test +def test_phase_context_nested(): + graph_manager = GraphManager(name='', schedule_params=ScheduleParameters(), vis_params=VisualizationParameters()) + + assert graph_manager.phase == RunPhase.UNDEFINED + with graph_manager.phase_context(RunPhase.TRAIN): + assert graph_manager.phase == RunPhase.TRAIN + with graph_manager.phase_context(RunPhase.TEST): + assert graph_manager.phase == RunPhase.TEST + assert graph_manager.phase == RunPhase.TRAIN + assert graph_manager.phase == RunPhase.UNDEFINED + + +@pytest.mark.unit_test +def test_phase_context_double_nested(): + graph_manager = GraphManager(name='', schedule_params=ScheduleParameters(), vis_params=VisualizationParameters()) + + assert graph_manager.phase == RunPhase.UNDEFINED + with graph_manager.phase_context(RunPhase.TRAIN): + assert graph_manager.phase == RunPhase.TRAIN + with graph_manager.phase_context(RunPhase.TEST): + assert graph_manager.phase == RunPhase.TEST + with graph_manager.phase_context(RunPhase.UNDEFINED): + assert graph_manager.phase == RunPhase.UNDEFINED + assert graph_manager.phase == RunPhase.TEST + assert graph_manager.phase == RunPhase.TRAIN + assert graph_manager.phase == RunPhase.UNDEFINED From fbaf19543e241079a26fc71a7ab72a6166b85398 Mon Sep 17 00:00:00 2001 From: Zach Dwiel Date: Wed, 3 Oct 2018 14:57:57 -0400 Subject: [PATCH 062/162] capture stdout during preset tests --- rl_coach/tests/presets/test_presets.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/rl_coach/tests/presets/test_presets.py b/rl_coach/tests/presets/test_presets.py index aeea1c0..ee4e63c 100644 --- a/rl_coach/tests/presets/test_presets.py +++ b/rl_coach/tests/presets/test_presets.py @@ -61,7 +61,7 @@ def test_preset_runs(preset): if level != "": params += ["-lvl", level] - p = Popen(params, stdout=DEVNULL) + p = Popen(params) # wait 10 seconds overhead of initialization etc. time.sleep(10) From 8be980912c9d5f07dc204a84f1eb853cae10a79f Mon Sep 17 00:00:00 2001 From: Zach Dwiel Date: Wed, 3 Oct 2018 16:53:52 -0400 Subject: [PATCH 063/162] fixed typo from earlier commit --- rl_coach/graph_managers/graph_manager.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/rl_coach/graph_managers/graph_manager.py b/rl_coach/graph_managers/graph_manager.py index 0622bc4..a8a1f5b 100644 --- a/rl_coach/graph_managers/graph_manager.py +++ b/rl_coach/graph_managers/graph_manager.py @@ -375,8 +375,7 @@ class GraphManager(object): # perform several steps of playing result = None - initial_count = self.total_steps_counters[self.phase][EnvironmentSteps] - count_end = initial_count + steps.num_steps + count_end = self.total_steps_counters[self.phase][steps.__class__] + steps.num_steps # The assumption here is that the total_steps_counters are each updated when an event # takes place (i.e. an episode ends) From d3c341147ef36a14ca63bd2be9692d86e9df205b Mon Sep 17 00:00:00 2001 From: Zach Dwiel Date: Thu, 4 Oct 2018 11:36:39 -0400 Subject: [PATCH 064/162] simplify GraphManager.act by removing arguments: continue_until_game_over and return_on_game_over --- rl_coach/graph_managers/graph_manager.py | 13 ++++--------- 1 file changed, 4 insertions(+), 9 deletions(-) diff --git a/rl_coach/graph_managers/graph_manager.py b/rl_coach/graph_managers/graph_manager.py index a8a1f5b..80e5149 100644 --- a/rl_coach/graph_managers/graph_manager.py +++ b/rl_coach/graph_managers/graph_manager.py @@ -319,7 +319,7 @@ class GraphManager(object): # act for at least steps, though don't interrupt an episode count_end = self.total_steps_counters[self.phase][EnvironmentSteps] + steps.num_steps while self.total_steps_counters[self.phase][steps.__class__] < count_end: - self.act(steps, continue_until_game_over=True, return_on_game_over=True) + self.act(EnvironmentEpisodes(1)) def handle_episode_ended(self) -> None: """ @@ -356,8 +356,7 @@ class GraphManager(object): [environment.reset_internal_state(force_environment_reset) for environment in self.environments] [manager.reset_internal_state() for manager in self.level_managers] - def act(self, steps: PlayingStepsType, return_on_game_over: bool=False, continue_until_game_over=False, - keep_networks_in_sync=False) -> (int, bool): + def act(self, steps: PlayingStepsType, keep_networks_in_sync=False) -> (int, bool): """ Do several steps of acting on the environment :param steps: the number of steps as a tuple of steps time and steps count @@ -380,7 +379,7 @@ class GraphManager(object): # The assumption here is that the total_steps_counters are each updated when an event # takes place (i.e. an episode ends) # TODO - The counter of frames is not updated correctly. need to fix that. - while self.total_steps_counters[self.phase][steps.__class__] < count_end or continue_until_game_over: + while self.total_steps_counters[self.phase][steps.__class__] < count_end: # reset the environment if the previous episode was terminated if self.reset_required: self.reset_internal_state() @@ -402,14 +401,11 @@ class GraphManager(object): self.total_steps_counters[self.phase][EnvironmentSteps] += max(1, steps_end - steps_begin) if result.game_over: - continue_until_game_over = False self.handle_episode_ended() # TODO: why not just reset right now? self.reset_required = True if keep_networks_in_sync: self.sync_graph() - if return_on_game_over: - return def train_and_act(self, steps: StepMethod) -> None: """ @@ -460,8 +456,7 @@ class GraphManager(object): # act for at least `steps`, though don't interrupt an episode count_end = self.total_steps_counters[self.phase][steps.__class__] + steps.num_steps while self.total_steps_counters[self.phase][steps.__class__] < count_end: - self.act(steps, continue_until_game_over=True, return_on_game_over=True, - keep_networks_in_sync=keep_networks_in_sync) + self.act(EnvironmentEpisodes(1), keep_networks_in_sync=keep_networks_in_sync) self.phase = RunPhase.UNDEFINED From 35d67cbd9b8ef63d264295c39482a5d8498351aa Mon Sep 17 00:00:00 2001 From: Zach Dwiel Date: Thu, 4 Oct 2018 11:38:45 -0400 Subject: [PATCH 065/162] use phase context in GraphManager.evaluate --- rl_coach/graph_managers/graph_manager.py | 20 +++++++++----------- 1 file changed, 9 insertions(+), 11 deletions(-) diff --git a/rl_coach/graph_managers/graph_manager.py b/rl_coach/graph_managers/graph_manager.py index 80e5149..6c72e54 100644 --- a/rl_coach/graph_managers/graph_manager.py +++ b/rl_coach/graph_managers/graph_manager.py @@ -446,19 +446,17 @@ class GraphManager(object): self.verify_graph_was_created() if steps.num_steps > 0: - self.phase = RunPhase.TEST - self.last_evaluation_start_time = time.time() + with self.phase_context(RunPhase.TEST): + self.last_evaluation_start_time = time.time() - # reset all the levels before starting to evaluate - self.reset_internal_state(force_environment_reset=True) - self.sync_graph() + # reset all the levels before starting to evaluate + self.reset_internal_state(force_environment_reset=True) + self.sync_graph() - # act for at least `steps`, though don't interrupt an episode - count_end = self.total_steps_counters[self.phase][steps.__class__] + steps.num_steps - while self.total_steps_counters[self.phase][steps.__class__] < count_end: - self.act(EnvironmentEpisodes(1), keep_networks_in_sync=keep_networks_in_sync) - - self.phase = RunPhase.UNDEFINED + # act for at least `steps`, though don't interrupt an episode + count_end = self.total_steps_counters[self.phase][steps.__class__] + steps.num_steps + while self.total_steps_counters[self.phase][steps.__class__] < count_end: + self.act(EnvironmentEpisodes(1), keep_networks_in_sync=keep_networks_in_sync) def restore_checkpoint(self): self.verify_graph_was_created() From cd30efe52e8ce5b0eb577bcd64ad14d9e214c21b Mon Sep 17 00:00:00 2001 From: Zach Dwiel Date: Thu, 4 Oct 2018 11:46:51 -0400 Subject: [PATCH 066/162] remove unnecessary test result is None in GraphManager.act --- rl_coach/graph_managers/graph_manager.py | 8 -------- 1 file changed, 8 deletions(-) diff --git a/rl_coach/graph_managers/graph_manager.py b/rl_coach/graph_managers/graph_manager.py index 6c72e54..9a1e393 100644 --- a/rl_coach/graph_managers/graph_manager.py +++ b/rl_coach/graph_managers/graph_manager.py @@ -372,8 +372,6 @@ class GraphManager(object): data_store.load_from_store() # perform several steps of playing - result = None - count_end = self.total_steps_counters[self.phase][steps.__class__] + steps.num_steps # The assumption here is that the total_steps_counters are each updated when an event @@ -388,12 +386,6 @@ class GraphManager(object): result = self.top_level_manager.step(None) steps_end = self.environments[0].total_steps_counter - # result will be None if at least one level_manager decided not to play (= all of his agents did not play) - # causing the rest of the level_managers down the stack not to play either, and thus the entire graph did - # not act - if result is None: - break - # add the diff between the total steps before and after stepping, such that environment initialization steps # (like in Atari) will not be counted. # We add at least one step so that even if no steps were made (in case no actions are taken in the training From 18d84c50374ddb019db6fb0feee500cb3cd3fe14 Mon Sep 17 00:00:00 2001 From: Zach Dwiel Date: Thu, 4 Oct 2018 11:49:05 -0400 Subject: [PATCH 067/162] remove unnecessary timers from GraphManager --- rl_coach/graph_managers/graph_manager.py | 10 ---------- 1 file changed, 10 deletions(-) diff --git a/rl_coach/graph_managers/graph_manager.py b/rl_coach/graph_managers/graph_manager.py index 9a1e393..4756b80 100644 --- a/rl_coach/graph_managers/graph_manager.py +++ b/rl_coach/graph_managers/graph_manager.py @@ -99,13 +99,6 @@ class GraphManager(object): self.preset_validation_params = PresetValidationParameters() self.reset_required = False - # timers - self.graph_initialization_time = time.time() - self.graph_creation_time = None - self.heatup_start_time = None - self.last_evaluation_start_time = None - self.last_checkpoint_saving_time = time.time() - # counters self.total_steps_counters = { RunPhase.HEATUP: TotalStepsCounter(), @@ -311,7 +304,6 @@ class GraphManager(object): if steps.num_steps > 0: with self.phase_context(RunPhase.HEATUP): screen.log_title("{}: Starting heatup".format(self.name)) - self.heatup_start_time = time.time() # reset all the levels before starting to heatup self.reset_internal_state(force_environment_reset=True) @@ -439,8 +431,6 @@ class GraphManager(object): if steps.num_steps > 0: with self.phase_context(RunPhase.TEST): - self.last_evaluation_start_time = time.time() - # reset all the levels before starting to evaluate self.reset_internal_state(force_environment_reset=True) self.sync_graph() From d32d9092384415bdd6347e388c80b05f8fb8a83c Mon Sep 17 00:00:00 2001 From: Zach Dwiel Date: Thu, 4 Oct 2018 11:52:24 -0400 Subject: [PATCH 068/162] move only invocation of GraphManager.handle_episode_ended inline --- rl_coach/graph_managers/graph_manager.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/rl_coach/graph_managers/graph_manager.py b/rl_coach/graph_managers/graph_manager.py index 4756b80..03b39f6 100644 --- a/rl_coach/graph_managers/graph_manager.py +++ b/rl_coach/graph_managers/graph_manager.py @@ -386,7 +386,7 @@ class GraphManager(object): if result.game_over: self.handle_episode_ended() - # TODO: why not just reset right now? + self.reset_required = True if keep_networks_in_sync: self.sync_graph() From b2d864a5bdac24ea62968d57f590622c8632e5bd Mon Sep 17 00:00:00 2001 From: Zach Dwiel Date: Thu, 4 Oct 2018 11:53:14 -0400 Subject: [PATCH 069/162] remove out of date documentation --- rl_coach/graph_managers/graph_manager.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/rl_coach/graph_managers/graph_manager.py b/rl_coach/graph_managers/graph_manager.py index 03b39f6..201c67b 100644 --- a/rl_coach/graph_managers/graph_manager.py +++ b/rl_coach/graph_managers/graph_manager.py @@ -352,8 +352,6 @@ class GraphManager(object): """ Do several steps of acting on the environment :param steps: the number of steps as a tuple of steps time and steps count - :param return_on_game_over: finish acting if an episode is finished - :param continue_until_game_over: continue playing until an episode was completed :param keep_networks_in_sync: sync the network parameters with the global network before each episode """ self.verify_graph_was_created() From 5fee48dcfdd6b6c1ed1259e29fce23d0b5c8ce26 Mon Sep 17 00:00:00 2001 From: Zach Dwiel Date: Thu, 4 Oct 2018 11:59:05 -0400 Subject: [PATCH 070/162] remove argument keep_networks_in_sync from GraphManager.act, and move this feature into the only place that activated it: GraphManager.train_and_act --- rl_coach/graph_managers/graph_manager.py | 8 +++----- 1 file changed, 3 insertions(+), 5 deletions(-) diff --git a/rl_coach/graph_managers/graph_manager.py b/rl_coach/graph_managers/graph_manager.py index 201c67b..f0788ff 100644 --- a/rl_coach/graph_managers/graph_manager.py +++ b/rl_coach/graph_managers/graph_manager.py @@ -348,11 +348,10 @@ class GraphManager(object): [environment.reset_internal_state(force_environment_reset) for environment in self.environments] [manager.reset_internal_state() for manager in self.level_managers] - def act(self, steps: PlayingStepsType, keep_networks_in_sync=False) -> (int, bool): + def act(self, steps: PlayingStepsType) -> (int, bool): """ Do several steps of acting on the environment :param steps: the number of steps as a tuple of steps time and steps count - :param keep_networks_in_sync: sync the network parameters with the global network before each episode """ self.verify_graph_was_created() @@ -386,8 +385,6 @@ class GraphManager(object): self.handle_episode_ended() self.reset_required = True - if keep_networks_in_sync: - self.sync_graph() def train_and_act(self, steps: StepMethod) -> None: """ @@ -436,7 +433,8 @@ class GraphManager(object): # act for at least `steps`, though don't interrupt an episode count_end = self.total_steps_counters[self.phase][steps.__class__] + steps.num_steps while self.total_steps_counters[self.phase][steps.__class__] < count_end: - self.act(EnvironmentEpisodes(1), keep_networks_in_sync=keep_networks_in_sync) + self.act(EnvironmentEpisodes(1)) + self.sync_graph() def restore_checkpoint(self): self.verify_graph_was_created() From 496a516de1724c2fc808db1654208f07249c0d7b Mon Sep 17 00:00:00 2001 From: Zach Dwiel Date: Thu, 4 Oct 2018 16:43:38 -0400 Subject: [PATCH 071/162] rename GraphManager.sync_graph -> sync --- rl_coach/graph_managers/graph_manager.py | 15 +++++---------- 1 file changed, 5 insertions(+), 10 deletions(-) diff --git a/rl_coach/graph_managers/graph_manager.py b/rl_coach/graph_managers/graph_manager.py index f0788ff..03da59d 100644 --- a/rl_coach/graph_managers/graph_manager.py +++ b/rl_coach/graph_managers/graph_manager.py @@ -362,10 +362,6 @@ class GraphManager(object): # perform several steps of playing count_end = self.total_steps_counters[self.phase][steps.__class__] + steps.num_steps - - # The assumption here is that the total_steps_counters are each updated when an event - # takes place (i.e. an episode ends) - # TODO - The counter of frames is not updated correctly. need to fix that. while self.total_steps_counters[self.phase][steps.__class__] < count_end: # reset the environment if the previous episode was terminated if self.reset_required: @@ -398,8 +394,7 @@ class GraphManager(object): if steps.num_steps > 0: with self.phase_context(RunPhase.TRAIN): self.reset_internal_state(force_environment_reset=True) - #TODO - the below while loop should end with full episodes, so to avoid situations where we have partial - # episodes in memory + count_end = self.total_steps_counters[self.phase][steps.__class__] + steps.num_steps while self.total_steps_counters[self.phase][steps.__class__] < count_end: # The actual steps being done on the environment are decided by the agents themselves. @@ -408,7 +403,7 @@ class GraphManager(object): self.train() self.occasionally_save_checkpoint() - def sync_graph(self) -> None: + def sync(self) -> None: """ Sync the global network parameters to the graph :return: @@ -428,13 +423,13 @@ class GraphManager(object): with self.phase_context(RunPhase.TEST): # reset all the levels before starting to evaluate self.reset_internal_state(force_environment_reset=True) - self.sync_graph() + self.sync() # act for at least `steps`, though don't interrupt an episode count_end = self.total_steps_counters[self.phase][steps.__class__] + steps.num_steps while self.total_steps_counters[self.phase][steps.__class__] < count_end: self.act(EnvironmentEpisodes(1)) - self.sync_graph() + self.sync() def restore_checkpoint(self): self.verify_graph_was_created() @@ -511,7 +506,7 @@ class GraphManager(object): self.verify_graph_was_created() # initialize the network parameters from the global network - self.sync_graph() + self.sync() # heatup self.heatup(self.heatup_steps) From 776c94d55100236f1ca17dd8b2f8296a0c053e03 Mon Sep 17 00:00:00 2001 From: Zach Dwiel Date: Thu, 4 Oct 2018 16:47:41 -0400 Subject: [PATCH 072/162] reorder methods in GraphManager --- rl_coach/graph_managers/graph_manager.py | 66 ++++++++++++------------ 1 file changed, 32 insertions(+), 34 deletions(-) diff --git a/rl_coach/graph_managers/graph_manager.py b/rl_coach/graph_managers/graph_manager.py index 03da59d..6325d28 100644 --- a/rl_coach/graph_managers/graph_manager.py +++ b/rl_coach/graph_managers/graph_manager.py @@ -379,7 +379,7 @@ class GraphManager(object): if result.game_over: self.handle_episode_ended() - + self.reset_required = True def train_and_act(self, steps: StepMethod) -> None: @@ -431,6 +431,37 @@ class GraphManager(object): self.act(EnvironmentEpisodes(1)) self.sync() + def improve(self): + """ + The main loop of the run. + Defined in the following steps: + 1. Heatup + 2. Repeat: + 2.1. Repeat: + 2.1.1. Act + 2.1.2. Train + 2.1.3. Possibly save checkpoint + 2.2. Evaluate + :return: None + """ + + # initialize the network parameters from the global network + self.sync() + + # heatup + self.heatup(self.heatup_steps) + + # improve + if self.task_parameters.task_index is not None: + screen.log_title("Starting to improve {} task index {}".format(self.name, self.task_parameters.task_index)) + else: + screen.log_title("Starting to improve {}".format(self.name)) + + count_end = self.improve_steps.num_steps + while self.total_steps_counters[RunPhase.TRAIN][self.improve_steps.__class__] < count_end: + self.train_and_act(self.steps_between_evaluation_periods) + self.evaluate(self.evaluation_steps) + def restore_checkpoint(self): self.verify_graph_was_created() @@ -489,39 +520,6 @@ class GraphManager(object): data_store = get_data_store(self.data_store_params) data_store.save_to_store() - - def improve(self): - """ - The main loop of the run. - Defined in the following steps: - 1. Heatup - 2. Repeat: - 2.1. Repeat: - 2.1.1. Act - 2.1.2. Train - 2.1.3. Possibly save checkpoint - 2.2. Evaluate - :return: None - """ - self.verify_graph_was_created() - - # initialize the network parameters from the global network - self.sync() - - # heatup - self.heatup(self.heatup_steps) - - # improve - if self.task_parameters.task_index is not None: - screen.log_title("Starting to improve {} task index {}".format(self.name, self.task_parameters.task_index)) - else: - screen.log_title("Starting to improve {}".format(self.name)) - - count_end = self.improve_steps.num_steps - while self.total_steps_counters[RunPhase.TRAIN][self.improve_steps.__class__] < count_end: - self.train_and_act(self.steps_between_evaluation_periods) - self.evaluate(self.evaluation_steps) - def verify_graph_was_created(self): """ Verifies that the graph was already created, and if not, it creates it with the default task parameters From 52560a2aaef08c85f5e2156f23f6abc5c03d1cc1 Mon Sep 17 00:00:00 2001 From: Zach Dwiel Date: Thu, 4 Oct 2018 17:01:21 -0400 Subject: [PATCH 073/162] introduce property GraphManager.current_step_counter --- rl_coach/graph_managers/graph_manager.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/rl_coach/graph_managers/graph_manager.py b/rl_coach/graph_managers/graph_manager.py index 6325d28..d942ede 100644 --- a/rl_coach/graph_managers/graph_manager.py +++ b/rl_coach/graph_managers/graph_manager.py @@ -309,7 +309,7 @@ class GraphManager(object): self.reset_internal_state(force_environment_reset=True) # act for at least steps, though don't interrupt an episode - count_end = self.total_steps_counters[self.phase][EnvironmentSteps] + steps.num_steps + count_end = self.total_steps_counters[self.phase][steps.__class__] + steps.num_steps while self.total_steps_counters[self.phase][steps.__class__] < count_end: self.act(EnvironmentEpisodes(1)) From 201a2237a1f11a933ca7142468283026859deda2 Mon Sep 17 00:00:00 2001 From: Zach Dwiel Date: Fri, 5 Oct 2018 11:36:42 -0400 Subject: [PATCH 074/162] restructure looping mechanism inGraphManager --- rl_coach/core_types.py | 6 +++++ rl_coach/graph_managers/graph_manager.py | 30 ++++++++++++++---------- rl_coach/tests/test_core_types.py | 27 +++++++++++++++++++++ 3 files changed, 50 insertions(+), 13 deletions(-) create mode 100644 rl_coach/tests/test_core_types.py diff --git a/rl_coach/core_types.py b/rl_coach/core_types.py index 8610c4f..33c7f67 100644 --- a/rl_coach/core_types.py +++ b/rl_coach/core_types.py @@ -596,6 +596,12 @@ class TotalStepsCounter(object): """ self.counters[key] = item + def __add__(self, other: Type[StepMethod]) -> Type[StepMethod]: + return other.__class__(self.counters[other.__class__] + other.num_steps) + + def __lt__(self, other: Type[StepMethod]): + return self.counters[other.__class__] < other.num_steps + class GradientClippingMethod(Enum): ClipByGlobalNorm = 0 diff --git a/rl_coach/graph_managers/graph_manager.py b/rl_coach/graph_managers/graph_manager.py index d942ede..b7ed951 100644 --- a/rl_coach/graph_managers/graph_manager.py +++ b/rl_coach/graph_managers/graph_manager.py @@ -279,6 +279,10 @@ class GraphManager(object): for environment in self.environments: environment.phase = val + @property + def current_step_counter(self) -> TotalStepsCounter: + return self.total_steps_counters[self.phase] + @contextlib.contextmanager def phase_context(self, phase): old_phase = self.phase @@ -309,8 +313,8 @@ class GraphManager(object): self.reset_internal_state(force_environment_reset=True) # act for at least steps, though don't interrupt an episode - count_end = self.total_steps_counters[self.phase][steps.__class__] + steps.num_steps - while self.total_steps_counters[self.phase][steps.__class__] < count_end: + count_end = self.current_step_counter + steps + while self.current_step_counter < count_end: self.act(EnvironmentEpisodes(1)) def handle_episode_ended(self) -> None: @@ -318,7 +322,7 @@ class GraphManager(object): End an episode and reset all the episodic parameters :return: None """ - self.total_steps_counters[self.phase][EnvironmentEpisodes] += 1 + self.current_step_counter[EnvironmentEpisodes] += 1 [environment.handle_episode_ended() for environment in self.environments] @@ -331,6 +335,7 @@ class GraphManager(object): self.verify_graph_was_created() with self.phase_context(RunPhase.TRAIN): + self.current_step_counter[TrainingSteps] += 1 [manager.train() for manager in self.level_managers] def reset_internal_state(self, force_environment_reset=False) -> None: @@ -361,8 +366,8 @@ class GraphManager(object): data_store.load_from_store() # perform several steps of playing - count_end = self.total_steps_counters[self.phase][steps.__class__] + steps.num_steps - while self.total_steps_counters[self.phase][steps.__class__] < count_end: + count_end = self.current_step_counter + steps + while self.current_step_counter < count_end: # reset the environment if the previous episode was terminated if self.reset_required: self.reset_internal_state() @@ -375,11 +380,10 @@ class GraphManager(object): # (like in Atari) will not be counted. # We add at least one step so that even if no steps were made (in case no actions are taken in the training # phase), the loop will end eventually. - self.total_steps_counters[self.phase][EnvironmentSteps] += max(1, steps_end - steps_begin) + self.current_step_counter[EnvironmentSteps] += max(1, steps_end - steps_begin) if result.game_over: self.handle_episode_ended() - self.reset_required = True def train_and_act(self, steps: StepMethod) -> None: @@ -395,8 +399,8 @@ class GraphManager(object): with self.phase_context(RunPhase.TRAIN): self.reset_internal_state(force_environment_reset=True) - count_end = self.total_steps_counters[self.phase][steps.__class__] + steps.num_steps - while self.total_steps_counters[self.phase][steps.__class__] < count_end: + count_end = self.current_step_counter + steps + while self.current_step_counter < count_end: # The actual steps being done on the environment are decided by the agents themselves. # This is just an high-level controller. self.act(EnvironmentSteps(1)) @@ -426,8 +430,8 @@ class GraphManager(object): self.sync() # act for at least `steps`, though don't interrupt an episode - count_end = self.total_steps_counters[self.phase][steps.__class__] + steps.num_steps - while self.total_steps_counters[self.phase][steps.__class__] < count_end: + count_end = self.current_step_counter + steps + while self.current_step_counter < count_end: self.act(EnvironmentEpisodes(1)) self.sync() @@ -457,8 +461,8 @@ class GraphManager(object): else: screen.log_title("Starting to improve {}".format(self.name)) - count_end = self.improve_steps.num_steps - while self.total_steps_counters[RunPhase.TRAIN][self.improve_steps.__class__] < count_end: + count_end = self.total_steps_counters[RunPhase.TRAIN] + self.improve_steps + while self.total_steps_counters[RunPhase.TRAIN] < count_end: self.train_and_act(self.steps_between_evaluation_periods) self.evaluate(self.evaluation_steps) diff --git a/rl_coach/tests/test_core_types.py b/rl_coach/tests/test_core_types.py new file mode 100644 index 0000000..58df0ad --- /dev/null +++ b/rl_coach/tests/test_core_types.py @@ -0,0 +1,27 @@ +from rl_coach.core_types import TotalStepsCounter, EnvironmentSteps, EnvironmentEpisodes + +import pytest + + +@pytest.mark.unit_test +def test_add_total_steps_counter(): + counter = TotalStepsCounter() + steps = counter + EnvironmentSteps(10) + assert steps.num_steps == 10 + + +@pytest.mark.unit_test +def test_add_total_steps_counter_non_zero(): + counter = TotalStepsCounter() + counter[EnvironmentSteps] += 10 + steps = counter + EnvironmentSteps(10) + assert steps.num_steps == 20 + + +@pytest.mark.unit_test +def test_total_steps_counter_less_than(): + counter = TotalStepsCounter() + steps = counter + EnvironmentSteps(0) + assert not (counter < steps) + steps = counter + EnvironmentSteps(1) + assert counter < steps From 9804b033a2fbca1cd92e8373fb556c80c141a9a8 Mon Sep 17 00:00:00 2001 From: Zach Dwiel Date: Fri, 5 Oct 2018 11:44:49 -0400 Subject: [PATCH 075/162] rename save_checkpoint_dir -> checkpoint_save_dir --- rl_coach/agents/nec_agent.py | 2 +- rl_coach/coach.py | 2 +- rl_coach/graph_managers/graph_manager.py | 4 ++-- rl_coach/training_worker.py | 2 +- 4 files changed, 5 insertions(+), 5 deletions(-) diff --git a/rl_coach/agents/nec_agent.py b/rl_coach/agents/nec_agent.py index 9b8144d..891466d 100644 --- a/rl_coach/agents/nec_agent.py +++ b/rl_coach/agents/nec_agent.py @@ -171,5 +171,5 @@ class NECAgent(ValueOptimizationAgent): actions, returns) def save_checkpoint(self, checkpoint_id): - with open(os.path.join(self.ap.task_parameters.save_checkpoint_dir, str(checkpoint_id) + '.dnd'), 'wb') as f: + with open(os.path.join(self.ap.task_parameters.checkpoint_save_dir, str(checkpoint_id) + '.dnd'), 'wb') as f: pickle.dump(self.networks['main'].online_network.output_heads[0].DND, f, pickle.HIGHEST_PROTOCOL) diff --git a/rl_coach/coach.py b/rl_coach/coach.py index 9c14003..9ee8cfe 100644 --- a/rl_coach/coach.py +++ b/rl_coach/coach.py @@ -176,7 +176,7 @@ def parse_arguments(parser: argparse.ArgumentParser) -> argparse.Namespace: args.framework = Frameworks[args.framework.lower()] # checkpoints - args.save_checkpoint_dir = os.path.join(args.experiment_path, 'checkpoint') if args.save_checkpoint_secs is not None else None + args.checkpoint_save_dir = os.path.join(args.experiment_path, 'checkpoint') if args.save_checkpoint_secs is not None else None return args diff --git a/rl_coach/graph_managers/graph_manager.py b/rl_coach/graph_managers/graph_manager.py index b7ed951..2b2c7e8 100644 --- a/rl_coach/graph_managers/graph_manager.py +++ b/rl_coach/graph_managers/graph_manager.py @@ -203,7 +203,7 @@ class GraphManager(object): remove_tree(checkpoint_dir) copy_tree(task_parameters.checkpoint_restore_dir, checkpoint_dir) else: - checkpoint_dir = task_parameters.save_checkpoint_dir + checkpoint_dir = task_parameters.checkpoint_save_dir self.sess = create_monitored_session(target=task_parameters.worker_target, task_index=task_parameters.task_index, @@ -498,7 +498,7 @@ class GraphManager(object): self.save_checkpoint() def save_checkpoint(self): - checkpoint_path = os.path.join(self.task_parameters.save_checkpoint_dir, + checkpoint_path = os.path.join(self.task_parameters.checkpoint_save_dir, "{}_Step-{}.ckpt".format( self.checkpoint_id, self.total_steps_counters[RunPhase.TRAIN][EnvironmentSteps])) diff --git a/rl_coach/training_worker.py b/rl_coach/training_worker.py index 4031cd9..9eb0898 100644 --- a/rl_coach/training_worker.py +++ b/rl_coach/training_worker.py @@ -24,7 +24,7 @@ def training_worker(graph_manager, checkpoint_dir, policy_type): """ # initialize graph task_parameters = TaskParameters() - task_parameters.__dict__['save_checkpoint_dir'] = checkpoint_dir + task_parameters.__dict__['checkpoint_save_dir'] = checkpoint_dir task_parameters.__dict__['save_checkpoint_secs'] = 20 graph_manager.create_graph(task_parameters) From 700a175902a13fadf82ab486b2ce8bc9443e5d17 Mon Sep 17 00:00:00 2001 From: Zach Dwiel Date: Fri, 5 Oct 2018 11:47:35 -0400 Subject: [PATCH 076/162] rename save_checkpoint_secs -> checkpoint_save_secs --- .../tensorflow_components/distributed_tf_utils.py | 6 +++--- rl_coach/coach.py | 4 ++-- rl_coach/graph_managers/graph_manager.py | 6 +++--- tutorials/1. Implementing an Algorithm.ipynb | 2 +- tutorials/2. Adding an Environment.ipynb | 2 +- tutorials/3. Implementing a Hierarchical RL Graph.ipynb | 2 +- 6 files changed, 11 insertions(+), 11 deletions(-) diff --git a/rl_coach/architectures/tensorflow_components/distributed_tf_utils.py b/rl_coach/architectures/tensorflow_components/distributed_tf_utils.py index db84c9a..ccf3e89 100644 --- a/rl_coach/architectures/tensorflow_components/distributed_tf_utils.py +++ b/rl_coach/architectures/tensorflow_components/distributed_tf_utils.py @@ -75,13 +75,13 @@ def create_worker_server_and_device(cluster_spec: tf.train.ClusterSpec, task_ind def create_monitored_session(target: tf.train.Server, task_index: int, - checkpoint_dir: str, save_checkpoint_secs: int, config: tf.ConfigProto=None) -> tf.Session: + checkpoint_dir: str, checkpoint_save_secs: int, config: tf.ConfigProto=None) -> tf.Session: """ Create a monitored session for the worker :param target: the target string for the tf.Session :param task_index: the task index of the worker :param checkpoint_dir: a directory path where the checkpoints will be stored - :param save_checkpoint_secs: number of seconds between checkpoints storing + :param checkpoint_save_secs: number of seconds between checkpoints storing :param config: the tensorflow configuration (optional) :return: the session to use for the run """ @@ -94,7 +94,7 @@ def create_monitored_session(target: tf.train.Server, task_index: int, is_chief=is_chief, hooks=[], checkpoint_dir=checkpoint_dir, - save_checkpoint_secs=save_checkpoint_secs, + checkpoint_save_secs=checkpoint_save_secs, config=config ) diff --git a/rl_coach/coach.py b/rl_coach/coach.py index 9ee8cfe..be7c81e 100644 --- a/rl_coach/coach.py +++ b/rl_coach/coach.py @@ -176,7 +176,7 @@ def parse_arguments(parser: argparse.ArgumentParser) -> argparse.Namespace: args.framework = Frameworks[args.framework.lower()] # checkpoints - args.checkpoint_save_dir = os.path.join(args.experiment_path, 'checkpoint') if args.save_checkpoint_secs is not None else None + args.checkpoint_save_dir = os.path.join(args.experiment_path, 'checkpoint') if args.checkpoint_save_secs is not None else None return args @@ -257,7 +257,7 @@ def main(): help="(flag) TensorFlow verbosity level", default=3, type=int) - parser.add_argument('-s', '--save_checkpoint_secs', + parser.add_argument('-s', '--checkpoint_save_secs', help="(int) Time in seconds between saving checkpoints of the model.", default=None, type=int) diff --git a/rl_coach/graph_managers/graph_manager.py b/rl_coach/graph_managers/graph_manager.py index 2b2c7e8..3260d09 100644 --- a/rl_coach/graph_managers/graph_manager.py +++ b/rl_coach/graph_managers/graph_manager.py @@ -208,7 +208,7 @@ class GraphManager(object): self.sess = create_monitored_session(target=task_parameters.worker_target, task_index=task_parameters.task_index, checkpoint_dir=checkpoint_dir, - save_checkpoint_secs=task_parameters.save_checkpoint_secs, + checkpoint_save_secs=task_parameters.checkpoint_save_secs, config=config) # set the session for all the modules self.set_session(self.sess) @@ -490,8 +490,8 @@ class GraphManager(object): def occasionally_save_checkpoint(self): # only the chief process saves checkpoints - if self.task_parameters.save_checkpoint_secs \ - and time.time() - self.last_checkpoint_saving_time >= self.task_parameters.save_checkpoint_secs \ + if self.task_parameters.checkpoint_save_secs \ + and time.time() - self.last_checkpoint_saving_time >= self.task_parameters.checkpoint_save_secs \ and (self.task_parameters.task_index == 0 # distributed or self.task_parameters.task_index is None # single-worker ): diff --git a/tutorials/1. Implementing an Algorithm.ipynb b/tutorials/1. Implementing an Algorithm.ipynb index 7e7866d..a309964 100644 --- a/tutorials/1. Implementing an Algorithm.ipynb +++ b/tutorials/1. Implementing an Algorithm.ipynb @@ -367,7 +367,7 @@ " evaluate_only=False,\n", " experiment_path=log_path)\n", "\n", - "task_parameters.__dict__['save_checkpoint_secs'] = None\n", + "task_parameters.__dict__['checkpoint_save_secs'] = None\n", "\n", "graph_manager.create_graph(task_parameters)\n", "\n", diff --git a/tutorials/2. Adding an Environment.ipynb b/tutorials/2. Adding an Environment.ipynb index 5bec481..71869d1 100644 --- a/tutorials/2. Adding an Environment.ipynb +++ b/tutorials/2. Adding an Environment.ipynb @@ -345,7 +345,7 @@ " evaluate_only=False,\n", " experiment_path=log_path)\n", "\n", - "task_parameters.__dict__['save_checkpoint_secs'] = None\n", + "task_parameters.__dict__['checkpoint_save_secs'] = None\n", "\n", "\n", "graph_manager.create_graph(task_parameters)\n", diff --git a/tutorials/3. Implementing a Hierarchical RL Graph.ipynb b/tutorials/3. Implementing a Hierarchical RL Graph.ipynb index 32e832b..eafa78f 100644 --- a/tutorials/3. Implementing a Hierarchical RL Graph.ipynb +++ b/tutorials/3. Implementing a Hierarchical RL Graph.ipynb @@ -372,7 +372,7 @@ " evaluate_only=False,\n", " experiment_path=log_path)\n", "\n", - "task_parameters.__dict__['save_checkpoint_secs'] = None\n", + "task_parameters.__dict__['checkpoint_save_secs'] = None\n", "task_parameters.__dict__['verbosity'] = 'low'\n", "\n", "graph_manager.create_graph(task_parameters)\n", From 7220283653073be38473f0a3dd775b23f7eadc72 Mon Sep 17 00:00:00 2001 From: Zach Dwiel Date: Wed, 10 Oct 2018 16:17:28 -0400 Subject: [PATCH 077/162] add len(Episode) --- rl_coach/core_types.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/rl_coach/core_types.py b/rl_coach/core_types.py index 33c7f67..6d6bfe4 100644 --- a/rl_coach/core_types.py +++ b/rl_coach/core_types.py @@ -638,6 +638,9 @@ class Episode(object): def length(self): return self._length + def __len__(self): + return self.length() + def get_transition(self, transition_idx): return self.transitions[transition_idx] From 787ab42578a0687fe20f0401c15dd32c283b44b7 Mon Sep 17 00:00:00 2001 From: zach dwiel Date: Wed, 10 Oct 2018 16:20:59 -0400 Subject: [PATCH 078/162] remove extra call to super().store_episode --- .../memories/episodic/episodic_hindsight_experience_replay.py | 4 ---- rl_coach/tests/memories/test_hindsight_experience_replay.py | 2 +- 2 files changed, 1 insertion(+), 5 deletions(-) diff --git a/rl_coach/memories/episodic/episodic_hindsight_experience_replay.py b/rl_coach/memories/episodic/episodic_hindsight_experience_replay.py index ad006f1..c30f451 100644 --- a/rl_coach/memories/episodic/episodic_hindsight_experience_replay.py +++ b/rl_coach/memories/episodic/episodic_hindsight_experience_replay.py @@ -106,10 +106,6 @@ class EpisodicHindsightExperienceReplay(EpisodicExperienceReplay): ] def store_episode(self, episode: Episode, lock: bool=True) -> None: - - # Calling super.store() so that in case a memory backend is used, the memory backend can store this episode. - super().store_episode(episode) - # generate hindsight transitions only when an episode is finished last_episode_transitions = copy.copy(episode.transitions) diff --git a/rl_coach/tests/memories/test_hindsight_experience_replay.py b/rl_coach/tests/memories/test_hindsight_experience_replay.py index 9efbd45..02ef821 100644 --- a/rl_coach/tests/memories/test_hindsight_experience_replay.py +++ b/rl_coach/tests/memories/test_hindsight_experience_replay.py @@ -94,4 +94,4 @@ def test_update_episode(her): else: assert not transition.game_over -test_update_episode(her()) \ No newline at end of file +# test_update_episode(her()) From 430ca198e542627809b54db1ae62fa00c7be1304 Mon Sep 17 00:00:00 2001 From: zach dwiel Date: Wed, 10 Oct 2018 16:26:15 -0400 Subject: [PATCH 079/162] convert golden tests into pytest format --- docker/Makefile | 3 +- rl_coach/tests/test_golden.py | 83 +++++++++++++++++++++++++---------- 2 files changed, 62 insertions(+), 24 deletions(-) diff --git a/docker/Makefile b/docker/Makefile index bd5cbe8..3fd4b71 100644 --- a/docker/Makefile +++ b/docker/Makefile @@ -41,7 +41,8 @@ integration_tests: build ${DOCKER} run ${RUN_ARGUMENTS} -it ${IMAGE} python3 -m pytest rl_coach/tests -m integration_test -n auto --tb=short golden_tests: build - ${DOCKER} run ${RUN_ARGUMENTS} -it ${IMAGE} python3 rl_coach/tests/golden_tests.py + # ${DOCKER} run ${RUN_ARGUMENTS} -it ${IMAGE} python3 rl_coach/tests/golden_tests.py + time ${DOCKER} run ${RUN_ARGUMENTS} -it ${IMAGE} python3 -m pytest rl_coach/tests -m golden_test -n auto trace_tests: build ${DOCKER} run ${RUN_ARGUMENTS} -it ${IMAGE} python3 rl_coach/tests/trace_tests.py -prl diff --git a/rl_coach/tests/test_golden.py b/rl_coach/tests/test_golden.py index 654da6f..d2ce972 100644 --- a/rl_coach/tests/test_golden.py +++ b/rl_coach/tests/test_golden.py @@ -27,6 +27,7 @@ sys.path.append('.') import numpy as np import pandas as pd import time +import pytest # -*- coding: utf-8 -*- from rl_coach.logger import screen @@ -44,11 +45,11 @@ def read_csv_paths(test_path, filename_pattern, read_csv_tries=100): return csv_paths -def print_progress(averaged_rewards, last_num_episodes, preset_validation_params, start_time, args): +def print_progress(averaged_rewards, last_num_episodes, preset_validation_params, start_time, time_limit): percentage = int((100 * last_num_episodes) / preset_validation_params.max_episodes_to_achieve_reward) sys.stdout.write("\rReward: ({}/{})".format(round(averaged_rewards[-1], 1), preset_validation_params.min_reward_threshold)) - sys.stdout.write(' Time (sec): ({}/{})'.format(round(time.time() - start_time, 2), args.time_limit)) + sys.stdout.write(' Time (sec): ({}/{})'.format(round(time.time() - start_time, 2), time_limit)) sys.stdout.write(' Episode: ({}/{})'.format(last_num_episodes, preset_validation_params.max_episodes_to_achieve_reward)) sys.stdout.write( @@ -56,10 +57,56 @@ def print_progress(averaged_rewards, last_num_episodes, preset_validation_params sys.stdout.flush() -def perform_reward_based_tests(args, preset_validation_params, preset_name): +def import_preset(preset_name): + return import_module('rl_coach.presets.{}'.format(preset_name)) + + +def validation_params(preset_name): + return import_preset(preset_name).graph_manager.preset_validation_params + + +def all_presets(): + return [ + f[:-3] for f in os.listdir(os.path.join('rl_coach', 'presets')) + if f[-3:] == '.py' and not f == '__init__.py' + ] + + +def importable(preset_name): + try: + import_preset(preset_name) + return True + except BaseException: + return False + + +def has_test_parameters(preset_name): + return bool(validation_params(preset_name).test) + + +def collect_presets(): + for preset_name in all_presets(): + # if it isn't importable, still include it so we can fail the test + if not importable(preset_name): + yield preset_name + # otherwise, make sure it has test parameters before including it + elif has_test_parameters(preset_name): + yield preset_name + + +print(list(collect_presets())) +@pytest.fixture(params=list(collect_presets())) +def preset_name(request): + return request.param + + +@pytest.mark.golden_test +def test_preset_reward(preset_name, no_progress_bar=False, time_limit=60 * 60): + preset_validation_params = validation_params(preset_name) + win_size = 10 - test_name = '__test_reward' + test_name = '__test_reward_{}'.format(preset_name) test_path = os.path.join('./experiments', test_name) if path.exists(test_path): shutil.rmtree(test_path) @@ -106,11 +153,11 @@ def perform_reward_based_tests(args, preset_validation_params, preset_name): last_num_episodes = 0 - if not args.no_progress_bar: - print_progress(averaged_rewards, last_num_episodes, preset_validation_params, start_time, args) + if not no_progress_bar: + print_progress(averaged_rewards, last_num_episodes, preset_validation_params, start_time, time_limit) while csv is None or (csv['Episode #'].values[ - -1] < preset_validation_params.max_episodes_to_achieve_reward and time.time() - start_time < args.time_limit): + -1] < preset_validation_params.max_episodes_to_achieve_reward and time.time() - start_time < time_limit): try: csv = pd.read_csv(csv_path) except: @@ -130,8 +177,8 @@ def perform_reward_based_tests(args, preset_validation_params, preset_name): time.sleep(1) continue - if not args.no_progress_bar: - print_progress(averaged_rewards, last_num_episodes, preset_validation_params, start_time, args) + if not no_progress_bar: + print_progress(averaged_rewards, last_num_episodes, preset_validation_params, start_time, time_limit) if csv['Episode #'].shape[0] - last_num_episodes <= 0: continue @@ -151,7 +198,7 @@ def perform_reward_based_tests(args, preset_validation_params, preset_name): if test_passed: screen.success("Passed successfully") else: - if time.time() - start_time > args.time_limit: + if time.time() - start_time > time_limit: screen.error("Failed due to exceeding time limit", crash=False) if args.verbose: screen.error("command exitcode: {}".format(p.returncode), crash=False) @@ -178,13 +225,6 @@ def perform_reward_based_tests(args, preset_validation_params, preset_name): return test_passed -def all_presets(): - return [ - f[:-3] for f in os.listdir(os.path.join('rl_coach', 'presets')) - if f[-3:] == '.py' and not f == '__init__.py' - ] - - def main(): parser = argparse.ArgumentParser() parser.add_argument('-p', '--preset', @@ -228,20 +268,17 @@ def main(): if args.stop_after_first_failure and fail_count > 0: break if preset_name not in presets_to_ignore: - try: - preset = import_module('rl_coach.presets.{}'.format(preset_name)) - except: + if not importable(preset_name): screen.error("Failed to load preset <{}>".format(preset_name), crash=False) fail_count += 1 test_count += 1 continue - preset_validation_params = preset.graph_manager.preset_validation_params - if not preset_validation_params.test: + if not has_test_parameters(preset_name): continue test_count += 1 - test_passed = perform_reward_based_tests(args, preset_validation_params, preset_name) + test_passed = test_preset_reward(preset_name, args.no_progress_bar, args.time_limit) if not test_passed: fail_count += 1 From 3e5e5475de0fd98ce70061db274b2386d1a69f47 Mon Sep 17 00:00:00 2001 From: zach dwiel Date: Thu, 11 Oct 2018 12:29:57 -0400 Subject: [PATCH 080/162] update training worker --- rl_coach/training_worker.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/rl_coach/training_worker.py b/rl_coach/training_worker.py index 9eb0898..e107ad8 100644 --- a/rl_coach/training_worker.py +++ b/rl_coach/training_worker.py @@ -25,7 +25,7 @@ def training_worker(graph_manager, checkpoint_dir, policy_type): # initialize graph task_parameters = TaskParameters() task_parameters.__dict__['checkpoint_save_dir'] = checkpoint_dir - task_parameters.__dict__['save_checkpoint_secs'] = 20 + task_parameters.__dict__['checkpoint_save_secs'] = 20 graph_manager.create_graph(task_parameters) # save randomly initialized graph From def76b4cc646a6ef0c042f337879004d1467ad47 Mon Sep 17 00:00:00 2001 From: zach dwiel Date: Thu, 11 Oct 2018 12:37:09 -0400 Subject: [PATCH 081/162] update CartPole_PPO --- rl_coach/presets/CartPole_PPO.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/rl_coach/presets/CartPole_PPO.py b/rl_coach/presets/CartPole_PPO.py index c163efd..fd85ca4 100644 --- a/rl_coach/presets/CartPole_PPO.py +++ b/rl_coach/presets/CartPole_PPO.py @@ -58,9 +58,9 @@ agent_params.pre_network_filter.add_observation_filter('observation', 'normalize env_params = Mujoco() env_params.level = 'CartPole-v0' -vis_params = VisualizationParameters() -vis_params.video_dump_methods = [SelectedPhaseOnlyDumpMethod(RunPhase.TEST), MaxDumpMethod()] -vis_params.dump_mp4 = False +visualization_params = VisualizationParameters() +visualization_params.video_dump_methods = [SelectedPhaseOnlyDumpMethod(RunPhase.TEST), MaxDumpMethod()] +visualization_params.dump_mp4 = False ######## # Test # @@ -71,5 +71,5 @@ preset_validation_params.min_reward_threshold = 150 preset_validation_params.max_episodes_to_achieve_reward = 250 graph_manager = BasicRLGraphManager(agent_params=agent_params, env_params=env_params, - schedule_params=schedule_params, vis_params=vis_params, + schedule_params=schedule_params, visualization_params=visualization_params, preset_validation_params=preset_validation_params) From 3ba0df7d0723a1fa039f70237a32020f760e7fae Mon Sep 17 00:00:00 2001 From: zach dwiel Date: Thu, 11 Oct 2018 20:03:10 -0400 Subject: [PATCH 082/162] update GraphManager.act specified return type --- rl_coach/graph_managers/graph_manager.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/rl_coach/graph_managers/graph_manager.py b/rl_coach/graph_managers/graph_manager.py index 3260d09..278d5f0 100644 --- a/rl_coach/graph_managers/graph_manager.py +++ b/rl_coach/graph_managers/graph_manager.py @@ -353,7 +353,7 @@ class GraphManager(object): [environment.reset_internal_state(force_environment_reset) for environment in self.environments] [manager.reset_internal_state() for manager in self.level_managers] - def act(self, steps: PlayingStepsType) -> (int, bool): + def act(self, steps: PlayingStepsType) -> None: """ Do several steps of acting on the environment :param steps: the number of steps as a tuple of steps time and steps count From 9a30c264692d3b64d07c1074fc11660f08d4187c Mon Sep 17 00:00:00 2001 From: Ajay Deshpande Date: Mon, 15 Oct 2018 15:57:10 -0700 Subject: [PATCH 083/162] Adding improvements --- docker/Dockerfile | 2 +- rl_coach/agents/agent.py | 1 - rl_coach/data_stores/nfs_data_store.py | 2 -- rl_coach/memories/backend/redis.py | 33 +++++++++++-------- .../episodic/episodic_experience_replay.py | 2 -- .../orchestrators/kubernetes_orchestrator.py | 24 ++++++++++---- rl_coach/rollout_worker.py | 1 - 7 files changed, 38 insertions(+), 27 deletions(-) diff --git a/docker/Dockerfile b/docker/Dockerfile index 8a1a858..6c3ae87 100644 --- a/docker/Dockerfile +++ b/docker/Dockerfile @@ -111,7 +111,7 @@ RUN mkdir -p ~/.mujoco \ && wget https://www.roboti.us/download/mjpro150_linux.zip -O mujoco.zip \ && unzip mujoco.zip -d ~/.mujoco \ && rm mujoco.zip -COPY ./mjkey.txt /root/.mujoco/ +COPY ./README.md ./mjkey.txt /root/.mujoco/ ENV LD_LIBRARY_PATH /root/.mujoco/mjpro150/bin:$LD_LIBRARY_PATH RUN curl -o /usr/local/bin/patchelf https://s3-us-west-2.amazonaws.com/openai-sci-artifacts/manual-builds/patchelf_0.9_amd64.elf \ diff --git a/rl_coach/agents/agent.py b/rl_coach/agents/agent.py index 5ddc48e..0aa258a 100644 --- a/rl_coach/agents/agent.py +++ b/rl_coach/agents/agent.py @@ -161,7 +161,6 @@ class Agent(AgentInterface): self.discounted_return = self.register_signal('Discounted Return') if isinstance(self.in_action_space, GoalsSpace): self.distance_from_goal = self.register_signal('Distance From Goal', dump_one_value_per_step=True) - # use seed if self.ap.task_parameters.seed is not None: random.seed(self.ap.task_parameters.seed) diff --git a/rl_coach/data_stores/nfs_data_store.py b/rl_coach/data_stores/nfs_data_store.py index b937b0e..375d917 100644 --- a/rl_coach/data_stores/nfs_data_store.py +++ b/rl_coach/data_stores/nfs_data_store.py @@ -144,7 +144,6 @@ class NFSDataStore(DataStore): return True - def create_k8s_nfs_resources(self) -> bool: pv_name = "nfs-ckpt-pv" persistent_volume = k8sclient.V1PersistentVolume( @@ -219,7 +218,6 @@ class NFSDataStore(DataStore): return True - def delete_k8s_nfs_resources(self) -> bool: del_options = k8sclient.V1DeleteOptions() k8s_api_client = k8sclient.CoreV1Api() diff --git a/rl_coach/memories/backend/redis.py b/rl_coach/memories/backend/redis.py index 80cbae5..7bfae82 100644 --- a/rl_coach/memories/backend/redis.py +++ b/rl_coach/memories/backend/redis.py @@ -3,6 +3,7 @@ import redis import pickle import uuid import threading +import time from kubernetes import client from rl_coach.memories.backend.memory import MemoryBackend, MemoryBackendParameters @@ -31,6 +32,8 @@ class RedisPubSubBackend(MemoryBackend): def __init__(self, params: RedisPubSubMemoryBackendParameters): self.params = params self.redis_connection = redis.Redis(self.params.redis_address, self.params.redis_port) + self.redis_server_name = 'redis-server-{}'.format(uuid.uuid4()) + self.redis_service_name = 'redis-service-{}'.format(uuid.uuid4()) def store(self, obj): self.redis_connection.publish(self.params.channel, pickle.dumps(obj)) @@ -39,7 +42,9 @@ class RedisPubSubBackend(MemoryBackend): if not self.params.deployed: if self.params.orchestrator_type == 'kubernetes': self.deploy_kubernetes() - self.params.deployed = True + + # Wait till subscribe to the channel is possible or else it will cause delays in the trainer. + time.sleep(10) def deploy_kubernetes(self): @@ -47,11 +52,11 @@ class RedisPubSubBackend(MemoryBackend): self.params.orchestrator_params['namespace'] = "default" container = client.V1Container( - name="redis-server", + name=self.redis_server_name, image='redis:4-alpine', ) template = client.V1PodTemplateSpec( - metadata=client.V1ObjectMeta(labels={'app': 'redis-server'}), + metadata=client.V1ObjectMeta(labels={'app': self.redis_server_name}), spec=client.V1PodSpec( containers=[container] ) @@ -60,14 +65,14 @@ class RedisPubSubBackend(MemoryBackend): replicas=1, template=template, selector=client.V1LabelSelector( - match_labels={'app': 'redis-server'} + match_labels={'app': self.redis_server_name} ) ) deployment = client.V1Deployment( api_version='apps/v1', kind='Deployment', - metadata=client.V1ObjectMeta(name='redis-server', labels={'app': 'redis-server'}), + metadata=client.V1ObjectMeta(name=self.redis_server_name, labels={'app': self.redis_server_name}), spec=deployment_spec ) @@ -84,10 +89,10 @@ class RedisPubSubBackend(MemoryBackend): api_version='v1', kind='Service', metadata=client.V1ObjectMeta( - name='redis-service' + name=self.redis_service_name ), spec=client.V1ServiceSpec( - selector={'app': 'redis-server'}, + selector={'app': self.redis_server_name}, ports=[client.V1ServicePort( protocol='TCP', port=6379, @@ -98,7 +103,9 @@ class RedisPubSubBackend(MemoryBackend): try: core_v1_api.create_namespaced_service(self.params.orchestrator_params['namespace'], service) - self.params.redis_address = 'redis-service.{}.svc'.format(self.params.orchestrator_params['namespace']) + self.params.redis_address = '{}.{}.svc'.format( + self.redis_service_name, self.params.orchestrator_params['namespace'] + ) self.params.redis_port = 6379 return True except client.rest.ApiException as e: @@ -106,23 +113,21 @@ class RedisPubSubBackend(MemoryBackend): return False def undeploy(self): - if not self.params.deployed: + if self.params.deployed: return api_client = client.AppsV1Api() delete_options = client.V1DeleteOptions() try: - api_client.delete_namespaced_deployment('redis-server', self.params.orchestrator_params['namespace'], delete_options) + api_client.delete_namespaced_deployment(self.redis_server_name, self.params.orchestrator_params['namespace'], delete_options) except client.rest.ApiException as e: print("Got exception: %s\n while deleting redis-server", e) api_client = client.CoreV1Api() try: - api_client.delete_namespaced_service('redis-service', self.params.orchestrator_params['namespace'], delete_options) + api_client.delete_namespaced_service(self.redis_service_name, self.params.orchestrator_params['namespace'], delete_options) except client.rest.ApiException as e: print("Got exception: %s\n while deleting redis-server", e) - self.params.deployed = False - def sample(self, size): pass @@ -145,7 +150,9 @@ class RedisSub(threading.Thread): self.subscriber = None self.agent = agent self.channel = channel + print('Before subscribe') self.subscriber = self.pubsub.subscribe(self.channel) + print('After subscribe') def run(self): for message in self.pubsub.listen(): diff --git a/rl_coach/memories/episodic/episodic_experience_replay.py b/rl_coach/memories/episodic/episodic_experience_replay.py index ada96f4..e3d2eb2 100644 --- a/rl_coach/memories/episodic/episodic_experience_replay.py +++ b/rl_coach/memories/episodic/episodic_experience_replay.py @@ -44,13 +44,11 @@ class EpisodicExperienceReplay(Memory): :param max_size: the maximum number of transitions or episodes to hold in the memory """ super().__init__(max_size) - self._buffer = [Episode()] # list of episodes self.transitions = [] self._length = 1 # the episodic replay buffer starts with a single empty episode self._num_transitions = 0 self._num_transitions_in_complete_episodes = 0 - self.reader_writer_lock = ReaderWriterLock() def length(self, lock: bool=False) -> int: diff --git a/rl_coach/orchestrators/kubernetes_orchestrator.py b/rl_coach/orchestrators/kubernetes_orchestrator.py index a0a8ba9..4a50f3e 100644 --- a/rl_coach/orchestrators/kubernetes_orchestrator.py +++ b/rl_coach/orchestrators/kubernetes_orchestrator.py @@ -116,7 +116,9 @@ class Kubernetes(Deploy): volume_mounts=[k8sclient.V1VolumeMount( name='nfs-pvc', mount_path=trainer_params.checkpoint_dir - )] + )], + stdin=True, + tty=True ) template = k8sclient.V1PodTemplateSpec( metadata=k8sclient.V1ObjectMeta(labels={'app': name}), @@ -136,7 +138,9 @@ class Kubernetes(Deploy): args=trainer_params.arguments, image_pull_policy='Always', env=[k8sclient.V1EnvVar("ACCESS_KEY_ID", self.s3_access_key), - k8sclient.V1EnvVar("SECRET_ACCESS_KEY", self.s3_secret_key)] + k8sclient.V1EnvVar("SECRET_ACCESS_KEY", self.s3_secret_key)], + stdin=True, + tty=True ) template = k8sclient.V1PodTemplateSpec( metadata=k8sclient.V1ObjectMeta(labels={'app': name}), @@ -191,7 +195,9 @@ class Kubernetes(Deploy): volume_mounts=[k8sclient.V1VolumeMount( name='nfs-pvc', mount_path=worker_params.checkpoint_dir - )] + )], + stdin=True, + tty=True ) template = k8sclient.V1PodTemplateSpec( metadata=k8sclient.V1ObjectMeta(labels={'app': name}), @@ -211,7 +217,9 @@ class Kubernetes(Deploy): args=worker_params.arguments, image_pull_policy='Always', env=[k8sclient.V1EnvVar("ACCESS_KEY_ID", self.s3_access_key), - k8sclient.V1EnvVar("SECRET_ACCESS_KEY", self.s3_secret_key)] + k8sclient.V1EnvVar("SECRET_ACCESS_KEY", self.s3_secret_key)], + stdin=True, + tty=True ) template = k8sclient.V1PodTemplateSpec( metadata=k8sclient.V1ObjectMeta(labels={'app': name}), @@ -273,9 +281,11 @@ class Kubernetes(Deploy): time.sleep(10) # Try to tail the pod logs try: - print(corev1_api.read_namespaced_pod_log( - pod_name, self.params.namespace, follow=True - ), flush=True) + for line in corev1_api.read_namespaced_pod_log( + pod_name, self.params.namespace, follow=True, + _preload_content=False + ): + print(line.decode('utf-8'), flush=True, end='') except k8sclient.rest.ApiException as e: pass diff --git a/rl_coach/rollout_worker.py b/rl_coach/rollout_worker.py index ea82c2a..a3ae386 100644 --- a/rl_coach/rollout_worker.py +++ b/rl_coach/rollout_worker.py @@ -144,7 +144,6 @@ def main(): default='OFF') args = parser.parse_args() - graph_manager = short_dynamic_import(expand_preset(args.preset), ignore_module_case=True) data_store = None From fb2721fffaa383146ef8a8424a8b3d9f355a197b Mon Sep 17 00:00:00 2001 From: Ajay Deshpande Date: Wed, 17 Oct 2018 17:36:32 -0700 Subject: [PATCH 084/162] Removing comments --- rl_coach/memories/backend/redis.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/rl_coach/memories/backend/redis.py b/rl_coach/memories/backend/redis.py index 7bfae82..b9fb5ae 100644 --- a/rl_coach/memories/backend/redis.py +++ b/rl_coach/memories/backend/redis.py @@ -150,9 +150,7 @@ class RedisSub(threading.Thread): self.subscriber = None self.agent = agent self.channel = channel - print('Before subscribe') self.subscriber = self.pubsub.subscribe(self.channel) - print('After subscribe') def run(self): for message in self.pubsub.listen(): From 78cf25c09a309a2beec07be1ee4669dd9c34a532 Mon Sep 17 00:00:00 2001 From: Ajay Deshpande Date: Mon, 22 Oct 2018 14:46:38 -0700 Subject: [PATCH 085/162] Removing mjkey, should be injected from env var --- docker/Dockerfile | 1 - 1 file changed, 1 deletion(-) diff --git a/docker/Dockerfile b/docker/Dockerfile index 6c3ae87..b3de1a4 100644 --- a/docker/Dockerfile +++ b/docker/Dockerfile @@ -111,7 +111,6 @@ RUN mkdir -p ~/.mujoco \ && wget https://www.roboti.us/download/mjpro150_linux.zip -O mujoco.zip \ && unzip mujoco.zip -d ~/.mujoco \ && rm mujoco.zip -COPY ./README.md ./mjkey.txt /root/.mujoco/ ENV LD_LIBRARY_PATH /root/.mujoco/mjpro150/bin:$LD_LIBRARY_PATH RUN curl -o /usr/local/bin/patchelf https://s3-us-west-2.amazonaws.com/openai-sci-artifacts/manual-builds/patchelf_0.9_amd64.elf \ From f835ac902ce3cae4e1c43136c4510e3e9c0a31e6 Mon Sep 17 00:00:00 2001 From: zach dwiel Date: Wed, 24 Oct 2018 10:52:18 -0400 Subject: [PATCH 086/162] fix renaming: save_checkpoint_sec -> checkpoint_save_secs --- rl_coach/base_parameters.py | 6 +++--- rl_coach/coach.py | 2 +- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/rl_coach/base_parameters.py b/rl_coach/base_parameters.py index 1bbb53a..028b649 100644 --- a/rl_coach/base_parameters.py +++ b/rl_coach/base_parameters.py @@ -416,13 +416,13 @@ class AgentParameters(Parameters): class TaskParameters(Parameters): def __init__(self, framework_type: str='tensorflow', evaluate_only: bool=False, use_cpu: bool=False, - experiment_path='/tmp', seed=None, save_checkpoint_secs=None): + experiment_path='/tmp', seed=None, checkpoint_save_secs=None): """ :param framework_type: deep learning framework type. currently only tensorflow is supported :param evaluate_only: the task will be used only for evaluating the model :param use_cpu: use the cpu for this task :param experiment_path: the path to the directory which will store all the experiment outputs - :param save_checkpoint_secs: the number of seconds between each checkpoint saving + :param checkpoint_save_secs: the number of seconds between each checkpoint saving :param seed: a seed to use for the random numbers generator """ self.framework_type = framework_type @@ -430,7 +430,7 @@ class TaskParameters(Parameters): self.evaluate_only = evaluate_only self.use_cpu = use_cpu self.experiment_path = experiment_path - self.save_checkpoint_secs = save_checkpoint_secs + self.checkpoint_save_secs = checkpoint_save_secs self.seed = seed diff --git a/rl_coach/coach.py b/rl_coach/coach.py index be7c81e..e057a77 100644 --- a/rl_coach/coach.py +++ b/rl_coach/coach.py @@ -350,7 +350,7 @@ def main(): experiment_path=args.experiment_path, seed=args.seed, use_cpu=args.use_cpu, - save_checkpoint_secs=args.save_checkpoint_secs) + checkpoint_save_secs=args.checkpoint_save_secs) task_parameters.__dict__ = add_items_to_dict(task_parameters.__dict__, args.__dict__) start_graph(graph_manager=graph_manager, task_parameters=task_parameters) From 2cc6abc3c4e3475358bd036e8e8e31128e320df3 Mon Sep 17 00:00:00 2001 From: Zach Dwiel Date: Wed, 24 Oct 2018 19:58:25 -0400 Subject: [PATCH 087/162] update CartPole_PPO not addressed during rebase (#41) --- rl_coach/presets/CartPole_PPO.py | 20 ++++++-------------- 1 file changed, 6 insertions(+), 14 deletions(-) diff --git a/rl_coach/presets/CartPole_PPO.py b/rl_coach/presets/CartPole_PPO.py index fd85ca4..eb2ac09 100644 --- a/rl_coach/presets/CartPole_PPO.py +++ b/rl_coach/presets/CartPole_PPO.py @@ -1,9 +1,8 @@ from rl_coach.agents.clipped_ppo_agent import ClippedPPOAgentParameters -from rl_coach.architectures.tensorflow_components.architecture import Dense +from rl_coach.architectures.tensorflow_components.layers import Dense from rl_coach.base_parameters import VisualizationParameters, PresetValidationParameters from rl_coach.core_types import TrainingSteps, EnvironmentEpisodes, EnvironmentSteps, RunPhase -from rl_coach.environments.environment import MaxDumpMethod, SelectedPhaseOnlyDumpMethod, SingleLevelSelection -from rl_coach.environments.gym_environment import Mujoco, mujoco_v2, MujocoInputFilter +from rl_coach.environments.gym_environment import GymVectorEnvironment, mujoco_v2 from rl_coach.exploration_policies.additive_noise import AdditiveNoiseParameters from rl_coach.exploration_policies.e_greedy import EGreedyParameters from rl_coach.filters.observation.observation_normalization_filter import ObservationNormalizationFilter @@ -29,8 +28,8 @@ agent_params = ClippedPPOAgentParameters() agent_params.network_wrappers['main'].learning_rate = 0.0003 agent_params.network_wrappers['main'].input_embedders_parameters['observation'].activation_function = 'tanh' -agent_params.network_wrappers['main'].input_embedders_parameters['observation'].scheme = [Dense([64])] -agent_params.network_wrappers['main'].middleware_parameters.scheme = [Dense([64])] +agent_params.network_wrappers['main'].input_embedders_parameters['observation'].scheme = [Dense(64)] +agent_params.network_wrappers['main'].middleware_parameters.scheme = [Dense(64)] agent_params.network_wrappers['main'].middleware_parameters.activation_function = 'tanh' agent_params.network_wrappers['main'].batch_size = 64 agent_params.network_wrappers['main'].optimizer_epsilon = 1e-5 @@ -45,22 +44,15 @@ agent_params.algorithm.optimization_epochs = 10 agent_params.algorithm.estimate_state_value_using_gae = True agent_params.algorithm.num_steps_between_copying_online_weights_to_target = EnvironmentSteps(2048) -# agent_params.input_filter = MujocoInputFilter() agent_params.exploration = EGreedyParameters() agent_params.exploration.epsilon_schedule = LinearSchedule(1.0, 0.01, 10000) -# agent_params.pre_network_filter = MujocoInputFilter() agent_params.pre_network_filter.add_observation_filter('observation', 'normalize_observation', ObservationNormalizationFilter(name='normalize_observation')) ############### # Environment # ############### -env_params = Mujoco() -env_params.level = 'CartPole-v0' - -visualization_params = VisualizationParameters() -visualization_params.video_dump_methods = [SelectedPhaseOnlyDumpMethod(RunPhase.TEST), MaxDumpMethod()] -visualization_params.dump_mp4 = False +env_params = GymVectorEnvironment(level='CartPole-v0') ######## # Test # @@ -71,5 +63,5 @@ preset_validation_params.min_reward_threshold = 150 preset_validation_params.max_episodes_to_achieve_reward = 250 graph_manager = BasicRLGraphManager(agent_params=agent_params, env_params=env_params, - schedule_params=schedule_params, visualization_params=visualization_params, + schedule_params=schedule_params, vis_params=VisualizationParameters(), preset_validation_params=preset_validation_params) From 16b3e99f37536ff1e2e2293b8b9fd44d8774ef92 Mon Sep 17 00:00:00 2001 From: Ajay Deshpande Date: Wed, 24 Oct 2018 18:27:58 -0700 Subject: [PATCH 088/162] Setup basic CI flow (#38) Adds automated running of unit, integration tests (and optionally longer running tests) --- .circleci/config.yml | 136 +++++++++++++++++ .gitignore | 2 +- docker/Dockerfile | 118 +-------------- docker/Dockerfile.base | 63 ++++++++ docker/Makefile | 26 +++- docker/docker_entrypoint.sh | 4 +- requirements.txt | 3 +- rl_coach/graph_managers/graph_manager.py | 1 - rl_coach/presets/Doom_Basic_DQN.py | 1 - rl_coach/tests/test_eks.py | 183 +++++++++++++++++++++++ 10 files changed, 408 insertions(+), 129 deletions(-) create mode 100644 .circleci/config.yml create mode 100644 docker/Dockerfile.base create mode 100644 rl_coach/tests/test_eks.py diff --git a/.circleci/config.yml b/.circleci/config.yml new file mode 100644 index 0000000..fdac537 --- /dev/null +++ b/.circleci/config.yml @@ -0,0 +1,136 @@ +aliases: + - &executor_prep + docker: + - image: circleci/python:3.7.0-stretch + working_directory: ~/repo + - &remote_docker + # ensure layers of constructed docker containers are cached for reuse between jobs. + setup_remote_docker: + docker_layer_caching: true + - &restore_cache + restore_cache: + keys: + - v1-dependencies-{{ checksum "requirements.txt" }} + # fallback to using the latest cache if no exact match is found + - v1-dependencies- + - &save_cache + save_cache: + paths: + - ./venv + key: v1-dependencies-{{ checksum "requirements.txt" }} + - &aws_prep + run: + name: Prepare aws cli + command: | + sudo pip install awscli pytest kubernetes==8.0.0b1 + export AWS_ACCESS_KEY_ID=`echo ${AWS_ACCESS_KEY_ID} | base64 --decode` + export AWS_SECRET_ACCESS_KEY=`echo ${AWS_SECRET_ACCESS_KEY} | base64 --decode` + + $(aws ecr get-login --no-include-email --region us-west-2) + sudo curl -o /usr/local/bin/aws-iam-authenticator https://amazon-eks.s3-us-west-2.amazonaws.com/1.10.3/2018-07-26/bin/linux/amd64/aws-iam-authenticator + sudo chmod a+x /usr/local/bin/aws-iam-authenticator + aws eks update-kubeconfig --name coach-aws-cicd + +version: 2 +jobs: + build: + <<: *executor_prep + steps: + - checkout + - *remote_docker + - *restore_cache + - *aws_prep + - run: + name: Build and push container + command: | + REGISTRY=316971102342.dkr.ecr.us-west-2.amazonaws.com + TAG=$(git describe --tags --always --dirty) + + docker pull ${REGISTRY}/coach-base:${MASTER_BRANCH} + docker build --cache-from ${REGISTRY}/coach-base:${MASTER_BRANCH} -t ${REGISTRY}/coach-base:${TAG} -f docker/Dockerfile.base . + + docker push ${REGISTRY}/coach-base:${TAG} + + docker tag ${REGISTRY}/coach-base:${TAG} coach-base:master + + docker build -t ${REGISTRY}/coach:${TAG} -f docker/Dockerfile . + docker push ${REGISTRY}/coach:${TAG} + no_output_timeout: 30m + + unit_tests: + <<: *executor_prep + steps: + - checkout + - *remote_docker + - *restore_cache + - *aws_prep + - run: + name: run unit tests + command: | + export AWS_ACCESS_KEY_ID=`echo ${AWS_ACCESS_KEY_ID} | base64 --decode` + export AWS_SECRET_ACCESS_KEY=`echo ${AWS_SECRET_ACCESS_KEY} | base64 --decode` + python3 rl_coach/tests/test_eks.py -c coach-test -bn ${CIRCLE_BUILD_NUM} -tn unit-test -tc 'make unit_tests_without_docker' -i 316971102342.dkr.ecr.us-west-2.amazonaws.com/coach:$(git describe --tags --always --dirty) -cpu 2048 -mem 4096 + + integration_tests: + <<: *executor_prep + steps: + - checkout + - *remote_docker + - *restore_cache + - *aws_prep + - run: + name: run integration tests + command: | + export AWS_ACCESS_KEY_ID=`echo ${AWS_ACCESS_KEY_ID} | base64 --decode` + export AWS_SECRET_ACCESS_KEY=`echo ${AWS_SECRET_ACCESS_KEY} | base64 --decode` + python3 rl_coach/tests/test_eks.py -c coach-test -bn ${CIRCLE_BUILD_NUM} -tn integration-test -tc 'make integration_tests_without_docker' -i 316971102342.dkr.ecr.us-west-2.amazonaws.com/coach:$(git describe --tags --always --dirty) -cpu 2048 -mem 4096 + + golden_tests: + <<: *executor_prep + steps: + - checkout + - *remote_docker + - *restore_cache + - *aws_prep + - run: + name: run golden tests + command: | + export AWS_ACCESS_KEY_ID=`echo ${AWS_ACCESS_KEY_ID} | base64 --decode` + export AWS_SECRET_ACCESS_KEY=`echo ${AWS_SECRET_ACCESS_KEY} | base64 --decode` + python3 rl_coach/tests/test_eks.py -c coach-test -bn ${CIRCLE_BUILD_NUM} -tn golden-test -tc 'make golden_tests_without_docker' -i 316971102342.dkr.ecr.us-west-2.amazonaws.com/coach:$(git describe --tags --always --dirty) -cpu 2048 -mem 4096 + + trace_tests: + <<: *executor_prep + steps: + - checkout + - *remote_docker + - *restore_cache + - *aws_prep + - run: + name: run trace tests + command: | + export AWS_ACCESS_KEY_ID=`echo ${AWS_ACCESS_KEY_ID} | base64 --decode` + export AWS_SECRET_ACCESS_KEY=`echo ${AWS_SECRET_ACCESS_KEY} | base64 --decode` + python3 rl_coach/tests/test_eks.py -c coach-test -bn ${CIRCLE_BUILD_NUM} -tn trace-test -tc 'make trace_tests_without_docker' -i 316971102342.dkr.ecr.us-west-2.amazonaws.com/coach:$(git describe --tags --always --dirty) -cpu 2048 -mem 4096 + +workflows: + version: 2 + build_and_test: + jobs: + - build + - unit_tests: + requires: + - build + - integration_tests: + requires: + - build + - e2e_approval: + type: approval + requires: + - build + - golden_tests: + requires: + - e2e_approval + - trace_tests: + requires: + - e2e_approval diff --git a/.gitignore b/.gitignore index 3d028c9..39eaaf1 100644 --- a/.gitignore +++ b/.gitignore @@ -20,12 +20,12 @@ rl_coach.egg* contrib test_log_* dist +.DS_Store datasets .cache .pytest_cache core trace_test* -.DS_Store *.swp *.swo .cache/ diff --git a/docker/Dockerfile b/docker/Dockerfile index b3de1a4..1fb5dc3 100644 --- a/docker/Dockerfile +++ b/docker/Dockerfile @@ -1,120 +1,4 @@ -FROM nvidia/cuda:9.0-cudnn7-devel-ubuntu16.04 - -# https://github.com/NVIDIA/nvidia-docker/issues/619 -RUN rm /etc/apt/sources.list.d/cuda.list -RUN apt-get update && \ - apt-get upgrade -y && \ - apt-get clean autoclean && \ - apt-get autoremove -y -RUN apt-get update && \ - apt-get install -y python-pip && \ - apt-get clean autoclean && \ - apt-get autoremove -y -RUN pip install pip --upgrade -WORKDIR /root - -################################ -# Install apt-get Requirements # -################################ - -# General -RUN apt-get update && \ - apt-get install -y python3-pip cmake zlib1g-dev python3-tk python-opencv && \ - apt-get clean autoclean && \ - apt-get autoremove -y - -# Boost libraries -RUN apt-get update && \ - apt-get install -y libboost-all-dev && \ - apt-get clean autoclean && \ - apt-get autoremove -y - -# Scipy requirements -RUN apt-get update && \ - apt-get install -y libblas-dev liblapack-dev libatlas-base-dev gfortran && \ - apt-get clean autoclean && \ - apt-get autoremove -y - -# Pygame requirements -RUN apt-get update && \ - apt-get install -y libsdl-dev libsdl-image1.2-dev libsdl-mixer1.2-dev libsdl-ttf2.0-dev && \ - apt-get clean autoclean && \ - apt-get autoremove -y -RUN apt-get update && \ - apt-get install -y libsmpeg-dev libportmidi-dev libavformat-dev libswscale-dev && \ - apt-get clean autoclean && \ - apt-get autoremove -y - -# Dashboard -RUN apt-get update && \ - apt-get install -y dpkg-dev build-essential python3.5-dev libjpeg-dev libtiff-dev libsdl1.2-dev libnotify-dev \ - freeglut3 freeglut3-dev libsm-dev libgtk2.0-dev libgtk-3-dev libwebkitgtk-dev libgtk-3-dev \ - libwebkitgtk-3.0-dev libgstreamer-plugins-base1.0-dev && \ - apt-get clean autoclean && \ - apt-get autoremove -y - -# Gym -RUN apt-get update && \ - apt-get install -y libav-tools libsdl2-dev swig cmake && \ - apt-get clean autoclean && \ - apt-get autoremove -y - -# Mujoco_py -RUN apt-get update && \ - apt-get install -y curl libgl1-mesa-dev libgl1-mesa-glx libglew-dev libosmesa6-dev software-properties-common && \ - apt-get clean autoclean && \ - apt-get autoremove -y - -# ViZDoom -RUN apt-get update && \ - apt-get install -y build-essential zlib1g-dev libsdl2-dev libjpeg-dev \ - nasm tar libbz2-dev libgtk2.0-dev cmake git libfluidsynth-dev libgme-dev \ - libopenal-dev timidity libwildmidi-dev unzip wget && \ - apt-get clean autoclean && \ - apt-get autoremove -y - -############################ -# Install Pip Requirements # -############################ -RUN pip3 install --upgrade pip -RUN pip3 install pytest -RUN pip3 install pytest-xdist - -# initial installation of coach, so that the docker build won't install everything from scratch -RUN pip3 install rl_coach>=0.10.0 - -# install additional environments -RUN pip3 install gym[atari]==0.10.5 -RUN pip3 install mujoco_py==1.50.1.56 -RUN pip3 install vizdoom==1.1.6 - -# FROM ubuntu:16.04 -# -# RUN apt-get update \ -# && apt-get install -y \ -# python3-pip cmake zlib1g-dev python3-tk python-opencv \ -# libboost-all-dev \ -# libblas-dev liblapack-dev libatlas-base-dev gfortran \ -# libsdl-dev libsdl-image1.2-dev libsdl-mixer1.2-dev libsdl-ttf2.0-dev \ -# libsmpeg-dev libportmidi-dev libavformat-dev libswscale-dev \ -# dpkg-dev build-essential python3.5-dev libjpeg-dev libtiff-dev \ -# libsdl1.2-dev libnotify-dev freeglut3 freeglut3-dev libsm-dev \ -# libgtk2.0-dev libgtk-3-dev libwebkitgtk-dev libgtk-3-dev \ -# libwebkitgtk-3.0-dev libgstreamer-plugins-base1.0-dev \ -# libav-tools libsdl2-dev swig -# -# # installing python dependencies -# RUN pip3 install --upgrade pip - -RUN apt-get update && apt-get install -y wget zip -RUN mkdir -p ~/.mujoco \ - && wget https://www.roboti.us/download/mjpro150_linux.zip -O mujoco.zip \ - && unzip mujoco.zip -d ~/.mujoco \ - && rm mujoco.zip -ENV LD_LIBRARY_PATH /root/.mujoco/mjpro150/bin:$LD_LIBRARY_PATH - -RUN curl -o /usr/local/bin/patchelf https://s3-us-west-2.amazonaws.com/openai-sci-artifacts/manual-builds/patchelf_0.9_amd64.elf \ - && chmod +x /usr/local/bin/patchelf +FROM coach-base:master RUN mkdir /root/src COPY setup.py /root/src/. diff --git a/docker/Dockerfile.base b/docker/Dockerfile.base new file mode 100644 index 0000000..8659096 --- /dev/null +++ b/docker/Dockerfile.base @@ -0,0 +1,63 @@ +FROM nvidia/cuda:9.0-cudnn7-devel-ubuntu16.04 + +# https://github.com/NVIDIA/nvidia-docker/issues/619 +RUN rm /etc/apt/sources.list.d/cuda.list +RUN apt-get update && \ + apt-get upgrade -y && \ + apt-get clean autoclean && \ + apt-get autoremove -y && apt-get update && \ + apt-get install -y python-pip && \ + apt-get clean autoclean && \ + apt-get autoremove -y +RUN pip install pip --upgrade +WORKDIR /root + +################################ +# Install apt-get Requirements # +################################ + +# General +RUN apt-get update && \ + apt-get install -y python3-pip cmake zlib1g-dev python3-tk python-opencv \ + # Boost libraries + libboost-all-dev \ + # Scipy requirements + libblas-dev liblapack-dev libatlas-base-dev gfortran \ + # Pygame requirements + libsdl-dev libsdl-image1.2-dev libsdl-mixer1.2-dev libsdl-ttf2.0-dev \ + libsmpeg-dev libportmidi-dev libavformat-dev libswscale-dev \ + # Dashboard + dpkg-dev build-essential python3.5-dev libjpeg-dev libtiff-dev libsdl1.2-dev libnotify-dev \ + freeglut3 freeglut3-dev libsm-dev libgtk2.0-dev libgtk-3-dev libwebkitgtk-dev libgtk-3-dev \ + libwebkitgtk-3.0-dev libgstreamer-plugins-base1.0-dev \ + # Gym + libav-tools libsdl2-dev swig cmake \ + # Mujoco_py + curl libgl1-mesa-dev libgl1-mesa-glx libglew-dev libosmesa6-dev software-properties-common \ + # ViZDoom + build-essential zlib1g-dev libsdl2-dev libjpeg-dev \ + nasm tar libbz2-dev libgtk2.0-dev cmake git libfluidsynth-dev libgme-dev \ + libopenal-dev timidity libwildmidi-dev unzip wget && \ + apt-get clean autoclean && \ + apt-get autoremove -y + +############################ +# Install Pip Requirements # +############################ +RUN pip3 install --upgrade pip +RUN pip3 install pytest +RUN pip3 install pytest-xdist + +# initial installation of coach, so that the docker build won't install everything from scratch +RUN pip3 install rl_coach>=0.10.0 && pip3 install gym[atari]==0.10.5 && \ + pip3 install mujoco_py==1.50.1.56 && pip3 install vizdoom==1.1.6 + +RUN mkdir -p ~/.mujoco \ + && wget https://www.roboti.us/download/mjpro150_linux.zip -O mujoco.zip \ + && unzip mujoco.zip -d ~/.mujoco \ + && rm mujoco.zip +# COPY ./mjkey.txt /root/.mujoco/ +ENV LD_LIBRARY_PATH /root/.mujoco/mjpro150/bin:$LD_LIBRARY_PATH + +RUN curl -o /usr/local/bin/patchelf https://s3-us-west-2.amazonaws.com/openai-sci-artifacts/manual-builds/patchelf_0.9_amd64.elf \ + && chmod +x /usr/local/bin/patchelf diff --git a/docker/Makefile b/docker/Makefile index 3fd4b71..c409f8a 100644 --- a/docker/Makefile +++ b/docker/Makefile @@ -20,6 +20,11 @@ RUN_ARGUMENTS+=--rm RUN_ARGUMENTS+=--net host RUN_ARGUMENTS+=-v /tmp/checkpoint:/checkpoint +UNIT_TESTS=python3 -m pytest rl_coach/tests -m unit_test +INTEGRATION_TESTS=python3 -m pytest rl_coach/tests -m integration_test -n auto --tb=short +GOLDEN_TESTS=python3 -m pytest rl_coach/tests -m golden_test -n auto +TRACE_TESTS=python3 rl_coach/tests/trace_tests.py -prl + CONTEXT = $(realpath ..) ifndef DOCKER @@ -35,17 +40,16 @@ shell: build ${DOCKER} run ${RUN_ARGUMENTS} -it ${IMAGE} /bin/bash unit_tests: build - ${DOCKER} run ${RUN_ARGUMENTS} -it ${IMAGE} python3 -m pytest rl_coach/tests -m unit_test -n 8 + ${DOCKER} run ${RUN_ARGUMENTS} -it ${IMAGE} ${UNIT_TESTS} -n 8 integration_tests: build - ${DOCKER} run ${RUN_ARGUMENTS} -it ${IMAGE} python3 -m pytest rl_coach/tests -m integration_test -n auto --tb=short + ${DOCKER} run ${RUN_ARGUMENTS} -it ${IMAGE} ${INTEGRATION_TESTS} golden_tests: build - # ${DOCKER} run ${RUN_ARGUMENTS} -it ${IMAGE} python3 rl_coach/tests/golden_tests.py - time ${DOCKER} run ${RUN_ARGUMENTS} -it ${IMAGE} python3 -m pytest rl_coach/tests -m golden_test -n auto + ${DOCKER} run ${RUN_ARGUMENTS} -it ${IMAGE} ${GOLDEN_TESTS} trace_tests: build - ${DOCKER} run ${RUN_ARGUMENTS} -it ${IMAGE} python3 rl_coach/tests/trace_tests.py -prl + ${DOCKER} run ${RUN_ARGUMENTS} -it ${IMAGE} ${TRACE_TESTS} run: build ${DOCKER} run ${RUN_ARGUMENTS} -it ${IMAGE} @@ -73,3 +77,15 @@ kubernetes: stop_kubernetes push: build ${DOCKER} tag ${IMAGE} ${REGISTRY}${IMAGE} ${DOCKER} push ${REGISTRY}${IMAGE} + +unit_tests_without_docker: + cd .. && ${UNIT_TESTS} + +integration_tests_without_docker: + cd .. && ${INTEGRATION_TESTS} + +golden_tests_without_docker: + cd .. && ${GOLDEN_TESTS} + +trace_tests_without_docker: + cd .. && ${TRACE_TESTS} diff --git a/docker/docker_entrypoint.sh b/docker/docker_entrypoint.sh index 9b19f90..feccda2 100644 --- a/docker/docker_entrypoint.sh +++ b/docker/docker_entrypoint.sh @@ -16,6 +16,4 @@ set -e export VIZDOOM_ROOT=`pip show vizdoom 2>/dev/null | awk '/Location/{print $2}'`/vizdoom -cd /root/src/ - -exec "$@" +bash -c "$@" diff --git a/requirements.txt b/requirements.txt index acd5ff7..90c6785 100644 --- a/requirements.txt +++ b/requirements.txt @@ -12,6 +12,7 @@ gym==0.10.5 bokeh==0.13.0 futures==3.1.1 wxPython==4.0.1 -kubernetes==7.0.0 +kubernetes==8.0.0b1 redis==2.10.6 minio==4.0.5 +pytest==3.8.2 diff --git a/rl_coach/graph_managers/graph_manager.py b/rl_coach/graph_managers/graph_manager.py index 278d5f0..4b5ae9f 100644 --- a/rl_coach/graph_managers/graph_manager.py +++ b/rl_coach/graph_managers/graph_manager.py @@ -364,7 +364,6 @@ class GraphManager(object): if self.agent_params.memory.memory_backend_params.run_type == "worker": data_store = get_data_store(self.data_store_params) data_store.load_from_store() - # perform several steps of playing count_end = self.current_step_counter + steps while self.current_step_counter < count_end: diff --git a/rl_coach/presets/Doom_Basic_DQN.py b/rl_coach/presets/Doom_Basic_DQN.py index 6c551d5..751a32b 100644 --- a/rl_coach/presets/Doom_Basic_DQN.py +++ b/rl_coach/presets/Doom_Basic_DQN.py @@ -36,7 +36,6 @@ agent_params.network_wrappers['main'].replace_mse_with_huber_loss = False ############### env_params = DoomEnvironmentParameters(level='basic') - ######## # Test # ######## diff --git a/rl_coach/tests/test_eks.py b/rl_coach/tests/test_eks.py new file mode 100644 index 0000000..f75ab04 --- /dev/null +++ b/rl_coach/tests/test_eks.py @@ -0,0 +1,183 @@ + +import argparse +import pytest +import time +from kubernetes import client, config + + +class EKSHandler(): + + def __init__(self, cluster, build_num, test_name, test_command, image, cpu, memory, working_dir): + self.cluster = cluster + self.build_num = build_num + self.test_name = test_name + self.test_command = test_command + self.image = image + self.cpu = cpu + self.memory = memory + config.load_kube_config() + self.namespace = '{}-{}'.format(test_name, build_num) + self.corev1_api = client.CoreV1Api() + self.create_namespace() + self.working_dir = working_dir + + def create_namespace(self): + namespace = client.V1Namespace( + api_version='v1', + kind="Namespace", + metadata=client.V1ObjectMeta(name=self.namespace) + ) + + try: + self.corev1_api.create_namespace(namespace) + except client.rest.ApiException as e: + raise RuntimeError("Failed to create namesapce. Got exception: {}".format(e)) + + def deploy(self): + container = client.V1Container( + name=self.test_name, + image=self.image, + args=[self.test_command], + image_pull_policy='Always', + working_dir=self.working_dir, + stdin=True, + tty=True + ) + pod_spec = client.V1PodSpec( + containers=[container], + restart_policy='Never' + ) + pod = client.V1Pod( + api_version="v1", + kind="Pod", + metadata=client.V1ObjectMeta(name=self.test_name), + spec=pod_spec + ) + + try: + self.corev1_api.create_namespaced_pod(self.namespace, pod) + except client.rest.ApiException as e: + print("Got exception: {} while creating a pod".format(e)) + return 1 + + return 0 + + def print_logs(self): + while True: + time.sleep(10) + # Try to tail the pod logs + try: + for line in self.corev1_api.read_namespaced_pod_log( + self.test_name, self.namespace, follow=True, + _preload_content=False + ): + print(line.decode('utf-8'), flush=True, end='') + + except client.rest.ApiException as e: + pass + + try: + pod = self.corev1_api.read_namespaced_pod(self.test_name, self.namespace) + except client.rest.ApiException as e: + continue + + if not hasattr(pod, 'status') or not pod.status: + continue + if not hasattr(pod.status, 'container_statuses') or not pod.status.container_statuses: + continue + + for container_status in pod.status.container_statuses: + if container_status.state.waiting is not None: + if container_status.state.waiting.reason == 'Error' or \ + container_status.state.waiting.reason == 'CrashLoopBackOff' or \ + container_status.state.waiting.reason == 'ImagePullBackOff' or \ + container_status.state.waiting.reason == 'ErrImagePull': + return + if container_status.state.terminated is not None: + return + + def get_return_status(self): + # This part will get executed if the pod is one of the following phases: not ready, failed or terminated. + # Check if the pod has errored out, else just try again. + # Get the pod + try: + pod = self.corev1_api.read_namespaced_pod(self.test_name, self.namespace) + except client.rest.ApiException as e: + return 1 + + if not hasattr(pod, 'status') or not pod.status: + return 0 + if not hasattr(pod.status, 'container_statuses') or not pod.status.container_statuses: + return 0 + + for container_status in pod.status.container_statuses: + if container_status.state.waiting is not None: + if container_status.state.waiting.reason == 'Error' or \ + container_status.state.waiting.reason == 'CrashLoopBackOff' or \ + container_status.state.waiting.reason == 'ImagePullBackOff' or \ + container_status.state.waiting.reason == 'ErrImagePull': + return 1 + if container_status.state.terminated is not None: + return container_status.state.terminated.exit_code + + def cleanup(self): + + # Delete pod + try: + self.corev1_api.delete_namespaced_pod(self.test_name, self.namespace, client.V1DeleteOptions()) + except client.rest.ApiException as e: + print("Got exception while deleting pod: {}".format(e)) + + # Delete namespace + try: + self.corev1_api.delete_namespace(self.namespace, client.V1DeleteOptions()) + except client.rest.ApiException as e: + print("Got exception while deleting namespace: {}".format(e)) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + + parser.add_argument( + '-c', '--cluster', help="(string) Name of the cluster", type=str, required=True + ) + parser.add_argument( + '-bn', '--build-num', help="(int) CI Build number", type=int, required=True + ) + parser.add_argument( + '-tn', '--test-name', help="(string) Name of the test", type=str, required=True + ) + parser.add_argument( + '-tc', '--test-command', help="(string) command to execute", type=str, required=True + ) + parser.add_argument( + '-i', '--image', help="(string) Container image", type=str, required=True + ) + parser.add_argument( + '-cpu', help="(string) Units of cpu to use", type=str, required=True + ) + parser.add_argument( + '-mem', help="(string) The amount in megabytes", type=str, required=True + ) + parser.add_argument( + '--working-dir', help="(string) The working dir in the container", type=str, required=False, + default='/root/src/docker' + ) + args = parser.parse_args() + + obj = EKSHandler( + args.cluster, args.build_num, args.test_name, args.test_command, + args.image, args.cpu, args.mem, args.working_dir + ) + + if obj.deploy() != 0: + obj.cleanup() + pytest.fail("Failed to deploy") + + obj.print_logs() + + if obj.get_return_status() != 0: + obj.cleanup() + pytest.fail("Failed to run tests") + + obj.cleanup() From a8882266412971ae25e38f370353ead854a8ccbd Mon Sep 17 00:00:00 2001 From: Sina Afrooze Date: Mon, 29 Oct 2018 14:46:40 -0700 Subject: [PATCH 089/162] Move embedder, middleware, and head parameters to framework agnostic modules. (#45) Part of #28 --- rl_coach/agents/actor_critic_agent.py | 7 +- rl_coach/agents/bc_agent.py | 6 +- rl_coach/agents/categorical_dqn_agent.py | 2 +- rl_coach/agents/cil_agent.py | 6 +- rl_coach/agents/clipped_ppo_agent.py | 7 +- rl_coach/agents/ddpg_agent.py | 7 +- rl_coach/agents/dfp_agent.py | 7 +- rl_coach/agents/dqn_agent.py | 6 +- rl_coach/agents/human_agent.py | 6 +- rl_coach/agents/n_step_q_agent.py | 6 +- rl_coach/agents/naf_agent.py | 6 +- rl_coach/agents/nec_agent.py | 6 +- rl_coach/agents/policy_gradients_agent.py | 6 +- rl_coach/agents/ppo_agent.py | 7 +- rl_coach/agents/qr_dqn_agent.py | 3 +- rl_coach/agents/rainbow_dqn_agent.py | 4 +- rl_coach/architectures/embedder_parameters.py | 41 +++++ rl_coach/architectures/head_parameters.py | 173 ++++++++++++++++++ .../architectures/middleware_parameters.py | 54 ++++++ .../embedders/__init__.py | 4 + .../embedders/embedder.py | 31 +--- .../tensorflow_components/general_network.py | 32 ++-- .../tensorflow_components/heads/__init__.py | 29 +++ .../heads/categorical_q_head.py | 12 +- .../tensorflow_components/heads/cil_head.py | 15 +- .../heads/ddpg_actor_head.py | 13 +- .../tensorflow_components/heads/dnd_q_head.py | 11 -- .../heads/dueling_q_head.py | 10 - .../tensorflow_components/heads/head.py | 15 +- .../heads/measurements_prediction_head.py | 13 +- .../tensorflow_components/heads/naf_head.py | 12 +- .../heads/policy_head.py | 13 +- .../tensorflow_components/heads/ppo_head.py | 12 +- .../tensorflow_components/heads/ppo_v_head.py | 13 +- .../tensorflow_components/heads/q_head.py | 13 +- .../heads/quantile_regression_q_head.py | 13 +- .../heads/rainbow_q_head.py | 12 +- .../tensorflow_components/heads/v_head.py | 13 +- .../middlewares/__init__.py | 4 + .../middlewares/fc_middleware.py | 12 +- .../middlewares/lstm_middleware.py | 13 +- .../middlewares/middleware.py | 17 +- rl_coach/presets/Atari_A3C.py | 2 +- rl_coach/presets/Atari_A3C_LSTM.py | 2 +- rl_coach/presets/Atari_Dueling_DDQN.py | 2 +- .../Atari_Dueling_DDQN_with_PER_OpenAI.py | 2 +- rl_coach/presets/BitFlip_DQN.py | 2 +- rl_coach/presets/BitFlip_DQN_HER.py | 2 +- rl_coach/presets/CARLA_CIL.py | 6 +- rl_coach/presets/CARLA_Dueling_DDQN.py | 2 +- rl_coach/presets/CartPole_Dueling_DDQN.py | 2 +- rl_coach/presets/Doom_Basic_Dueling_DDQN.py | 2 +- .../presets/ExplorationChain_Dueling_DDQN.py | 2 +- rl_coach/presets/Fetch_DDPG_HER_baselines.py | 4 +- rl_coach/presets/Mujoco_A3C_LSTM.py | 4 +- rl_coach/presets/Pendulum_HAC.py | 2 +- .../presets/Starcraft_CollectMinerals_A3C.py | 2 +- .../Starcraft_CollectMinerals_Dueling_DDQN.py | 4 +- tutorials/0. Quick Start Guide.ipynb | 2 +- ...Implementing a Hierarchical RL Graph.ipynb | 4 +- 60 files changed, 410 insertions(+), 330 deletions(-) create mode 100644 rl_coach/architectures/embedder_parameters.py create mode 100644 rl_coach/architectures/head_parameters.py create mode 100644 rl_coach/architectures/middleware_parameters.py diff --git a/rl_coach/agents/actor_critic_agent.py b/rl_coach/agents/actor_critic_agent.py index 7c74f1e..732c7ea 100644 --- a/rl_coach/agents/actor_critic_agent.py +++ b/rl_coach/agents/actor_critic_agent.py @@ -20,9 +20,9 @@ import numpy as np import scipy.signal from rl_coach.agents.policy_optimization_agent import PolicyOptimizationAgent, PolicyGradientRescaler -from rl_coach.architectures.tensorflow_components.heads.policy_head import PolicyHeadParameters -from rl_coach.architectures.tensorflow_components.heads.v_head import VHeadParameters -from rl_coach.architectures.tensorflow_components.middlewares.fc_middleware import FCMiddlewareParameters +from rl_coach.architectures.embedder_parameters import InputEmbedderParameters +from rl_coach.architectures.head_parameters import PolicyHeadParameters, VHeadParameters +from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters from rl_coach.base_parameters import AlgorithmParameters, NetworkParameters, \ AgentParameters from rl_coach.exploration_policies.categorical import CategoricalParameters @@ -31,7 +31,6 @@ from rl_coach.logger import screen from rl_coach.memories.episodic.single_episode_buffer import SingleEpisodeBufferParameters from rl_coach.spaces import DiscreteActionSpace, BoxActionSpace from rl_coach.utils import last_sample -from rl_coach.architectures.tensorflow_components.embedders.embedder import InputEmbedderParameters class ActorCriticAlgorithmParameters(AlgorithmParameters): diff --git a/rl_coach/agents/bc_agent.py b/rl_coach/agents/bc_agent.py index 7f8e166..044dd71 100644 --- a/rl_coach/agents/bc_agent.py +++ b/rl_coach/agents/bc_agent.py @@ -19,13 +19,13 @@ from typing import Union import numpy as np from rl_coach.agents.imitation_agent import ImitationAgent -from rl_coach.architectures.tensorflow_components.heads.policy_head import PolicyHeadParameters -from rl_coach.architectures.tensorflow_components.middlewares.fc_middleware import FCMiddlewareParameters +from rl_coach.architectures.head_parameters import PolicyHeadParameters +from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters +from rl_coach.architectures.embedder_parameters import InputEmbedderParameters from rl_coach.base_parameters import AgentParameters, AlgorithmParameters, NetworkParameters, \ MiddlewareScheme from rl_coach.exploration_policies.e_greedy import EGreedyParameters from rl_coach.memories.episodic.episodic_experience_replay import EpisodicExperienceReplayParameters -from rl_coach.architectures.tensorflow_components.embedders.embedder import InputEmbedderParameters from rl_coach.memories.non_episodic.experience_replay import ExperienceReplayParameters diff --git a/rl_coach/agents/categorical_dqn_agent.py b/rl_coach/agents/categorical_dqn_agent.py index 5af83c1..24a610b 100644 --- a/rl_coach/agents/categorical_dqn_agent.py +++ b/rl_coach/agents/categorical_dqn_agent.py @@ -20,7 +20,7 @@ import numpy as np from rl_coach.agents.dqn_agent import DQNNetworkParameters, DQNAlgorithmParameters, DQNAgentParameters from rl_coach.agents.value_optimization_agent import ValueOptimizationAgent -from rl_coach.architectures.tensorflow_components.heads.categorical_q_head import CategoricalQHeadParameters +from rl_coach.architectures.head_parameters import CategoricalQHeadParameters from rl_coach.base_parameters import AgentParameters from rl_coach.core_types import StateType from rl_coach.exploration_policies.e_greedy import EGreedyParameters diff --git a/rl_coach/agents/cil_agent.py b/rl_coach/agents/cil_agent.py index 1940acf..a0d4af0 100644 --- a/rl_coach/agents/cil_agent.py +++ b/rl_coach/agents/cil_agent.py @@ -17,9 +17,9 @@ from typing import Union from rl_coach.agents.imitation_agent import ImitationAgent -from rl_coach.architectures.tensorflow_components.embedders.embedder import InputEmbedderParameters -from rl_coach.architectures.tensorflow_components.heads.cil_head import RegressionHeadParameters -from rl_coach.architectures.tensorflow_components.middlewares.fc_middleware import FCMiddlewareParameters +from rl_coach.architectures.embedder_parameters import InputEmbedderParameters +from rl_coach.architectures.head_parameters import RegressionHeadParameters +from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters from rl_coach.base_parameters import AgentParameters, MiddlewareScheme, NetworkParameters, AlgorithmParameters from rl_coach.exploration_policies.e_greedy import EGreedyParameters from rl_coach.memories.non_episodic.balanced_experience_replay import BalancedExperienceReplayParameters diff --git a/rl_coach/agents/clipped_ppo_agent.py b/rl_coach/agents/clipped_ppo_agent.py index 4414966..080525f 100644 --- a/rl_coach/agents/clipped_ppo_agent.py +++ b/rl_coach/agents/clipped_ppo_agent.py @@ -23,12 +23,11 @@ import numpy as np from rl_coach.agents.actor_critic_agent import ActorCriticAgent from rl_coach.agents.policy_optimization_agent import PolicyGradientRescaler -from rl_coach.architectures.tensorflow_components.heads.ppo_head import PPOHeadParameters -from rl_coach.architectures.tensorflow_components.heads.v_head import VHeadParameters -from rl_coach.architectures.tensorflow_components.middlewares.fc_middleware import FCMiddlewareParameters +from rl_coach.architectures.embedder_parameters import InputEmbedderParameters +from rl_coach.architectures.head_parameters import PPOHeadParameters, VHeadParameters +from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters from rl_coach.base_parameters import AlgorithmParameters, NetworkParameters, \ AgentParameters -from rl_coach.architectures.tensorflow_components.embedders.embedder import InputEmbedderParameters from rl_coach.core_types import EnvironmentSteps, Batch, EnvResponse, StateType from rl_coach.exploration_policies.additive_noise import AdditiveNoiseParameters from rl_coach.exploration_policies.categorical import CategoricalParameters diff --git a/rl_coach/agents/ddpg_agent.py b/rl_coach/agents/ddpg_agent.py index 8fa0eb7..79d2ef8 100644 --- a/rl_coach/agents/ddpg_agent.py +++ b/rl_coach/agents/ddpg_agent.py @@ -22,10 +22,9 @@ import numpy as np from rl_coach.agents.actor_critic_agent import ActorCriticAgent from rl_coach.agents.agent import Agent -from rl_coach.architectures.tensorflow_components.embedders.embedder import InputEmbedderParameters -from rl_coach.architectures.tensorflow_components.heads.ddpg_actor_head import DDPGActorHeadParameters -from rl_coach.architectures.tensorflow_components.heads.v_head import VHeadParameters -from rl_coach.architectures.tensorflow_components.middlewares.fc_middleware import FCMiddlewareParameters +from rl_coach.architectures.embedder_parameters import InputEmbedderParameters +from rl_coach.architectures.head_parameters import DDPGActorHeadParameters, VHeadParameters +from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters from rl_coach.base_parameters import NetworkParameters, AlgorithmParameters, \ AgentParameters, EmbedderScheme from rl_coach.core_types import ActionInfo, EnvironmentSteps diff --git a/rl_coach/agents/dfp_agent.py b/rl_coach/agents/dfp_agent.py index f42f948..9fed1c3 100644 --- a/rl_coach/agents/dfp_agent.py +++ b/rl_coach/agents/dfp_agent.py @@ -21,14 +21,13 @@ from typing import Union import numpy as np from rl_coach.agents.agent import Agent +from rl_coach.architectures.head_parameters import MeasurementsPredictionHeadParameters +from rl_coach.architectures.embedder_parameters import InputEmbedderParameters +from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters from rl_coach.architectures.tensorflow_components.layers import Conv2d, Dense -from rl_coach.architectures.tensorflow_components.heads.measurements_prediction_head import \ - MeasurementsPredictionHeadParameters -from rl_coach.architectures.tensorflow_components.middlewares.fc_middleware import FCMiddlewareParameters from rl_coach.base_parameters import AlgorithmParameters, AgentParameters, NetworkParameters, \ MiddlewareScheme from rl_coach.core_types import ActionInfo, EnvironmentSteps, RunPhase -from rl_coach.architectures.tensorflow_components.embedders.embedder import InputEmbedderParameters from rl_coach.exploration_policies.e_greedy import EGreedyParameters from rl_coach.memories.episodic.episodic_experience_replay import EpisodicExperienceReplayParameters from rl_coach.memories.memory import MemoryGranularity diff --git a/rl_coach/agents/dqn_agent.py b/rl_coach/agents/dqn_agent.py index f261a08..a60aac2 100644 --- a/rl_coach/agents/dqn_agent.py +++ b/rl_coach/agents/dqn_agent.py @@ -19,11 +19,11 @@ from typing import Union import numpy as np from rl_coach.agents.value_optimization_agent import ValueOptimizationAgent -from rl_coach.architectures.tensorflow_components.heads.q_head import QHeadParameters -from rl_coach.architectures.tensorflow_components.middlewares.fc_middleware import FCMiddlewareParameters +from rl_coach.architectures.embedder_parameters import InputEmbedderParameters +from rl_coach.architectures.head_parameters import QHeadParameters +from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters from rl_coach.base_parameters import AlgorithmParameters, NetworkParameters, AgentParameters, \ MiddlewareScheme -from rl_coach.architectures.tensorflow_components.embedders.embedder import InputEmbedderParameters from rl_coach.core_types import EnvironmentSteps from rl_coach.exploration_policies.e_greedy import EGreedyParameters from rl_coach.memories.non_episodic.experience_replay import ExperienceReplayParameters diff --git a/rl_coach/agents/human_agent.py b/rl_coach/agents/human_agent.py index e553403..2b92058 100644 --- a/rl_coach/agents/human_agent.py +++ b/rl_coach/agents/human_agent.py @@ -23,11 +23,11 @@ from pandas import to_pickle from rl_coach.agents.agent import Agent from rl_coach.agents.bc_agent import BCNetworkParameters -from rl_coach.architectures.tensorflow_components.heads.policy_head import PolicyHeadParameters -from rl_coach.architectures.tensorflow_components.middlewares.fc_middleware import FCMiddlewareParameters +from rl_coach.architectures.embedder_parameters import InputEmbedderParameters +from rl_coach.architectures.head_parameters import PolicyHeadParameters +from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters from rl_coach.base_parameters import AlgorithmParameters, NetworkParameters, EmbedderScheme, \ AgentParameters -from rl_coach.architectures.tensorflow_components.embedders.embedder import InputEmbedderParameters from rl_coach.core_types import ActionInfo from rl_coach.exploration_policies.e_greedy import EGreedyParameters from rl_coach.logger import screen diff --git a/rl_coach/agents/n_step_q_agent.py b/rl_coach/agents/n_step_q_agent.py index 787544b..1f01314 100644 --- a/rl_coach/agents/n_step_q_agent.py +++ b/rl_coach/agents/n_step_q_agent.py @@ -20,10 +20,10 @@ import numpy as np from rl_coach.agents.policy_optimization_agent import PolicyOptimizationAgent from rl_coach.agents.value_optimization_agent import ValueOptimizationAgent -from rl_coach.architectures.tensorflow_components.heads.q_head import QHeadParameters -from rl_coach.architectures.tensorflow_components.middlewares.fc_middleware import FCMiddlewareParameters +from rl_coach.architectures.embedder_parameters import InputEmbedderParameters +from rl_coach.architectures.head_parameters import QHeadParameters +from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters from rl_coach.base_parameters import AlgorithmParameters, AgentParameters, NetworkParameters -from rl_coach.architectures.tensorflow_components.embedders.embedder import InputEmbedderParameters from rl_coach.core_types import EnvironmentSteps from rl_coach.exploration_policies.e_greedy import EGreedyParameters diff --git a/rl_coach/agents/naf_agent.py b/rl_coach/agents/naf_agent.py index 0401ba5..df7c60d 100644 --- a/rl_coach/agents/naf_agent.py +++ b/rl_coach/agents/naf_agent.py @@ -19,11 +19,11 @@ from typing import Union import numpy as np from rl_coach.agents.value_optimization_agent import ValueOptimizationAgent -from rl_coach.architectures.tensorflow_components.heads.naf_head import NAFHeadParameters -from rl_coach.architectures.tensorflow_components.middlewares.fc_middleware import FCMiddlewareParameters +from rl_coach.architectures.embedder_parameters import InputEmbedderParameters +from rl_coach.architectures.head_parameters import NAFHeadParameters +from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters from rl_coach.base_parameters import AlgorithmParameters, AgentParameters, \ NetworkParameters -from rl_coach.architectures.tensorflow_components.embedders.embedder import InputEmbedderParameters from rl_coach.core_types import ActionInfo, EnvironmentSteps from rl_coach.exploration_policies.ou_process import OUProcessParameters diff --git a/rl_coach/agents/nec_agent.py b/rl_coach/agents/nec_agent.py index 891466d..6f168bb 100644 --- a/rl_coach/agents/nec_agent.py +++ b/rl_coach/agents/nec_agent.py @@ -21,10 +21,10 @@ from typing import Union import numpy as np from rl_coach.agents.value_optimization_agent import ValueOptimizationAgent -from rl_coach.architectures.tensorflow_components.heads.dnd_q_head import DNDQHeadParameters -from rl_coach.architectures.tensorflow_components.middlewares.fc_middleware import FCMiddlewareParameters +from rl_coach.architectures.embedder_parameters import InputEmbedderParameters +from rl_coach.architectures.head_parameters import DNDQHeadParameters +from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters from rl_coach.base_parameters import AlgorithmParameters, NetworkParameters, AgentParameters -from rl_coach.architectures.tensorflow_components.embedders.embedder import InputEmbedderParameters from rl_coach.core_types import RunPhase, EnvironmentSteps, Episode, StateType from rl_coach.exploration_policies.e_greedy import EGreedyParameters diff --git a/rl_coach/agents/policy_gradients_agent.py b/rl_coach/agents/policy_gradients_agent.py index 177da8d..7db5fd8 100644 --- a/rl_coach/agents/policy_gradients_agent.py +++ b/rl_coach/agents/policy_gradients_agent.py @@ -19,11 +19,11 @@ from typing import Union import numpy as np from rl_coach.agents.policy_optimization_agent import PolicyOptimizationAgent, PolicyGradientRescaler -from rl_coach.architectures.tensorflow_components.heads.policy_head import PolicyHeadParameters -from rl_coach.architectures.tensorflow_components.middlewares.fc_middleware import FCMiddlewareParameters +from rl_coach.architectures.embedder_parameters import InputEmbedderParameters +from rl_coach.architectures.head_parameters import PolicyHeadParameters +from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters from rl_coach.base_parameters import NetworkParameters, AlgorithmParameters, \ AgentParameters -from rl_coach.architectures.tensorflow_components.embedders.embedder import InputEmbedderParameters from rl_coach.exploration_policies.additive_noise import AdditiveNoiseParameters from rl_coach.exploration_policies.categorical import CategoricalParameters diff --git a/rl_coach/agents/ppo_agent.py b/rl_coach/agents/ppo_agent.py index fdb175e..83b6fc4 100644 --- a/rl_coach/agents/ppo_agent.py +++ b/rl_coach/agents/ppo_agent.py @@ -22,12 +22,11 @@ import numpy as np from rl_coach.agents.actor_critic_agent import ActorCriticAgent from rl_coach.agents.policy_optimization_agent import PolicyGradientRescaler -from rl_coach.architectures.tensorflow_components.heads.ppo_head import PPOHeadParameters -from rl_coach.architectures.tensorflow_components.heads.v_head import VHeadParameters -from rl_coach.architectures.tensorflow_components.middlewares.fc_middleware import FCMiddlewareParameters +from rl_coach.architectures.embedder_parameters import InputEmbedderParameters +from rl_coach.architectures.head_parameters import PPOHeadParameters, VHeadParameters +from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters from rl_coach.base_parameters import AlgorithmParameters, NetworkParameters, \ AgentParameters, DistributedTaskParameters -from rl_coach.architectures.tensorflow_components.embedders.embedder import InputEmbedderParameters from rl_coach.core_types import EnvironmentSteps, Batch from rl_coach.exploration_policies.additive_noise import AdditiveNoiseParameters diff --git a/rl_coach/agents/qr_dqn_agent.py b/rl_coach/agents/qr_dqn_agent.py index 479cd80..ac525ea 100644 --- a/rl_coach/agents/qr_dqn_agent.py +++ b/rl_coach/agents/qr_dqn_agent.py @@ -20,8 +20,7 @@ import numpy as np from rl_coach.agents.dqn_agent import DQNAgentParameters, DQNNetworkParameters, DQNAlgorithmParameters from rl_coach.agents.value_optimization_agent import ValueOptimizationAgent -from rl_coach.architectures.tensorflow_components.heads.quantile_regression_q_head import \ - QuantileRegressionQHeadParameters +from rl_coach.architectures.head_parameters import QuantileRegressionQHeadParameters from rl_coach.core_types import StateType from rl_coach.schedules import LinearSchedule diff --git a/rl_coach/agents/rainbow_dqn_agent.py b/rl_coach/agents/rainbow_dqn_agent.py index e39024a..609ea0b 100644 --- a/rl_coach/agents/rainbow_dqn_agent.py +++ b/rl_coach/agents/rainbow_dqn_agent.py @@ -21,8 +21,8 @@ import numpy as np from rl_coach.agents.categorical_dqn_agent import CategoricalDQNAlgorithmParameters, \ CategoricalDQNAgent, CategoricalDQNAgentParameters from rl_coach.agents.dqn_agent import DQNNetworkParameters -from rl_coach.architectures.tensorflow_components.heads.rainbow_q_head import RainbowQHeadParameters -from rl_coach.architectures.tensorflow_components.middlewares.fc_middleware import FCMiddlewareParameters +from rl_coach.architectures.head_parameters import RainbowQHeadParameters +from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters from rl_coach.base_parameters import MiddlewareScheme from rl_coach.exploration_policies.parameter_noise import ParameterNoiseParameters from rl_coach.memories.non_episodic.prioritized_experience_replay import PrioritizedExperienceReplayParameters, \ diff --git a/rl_coach/architectures/embedder_parameters.py b/rl_coach/architectures/embedder_parameters.py new file mode 100644 index 0000000..2731a52 --- /dev/null +++ b/rl_coach/architectures/embedder_parameters.py @@ -0,0 +1,41 @@ +# +# Copyright (c) 2017 Intel Corporation +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +from typing import List, Union + +from rl_coach.base_parameters import EmbedderScheme, NetworkComponentParameters + + +class InputEmbedderParameters(NetworkComponentParameters): + def __init__(self, activation_function: str='relu', scheme: Union[List, EmbedderScheme]=EmbedderScheme.Medium, + batchnorm: bool=False, dropout=False, name: str='embedder', input_rescaling=None, input_offset=None, + input_clipping=None, dense_layer=None, is_training=False): + super().__init__(dense_layer=dense_layer) + self.activation_function = activation_function + self.scheme = scheme + self.batchnorm = batchnorm + self.dropout = dropout + + if input_rescaling is None: + input_rescaling = {'image': 255.0, 'vector': 1.0} + if input_offset is None: + input_offset = {'image': 0.0, 'vector': 0.0} + + self.input_rescaling = input_rescaling + self.input_offset = input_offset + self.input_clipping = input_clipping + self.name = name + self.is_training = is_training diff --git a/rl_coach/architectures/head_parameters.py b/rl_coach/architectures/head_parameters.py new file mode 100644 index 0000000..e29d656 --- /dev/null +++ b/rl_coach/architectures/head_parameters.py @@ -0,0 +1,173 @@ +# +# Copyright (c) 2017 Intel Corporation +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +from typing import Type + +from rl_coach.base_parameters import NetworkComponentParameters + + +class HeadParameters(NetworkComponentParameters): + def __init__(self, parameterized_class_name: str, activation_function: str = 'relu', name: str= 'head', + num_output_head_copies: int=1, rescale_gradient_from_head_by_factor: float=1.0, + loss_weight: float=1.0, dense_layer=None): + super().__init__(dense_layer=dense_layer) + self.activation_function = activation_function + self.name = name + self.num_output_head_copies = num_output_head_copies + self.rescale_gradient_from_head_by_factor = rescale_gradient_from_head_by_factor + self.loss_weight = loss_weight + self.parameterized_class_name = parameterized_class_name + + +class PPOHeadParameters(HeadParameters): + def __init__(self, activation_function: str ='tanh', name: str='ppo_head_params', + num_output_head_copies: int = 1, rescale_gradient_from_head_by_factor: float = 1.0, + loss_weight: float = 1.0, dense_layer=None): + super().__init__(parameterized_class_name="PPOHead", activation_function=activation_function, name=name, + dense_layer=dense_layer, num_output_head_copies=num_output_head_copies, + rescale_gradient_from_head_by_factor=rescale_gradient_from_head_by_factor, + loss_weight=loss_weight) + + +class VHeadParameters(HeadParameters): + def __init__(self, activation_function: str ='relu', name: str='v_head_params', + num_output_head_copies: int = 1, rescale_gradient_from_head_by_factor: float = 1.0, + loss_weight: float = 1.0, dense_layer=None): + super().__init__(parameterized_class_name="VHead", activation_function=activation_function, name=name, + dense_layer=dense_layer, num_output_head_copies=num_output_head_copies, + rescale_gradient_from_head_by_factor=rescale_gradient_from_head_by_factor, + loss_weight=loss_weight) + + +class CategoricalQHeadParameters(HeadParameters): + def __init__(self, activation_function: str ='relu', name: str='categorical_q_head_params', + num_output_head_copies: int = 1, rescale_gradient_from_head_by_factor: float = 1.0, + loss_weight: float = 1.0, dense_layer=None): + super().__init__(parameterized_class_name="CategoricalQHead", activation_function=activation_function, name=name, + dense_layer=dense_layer, num_output_head_copies=num_output_head_copies, + rescale_gradient_from_head_by_factor=rescale_gradient_from_head_by_factor, + loss_weight=loss_weight) + + +class RegressionHeadParameters(HeadParameters): + def __init__(self, activation_function: str ='relu', name: str='q_head_params', + num_output_head_copies: int = 1, rescale_gradient_from_head_by_factor: float = 1.0, + loss_weight: float = 1.0, dense_layer=None, scheme=None): + super().__init__(parameterized_class_name="RegressionHead", activation_function=activation_function, name=name, + dense_layer=dense_layer, num_output_head_copies=num_output_head_copies, + rescale_gradient_from_head_by_factor=rescale_gradient_from_head_by_factor, + loss_weight=loss_weight) + + +class DDPGActorHeadParameters(HeadParameters): + def __init__(self, activation_function: str ='tanh', name: str='policy_head_params', batchnorm: bool=True, + num_output_head_copies: int = 1, rescale_gradient_from_head_by_factor: float = 1.0, + loss_weight: float = 1.0, dense_layer=None): + super().__init__(parameterized_class_name="DDPGActor", activation_function=activation_function, name=name, + dense_layer=dense_layer, num_output_head_copies=num_output_head_copies, + rescale_gradient_from_head_by_factor=rescale_gradient_from_head_by_factor, + loss_weight=loss_weight) + self.batchnorm = batchnorm + + +class DNDQHeadParameters(HeadParameters): + def __init__(self, activation_function: str ='relu', name: str='dnd_q_head_params', + num_output_head_copies: int = 1, rescale_gradient_from_head_by_factor: float = 1.0, + loss_weight: float = 1.0, dense_layer=None): + super().__init__(parameterized_class_name="DNDQHead", activation_function=activation_function, name=name, + dense_layer=dense_layer, num_output_head_copies=num_output_head_copies, + rescale_gradient_from_head_by_factor=rescale_gradient_from_head_by_factor, + loss_weight=loss_weight) + + +class DuelingQHeadParameters(HeadParameters): + def __init__(self, activation_function: str ='relu', name: str='dueling_q_head_params', + num_output_head_copies: int = 1, rescale_gradient_from_head_by_factor: float = 1.0, + loss_weight: float = 1.0, dense_layer=None): + super().__init__(parameterized_class_name="DuelingQHead", activation_function=activation_function, name=name, + dense_layer=dense_layer, num_output_head_copies=num_output_head_copies, + rescale_gradient_from_head_by_factor=rescale_gradient_from_head_by_factor, + loss_weight=loss_weight) + + +class MeasurementsPredictionHeadParameters(HeadParameters): + def __init__(self, activation_function: str ='relu', name: str='measurements_prediction_head_params', + num_output_head_copies: int = 1, rescale_gradient_from_head_by_factor: float = 1.0, + loss_weight: float = 1.0, dense_layer=None): + super().__init__(parameterized_class_name="MeasurementsPredictionHead", activation_function=activation_function, name=name, + dense_layer=dense_layer, num_output_head_copies=num_output_head_copies, + rescale_gradient_from_head_by_factor=rescale_gradient_from_head_by_factor, + loss_weight=loss_weight) + + +class NAFHeadParameters(HeadParameters): + def __init__(self, activation_function: str ='tanh', name: str='naf_head_params', + num_output_head_copies: int = 1, rescale_gradient_from_head_by_factor: float = 1.0, + loss_weight: float = 1.0, dense_layer=None): + super().__init__(parameterized_class_name="NAFHead", activation_function=activation_function, name=name, + dense_layer=dense_layer, num_output_head_copies=num_output_head_copies, + rescale_gradient_from_head_by_factor=rescale_gradient_from_head_by_factor, + loss_weight=loss_weight) + + +class PolicyHeadParameters(HeadParameters): + def __init__(self, activation_function: str ='tanh', name: str='policy_head_params', + num_output_head_copies: int = 1, rescale_gradient_from_head_by_factor: float = 1.0, + loss_weight: float = 1.0, dense_layer=None): + super().__init__(parameterized_class_name="PolicyHead", activation_function=activation_function, name=name, + dense_layer=dense_layer, num_output_head_copies=num_output_head_copies, + rescale_gradient_from_head_by_factor=rescale_gradient_from_head_by_factor, + loss_weight=loss_weight) + + +class PPOVHeadParameters(HeadParameters): + def __init__(self, activation_function: str ='relu', name: str='ppo_v_head_params', + num_output_head_copies: int = 1, rescale_gradient_from_head_by_factor: float = 1.0, + loss_weight: float = 1.0, dense_layer=None): + super().__init__(parameterized_class_name="PPOVHead", activation_function=activation_function, name=name, + dense_layer=dense_layer, num_output_head_copies=num_output_head_copies, + rescale_gradient_from_head_by_factor=rescale_gradient_from_head_by_factor, + loss_weight=loss_weight) + + +class QHeadParameters(HeadParameters): + def __init__(self, activation_function: str ='relu', name: str='q_head_params', + num_output_head_copies: int = 1, rescale_gradient_from_head_by_factor: float = 1.0, + loss_weight: float = 1.0, dense_layer=None): + super().__init__(parameterized_class_name="QHead", activation_function=activation_function, name=name, + dense_layer=dense_layer, num_output_head_copies=num_output_head_copies, + rescale_gradient_from_head_by_factor=rescale_gradient_from_head_by_factor, + loss_weight=loss_weight) + + +class QuantileRegressionQHeadParameters(HeadParameters): + def __init__(self, activation_function: str ='relu', name: str='quantile_regression_q_head_params', + num_output_head_copies: int = 1, rescale_gradient_from_head_by_factor: float = 1.0, + loss_weight: float = 1.0, dense_layer=None): + super().__init__(parameterized_class_name="QuantileRegressionQHead", activation_function=activation_function, name=name, + dense_layer=dense_layer, num_output_head_copies=num_output_head_copies, + rescale_gradient_from_head_by_factor=rescale_gradient_from_head_by_factor, + loss_weight=loss_weight) + + +class RainbowQHeadParameters(HeadParameters): + def __init__(self, activation_function: str ='relu', name: str='rainbow_q_head_params', + num_output_head_copies: int = 1, rescale_gradient_from_head_by_factor: float = 1.0, + loss_weight: float = 1.0, dense_layer=None): + super().__init__(parameterized_class_name="RainbowQHead", activation_function=activation_function, name=name, + dense_layer=dense_layer, num_output_head_copies=num_output_head_copies, + rescale_gradient_from_head_by_factor=rescale_gradient_from_head_by_factor, + loss_weight=loss_weight) diff --git a/rl_coach/architectures/middleware_parameters.py b/rl_coach/architectures/middleware_parameters.py new file mode 100644 index 0000000..711ec06 --- /dev/null +++ b/rl_coach/architectures/middleware_parameters.py @@ -0,0 +1,54 @@ +# +# Copyright (c) 2017 Intel Corporation +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +from typing import List, Type, Union + +from rl_coach.base_parameters import MiddlewareScheme, NetworkComponentParameters + + +class MiddlewareParameters(NetworkComponentParameters): + def __init__(self, parameterized_class_name: str, + activation_function: str='relu', scheme: Union[List, MiddlewareScheme]=MiddlewareScheme.Medium, + batchnorm: bool=False, dropout: bool=False, name='middleware', dense_layer=None, is_training=False): + super().__init__(dense_layer=dense_layer) + self.activation_function = activation_function + self.scheme = scheme + self.batchnorm = batchnorm + self.dropout = dropout + self.name = name + self.is_training = is_training + self.parameterized_class_name = parameterized_class_name + + +class FCMiddlewareParameters(MiddlewareParameters): + def __init__(self, activation_function='relu', + scheme: Union[List, MiddlewareScheme] = MiddlewareScheme.Medium, + batchnorm: bool = False, dropout: bool = False, + name="middleware_fc_embedder", dense_layer=None, is_training=False): + super().__init__(parameterized_class_name="FCMiddleware", activation_function=activation_function, + scheme=scheme, batchnorm=batchnorm, dropout=dropout, name=name, dense_layer=dense_layer, + is_training=is_training) + + +class LSTMMiddlewareParameters(MiddlewareParameters): + def __init__(self, activation_function='relu', number_of_lstm_cells=256, + scheme: MiddlewareScheme = MiddlewareScheme.Medium, + batchnorm: bool = False, dropout: bool = False, + name="middleware_lstm_embedder", dense_layer=None, is_training=False): + super().__init__(parameterized_class_name="LSTMMiddleware", activation_function=activation_function, + scheme=scheme, batchnorm=batchnorm, dropout=dropout, name=name, dense_layer=dense_layer, + is_training=is_training) + self.number_of_lstm_cells = number_of_lstm_cells \ No newline at end of file diff --git a/rl_coach/architectures/tensorflow_components/embedders/__init__.py b/rl_coach/architectures/tensorflow_components/embedders/__init__.py index e69de29..eb0482f 100644 --- a/rl_coach/architectures/tensorflow_components/embedders/__init__.py +++ b/rl_coach/architectures/tensorflow_components/embedders/__init__.py @@ -0,0 +1,4 @@ +from .image_embedder import ImageEmbedder +from .vector_embedder import VectorEmbedder + +__all__ = ['ImageEmbedder', 'VectorEmbedder'] diff --git a/rl_coach/architectures/tensorflow_components/embedders/embedder.py b/rl_coach/architectures/tensorflow_components/embedders/embedder.py index 66b81ef..004c5c4 100644 --- a/rl_coach/architectures/tensorflow_components/embedders/embedder.py +++ b/rl_coach/architectures/tensorflow_components/embedders/embedder.py @@ -28,35 +28,6 @@ from rl_coach.core_types import InputEmbedding from rl_coach.utils import force_list -class InputEmbedderParameters(NetworkComponentParameters): - def __init__(self, activation_function: str='relu', scheme: Union[List, EmbedderScheme]=EmbedderScheme.Medium, - batchnorm: bool=False, dropout=False, name: str='embedder', input_rescaling=None, input_offset=None, - input_clipping=None, dense_layer=Dense, is_training=False): - super().__init__(dense_layer=dense_layer) - self.activation_function = activation_function - self.scheme = scheme - self.batchnorm = batchnorm - self.dropout = dropout - - if input_rescaling is None: - input_rescaling = {'image': 255.0, 'vector': 1.0} - if input_offset is None: - input_offset = {'image': 0.0, 'vector': 0.0} - - self.input_rescaling = input_rescaling - self.input_offset = input_offset - self.input_clipping = input_clipping - self.name = name - self.is_training = is_training - - @property - def path(self): - return { - "image": 'image_embedder:ImageEmbedder', - "vector": 'vector_embedder:VectorEmbedder' - } - - class InputEmbedder(object): """ An input embedder is the first part of the network, which takes the input from the state and produces a vector @@ -83,6 +54,8 @@ class InputEmbedder(object): self.input_offset = input_offset self.input_clipping = input_clipping self.dense_layer = dense_layer + if self.dense_layer is None: + self.dense_layer = Dense self.is_training = is_training # layers order is conv -> batchnorm -> activation -> dropout diff --git a/rl_coach/architectures/tensorflow_components/general_network.py b/rl_coach/architectures/tensorflow_components/general_network.py index 0872931..fa494b5 100644 --- a/rl_coach/architectures/tensorflow_components/general_network.py +++ b/rl_coach/architectures/tensorflow_components/general_network.py @@ -20,10 +20,10 @@ from typing import Dict import numpy as np import tensorflow as tf -from rl_coach.architectures.tensorflow_components.embedders.embedder import InputEmbedderParameters +from rl_coach.architectures.embedder_parameters import InputEmbedderParameters +from rl_coach.architectures.head_parameters import HeadParameters +from rl_coach.architectures.middleware_parameters import MiddlewareParameters from rl_coach.architectures.tensorflow_components.architecture import TensorFlowArchitecture -from rl_coach.architectures.tensorflow_components.heads.head import HeadParameters -from rl_coach.architectures.tensorflow_components.middlewares.middleware import MiddlewareParameters from rl_coach.base_parameters import AgentParameters, EmbeddingMergerType from rl_coach.core_types import PredictionType from rl_coach.spaces import SpacesDefinition, PlanarMapsObservationSpace @@ -136,15 +136,17 @@ class GeneralTensorFlowNetwork(TensorFlowArchitecture): raise ValueError("The key for the input embedder ({}) must match one of the following keys: {}" .format(input_name, allowed_inputs.keys())) - type = "vector" - if isinstance(allowed_inputs[input_name], PlanarMapsObservationSpace): - type = "image" + mod_names = {'image': 'ImageEmbedder', 'vector': 'VectorEmbedder'} - embedder_path = 'rl_coach.architectures.tensorflow_components.embedders.' + embedder_params.path[type] + emb_type = "vector" + if isinstance(allowed_inputs[input_name], PlanarMapsObservationSpace): + emb_type = "image" + + embedder_path = 'rl_coach.architectures.tensorflow_components.embedders:' + mod_names[emb_type] embedder_params_copy = copy.copy(embedder_params) embedder_params_copy.activation_function = self.get_activation_function(embedder_params.activation_function) - embedder_params_copy.input_rescaling = embedder_params_copy.input_rescaling[type] - embedder_params_copy.input_offset = embedder_params_copy.input_offset[type] + embedder_params_copy.input_rescaling = embedder_params_copy.input_rescaling[emb_type] + embedder_params_copy.input_offset = embedder_params_copy.input_offset[emb_type] embedder_params_copy.name = input_name module = dynamic_import_and_instantiate_module_from_params(embedder_params_copy, path=embedder_path, @@ -157,25 +159,25 @@ class GeneralTensorFlowNetwork(TensorFlowArchitecture): :param middleware_params: the paramaeters of the middleware class :return: the middleware instance """ + mod_name = middleware_params.parameterized_class_name + middleware_path = 'rl_coach.architectures.tensorflow_components.middlewares:' + mod_name middleware_params_copy = copy.copy(middleware_params) middleware_params_copy.activation_function = self.get_activation_function(middleware_params.activation_function) - module = dynamic_import_and_instantiate_module_from_params(middleware_params_copy) + module = dynamic_import_and_instantiate_module_from_params(middleware_params_copy, path=middleware_path) return module def get_output_head(self, head_params: HeadParameters, head_idx: int): """ Given a head type, creates the head and returns it :param head_params: the parameters of the head to create - :param head_type: the path to the class of the head under the embedders directory or a full path to a head class. - the path should be in the following structure: : :param head_idx: the head index - :param loss_weight: the weight to assign for the embedders loss :return: the head """ - + mod_name = head_params.parameterized_class_name + head_path = 'rl_coach.architectures.tensorflow_components.heads:' + mod_name head_params_copy = copy.copy(head_params) head_params_copy.activation_function = self.get_activation_function(head_params_copy.activation_function) - return dynamic_import_and_instantiate_module_from_params(head_params_copy, extra_kwargs={ + return dynamic_import_and_instantiate_module_from_params(head_params_copy, path=head_path, extra_kwargs={ 'agent_parameters': self.ap, 'spaces': self.spaces, 'network_name': self.network_wrapper_name, 'head_idx': head_idx, 'is_local': self.network_is_local}) diff --git a/rl_coach/architectures/tensorflow_components/heads/__init__.py b/rl_coach/architectures/tensorflow_components/heads/__init__.py index e69de29..7e64234 100644 --- a/rl_coach/architectures/tensorflow_components/heads/__init__.py +++ b/rl_coach/architectures/tensorflow_components/heads/__init__.py @@ -0,0 +1,29 @@ +from .categorical_q_head import CategoricalQHead +from .ddpg_actor_head import DDPGActor +from .dnd_q_head import DNDQHead +from .dueling_q_head import DuelingQHead +from .measurements_prediction_head import MeasurementsPredictionHead +from .naf_head import NAFHead +from .policy_head import PolicyHead +from .ppo_head import PPOHead +from .ppo_v_head import PPOVHead +from .q_head import QHead +from .quantile_regression_q_head import QuantileRegressionQHead +from .rainbow_q_head import RainbowQHead +from .v_head import VHead + +__all__ = [ + 'CategoricalQHead', + 'DDPGActor', + 'DNDQHead', + 'DuelingQHead', + 'MeasurementsPredictionHead', + 'NAFHead', + 'PolicyHead', + 'PPOHead', + 'PPOVHead', + 'QHead', + 'QuantileRegressionQHead', + 'RainbowQHead', + 'VHead' +] diff --git a/rl_coach/architectures/tensorflow_components/heads/categorical_q_head.py b/rl_coach/architectures/tensorflow_components/heads/categorical_q_head.py index 6f60a05..1f19a59 100644 --- a/rl_coach/architectures/tensorflow_components/heads/categorical_q_head.py +++ b/rl_coach/architectures/tensorflow_components/heads/categorical_q_head.py @@ -18,22 +18,12 @@ import tensorflow as tf from rl_coach.architectures.tensorflow_components.layers import Dense -from rl_coach.architectures.tensorflow_components.heads.head import Head, HeadParameters +from rl_coach.architectures.tensorflow_components.heads.head import Head from rl_coach.base_parameters import AgentParameters from rl_coach.core_types import QActionStateValue from rl_coach.spaces import SpacesDefinition -class CategoricalQHeadParameters(HeadParameters): - def __init__(self, activation_function: str ='relu', name: str='categorical_q_head_params', - num_output_head_copies: int = 1, rescale_gradient_from_head_by_factor: float = 1.0, - loss_weight: float = 1.0, dense_layer=Dense): - super().__init__(parameterized_class=CategoricalQHead, activation_function=activation_function, name=name, - dense_layer=dense_layer, num_output_head_copies=num_output_head_copies, - rescale_gradient_from_head_by_factor=rescale_gradient_from_head_by_factor, - loss_weight=loss_weight) - - class CategoricalQHead(Head): def __init__(self, agent_parameters: AgentParameters, spaces: SpacesDefinition, network_name: str, head_idx: int = 0, loss_weight: float = 1., is_local: bool = True, activation_function: str ='relu', diff --git a/rl_coach/architectures/tensorflow_components/heads/cil_head.py b/rl_coach/architectures/tensorflow_components/heads/cil_head.py index 27bb0af..15f9de1 100644 --- a/rl_coach/architectures/tensorflow_components/heads/cil_head.py +++ b/rl_coach/architectures/tensorflow_components/heads/cil_head.py @@ -16,25 +16,14 @@ import tensorflow as tf -from rl_coach.architectures.tensorflow_components.layers import Dense, batchnorm_activation_dropout - -from rl_coach.architectures.tensorflow_components.heads.head import Head, HeadParameters +from rl_coach.architectures.tensorflow_components.layers import Dense +from rl_coach.architectures.tensorflow_components.heads.head import Head from rl_coach.base_parameters import AgentParameters from rl_coach.core_types import QActionStateValue from rl_coach.spaces import SpacesDefinition, BoxActionSpace, DiscreteActionSpace from rl_coach.utils import force_list -class RegressionHeadParameters(HeadParameters): - def __init__(self, activation_function: str ='relu', name: str='q_head_params', - num_output_head_copies: int = 1, rescale_gradient_from_head_by_factor: float = 1.0, - loss_weight: float = 1.0, dense_layer=Dense, scheme=[Dense(256), Dense(256)]): - super().__init__(parameterized_class=RegressionHead, activation_function=activation_function, name=name, - dense_layer=dense_layer, num_output_head_copies=num_output_head_copies, - rescale_gradient_from_head_by_factor=rescale_gradient_from_head_by_factor, - loss_weight=loss_weight) - - class RegressionHead(Head): def __init__(self, agent_parameters: AgentParameters, spaces: SpacesDefinition, network_name: str, head_idx: int = 0, loss_weight: float = 1., is_local: bool = True, activation_function: str='relu', diff --git a/rl_coach/architectures/tensorflow_components/heads/ddpg_actor_head.py b/rl_coach/architectures/tensorflow_components/heads/ddpg_actor_head.py index 58011c6..6b3112a 100644 --- a/rl_coach/architectures/tensorflow_components/heads/ddpg_actor_head.py +++ b/rl_coach/architectures/tensorflow_components/heads/ddpg_actor_head.py @@ -17,23 +17,12 @@ import tensorflow as tf from rl_coach.architectures.tensorflow_components.layers import batchnorm_activation_dropout, Dense -from rl_coach.architectures.tensorflow_components.heads.head import Head, HeadParameters +from rl_coach.architectures.tensorflow_components.heads.head import Head from rl_coach.base_parameters import AgentParameters from rl_coach.core_types import ActionProbabilities from rl_coach.spaces import SpacesDefinition -class DDPGActorHeadParameters(HeadParameters): - def __init__(self, activation_function: str ='tanh', name: str='policy_head_params', batchnorm: bool=True, - num_output_head_copies: int = 1, rescale_gradient_from_head_by_factor: float = 1.0, - loss_weight: float = 1.0, dense_layer=Dense): - super().__init__(parameterized_class=DDPGActor, activation_function=activation_function, name=name, - dense_layer=dense_layer, num_output_head_copies=num_output_head_copies, - rescale_gradient_from_head_by_factor=rescale_gradient_from_head_by_factor, - loss_weight=loss_weight) - self.batchnorm = batchnorm - - class DDPGActor(Head): def __init__(self, agent_parameters: AgentParameters, spaces: SpacesDefinition, network_name: str, head_idx: int = 0, loss_weight: float = 1., is_local: bool = True, activation_function: str='tanh', diff --git a/rl_coach/architectures/tensorflow_components/heads/dnd_q_head.py b/rl_coach/architectures/tensorflow_components/heads/dnd_q_head.py index a880138..5c45146 100644 --- a/rl_coach/architectures/tensorflow_components/heads/dnd_q_head.py +++ b/rl_coach/architectures/tensorflow_components/heads/dnd_q_head.py @@ -16,23 +16,12 @@ import tensorflow as tf from rl_coach.architectures.tensorflow_components.layers import Dense -from rl_coach.architectures.tensorflow_components.heads.head import HeadParameters from rl_coach.architectures.tensorflow_components.heads.q_head import QHead from rl_coach.base_parameters import AgentParameters from rl_coach.memories.non_episodic import differentiable_neural_dictionary from rl_coach.spaces import SpacesDefinition -class DNDQHeadParameters(HeadParameters): - def __init__(self, activation_function: str ='relu', name: str='dnd_q_head_params', - num_output_head_copies: int = 1, rescale_gradient_from_head_by_factor: float = 1.0, - loss_weight: float = 1.0, dense_layer=Dense): - super().__init__(parameterized_class=DNDQHead, activation_function=activation_function, name=name, - dense_layer=dense_layer, num_output_head_copies=num_output_head_copies, - rescale_gradient_from_head_by_factor=rescale_gradient_from_head_by_factor, - loss_weight=loss_weight) - - class DNDQHead(QHead): def __init__(self, agent_parameters: AgentParameters, spaces: SpacesDefinition, network_name: str, head_idx: int = 0, loss_weight: float = 1., is_local: bool = True, activation_function: str='relu', diff --git a/rl_coach/architectures/tensorflow_components/heads/dueling_q_head.py b/rl_coach/architectures/tensorflow_components/heads/dueling_q_head.py index 05f730e..8237a91 100644 --- a/rl_coach/architectures/tensorflow_components/heads/dueling_q_head.py +++ b/rl_coach/architectures/tensorflow_components/heads/dueling_q_head.py @@ -17,21 +17,11 @@ import tensorflow as tf from rl_coach.architectures.tensorflow_components.layers import Dense -from rl_coach.architectures.tensorflow_components.heads.head import HeadParameters from rl_coach.architectures.tensorflow_components.heads.q_head import QHead from rl_coach.base_parameters import AgentParameters from rl_coach.spaces import SpacesDefinition -class DuelingQHeadParameters(HeadParameters): - def __init__(self, activation_function: str ='relu', name: str='dueling_q_head_params', - num_output_head_copies: int = 1, rescale_gradient_from_head_by_factor: float = 1.0, - loss_weight: float = 1.0, dense_layer=Dense): - super().__init__(parameterized_class=DuelingQHead, activation_function=activation_function, name=name, - dense_layer=dense_layer, num_output_head_copies=num_output_head_copies, - rescale_gradient_from_head_by_factor=rescale_gradient_from_head_by_factor, - loss_weight=loss_weight) - class DuelingQHead(QHead): def __init__(self, agent_parameters: AgentParameters, spaces: SpacesDefinition, network_name: str, head_idx: int = 0, loss_weight: float = 1., is_local: bool = True, activation_function: str='relu', diff --git a/rl_coach/architectures/tensorflow_components/heads/head.py b/rl_coach/architectures/tensorflow_components/heads/head.py index 7439f9a..956bd5c 100644 --- a/rl_coach/architectures/tensorflow_components/heads/head.py +++ b/rl_coach/architectures/tensorflow_components/heads/head.py @@ -33,19 +33,6 @@ def normalized_columns_initializer(std=1.0): return _initializer -class HeadParameters(NetworkComponentParameters): - def __init__(self, parameterized_class: Type['Head'], activation_function: str = 'relu', name: str= 'head', - num_output_head_copies: int=1, rescale_gradient_from_head_by_factor: float=1.0, - loss_weight: float=1.0, dense_layer=Dense): - super().__init__(dense_layer=dense_layer) - self.activation_function = activation_function - self.name = name - self.num_output_head_copies = num_output_head_copies - self.rescale_gradient_from_head_by_factor = rescale_gradient_from_head_by_factor - self.loss_weight = loss_weight - self.parameterized_class_name = parameterized_class.__name__ - - class Head(object): """ A head is the final part of the network. It takes the embedding from the middleware embedder and passes it through @@ -74,6 +61,8 @@ class Head(object): self.return_type = None self.activation_function = activation_function self.dense_layer = dense_layer + if self.dense_layer is None: + self.dense_layer = Dense def __call__(self, input_layer): """ diff --git a/rl_coach/architectures/tensorflow_components/heads/measurements_prediction_head.py b/rl_coach/architectures/tensorflow_components/heads/measurements_prediction_head.py index f1172bc..647abc3 100644 --- a/rl_coach/architectures/tensorflow_components/heads/measurements_prediction_head.py +++ b/rl_coach/architectures/tensorflow_components/heads/measurements_prediction_head.py @@ -17,23 +17,12 @@ import tensorflow as tf from rl_coach.architectures.tensorflow_components.layers import Dense - -from rl_coach.architectures.tensorflow_components.heads.head import Head, HeadParameters +from rl_coach.architectures.tensorflow_components.heads.head import Head from rl_coach.base_parameters import AgentParameters from rl_coach.core_types import Measurements from rl_coach.spaces import SpacesDefinition -class MeasurementsPredictionHeadParameters(HeadParameters): - def __init__(self, activation_function: str ='relu', name: str='measurements_prediction_head_params', - num_output_head_copies: int = 1, rescale_gradient_from_head_by_factor: float = 1.0, - loss_weight: float = 1.0, dense_layer=Dense): - super().__init__(parameterized_class=MeasurementsPredictionHead, activation_function=activation_function, name=name, - dense_layer=dense_layer, num_output_head_copies=num_output_head_copies, - rescale_gradient_from_head_by_factor=rescale_gradient_from_head_by_factor, - loss_weight=loss_weight) - - class MeasurementsPredictionHead(Head): def __init__(self, agent_parameters: AgentParameters, spaces: SpacesDefinition, network_name: str, head_idx: int = 0, loss_weight: float = 1., is_local: bool = True, activation_function: str='relu', diff --git a/rl_coach/architectures/tensorflow_components/heads/naf_head.py b/rl_coach/architectures/tensorflow_components/heads/naf_head.py index c2768cc..9071fed 100644 --- a/rl_coach/architectures/tensorflow_components/heads/naf_head.py +++ b/rl_coach/architectures/tensorflow_components/heads/naf_head.py @@ -17,23 +17,13 @@ import tensorflow as tf from rl_coach.architectures.tensorflow_components.layers import Dense -from rl_coach.architectures.tensorflow_components.heads.head import Head, HeadParameters +from rl_coach.architectures.tensorflow_components.heads.head import Head from rl_coach.base_parameters import AgentParameters from rl_coach.core_types import QActionStateValue from rl_coach.spaces import BoxActionSpace from rl_coach.spaces import SpacesDefinition -class NAFHeadParameters(HeadParameters): - def __init__(self, activation_function: str ='tanh', name: str='naf_head_params', - num_output_head_copies: int = 1, rescale_gradient_from_head_by_factor: float = 1.0, - loss_weight: float = 1.0, dense_layer=Dense): - super().__init__(parameterized_class=NAFHead, activation_function=activation_function, name=name, - dense_layer=dense_layer, num_output_head_copies=num_output_head_copies, - rescale_gradient_from_head_by_factor=rescale_gradient_from_head_by_factor, - loss_weight=loss_weight) - - class NAFHead(Head): def __init__(self, agent_parameters: AgentParameters, spaces: SpacesDefinition, network_name: str, head_idx: int = 0, loss_weight: float = 1., is_local: bool = True,activation_function: str='relu', diff --git a/rl_coach/architectures/tensorflow_components/heads/policy_head.py b/rl_coach/architectures/tensorflow_components/heads/policy_head.py index da53a14..99c9958 100644 --- a/rl_coach/architectures/tensorflow_components/heads/policy_head.py +++ b/rl_coach/architectures/tensorflow_components/heads/policy_head.py @@ -18,7 +18,7 @@ import numpy as np import tensorflow as tf from rl_coach.architectures.tensorflow_components.layers import Dense -from rl_coach.architectures.tensorflow_components.heads.head import Head, normalized_columns_initializer, HeadParameters +from rl_coach.architectures.tensorflow_components.heads.head import Head, normalized_columns_initializer from rl_coach.base_parameters import AgentParameters from rl_coach.core_types import ActionProbabilities from rl_coach.exploration_policies.continuous_entropy import ContinuousEntropyParameters @@ -27,17 +27,6 @@ from rl_coach.spaces import SpacesDefinition from rl_coach.utils import eps, indent_string -class PolicyHeadParameters(HeadParameters): - def __init__(self, activation_function: str ='tanh', name: str='policy_head_params', - num_output_head_copies: int = 1, rescale_gradient_from_head_by_factor: float = 1.0, - loss_weight: float = 1.0, dense_layer=Dense): - super().__init__(parameterized_class=PolicyHead, activation_function=activation_function, name=name, - dense_layer=dense_layer, num_output_head_copies=num_output_head_copies, - rescale_gradient_from_head_by_factor=rescale_gradient_from_head_by_factor, - loss_weight=loss_weight) - - - class PolicyHead(Head): def __init__(self, agent_parameters: AgentParameters, spaces: SpacesDefinition, network_name: str, head_idx: int = 0, loss_weight: float = 1., is_local: bool = True, activation_function: str='tanh', diff --git a/rl_coach/architectures/tensorflow_components/heads/ppo_head.py b/rl_coach/architectures/tensorflow_components/heads/ppo_head.py index c76c035..6ce7898 100644 --- a/rl_coach/architectures/tensorflow_components/heads/ppo_head.py +++ b/rl_coach/architectures/tensorflow_components/heads/ppo_head.py @@ -18,7 +18,7 @@ import numpy as np import tensorflow as tf from rl_coach.architectures.tensorflow_components.layers import Dense -from rl_coach.architectures.tensorflow_components.heads.head import Head, HeadParameters, normalized_columns_initializer +from rl_coach.architectures.tensorflow_components.heads.head import Head, normalized_columns_initializer from rl_coach.base_parameters import AgentParameters from rl_coach.core_types import ActionProbabilities from rl_coach.spaces import BoxActionSpace, DiscreteActionSpace @@ -26,16 +26,6 @@ from rl_coach.spaces import SpacesDefinition from rl_coach.utils import eps -class PPOHeadParameters(HeadParameters): - def __init__(self, activation_function: str ='tanh', name: str='ppo_head_params', - num_output_head_copies: int = 1, rescale_gradient_from_head_by_factor: float = 1.0, - loss_weight: float = 1.0, dense_layer=Dense): - super().__init__(parameterized_class=PPOHead, activation_function=activation_function, name=name, - dense_layer=dense_layer, num_output_head_copies=num_output_head_copies, - rescale_gradient_from_head_by_factor=rescale_gradient_from_head_by_factor, - loss_weight=loss_weight) - - class PPOHead(Head): def __init__(self, agent_parameters: AgentParameters, spaces: SpacesDefinition, network_name: str, head_idx: int = 0, loss_weight: float = 1., is_local: bool = True, activation_function: str='tanh', diff --git a/rl_coach/architectures/tensorflow_components/heads/ppo_v_head.py b/rl_coach/architectures/tensorflow_components/heads/ppo_v_head.py index 7253ecc..968a97a 100644 --- a/rl_coach/architectures/tensorflow_components/heads/ppo_v_head.py +++ b/rl_coach/architectures/tensorflow_components/heads/ppo_v_head.py @@ -17,23 +17,12 @@ import tensorflow as tf from rl_coach.architectures.tensorflow_components.layers import Dense - -from rl_coach.architectures.tensorflow_components.heads.head import Head, normalized_columns_initializer, HeadParameters +from rl_coach.architectures.tensorflow_components.heads.head import Head, normalized_columns_initializer from rl_coach.base_parameters import AgentParameters from rl_coach.core_types import ActionProbabilities from rl_coach.spaces import SpacesDefinition -class PPOVHeadParameters(HeadParameters): - def __init__(self, activation_function: str ='relu', name: str='ppo_v_head_params', - num_output_head_copies: int = 1, rescale_gradient_from_head_by_factor: float = 1.0, - loss_weight: float = 1.0, dense_layer=Dense): - super().__init__(parameterized_class=PPOVHead, activation_function=activation_function, name=name, - dense_layer=dense_layer, num_output_head_copies=num_output_head_copies, - rescale_gradient_from_head_by_factor=rescale_gradient_from_head_by_factor, - loss_weight=loss_weight) - - class PPOVHead(Head): def __init__(self, agent_parameters: AgentParameters, spaces: SpacesDefinition, network_name: str, head_idx: int = 0, loss_weight: float = 1., is_local: bool = True, activation_function: str='relu', diff --git a/rl_coach/architectures/tensorflow_components/heads/q_head.py b/rl_coach/architectures/tensorflow_components/heads/q_head.py index 56a9b97..32c6946 100644 --- a/rl_coach/architectures/tensorflow_components/heads/q_head.py +++ b/rl_coach/architectures/tensorflow_components/heads/q_head.py @@ -17,23 +17,12 @@ import tensorflow as tf from rl_coach.architectures.tensorflow_components.layers import Dense - -from rl_coach.architectures.tensorflow_components.heads.head import Head, HeadParameters +from rl_coach.architectures.tensorflow_components.heads.head import Head from rl_coach.base_parameters import AgentParameters from rl_coach.core_types import QActionStateValue from rl_coach.spaces import SpacesDefinition, BoxActionSpace, DiscreteActionSpace -class QHeadParameters(HeadParameters): - def __init__(self, activation_function: str ='relu', name: str='q_head_params', - num_output_head_copies: int = 1, rescale_gradient_from_head_by_factor: float = 1.0, - loss_weight: float = 1.0, dense_layer=Dense): - super().__init__(parameterized_class=QHead, activation_function=activation_function, name=name, - dense_layer=dense_layer, num_output_head_copies=num_output_head_copies, - rescale_gradient_from_head_by_factor=rescale_gradient_from_head_by_factor, - loss_weight=loss_weight) - - class QHead(Head): def __init__(self, agent_parameters: AgentParameters, spaces: SpacesDefinition, network_name: str, head_idx: int = 0, loss_weight: float = 1., is_local: bool = True, activation_function: str='relu', diff --git a/rl_coach/architectures/tensorflow_components/heads/quantile_regression_q_head.py b/rl_coach/architectures/tensorflow_components/heads/quantile_regression_q_head.py index 012bbfa..fa6e1e9 100644 --- a/rl_coach/architectures/tensorflow_components/heads/quantile_regression_q_head.py +++ b/rl_coach/architectures/tensorflow_components/heads/quantile_regression_q_head.py @@ -17,23 +17,12 @@ import tensorflow as tf from rl_coach.architectures.tensorflow_components.layers import Dense - -from rl_coach.architectures.tensorflow_components.heads.head import Head, HeadParameters +from rl_coach.architectures.tensorflow_components.heads.head import Head from rl_coach.base_parameters import AgentParameters from rl_coach.core_types import QActionStateValue from rl_coach.spaces import SpacesDefinition -class QuantileRegressionQHeadParameters(HeadParameters): - def __init__(self, activation_function: str ='relu', name: str='quantile_regression_q_head_params', - num_output_head_copies: int = 1, rescale_gradient_from_head_by_factor: float = 1.0, - loss_weight: float = 1.0, dense_layer=Dense): - super().__init__(parameterized_class=QuantileRegressionQHead, activation_function=activation_function, name=name, - dense_layer=dense_layer, num_output_head_copies=num_output_head_copies, - rescale_gradient_from_head_by_factor=rescale_gradient_from_head_by_factor, - loss_weight=loss_weight) - - class QuantileRegressionQHead(Head): def __init__(self, agent_parameters: AgentParameters, spaces: SpacesDefinition, network_name: str, head_idx: int = 0, loss_weight: float = 1., is_local: bool = True, activation_function: str='relu', diff --git a/rl_coach/architectures/tensorflow_components/heads/rainbow_q_head.py b/rl_coach/architectures/tensorflow_components/heads/rainbow_q_head.py index 6c21623..2d2fb6e 100644 --- a/rl_coach/architectures/tensorflow_components/heads/rainbow_q_head.py +++ b/rl_coach/architectures/tensorflow_components/heads/rainbow_q_head.py @@ -17,22 +17,12 @@ import tensorflow as tf from rl_coach.architectures.tensorflow_components.layers import Dense -from rl_coach.architectures.tensorflow_components.heads.head import HeadParameters, Head +from rl_coach.architectures.tensorflow_components.heads.head import Head from rl_coach.base_parameters import AgentParameters from rl_coach.core_types import QActionStateValue from rl_coach.spaces import SpacesDefinition -class RainbowQHeadParameters(HeadParameters): - def __init__(self, activation_function: str ='relu', name: str='rainbow_q_head_params', - num_output_head_copies: int = 1, rescale_gradient_from_head_by_factor: float = 1.0, - loss_weight: float = 1.0, dense_layer=Dense): - super().__init__(parameterized_class=RainbowQHead, activation_function=activation_function, name=name, - dense_layer=dense_layer, num_output_head_copies=num_output_head_copies, - rescale_gradient_from_head_by_factor=rescale_gradient_from_head_by_factor, - loss_weight=loss_weight) - - class RainbowQHead(Head): def __init__(self, agent_parameters: AgentParameters, spaces: SpacesDefinition, network_name: str, head_idx: int = 0, loss_weight: float = 1., is_local: bool = True, activation_function: str='relu', diff --git a/rl_coach/architectures/tensorflow_components/heads/v_head.py b/rl_coach/architectures/tensorflow_components/heads/v_head.py index 6b2b67a..07dbf25 100644 --- a/rl_coach/architectures/tensorflow_components/heads/v_head.py +++ b/rl_coach/architectures/tensorflow_components/heads/v_head.py @@ -17,23 +17,12 @@ import tensorflow as tf from rl_coach.architectures.tensorflow_components.layers import Dense - -from rl_coach.architectures.tensorflow_components.heads.head import Head, normalized_columns_initializer, HeadParameters +from rl_coach.architectures.tensorflow_components.heads.head import Head, normalized_columns_initializer from rl_coach.base_parameters import AgentParameters from rl_coach.core_types import VStateValue from rl_coach.spaces import SpacesDefinition -class VHeadParameters(HeadParameters): - def __init__(self, activation_function: str ='relu', name: str='v_head_params', - num_output_head_copies: int = 1, rescale_gradient_from_head_by_factor: float = 1.0, - loss_weight: float = 1.0, dense_layer=Dense): - super().__init__(parameterized_class=VHead, activation_function=activation_function, name=name, - dense_layer=dense_layer, num_output_head_copies=num_output_head_copies, - rescale_gradient_from_head_by_factor=rescale_gradient_from_head_by_factor, - loss_weight=loss_weight) - - class VHead(Head): def __init__(self, agent_parameters: AgentParameters, spaces: SpacesDefinition, network_name: str, head_idx: int = 0, loss_weight: float = 1., is_local: bool = True, activation_function: str='relu', diff --git a/rl_coach/architectures/tensorflow_components/middlewares/__init__.py b/rl_coach/architectures/tensorflow_components/middlewares/__init__.py index e69de29..481eab0 100644 --- a/rl_coach/architectures/tensorflow_components/middlewares/__init__.py +++ b/rl_coach/architectures/tensorflow_components/middlewares/__init__.py @@ -0,0 +1,4 @@ +from .fc_middleware import FCMiddleware +from .lstm_middleware import LSTMMiddleware + +__all__ = ["FCMiddleware", "LSTMMiddleware"] diff --git a/rl_coach/architectures/tensorflow_components/middlewares/fc_middleware.py b/rl_coach/architectures/tensorflow_components/middlewares/fc_middleware.py index 5d3cb0c..f85db82 100644 --- a/rl_coach/architectures/tensorflow_components/middlewares/fc_middleware.py +++ b/rl_coach/architectures/tensorflow_components/middlewares/fc_middleware.py @@ -18,22 +18,12 @@ from typing import Union, List import tensorflow as tf from rl_coach.architectures.tensorflow_components.layers import batchnorm_activation_dropout, Dense -from rl_coach.architectures.tensorflow_components.middlewares.middleware import Middleware, MiddlewareParameters +from rl_coach.architectures.tensorflow_components.middlewares.middleware import Middleware from rl_coach.base_parameters import MiddlewareScheme from rl_coach.core_types import Middleware_FC_Embedding from rl_coach.utils import force_list -class FCMiddlewareParameters(MiddlewareParameters): - def __init__(self, activation_function='relu', - scheme: Union[List, MiddlewareScheme] = MiddlewareScheme.Medium, - batchnorm: bool = False, dropout: bool = False, - name="middleware_fc_embedder", dense_layer=Dense, is_training=False): - super().__init__(parameterized_class=FCMiddleware, activation_function=activation_function, - scheme=scheme, batchnorm=batchnorm, dropout=dropout, name=name, dense_layer=dense_layer, - is_training=is_training) - - class FCMiddleware(Middleware): def __init__(self, activation_function=tf.nn.relu, scheme: MiddlewareScheme = MiddlewareScheme.Medium, diff --git a/rl_coach/architectures/tensorflow_components/middlewares/lstm_middleware.py b/rl_coach/architectures/tensorflow_components/middlewares/lstm_middleware.py index 1955554..7c4a1b0 100644 --- a/rl_coach/architectures/tensorflow_components/middlewares/lstm_middleware.py +++ b/rl_coach/architectures/tensorflow_components/middlewares/lstm_middleware.py @@ -19,23 +19,12 @@ import numpy as np import tensorflow as tf from rl_coach.architectures.tensorflow_components.layers import batchnorm_activation_dropout, Dense -from rl_coach.architectures.tensorflow_components.middlewares.middleware import Middleware, MiddlewareParameters +from rl_coach.architectures.tensorflow_components.middlewares.middleware import Middleware from rl_coach.base_parameters import MiddlewareScheme from rl_coach.core_types import Middleware_LSTM_Embedding from rl_coach.utils import force_list -class LSTMMiddlewareParameters(MiddlewareParameters): - def __init__(self, activation_function='relu', number_of_lstm_cells=256, - scheme: MiddlewareScheme = MiddlewareScheme.Medium, - batchnorm: bool = False, dropout: bool = False, - name="middleware_lstm_embedder", dense_layer=Dense, is_training=False): - super().__init__(parameterized_class=LSTMMiddleware, activation_function=activation_function, - scheme=scheme, batchnorm=batchnorm, dropout=dropout, name=name, dense_layer=dense_layer, - is_training=is_training) - self.number_of_lstm_cells = number_of_lstm_cells - - class LSTMMiddleware(Middleware): def __init__(self, activation_function=tf.nn.relu, number_of_lstm_cells: int=256, scheme: MiddlewareScheme = MiddlewareScheme.Medium, diff --git a/rl_coach/architectures/tensorflow_components/middlewares/middleware.py b/rl_coach/architectures/tensorflow_components/middlewares/middleware.py index 6011ef3..02376de 100644 --- a/rl_coach/architectures/tensorflow_components/middlewares/middleware.py +++ b/rl_coach/architectures/tensorflow_components/middlewares/middleware.py @@ -14,7 +14,6 @@ # limitations under the License. # import copy -from typing import Type, Union, List import tensorflow as tf @@ -23,20 +22,6 @@ from rl_coach.base_parameters import MiddlewareScheme, NetworkComponentParameter from rl_coach.core_types import MiddlewareEmbedding -class MiddlewareParameters(NetworkComponentParameters): - def __init__(self, parameterized_class: Type['Middleware'], - activation_function: str='relu', scheme: Union[List, MiddlewareScheme]=MiddlewareScheme.Medium, - batchnorm: bool=False, dropout: bool=False, name='middleware', dense_layer=Dense, is_training=False): - super().__init__(dense_layer=dense_layer) - self.activation_function = activation_function - self.scheme = scheme - self.batchnorm = batchnorm - self.dropout = dropout - self.name = name - self.is_training = is_training - self.parameterized_class_name = parameterized_class.__name__ - - class Middleware(object): """ A middleware embedder is the middle part of the network. It takes the embeddings from the input embedders, @@ -57,6 +42,8 @@ class Middleware(object): self.scheme = scheme self.return_type = MiddlewareEmbedding self.dense_layer = dense_layer + if self.dense_layer is None: + self.dense_layer = Dense self.is_training = is_training # layers order is conv -> batchnorm -> activation -> dropout diff --git a/rl_coach/presets/Atari_A3C.py b/rl_coach/presets/Atari_A3C.py index defcaaf..cda5659 100644 --- a/rl_coach/presets/Atari_A3C.py +++ b/rl_coach/presets/Atari_A3C.py @@ -1,5 +1,5 @@ from rl_coach.agents.actor_critic_agent import ActorCriticAgentParameters -from rl_coach.architectures.tensorflow_components.middlewares.fc_middleware import FCMiddlewareParameters +from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters from rl_coach.base_parameters import VisualizationParameters, PresetValidationParameters from rl_coach.core_types import TrainingSteps, EnvironmentEpisodes, EnvironmentSteps from rl_coach.environments.environment import SingleLevelSelection diff --git a/rl_coach/presets/Atari_A3C_LSTM.py b/rl_coach/presets/Atari_A3C_LSTM.py index a5ff223..d2edc56 100644 --- a/rl_coach/presets/Atari_A3C_LSTM.py +++ b/rl_coach/presets/Atari_A3C_LSTM.py @@ -1,5 +1,5 @@ from rl_coach.agents.actor_critic_agent import ActorCriticAgentParameters -from rl_coach.architectures.tensorflow_components.middlewares.lstm_middleware import LSTMMiddlewareParameters +from rl_coach.architectures.middleware_parameters import LSTMMiddlewareParameters from rl_coach.base_parameters import VisualizationParameters, MiddlewareScheme, PresetValidationParameters from rl_coach.core_types import TrainingSteps, EnvironmentEpisodes, EnvironmentSteps from rl_coach.environments.environment import SingleLevelSelection diff --git a/rl_coach/presets/Atari_Dueling_DDQN.py b/rl_coach/presets/Atari_Dueling_DDQN.py index 39d87e2..a163b51 100644 --- a/rl_coach/presets/Atari_Dueling_DDQN.py +++ b/rl_coach/presets/Atari_Dueling_DDQN.py @@ -1,7 +1,7 @@ import math from rl_coach.agents.ddqn_agent import DDQNAgentParameters -from rl_coach.architectures.tensorflow_components.heads.dueling_q_head import DuelingQHeadParameters +from rl_coach.architectures.head_parameters import DuelingQHeadParameters from rl_coach.base_parameters import VisualizationParameters, MiddlewareScheme, PresetValidationParameters from rl_coach.environments.environment import SingleLevelSelection from rl_coach.environments.gym_environment import Atari, atari_deterministic_v4, atari_schedule diff --git a/rl_coach/presets/Atari_Dueling_DDQN_with_PER_OpenAI.py b/rl_coach/presets/Atari_Dueling_DDQN_with_PER_OpenAI.py index 73f5ff2..694590c 100644 --- a/rl_coach/presets/Atari_Dueling_DDQN_with_PER_OpenAI.py +++ b/rl_coach/presets/Atari_Dueling_DDQN_with_PER_OpenAI.py @@ -1,5 +1,5 @@ from rl_coach.agents.ddqn_agent import DDQNAgentParameters -from rl_coach.architectures.tensorflow_components.heads.dueling_q_head import DuelingQHeadParameters +from rl_coach.architectures.head_parameters import DuelingQHeadParameters from rl_coach.base_parameters import VisualizationParameters, MiddlewareScheme, PresetValidationParameters from rl_coach.core_types import EnvironmentSteps from rl_coach.environments.environment import SingleLevelSelection diff --git a/rl_coach/presets/BitFlip_DQN.py b/rl_coach/presets/BitFlip_DQN.py index 621e875..ed849f3 100644 --- a/rl_coach/presets/BitFlip_DQN.py +++ b/rl_coach/presets/BitFlip_DQN.py @@ -1,5 +1,5 @@ from rl_coach.agents.dqn_agent import DQNAgentParameters -from rl_coach.architectures.tensorflow_components.embedders.embedder import InputEmbedderParameters +from rl_coach.architectures.embedder_parameters import InputEmbedderParameters from rl_coach.architectures.tensorflow_components.layers import Dense from rl_coach.base_parameters import VisualizationParameters, EmbedderScheme, \ PresetValidationParameters diff --git a/rl_coach/presets/BitFlip_DQN_HER.py b/rl_coach/presets/BitFlip_DQN_HER.py index 6bd1142..3d9c2f9 100644 --- a/rl_coach/presets/BitFlip_DQN_HER.py +++ b/rl_coach/presets/BitFlip_DQN_HER.py @@ -1,5 +1,5 @@ from rl_coach.agents.dqn_agent import DQNAgentParameters -from rl_coach.architectures.tensorflow_components.embedders.embedder import InputEmbedderParameters +from rl_coach.architectures.embedder_parameters import InputEmbedderParameters from rl_coach.architectures.tensorflow_components.layers import Dense from rl_coach.base_parameters import VisualizationParameters, EmbedderScheme, \ PresetValidationParameters diff --git a/rl_coach/presets/CARLA_CIL.py b/rl_coach/presets/CARLA_CIL.py index 3e04b4f..8477cdf 100644 --- a/rl_coach/presets/CARLA_CIL.py +++ b/rl_coach/presets/CARLA_CIL.py @@ -7,10 +7,10 @@ from carla.driving_benchmark.experiment_suites import CoRL2017 from rl_coach.logger import screen from rl_coach.agents.cil_agent import CILAgentParameters -from rl_coach.architectures.tensorflow_components.embedders.embedder import InputEmbedderParameters -from rl_coach.architectures.tensorflow_components.heads.cil_head import RegressionHeadParameters +from rl_coach.architectures.embedder_parameters import InputEmbedderParameters +from rl_coach.architectures.head_parameters import RegressionHeadParameters +from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters from rl_coach.architectures.tensorflow_components.layers import Conv2d, Dense, BatchnormActivationDropout -from rl_coach.architectures.tensorflow_components.middlewares.fc_middleware import FCMiddlewareParameters from rl_coach.base_parameters import VisualizationParameters from rl_coach.core_types import TrainingSteps, EnvironmentEpisodes, EnvironmentSteps from rl_coach.environments.carla_environment import CarlaEnvironmentParameters diff --git a/rl_coach/presets/CARLA_Dueling_DDQN.py b/rl_coach/presets/CARLA_Dueling_DDQN.py index 8bad8b6..561ed05 100644 --- a/rl_coach/presets/CARLA_Dueling_DDQN.py +++ b/rl_coach/presets/CARLA_Dueling_DDQN.py @@ -1,7 +1,7 @@ import math from rl_coach.agents.ddqn_agent import DDQNAgentParameters -from rl_coach.architectures.tensorflow_components.heads.dueling_q_head import DuelingQHeadParameters +from rl_coach.architectures.head_parameters import DuelingQHeadParameters from rl_coach.base_parameters import VisualizationParameters, MiddlewareScheme from rl_coach.core_types import TrainingSteps, EnvironmentEpisodes, EnvironmentSteps from rl_coach.environments.carla_environment import CarlaEnvironmentParameters diff --git a/rl_coach/presets/CartPole_Dueling_DDQN.py b/rl_coach/presets/CartPole_Dueling_DDQN.py index 7463df4..861fdc5 100644 --- a/rl_coach/presets/CartPole_Dueling_DDQN.py +++ b/rl_coach/presets/CartPole_Dueling_DDQN.py @@ -1,7 +1,7 @@ import math from rl_coach.agents.ddqn_agent import DDQNAgentParameters -from rl_coach.architectures.tensorflow_components.heads.dueling_q_head import DuelingQHeadParameters +from rl_coach.architectures.head_parameters import DuelingQHeadParameters from rl_coach.base_parameters import VisualizationParameters, PresetValidationParameters from rl_coach.core_types import TrainingSteps, EnvironmentEpisodes, EnvironmentSteps from rl_coach.environments.gym_environment import GymVectorEnvironment diff --git a/rl_coach/presets/Doom_Basic_Dueling_DDQN.py b/rl_coach/presets/Doom_Basic_Dueling_DDQN.py index 4fc3265..a0ed0b3 100644 --- a/rl_coach/presets/Doom_Basic_Dueling_DDQN.py +++ b/rl_coach/presets/Doom_Basic_Dueling_DDQN.py @@ -1,5 +1,5 @@ from rl_coach.agents.ddqn_agent import DDQNAgentParameters -from rl_coach.architectures.tensorflow_components.heads.dueling_q_head import DuelingQHeadParameters +from rl_coach.architectures.head_parameters import DuelingQHeadParameters from rl_coach.base_parameters import VisualizationParameters from rl_coach.core_types import TrainingSteps, EnvironmentEpisodes, EnvironmentSteps from rl_coach.environments.doom_environment import DoomEnvironmentParameters diff --git a/rl_coach/presets/ExplorationChain_Dueling_DDQN.py b/rl_coach/presets/ExplorationChain_Dueling_DDQN.py index f6575b3..b55e816 100644 --- a/rl_coach/presets/ExplorationChain_Dueling_DDQN.py +++ b/rl_coach/presets/ExplorationChain_Dueling_DDQN.py @@ -1,5 +1,5 @@ from rl_coach.agents.ddqn_agent import DDQNAgentParameters -from rl_coach.architectures.tensorflow_components.heads.dueling_q_head import DuelingQHeadParameters +from rl_coach.architectures.head_parameters import DuelingQHeadParameters from rl_coach.base_parameters import VisualizationParameters from rl_coach.core_types import EnvironmentEpisodes, EnvironmentSteps from rl_coach.environments.gym_environment import GymEnvironmentParameters diff --git a/rl_coach/presets/Fetch_DDPG_HER_baselines.py b/rl_coach/presets/Fetch_DDPG_HER_baselines.py index 1119fca..d3fa643 100644 --- a/rl_coach/presets/Fetch_DDPG_HER_baselines.py +++ b/rl_coach/presets/Fetch_DDPG_HER_baselines.py @@ -1,7 +1,7 @@ from rl_coach.agents.ddpg_agent import DDPGAgentParameters -from rl_coach.architectures.tensorflow_components.embedders.embedder import InputEmbedderParameters +from rl_coach.architectures.embedder_parameters import InputEmbedderParameters +from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters from rl_coach.architectures.tensorflow_components.layers import Dense -from rl_coach.architectures.tensorflow_components.middlewares.fc_middleware import FCMiddlewareParameters from rl_coach.base_parameters import VisualizationParameters, EmbedderScheme, PresetValidationParameters from rl_coach.core_types import EnvironmentEpisodes, EnvironmentSteps, TrainingSteps from rl_coach.environments.environment import SingleLevelSelection diff --git a/rl_coach/presets/Mujoco_A3C_LSTM.py b/rl_coach/presets/Mujoco_A3C_LSTM.py index 729f1ce..1027c01 100644 --- a/rl_coach/presets/Mujoco_A3C_LSTM.py +++ b/rl_coach/presets/Mujoco_A3C_LSTM.py @@ -1,7 +1,7 @@ from rl_coach.agents.actor_critic_agent import ActorCriticAgentParameters -from rl_coach.architectures.tensorflow_components.embedders.embedder import InputEmbedderParameters +from rl_coach.architectures.embedder_parameters import InputEmbedderParameters +from rl_coach.architectures.middleware_parameters import LSTMMiddlewareParameters from rl_coach.architectures.tensorflow_components.layers import Dense -from rl_coach.architectures.tensorflow_components.middlewares.lstm_middleware import LSTMMiddlewareParameters from rl_coach.base_parameters import VisualizationParameters, MiddlewareScheme, PresetValidationParameters from rl_coach.core_types import TrainingSteps, EnvironmentEpisodes, EnvironmentSteps from rl_coach.environments.environment import SingleLevelSelection diff --git a/rl_coach/presets/Pendulum_HAC.py b/rl_coach/presets/Pendulum_HAC.py index 8b0826d..b6fa02c 100644 --- a/rl_coach/presets/Pendulum_HAC.py +++ b/rl_coach/presets/Pendulum_HAC.py @@ -1,7 +1,7 @@ import numpy as np from rl_coach.agents.hac_ddpg_agent import HACDDPGAgentParameters -from rl_coach.architectures.tensorflow_components.embedders.embedder import InputEmbedderParameters +from rl_coach.architectures.embedder_parameters import InputEmbedderParameters from rl_coach.architectures.tensorflow_components.layers import Dense from rl_coach.base_parameters import VisualizationParameters, EmbeddingMergerType, EmbedderScheme from rl_coach.core_types import EnvironmentEpisodes, EnvironmentSteps, TrainingSteps diff --git a/rl_coach/presets/Starcraft_CollectMinerals_A3C.py b/rl_coach/presets/Starcraft_CollectMinerals_A3C.py index f3cef54..2559333 100644 --- a/rl_coach/presets/Starcraft_CollectMinerals_A3C.py +++ b/rl_coach/presets/Starcraft_CollectMinerals_A3C.py @@ -1,6 +1,6 @@ from rl_coach.agents.actor_critic_agent import ActorCriticAgentParameters from rl_coach.agents.policy_optimization_agent import PolicyGradientRescaler -from rl_coach.architectures.tensorflow_components.embedders.embedder import InputEmbedderParameters +from rl_coach.architectures.embedder_parameters import InputEmbedderParameters from rl_coach.base_parameters import VisualizationParameters from rl_coach.core_types import TrainingSteps, EnvironmentEpisodes, EnvironmentSteps from rl_coach.environments.starcraft2_environment import StarCraft2EnvironmentParameters diff --git a/rl_coach/presets/Starcraft_CollectMinerals_Dueling_DDQN.py b/rl_coach/presets/Starcraft_CollectMinerals_Dueling_DDQN.py index cb1c6a8..155bfce 100644 --- a/rl_coach/presets/Starcraft_CollectMinerals_Dueling_DDQN.py +++ b/rl_coach/presets/Starcraft_CollectMinerals_Dueling_DDQN.py @@ -1,8 +1,8 @@ from collections import OrderedDict from rl_coach.agents.ddqn_agent import DDQNAgentParameters -from rl_coach.architectures.tensorflow_components.embedders.embedder import InputEmbedderParameters -from rl_coach.architectures.tensorflow_components.heads.dueling_q_head import DuelingQHeadParameters +from rl_coach.architectures.embedder_parameters import InputEmbedderParameters +from rl_coach.architectures.head_parameters import DuelingQHeadParameters from rl_coach.base_parameters import VisualizationParameters from rl_coach.core_types import TrainingSteps, EnvironmentEpisodes, EnvironmentSteps from rl_coach.environments.starcraft2_environment import StarCraft2EnvironmentParameters diff --git a/tutorials/0. Quick Start Guide.ipynb b/tutorials/0. Quick Start Guide.ipynb index 6e29812..963ec74 100644 --- a/tutorials/0. Quick Start Guide.ipynb +++ b/tutorials/0. Quick Start Guide.ipynb @@ -134,7 +134,7 @@ "from rl_coach.environments.gym_environment import GymVectorEnvironment\n", "from rl_coach.graph_managers.basic_rl_graph_manager import BasicRLGraphManager\n", "from rl_coach.graph_managers.graph_manager import SimpleSchedule\n", - "from rl_coach.architectures.tensorflow_components.embedders.embedder import InputEmbedderParameters\n", + "from rl_coach.architectures.embedder_parameters import InputEmbedderParameters\n", "\n", "# define the environment parameters\n", "bit_length = 10\n", diff --git a/tutorials/3. Implementing a Hierarchical RL Graph.ipynb b/tutorials/3. Implementing a Hierarchical RL Graph.ipynb index eafa78f..f200c2c 100644 --- a/tutorials/3. Implementing a Hierarchical RL Graph.ipynb +++ b/tutorials/3. Implementing a Hierarchical RL Graph.ipynb @@ -162,9 +162,9 @@ "metadata": {}, "outputs": [], "source": [ - "from rl_coach.architectures.tensorflow_components.architecture import Dense\n", + "from rl_coach.architectures.tensorflow_components.layers import Dense\n", "from rl_coach.base_parameters import VisualizationParameters, EmbeddingMergerType, EmbedderScheme\n", - "from rl_coach.architectures.tensorflow_components.embedders.embedder import InputEmbedderParameters\n", + "from rl_coach.architectures.embedder_parameters import InputEmbedderParameters\n", "from rl_coach.memories.episodic.episodic_hindsight_experience_replay import HindsightGoalSelectionMethod, \\\n", " EpisodicHindsightExperienceReplayParameters\n", "from rl_coach.memories.episodic.episodic_hrl_hindsight_experience_replay import \\\n", From 324c67d6149491c75411e0e217051929dfdfc601 Mon Sep 17 00:00:00 2001 From: Thom Lane Date: Mon, 29 Oct 2018 22:29:22 -0700 Subject: [PATCH 090/162] Bug fix: Removed reference to args which is out of scope. Conditioning now performed one level above. (#54) --- rl_coach/coach.py | 9 ++++----- 1 file changed, 4 insertions(+), 5 deletions(-) diff --git a/rl_coach/coach.py b/rl_coach/coach.py index e057a77..e479974 100644 --- a/rl_coach/coach.py +++ b/rl_coach/coach.py @@ -93,11 +93,10 @@ def get_graph_manager_from_args(args: argparse.Namespace) -> 'GraphManager': def display_all_presets_and_exit(): # list available presets - if args.list: - screen.log_title("Available Presets:") - for preset in sorted(list_all_presets()): - print(preset) - sys.exit(0) + screen.log_title("Available Presets:") + for preset in sorted(list_all_presets()): + print(preset) + sys.exit(0) def expand_preset(preset): if preset.lower() in [p.lower() for p in list_all_presets()]: From 2046358ab0633f5b2aac71159d16b43354f5002c Mon Sep 17 00:00:00 2001 From: Sina Afrooze Date: Tue, 30 Oct 2018 02:02:37 -0700 Subject: [PATCH 091/162] Add docstring for architecture (#47) - Removed get_model() from architecture because it is only implementation detail of architecture. --- rl_coach/architectures/architecture.py | 153 ++++++++++++++++-- .../tensorflow_components/architecture.py | 8 + 2 files changed, 146 insertions(+), 15 deletions(-) diff --git a/rl_coach/architectures/architecture.py b/rl_coach/architectures/architecture.py index 2d0377a..1ae2d47 100644 --- a/rl_coach/architectures/architecture.py +++ b/rl_coach/architectures/architecture.py @@ -14,6 +14,10 @@ # limitations under the License. # +from typing import Any, Dict, List, Tuple + +import numpy as np + from rl_coach.base_parameters import AgentParameters from rl_coach.spaces import SpacesDefinition @@ -21,15 +25,15 @@ from rl_coach.spaces import SpacesDefinition class Architecture(object): def __init__(self, agent_parameters: AgentParameters, spaces: SpacesDefinition, name: str= ""): """ + Creates a neural network 'architecture', that can be trained and used for inference. + :param agent_parameters: the agent parameters :param spaces: the spaces (observation, action, etc.) definition of the agent :param name: the name of the network """ - # spaces self.spaces = spaces - self.name = name - self.network_wrapper_name = self.name.split('/')[0] # the name can be main/online and the network_wrapper_name will be main + self.network_wrapper_name = self.name.split('/')[0] # e.g. 'main/online' --> 'main' self.full_name = "{}/{}".format(agent_parameters.full_name_id, name) self.network_parameters = agent_parameters.network_wrappers[self.network_wrapper_name] self.batch_size = self.network_parameters.batch_size @@ -37,35 +41,154 @@ class Architecture(object): self.optimizer = None self.ap = agent_parameters - def get_model(self): + def predict(self, inputs: Dict[str, np.ndarray]) -> List[np.ndarray]: + """ + Given input observations, use the model to make predictions (e.g. action or value). + + :param inputs: current state (i.e. observations, measurements, goals, etc.) + (e.g. `{'observation': numpy.ndarray}` of shape (batch_size, observation_space_size)) + :return: predictions of action or value of shape (batch_size, action_space_size) for action predictions) + """ pass - def predict(self, inputs): + def train_on_batch(self, + inputs: Dict[str, np.ndarray], + targets: List[np.ndarray], + scaler: float=1., + additional_fetches: list=None, + importance_weights: np.ndarray=None) -> tuple: + """ + Given a batch of inputs (e.g. states) and targets (e.g. discounted rewards), takes a training step: i.e. runs a + forward pass and backward pass of the network, accumulates the gradients and applies an optimization step to + update the weights. + Calls `accumulate_gradients` followed by `apply_and_reset_gradients`. + Note: Currently an unused method. + + :param inputs: typically the environment states (but can also contain other data necessary for loss). + (e.g. `{'observation': numpy.ndarray}` with `observation` of shape (batch_size, observation_space_size) or + (batch_size, observation_space_size, stack_size) or + `{'observation': numpy.ndarray, 'output_0_0': numpy.ndarray}` with `output_0_0` of shape (batch_size,)) + :param targets: target values of shape (batch_size, ). For example discounted rewards for value network + for calculating the value-network loss would be a target. Length of list and order of arrays in + the list matches that of network losses which are defined by network parameters + :param scaler: value to scale gradients by before optimizing network weights + :param additional_fetches: list of additional values to fetch and return. The type of each list + element is framework dependent. + :param importance_weights: ndarray of shape (batch_size,) to multiply with batch loss. + :return: tuple of total_loss, losses, norm_unclipped_grads, fetched_tensors + total_loss (float): sum of all head losses + losses (list of float): list of all losses. The order is list of target losses followed by list + of regularization losses. The specifics of losses is dependant on the network parameters + (number of heads, etc.) + norm_unclippsed_grads (float): global norm of all gradients before any gradient clipping is applied + fetched_tensors: all values for additional_fetches + """ pass - def train_on_batch(self, inputs, targets): + def get_weights(self) -> List[np.ndarray]: + """ + Gets model weights as a list of ndarrays. It is used for synchronizing weight between two identical networks. + + :return: list weights as ndarray + """ pass - def get_weights(self): + def set_weights(self, weights: List[np.ndarray], rate: float=1.0) -> None: + """ + Sets model weights for provided layer parameters. + + :param weights: list of model weights in the same order as received in get_weights + :param rate: controls the mixture of given weight values versus old weight values. + i.e. new_weight = rate * given_weight + (1 - rate) * old_weight + :return: None + """ pass - def set_weights(self, weights, rate=1.0): + def reset_accumulated_gradients(self) -> None: + """ + Sets gradient of all parameters to 0. + + Once gradients are reset, they must be accessible by `accumulated_gradients` property of this class, + which must return a list of numpy ndarrays. Child class must ensure that `accumulated_gradients` is set. + """ pass - def reset_accumulated_gradients(self): + def accumulate_gradients(self, + inputs: Dict[str, np.ndarray], + targets: List[np.ndarray], + additional_fetches: list=None, + importance_weights: np.ndarray=None, + no_accumulation: bool=False) ->\ + Tuple[float, List[float], float, list]: + """ + Given a batch of inputs (i.e. states) and targets (e.g. discounted rewards), computes and accumulates the + gradients for model parameters. Will run forward and backward pass to compute gradients, clip the gradient + values if required and then accumulate gradients from all learners. It does not update the model weights, + that's performed in `apply_and_reset_gradients` method. + + Once gradients are accumulated, they are accessed by `accumulated_gradients` property of this class.å + + :param inputs: typically the environment states (but can also contain other data for loss) + (e.g. `{'observation': numpy.ndarray}` with `observation` of shape (batch_size, observation_space_size) or + (batch_size, observation_space_size, stack_size) or + `{'observation': numpy.ndarray, 'output_0_0': numpy.ndarray}` with `output_0_0` of shape (batch_size,)) + :param targets: targets for calculating loss. For example discounted rewards for value network + for calculating the value-network loss would be a target. Length of list and order of arrays in + the list matches that of network losses which are defined by network parameters + :param additional_fetches: list of additional values to fetch and return. The type of each list + element is framework dependent. + :param importance_weights: ndarray of shape (batch_size,) to multiply with batch loss. + :param no_accumulation: if True, set gradient values to the new gradients, otherwise sum with previously + calculated gradients + :return: tuple of total_loss, losses, norm_unclipped_grads, fetched_tensors + total_loss (float): sum of all head losses + losses (list of float): list of all losses. The order is list of target losses followed by list of regularization losses. + The specifics of losses is dependant on the network parameters (number of heads, etc.) + norm_unclippsed_grads (float): global norm of all gradients before any gradient clipping is applied + fetched_tensors: all values for additional_fetches + """ pass - def accumulate_gradients(self, inputs, targets): + def apply_and_reset_gradients(self, gradients: List[np.ndarray]) -> None: + """ + Applies the given gradients to the network weights and resets the gradient accumulations. + Has the same impact as calling `apply_gradients`, then `reset_accumulated_gradients`. + + :param gradients: gradients for the parameter weights, taken from `accumulated_gradients` property + of an identical network (either self or another identical network) + """ pass - def apply_and_reset_gradients(self, gradients): + def apply_gradients(self, gradients: List[np.ndarray]) -> None: + """ + Applies the given gradients to the network weights. + Will be performed sync or async depending on `network_parameters.async_training` + + :param gradients: gradients for the parameter weights, taken from `accumulated_gradients` property + of an identical network (either self or another identical network) + """ pass - def apply_gradients(self, gradients): + def get_variable_value(self, variable: Any) -> np.ndarray: + """ + Gets value of a specified variable. Type of variable is dependant on the framework. + Example of a variable is head.kl_coefficient, which could be a symbol for evaluation + or could be a string representing the value. + + :param variable: variable of interest + :return: value of the specified variable + """ pass - def get_variable_value(self, variable): - pass + def set_variable_value(self, assign_op: Any, value: np.ndarray, placeholder: Any): + """ + Updates the value of a specified variable. Type of assign_op is dependant on the framework + and is a unique identifier for assigning value to a variable. For example an agent may use + head.assign_kl_coefficient. There is a one to one mapping between assign_op and placeholder + (in the example above, placeholder would be head.kl_coefficient_ph). - def set_variable_value(self, assign_op, value, placeholder=None): + :param assign_op: a parameter representing the operation for assigning value to a specific variable + :param value: value of the specified variable used for update + :param placeholder: a placeholder for binding the value to assign_op. + """ pass diff --git a/rl_coach/architectures/tensorflow_components/architecture.py b/rl_coach/architectures/tensorflow_components/architecture.py index e731920..7c5c248 100644 --- a/rl_coach/architectures/tensorflow_components/architecture.py +++ b/rl_coach/architectures/tensorflow_components/architecture.py @@ -146,6 +146,14 @@ class TensorFlowArchitecture(Architecture): # set the fetches for training self._set_initial_fetch_list() + def get_model(self) -> None: + """ + Constructs the model using `network_parameters` and sets `input_embedders`, `middleware`, + `output_heads`, `outputs`, `losses`, `total_loss`, `adaptive_learning_rate_scheme`, + `current_learning_rate`, and `optimizer` + """ + raise NotImplementedError + def _set_initial_fetch_list(self): """ Create an initial list of tensors to fetch in each training iteration From 95b4fc68883d71d122d082a3a4a0073a7daf1c24 Mon Sep 17 00:00:00 2001 From: Sina Afrooze Date: Tue, 30 Oct 2018 15:29:34 -0700 Subject: [PATCH 092/162] Added ability to switch between tensorflow and mxnet using -f commandline argument. (#48) NOTE: tensorflow framework works fine if mxnet is not installed in env, but mxnet will not work if tensorflow is not installed because of the code in network_wrapper. --- rl_coach/architectures/network_wrapper.py | 19 ++++++++++++++++--- rl_coach/base_parameters.py | 5 +++-- rl_coach/coach.py | 14 ++++++++++++-- .../test_agent_external_communication.py | 4 ++-- .../test_basic_rl_graph_manager.py | 8 ++++---- tutorials/1. Implementing an Algorithm.ipynb | 6 +++--- tutorials/2. Adding an Environment.ipynb | 10 ++++++---- ...Implementing a Hierarchical RL Graph.ipynb | 2 +- 8 files changed, 47 insertions(+), 21 deletions(-) diff --git a/rl_coach/architectures/network_wrapper.py b/rl_coach/architectures/network_wrapper.py index 5c868d5..042c93c 100644 --- a/rl_coach/architectures/network_wrapper.py +++ b/rl_coach/architectures/network_wrapper.py @@ -19,12 +19,17 @@ from typing import List, Tuple from rl_coach.base_parameters import Frameworks, AgentParameters from rl_coach.logger import failed_imports from rl_coach.spaces import SpacesDefinition - try: import tensorflow as tf from rl_coach.architectures.tensorflow_components.general_network import GeneralTensorFlowNetwork except ImportError: - failed_imports.append("TensorFlow") + failed_imports.append("tensorflow") + +try: + import mxnet as mx + from rl_coach.architectures.mxnet_components.general_network import GeneralMxnetNetwork +except ImportError: + failed_imports.append("mxnet") class NetworkWrapper(object): @@ -42,7 +47,15 @@ class NetworkWrapper(object): self.sess = None if self.network_parameters.framework == Frameworks.tensorflow: - general_network = GeneralTensorFlowNetwork + if "tensorflow" not in failed_imports: + general_network = GeneralTensorFlowNetwork + else: + raise Exception('Install tensorflow before using it as framework') + elif self.network_parameters.framework == Frameworks.mxnet: + if "mxnet" not in failed_imports: + general_network = GeneralMxnetNetwork + else: + raise Exception('Install mxnet before using it as framework') else: raise Exception("{} Framework is not supported" .format(Frameworks().to_string(self.network_parameters.framework))) diff --git a/rl_coach/base_parameters.py b/rl_coach/base_parameters.py index 028b649..a48a22c 100644 --- a/rl_coach/base_parameters.py +++ b/rl_coach/base_parameters.py @@ -29,6 +29,7 @@ from rl_coach.filters.filter import NoInputFilter class Frameworks(Enum): tensorflow = "TensorFlow" + mxnet = "MXNet" class EmbedderScheme(Enum): @@ -415,7 +416,7 @@ class AgentParameters(Parameters): class TaskParameters(Parameters): - def __init__(self, framework_type: str='tensorflow', evaluate_only: bool=False, use_cpu: bool=False, + def __init__(self, framework_type: Frameworks=Frameworks.tensorflow, evaluate_only: bool=False, use_cpu: bool=False, experiment_path='/tmp', seed=None, checkpoint_save_secs=None): """ :param framework_type: deep learning framework type. currently only tensorflow is supported @@ -435,7 +436,7 @@ class TaskParameters(Parameters): class DistributedTaskParameters(TaskParameters): - def __init__(self, framework_type: str, parameters_server_hosts: str, worker_hosts: str, job_type: str, + def __init__(self, framework_type: Frameworks, parameters_server_hosts: str, worker_hosts: str, job_type: str, task_index: int, evaluate_only: bool=False, num_tasks: int=None, num_training_tasks: int=None, use_cpu: bool=False, experiment_path=None, dnd=None, shared_memory_scratchpad=None, seed=None): diff --git a/rl_coach/coach.py b/rl_coach/coach.py index e479974..cab0492 100644 --- a/rl_coach/coach.py +++ b/rl_coach/coach.py @@ -61,6 +61,16 @@ def get_graph_manager_from_args(args: argparse.Namespace) -> 'GraphManager': schedule_params = HumanPlayScheduleParameters() graph_manager = BasicRLGraphManager(HumanAgentParameters(), env_params, schedule_params, VisualizationParameters()) + # Set framework + # Note: Some graph managers (e.g. HAC preset) create multiple agents and the attribute is called agents_params + if hasattr(graph_manager, 'agent_params'): + for network_parameters in graph_manager.agent_params.network_wrappers.values(): + network_parameters.framework = args.framework + elif hasattr(graph_manager, 'agents_params'): + for ap in graph_manager.agents_params: + for network_parameters in ap.network_wrappers.values(): + network_parameters.framework = args.framework + if args.level: if isinstance(graph_manager.env_params.level, SingleLevelSelection): graph_manager.env_params.level.select(args.level) @@ -344,7 +354,7 @@ def main(): # Single-threaded runs if args.num_workers == 1: # Start the training or evaluation - task_parameters = TaskParameters(framework_type="tensorflow", # TODO: tensorflow shouldn't be hardcoded + task_parameters = TaskParameters(framework_type=args.framework, evaluate_only=args.evaluate, experiment_path=args.experiment_path, seed=args.seed, @@ -373,7 +383,7 @@ def main(): def start_distributed_task(job_type, task_index, evaluation_worker=False, shared_memory_scratchpad=shared_memory_scratchpad): - task_parameters = DistributedTaskParameters(framework_type="tensorflow", # TODO: tensorflow should'nt be hardcoded + task_parameters = DistributedTaskParameters(framework_type=args.framework, parameters_server_hosts=ps_hosts, worker_hosts=worker_hosts, job_type=job_type, diff --git a/rl_coach/tests/agents/test_agent_external_communication.py b/rl_coach/tests/agents/test_agent_external_communication.py index 0bef271..77f0a89 100644 --- a/rl_coach/tests/agents/test_agent_external_communication.py +++ b/rl_coach/tests/agents/test_agent_external_communication.py @@ -1,7 +1,7 @@ import os import sys -from rl_coach.base_parameters import TaskParameters +from rl_coach.base_parameters import TaskParameters, Frameworks sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(__file__)))) import tensorflow as tf @@ -16,7 +16,7 @@ def test_get_QActionStateValue_predictions(): from rl_coach.presets.CartPole_DQN import graph_manager as cartpole_dqn_graph_manager assert cartpole_dqn_graph_manager cartpole_dqn_graph_manager.create_graph(task_parameters= - TaskParameters(framework_type="tensorflow", + TaskParameters(framework_type=Frameworks.tensorflow, experiment_path="./experiments/test")) cartpole_dqn_graph_manager.improve_steps.num_steps = 1 cartpole_dqn_graph_manager.steps_between_evaluation_periods.num_steps = 5 diff --git a/rl_coach/tests/graph_managers/test_basic_rl_graph_manager.py b/rl_coach/tests/graph_managers/test_basic_rl_graph_manager.py index e489373..214ef31 100644 --- a/rl_coach/tests/graph_managers/test_basic_rl_graph_manager.py +++ b/rl_coach/tests/graph_managers/test_basic_rl_graph_manager.py @@ -2,7 +2,7 @@ import os import sys sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(__file__)))) import tensorflow as tf -from rl_coach.base_parameters import TaskParameters, DistributedTaskParameters +from rl_coach.base_parameters import TaskParameters, DistributedTaskParameters, Frameworks from rl_coach.utils import get_open_port from multiprocessing import Process from tensorflow import logging @@ -16,7 +16,7 @@ def test_basic_rl_graph_manager_with_pong_a3c(): from rl_coach.presets.Atari_A3C import graph_manager assert graph_manager graph_manager.env_params.level = "PongDeterministic-v4" - graph_manager.create_graph(task_parameters=TaskParameters(framework_type="tensorflow", + graph_manager.create_graph(task_parameters=TaskParameters(framework_type=Frameworks.tensorflow, experiment_path="./experiments/test")) # graph_manager.improve() @@ -27,7 +27,7 @@ def test_basic_rl_graph_manager_with_pong_nec(): from rl_coach.presets.Atari_NEC import graph_manager assert graph_manager graph_manager.env_params.level = "PongDeterministic-v4" - graph_manager.create_graph(task_parameters=TaskParameters(framework_type="tensorflow", + graph_manager.create_graph(task_parameters=TaskParameters(framework_type=Frameworks.tensorflow, experiment_path="./experiments/test")) # graph_manager.improve() @@ -37,7 +37,7 @@ def test_basic_rl_graph_manager_with_cartpole_dqn(): tf.reset_default_graph() from rl_coach.presets.CartPole_DQN import graph_manager assert graph_manager - graph_manager.create_graph(task_parameters=TaskParameters(framework_type="tensorflow", + graph_manager.create_graph(task_parameters=TaskParameters(framework_type=Frameworks.tensorflow, experiment_path="./experiments/test")) # graph_manager.improve() diff --git a/tutorials/1. Implementing an Algorithm.ipynb b/tutorials/1. Implementing an Algorithm.ipynb index a309964..459a958 100644 --- a/tutorials/1. Implementing an Algorithm.ipynb +++ b/tutorials/1. Implementing an Algorithm.ipynb @@ -363,9 +363,9 @@ "if not os.path.exists(log_path):\n", " os.makedirs(log_path)\n", " \n", - "task_parameters = TaskParameters(framework_type=\"tensorflow\", \n", - " evaluate_only=False,\n", - " experiment_path=log_path)\n", + "task_parameters = TaskParameters(framework_type=Frameworks.tensorflow, \n", + " evaluate_only=False,\n", + " experiment_path=log_path)\n", "\n", "task_parameters.__dict__['checkpoint_save_secs'] = None\n", "\n", diff --git a/tutorials/2. Adding an Environment.ipynb b/tutorials/2. Adding an Environment.ipynb index 71869d1..9f0fa71 100644 --- a/tutorials/2. Adding an Environment.ipynb +++ b/tutorials/2. Adding an Environment.ipynb @@ -1,8 +1,10 @@ { "cells": [ { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": null, "metadata": {}, + "outputs": [], "source": [ "In this tutorial we'll add the DeepMind Control Suite environment to Coach, and create a preset that trains the DDPG agent on the new environment." ] @@ -341,9 +343,9 @@ "if not os.path.exists(log_path):\n", " os.makedirs(log_path)\n", " \n", - "task_parameters = TaskParameters(framework_type=\"tensorflow\", \n", - " evaluate_only=False,\n", - " experiment_path=log_path)\n", + "task_parameters = TaskParameters(framework_type=Frameworks.tensorflow, \n", + " evaluate_only=False,\n", + " experiment_path=log_path)\n", "\n", "task_parameters.__dict__['checkpoint_save_secs'] = None\n", "\n", diff --git a/tutorials/3. Implementing a Hierarchical RL Graph.ipynb b/tutorials/3. Implementing a Hierarchical RL Graph.ipynb index f200c2c..191d267 100644 --- a/tutorials/3. Implementing a Hierarchical RL Graph.ipynb +++ b/tutorials/3. Implementing a Hierarchical RL Graph.ipynb @@ -368,7 +368,7 @@ "if not os.path.exists(log_path):\n", " os.makedirs(log_path)\n", " \n", - "task_parameters = TaskParameters(framework_type=\"tensorflow\", \n", + "task_parameters = TaskParameters(framework_type=Frameworks.tensorflow, \n", " evaluate_only=False,\n", " experiment_path=log_path)\n", "\n", From 7e7006305a73460ae5883ed43431617f3f965a7b Mon Sep 17 00:00:00 2001 From: Balaji Subramaniam Date: Mon, 5 Nov 2018 09:33:30 -0800 Subject: [PATCH 093/162] Integrate coach.py params with distributed Coach. (#42) * Integrate coach.py params with distributed Coach. * Minor improvements - Use enums instead of constants. - Reduce code duplication. - Ask experiment name with timeout. --- dist-coach-config.template | 7 + rl_coach/base_parameters.py | 15 ++ rl_coach/coach.py | 176 +++++++++++++++++- rl_coach/graph_managers/graph_manager.py | 4 +- rl_coach/logger.py | 25 ++- .../orchestrators/kubernetes_orchestrator.py | 32 ++-- rl_coach/orchestrators/start_training.py | 120 ------------ rl_coach/orchestrators/test.py | 18 -- rl_coach/presets/CartPole_DQN.py | 2 +- rl_coach/presets/CartPole_PPO.py | 5 +- rl_coach/rollout_worker.py | 77 +------- rl_coach/training_worker.py | 64 +------ rl_coach/utils.py | 3 + 13 files changed, 263 insertions(+), 285 deletions(-) create mode 100644 dist-coach-config.template delete mode 100644 rl_coach/orchestrators/start_training.py delete mode 100644 rl_coach/orchestrators/test.py diff --git a/dist-coach-config.template b/dist-coach-config.template new file mode 100644 index 0000000..1566345 --- /dev/null +++ b/dist-coach-config.template @@ -0,0 +1,7 @@ +[coach] +image = +memory_backend = redispubsub +data_store = s3 +s3_end_point = s3.amazonaws.com +s3_bucket_name = +s3_creds_file = diff --git a/rl_coach/base_parameters.py b/rl_coach/base_parameters.py index a48a22c..e23226e 100644 --- a/rl_coach/base_parameters.py +++ b/rl_coach/base_parameters.py @@ -53,6 +53,18 @@ class EmbeddingMergerType(Enum): #Multiply = 3 +# DistributedCoachSynchronizationType provides the synchronization type for distributed Coach. +# The default value is None, which means the algorithm or preset cannot be used with distributed Coach. +class DistributedCoachSynchronizationType(Enum): + # In SYNC mode, the trainer waits for all the experiences to be gathered from distributed rollout workers before + # training a new policy and the rollout workers wait for a new policy before gathering experiences. + SYNC = "sync" + + # In ASYNC mode, the trainer doesn't wait for any set of experiences to be gathered from distributed rollout workers + # and the rollout workers continously gather experiences loading new policies, whenever they become available. + ASYNC = "async" + + def iterable_to_items(obj): if isinstance(obj, dict) or isinstance(obj, OrderedDict) or isinstance(obj, types.MappingProxyType): items = obj.items() @@ -154,6 +166,9 @@ class AlgorithmParameters(Parameters): # intrinsic reward self.scale_external_reward_by_intrinsic_reward_value = False + # Distributed Coach params + self.distributed_coach_synchronization_type = None + class PresetValidationParameters(Parameters): def __init__(self): diff --git a/rl_coach/coach.py b/rl_coach/coach.py index cab0492..eecfc70 100644 --- a/rl_coach/coach.py +++ b/rl_coach/coach.py @@ -17,6 +17,7 @@ import sys sys.path.append('.') import copy +from configparser import ConfigParser, Error from rl_coach.core_types import EnvironmentSteps import os from rl_coach import logger @@ -26,6 +27,7 @@ import argparse import atexit import time import sys +import json from rl_coach.base_parameters import Frameworks, VisualizationParameters, TaskParameters, DistributedTaskParameters from multiprocessing import Process from multiprocessing.managers import BaseManager @@ -35,6 +37,14 @@ from rl_coach.utils import list_all_presets, short_dynamic_import, get_open_port from rl_coach.agents.human_agent import HumanAgentParameters from rl_coach.graph_managers.basic_rl_graph_manager import BasicRLGraphManager from rl_coach.environments.environment import SingleLevelSelection +from rl_coach.orchestrators.kubernetes_orchestrator import KubernetesParameters, Kubernetes, RunType, RunTypeParameters +from rl_coach.memories.backend.redis import RedisPubSubMemoryBackendParameters +from rl_coach.memories.backend.memory_impl import construct_memory_params +from rl_coach.data_stores.data_store import DataStoreParameters +from rl_coach.data_stores.s3_data_store import S3DataStoreParameters +from rl_coach.data_stores.data_store_impl import get_data_store, construct_data_store_params +from rl_coach.training_worker import training_worker +from rl_coach.rollout_worker import rollout_worker, wait_for_checkpoint if len(set(failed_imports)) > 0: @@ -108,6 +118,7 @@ def display_all_presets_and_exit(): print(preset) sys.exit(0) + def expand_preset(preset): if preset.lower() in [p.lower() for p in list_all_presets()]: preset = "{}.py:graph_manager".format(os.path.join(get_base_dir(), 'presets', preset)) @@ -150,6 +161,49 @@ def parse_arguments(parser: argparse.ArgumentParser) -> argparse.Namespace: if args.list: display_all_presets_and_exit() + # Read args from config file for distributed Coach. + if args.distributed_coach and args.distributed_coach_run_type == RunType.ORCHESTRATOR: + coach_config = ConfigParser({ + 'image': '', + 'memory_backend': 'redispubsub', + 'data_store': 's3', + 's3_end_point': 's3.amazonaws.com', + 's3_bucket_name': '', + 's3_creds_file': '' + }) + try: + coach_config.read(args.distributed_coach_config_path) + args.image = coach_config.get('coach', 'image') + args.memory_backend = coach_config.get('coach', 'memory_backend') + args.data_store = coach_config.get('coach', 'data_store') + args.s3_end_point = coach_config.get('coach', 's3_end_point') + args.s3_bucket_name = coach_config.get('coach', 's3_bucket_name') + args.s3_creds_file = coach_config.get('coach', 's3_creds_file') + except Error as e: + screen.error("Error when reading distributed Coach config file: {}".format(e)) + + if args.image == '': + screen.error("Image cannot be empty.") + + data_store_choices = ['s3'] + if args.data_store not in data_store_choices: + screen.warning("{} data store is unsupported.".format(args.data_store)) + screen.error("Supported data stores are {}.".format(data_store_choices)) + + memory_backend_choices = ['redispubsub'] + if args.memory_backend not in memory_backend_choices: + screen.warning("{} memory backend is not supported.".format(args.memory_backend)) + screen.error("Supported memory backends are {}.".format(memory_backend_choices)) + + if args.s3_bucket_name == '': + screen.error("S3 bucket name cannot be empty.") + + if args.s3_creds_file == '': + args.s3_creds_file = None + + if args.play and args.distributed_coach: + screen.error("Playing is not supported in distributed Coach.") + # replace a short preset name with the full path if args.preset is not None: args.preset = expand_preset(args.preset) @@ -217,6 +271,94 @@ def start_graph(graph_manager: 'GraphManager', task_parameters: 'TaskParameters' graph_manager.improve() +def handle_distributed_coach_tasks(graph_manager, args): + ckpt_inside_container = "/checkpoint" + + memory_backend_params = None + if args.memory_backend_params: + memory_backend_params = json.loads(args.memory_backend_params) + memory_backend_params['run_type'] = str(args.distributed_coach_run_type) + graph_manager.agent_params.memory.register_var('memory_backend_params', construct_memory_params(memory_backend_params)) + + data_store_params = None + if args.data_store_params: + data_store_params = construct_data_store_params(json.loads(args.data_store_params)) + data_store_params.checkpoint_dir = ckpt_inside_container + graph_manager.data_store_params = data_store_params + + if args.distributed_coach_run_type == RunType.TRAINER: + training_worker( + graph_manager=graph_manager, + checkpoint_dir=ckpt_inside_container + ) + + if args.distributed_coach_run_type == RunType.ROLLOUT_WORKER: + data_store = None + if args.data_store_params: + data_store = get_data_store(data_store_params) + wait_for_checkpoint(checkpoint_dir=ckpt_inside_container, data_store=data_store) + + rollout_worker( + graph_manager=graph_manager, + checkpoint_dir=ckpt_inside_container, + data_store=data_store, + num_workers=args.num_workers + ) + + +def handle_distributed_coach_orchestrator(graph_manager, args): + ckpt_inside_container = "/checkpoint" + rollout_command = ['python3', 'rl_coach/coach.py', '--distributed_coach_run_type', str(RunType.ROLLOUT_WORKER)] + sys.argv[1:] + trainer_command = ['python3', 'rl_coach/coach.py', '--distributed_coach_run_type', str(RunType.TRAINER)] + sys.argv[1:] + + if '--experiment_name' not in rollout_command: + rollout_command = rollout_command + ['--experiment_name', args.experiment_name] + + if '--experiment_name' not in trainer_command: + trainer_command = trainer_command + ['--experiment_name', args.experiment_name] + + memory_backend_params = None + if args.memory_backend == "redispubsub": + memory_backend_params = RedisPubSubMemoryBackendParameters() + + ds_params_instance = None + if args.data_store == "s3": + ds_params = DataStoreParameters("s3", "", "") + ds_params_instance = S3DataStoreParameters(ds_params=ds_params, end_point=args.s3_end_point, bucket_name=args.s3_bucket_name, + creds_file=args.s3_creds_file, checkpoint_dir=ckpt_inside_container) + + worker_run_type_params = RunTypeParameters(args.image, rollout_command, run_type=str(RunType.ROLLOUT_WORKER), num_replicas=args.num_workers) + trainer_run_type_params = RunTypeParameters(args.image, trainer_command, run_type=str(RunType.TRAINER)) + + orchestration_params = KubernetesParameters([worker_run_type_params, trainer_run_type_params], + kubeconfig='~/.kube/config', + memory_backend_parameters=memory_backend_params, + data_store_params=ds_params_instance) + orchestrator = Kubernetes(orchestration_params) + if not orchestrator.setup(): + print("Could not setup.") + return + + if orchestrator.deploy_trainer(): + print("Successfully deployed trainer.") + else: + print("Could not deploy trainer.") + return + + if orchestrator.deploy_worker(): + print("Successfully deployed rollout worker(s).") + else: + print("Could not deploy rollout worker(s).") + return + + try: + orchestrator.trainer_logs() + except KeyboardInterrupt: + pass + + orchestrator.undeploy() + + def main(): parser = argparse.ArgumentParser() parser.add_argument('-p', '--preset', @@ -329,11 +471,35 @@ def main(): help="(int) A seed to use for running the experiment", default=None, type=int) + parser.add_argument('-dc', '--distributed_coach', + help="(flag) Use distributed Coach.", + action='store_true') + parser.add_argument('-dcp', '--distributed_coach_config_path', + help="(string) Path to config file when using distributed rollout workers." + "Only distributed Coach parameters should be provided through this config file." + "Rest of the parameters are provided using Coach command line options." + "Used only with --distributed_coach flag." + "Ignored if --distributed_coach flag is not used.", + type=str) + parser.add_argument('--memory_backend_params', + help=argparse.SUPPRESS, + type=str) + parser.add_argument('--data_store_params', + help=argparse.SUPPRESS, + type=str) + parser.add_argument('--distributed_coach_run_type', + help=argparse.SUPPRESS, + type=RunType, + default=RunType.ORCHESTRATOR, + choices=list(RunType)) args = parse_arguments(parser) graph_manager = get_graph_manager_from_args(args) + if args.distributed_coach and not graph_manager.agent_params.algorithm.distributed_coach_synchronization_type: + screen.error("{} preset is not supported using distributed Coach.".format(args.preset)) + # Intel optimized TF seems to run significantly faster when limiting to a single OMP thread. # This will not affect GPU runs. os.environ["OMP_NUM_THREADS"] = "1" @@ -343,7 +509,7 @@ def main(): os.environ['TF_CPP_MIN_LOG_LEVEL'] = str(args.tf_verbosity) # turn off the summary at the end of the run if necessary - if not args.no_summary: + if not args.no_summary and not args.distributed_coach: atexit.register(logger.summarize_experiment) screen.change_terminal_title(args.experiment_name) @@ -351,6 +517,14 @@ def main(): if args.open_dashboard: open_dashboard(args.experiment_path) + if args.distributed_coach and args.distributed_coach_run_type != RunType.ORCHESTRATOR: + handle_distributed_coach_tasks(graph_manager, args) + return + + if args.distributed_coach and args.distributed_coach_run_type == RunType.ORCHESTRATOR: + handle_distributed_coach_orchestrator(graph_manager, args) + return + # Single-threaded runs if args.num_workers == 1: # Start the training or evaluation diff --git a/rl_coach/graph_managers/graph_manager.py b/rl_coach/graph_managers/graph_manager.py index 4b5ae9f..b373c1c 100644 --- a/rl_coach/graph_managers/graph_manager.py +++ b/rl_coach/graph_managers/graph_manager.py @@ -33,6 +33,7 @@ from rl_coach.level_manager import LevelManager from rl_coach.logger import screen, Logger from rl_coach.utils import set_cpu, start_shell_command_and_wait from rl_coach.data_stores.data_store_impl import get_data_store +from rl_coach.orchestrators.kubernetes_orchestrator import RunType class ScheduleParameters(Parameters): @@ -361,9 +362,10 @@ class GraphManager(object): self.verify_graph_was_created() if hasattr(self, 'data_store_params') and hasattr(self.agent_params.memory, 'memory_backend_params'): - if self.agent_params.memory.memory_backend_params.run_type == "worker": + if self.agent_params.memory.memory_backend_params.run_type == str(RunType.ROLLOUT_WORKER): data_store = get_data_store(self.data_store_params) data_store.load_from_store() + # perform several steps of playing count_end = self.current_step_counter + steps while self.current_step_counter < count_end: diff --git a/rl_coach/logger.py b/rl_coach/logger.py index 02f1a9c..0a42296 100644 --- a/rl_coach/logger.py +++ b/rl_coach/logger.py @@ -18,7 +18,9 @@ import datetime import os import re import shutil +import signal import time +import uuid from subprocess import Popen, PIPE from typing import Union @@ -90,6 +92,23 @@ class ScreenLogger(object): def ask_input(self, title): return input("{}{}{}".format(Colors.BG_CYAN, title, Colors.END)) + def ask_input_with_timeout(self, title, timeout, msg_if_timeout='Timeout expired.'): + class TimeoutExpired(Exception): + pass + + def timeout_alarm_handler(signum, frame): + raise TimeoutExpired + + signal.signal(signal.SIGALRM, timeout_alarm_handler) + signal.alarm(timeout) + + try: + return input("{}{}{}".format(Colors.BG_CYAN, title, Colors.END)) + except TimeoutExpired: + self.warning(msg_if_timeout) + finally: + signal.alarm(0) + def ask_yes_no(self, title: str, default: Union[None, bool] = None): """ Ask the user for a yes / no question and return True if the answer is yes and False otherwise. @@ -333,10 +352,14 @@ def get_experiment_name(initial_experiment_name=''): match = None while match is None: if initial_experiment_name == '': - experiment_name = screen.ask_input("Please enter an experiment name: ") + msg_if_timeout = "Timeout waiting for experiement name." + experiment_name = screen.ask_input_with_timeout("Please enter an experiment name: ", 60, msg_if_timeout) else: experiment_name = initial_experiment_name + if not experiment_name: + experiment_name = '' + experiment_name = experiment_name.replace(" ", "_") match = re.match("^$|^[\w -/]{1,1000}$", experiment_name) diff --git a/rl_coach/orchestrators/kubernetes_orchestrator.py b/rl_coach/orchestrators/kubernetes_orchestrator.py index 4a50f3e..d2afb4d 100644 --- a/rl_coach/orchestrators/kubernetes_orchestrator.py +++ b/rl_coach/orchestrators/kubernetes_orchestrator.py @@ -2,6 +2,7 @@ import os import uuid import json import time +from enum import Enum from typing import List from configparser import ConfigParser, Error from rl_coach.orchestrators.deploy import Deploy, DeployParameters @@ -12,10 +13,19 @@ from rl_coach.data_stores.data_store import DataStoreParameters from rl_coach.data_stores.data_store_impl import get_data_store +class RunType(Enum): + ORCHESTRATOR = "orchestrator" + TRAINER = "trainer" + ROLLOUT_WORKER = "rollout-worker" + + def __str__(self): + return self.value + + class RunTypeParameters(): def __init__(self, image: str, command: list(), arguments: list() = None, - run_type: str = "trainer", checkpoint_dir: str = "/checkpoint", + run_type: str = str(RunType.TRAINER), checkpoint_dir: str = "/checkpoint", num_replicas: int = 1, orchestration_params: dict=None): self.image = image self.command = command @@ -97,12 +107,12 @@ class Kubernetes(Deploy): def deploy_trainer(self) -> bool: - trainer_params = self.params.run_type_params.get('trainer', None) + trainer_params = self.params.run_type_params.get(str(RunType.TRAINER), None) if not trainer_params: return False - trainer_params.command += ['--memory-backend-params', json.dumps(self.params.memory_backend_parameters.__dict__)] - trainer_params.command += ['--data-store-params', json.dumps(self.params.data_store_params.__dict__)] + trainer_params.command += ['--memory_backend_params', json.dumps(self.params.memory_backend_parameters.__dict__)] + trainer_params.command += ['--data_store_params', json.dumps(self.params.data_store_params.__dict__)] name = "{}-{}".format(trainer_params.run_type, uuid.uuid4()) @@ -175,13 +185,13 @@ class Kubernetes(Deploy): def deploy_worker(self): - worker_params = self.params.run_type_params.get('worker', None) + worker_params = self.params.run_type_params.get(str(RunType.ROLLOUT_WORKER), None) if not worker_params: return False - worker_params.command += ['--memory-backend-params', json.dumps(self.params.memory_backend_parameters.__dict__)] - worker_params.command += ['--data-store-params', json.dumps(self.params.data_store_params.__dict__)] - worker_params.command += ['--num-workers', '{}'.format(worker_params.num_replicas)] + worker_params.command += ['--memory_backend_params', json.dumps(self.params.memory_backend_parameters.__dict__)] + worker_params.command += ['--data_store_params', json.dumps(self.params.data_store_params.__dict__)] + worker_params.command += ['--num_workers', '{}'.format(worker_params.num_replicas)] name = "{}-{}".format(worker_params.run_type, uuid.uuid4()) @@ -255,7 +265,7 @@ class Kubernetes(Deploy): pass def trainer_logs(self): - trainer_params = self.params.run_type_params.get('trainer', None) + trainer_params = self.params.run_type_params.get(str(RunType.TRAINER), None) if not trainer_params: return @@ -313,7 +323,7 @@ class Kubernetes(Deploy): return def undeploy(self): - trainer_params = self.params.run_type_params.get('trainer', None) + trainer_params = self.params.run_type_params.get(str(RunType.TRAINER), None) api_client = k8sclient.AppsV1Api() delete_options = k8sclient.V1DeleteOptions() if trainer_params: @@ -321,7 +331,7 @@ class Kubernetes(Deploy): api_client.delete_namespaced_deployment(trainer_params.orchestration_params['deployment_name'], self.params.namespace, delete_options) except k8sclient.rest.ApiException as e: print("Got exception: %s\n while deleting trainer", e) - worker_params = self.params.run_type_params.get('worker', None) + worker_params = self.params.run_type_params.get(str(RunType.ROLLOUT_WORKER), None) if worker_params: try: api_client.delete_namespaced_deployment(worker_params.orchestration_params['deployment_name'], self.params.namespace, delete_options) diff --git a/rl_coach/orchestrators/start_training.py b/rl_coach/orchestrators/start_training.py deleted file mode 100644 index 100a870..0000000 --- a/rl_coach/orchestrators/start_training.py +++ /dev/null @@ -1,120 +0,0 @@ -import argparse - -from rl_coach.orchestrators.kubernetes_orchestrator import KubernetesParameters, Kubernetes, RunTypeParameters -from rl_coach.memories.backend.redis import RedisPubSubMemoryBackendParameters -from rl_coach.data_stores.data_store import DataStoreParameters -from rl_coach.data_stores.s3_data_store import S3DataStoreParameters -from rl_coach.data_stores.nfs_data_store import NFSDataStoreParameters - - -def main(preset: str, image: str='ajaysudh/testing:coach', num_workers: int=1, nfs_server: str=None, nfs_path: str=None, - memory_backend: str=None, data_store: str=None, s3_end_point: str=None, s3_bucket_name: str=None, - s3_creds_file: str=None, policy_type: str="OFF"): - rollout_command = ['python3', 'rl_coach/rollout_worker.py', '-p', preset, '--policy-type', policy_type] - training_command = ['python3', 'rl_coach/training_worker.py', '-p', preset, '--policy-type', policy_type] - - memory_backend_params = None - if memory_backend == "redispubsub": - memory_backend_params = RedisPubSubMemoryBackendParameters() - - ds_params_instance = None - if data_store == "s3": - ds_params = DataStoreParameters("s3", "", "") - ds_params_instance = S3DataStoreParameters(ds_params=ds_params, end_point=s3_end_point, bucket_name=s3_bucket_name, - creds_file=s3_creds_file, checkpoint_dir="/checkpoint") - elif data_store == "nfs": - ds_params = DataStoreParameters("nfs", "kubernetes", {"namespace": "default"}) - ds_params_instance = NFSDataStoreParameters(ds_params) - - worker_run_type_params = RunTypeParameters(image, rollout_command, run_type="worker", num_replicas=num_workers) - trainer_run_type_params = RunTypeParameters(image, training_command, run_type="trainer") - - orchestration_params = KubernetesParameters([worker_run_type_params, trainer_run_type_params], - kubeconfig='~/.kube/config', nfs_server=nfs_server, nfs_path=nfs_path, - memory_backend_parameters=memory_backend_params, - data_store_params=ds_params_instance) - orchestrator = Kubernetes(orchestration_params) - if not orchestrator.setup(): - print("Could not setup") - return - - if orchestrator.deploy_trainer(): - print("Successfully deployed") - else: - print("Could not deploy") - return - - if orchestrator.deploy_worker(): - print("Successfully deployed") - else: - print("Could not deploy") - return - - try: - orchestrator.trainer_logs() - except KeyboardInterrupt: - pass - orchestrator.undeploy() - - -if __name__ == '__main__': - parser = argparse.ArgumentParser() - parser.add_argument('--image', - help="(string) Name of a docker image.", - type=str, - required=True) - parser.add_argument('-p', '--preset', - help="(string) Name of a preset to run (class name from the 'presets' directory).", - type=str, - required=True) - parser.add_argument('--memory-backend', - help="(string) Memory backend to use.", - type=str, - choices=['redispubsub'], - default="redispubsub") - parser.add_argument('--data-store', - help="(string) Data store to use.", - type=str, - choices=['s3', 'nfs'], - default="s3") - parser.add_argument('--nfs-server', - help="(string) Addresss of the nfs server.", - type=str, - required=False) - parser.add_argument('--nfs-path', - help="(string) Exported path for the nfs server.", - type=str, - required=False) - parser.add_argument('--s3-end-point', - help="(string) S3 endpoint to use when S3 data store is used.", - type=str, - required=False) - parser.add_argument('--s3-bucket-name', - help="(string) S3 bucket name to use when S3 data store is used.", - type=str, - required=False) - parser.add_argument('--s3-creds-file', - help="(string) S3 credentials file to use when S3 data store is used.", - type=str, - required=False) - parser.add_argument('--num-workers', - help="(string) Number of rollout workers.", - type=int, - required=False, - default=1) - parser.add_argument('--policy-type', - help="(string) The type of policy: OFF/ON.", - type=str, - choices=['ON', 'OFF'], - default='OFF') - - # parser.add_argument('--checkpoint_dir', - # help='(string) Path to a folder containing a checkpoint to write the model to.', - # type=str, - # default='/checkpoint') - args = parser.parse_args() - - main(preset=args.preset, image=args.image, nfs_server=args.nfs_server, nfs_path=args.nfs_path, - memory_backend=args.memory_backend, data_store=args.data_store, s3_end_point=args.s3_end_point, - s3_bucket_name=args.s3_bucket_name, s3_creds_file=args.s3_creds_file, num_workers=args.num_workers, - policy_type=args.policy_type) diff --git a/rl_coach/orchestrators/test.py b/rl_coach/orchestrators/test.py deleted file mode 100644 index 3142b6a..0000000 --- a/rl_coach/orchestrators/test.py +++ /dev/null @@ -1,18 +0,0 @@ -from rl_coach.orchestrators.kubernetes_orchestrator import KubernetesParameters, Kubernetes - -# image = 'gcr.io/constant-cubist-173123/coach:latest' -image = 'ajaysudh/testing:coach' -command = ['python3', 'rl_coach/rollout_worker.py', '-p', 'CartPole_DQN_distributed'] -# command = ['sleep', '10h'] - -params = KubernetesParameters(image, command, kubeconfig='~/.kube/config', redis_ip='redis-service.ajay.svc', redis_port=6379, num_workers=1) -# params = KubernetesParameters(image, command, kubeconfig='~/.kube/config') - -obj = Kubernetes(params) -if not obj.setup(): - print("Could not setup") - -if obj.deploy(): - print("Successfully deployed") -else: - print("Could not deploy") diff --git a/rl_coach/presets/CartPole_DQN.py b/rl_coach/presets/CartPole_DQN.py index 02a38d7..ba4472f 100644 --- a/rl_coach/presets/CartPole_DQN.py +++ b/rl_coach/presets/CartPole_DQN.py @@ -1,5 +1,5 @@ from rl_coach.agents.dqn_agent import DQNAgentParameters -from rl_coach.base_parameters import VisualizationParameters, PresetValidationParameters +from rl_coach.base_parameters import VisualizationParameters, PresetValidationParameters, DistributedCoachSynchronizationType from rl_coach.core_types import TrainingSteps, EnvironmentEpisodes, EnvironmentSteps from rl_coach.environments.gym_environment import GymVectorEnvironment from rl_coach.graph_managers.basic_rl_graph_manager import BasicRLGraphManager diff --git a/rl_coach/presets/CartPole_PPO.py b/rl_coach/presets/CartPole_PPO.py index eb2ac09..40e06d1 100644 --- a/rl_coach/presets/CartPole_PPO.py +++ b/rl_coach/presets/CartPole_PPO.py @@ -1,6 +1,6 @@ from rl_coach.agents.clipped_ppo_agent import ClippedPPOAgentParameters from rl_coach.architectures.tensorflow_components.layers import Dense -from rl_coach.base_parameters import VisualizationParameters, PresetValidationParameters +from rl_coach.base_parameters import VisualizationParameters, PresetValidationParameters, DistributedCoachSynchronizationType from rl_coach.core_types import TrainingSteps, EnvironmentEpisodes, EnvironmentSteps, RunPhase from rl_coach.environments.gym_environment import GymVectorEnvironment, mujoco_v2 from rl_coach.exploration_policies.additive_noise import AdditiveNoiseParameters @@ -44,6 +44,9 @@ agent_params.algorithm.optimization_epochs = 10 agent_params.algorithm.estimate_state_value_using_gae = True agent_params.algorithm.num_steps_between_copying_online_weights_to_target = EnvironmentSteps(2048) +# Distributed Coach synchronization type. +agent_params.algorithm.distributed_coach_synchronization_type = DistributedCoachSynchronizationType.SYNC + agent_params.exploration = EGreedyParameters() agent_params.exploration.epsilon_schedule = LinearSchedule(1.0, 0.01, 10000) agent_params.pre_network_filter.add_observation_filter('observation', 'normalize_observation', diff --git a/rl_coach/rollout_worker.py b/rl_coach/rollout_worker.py index a3ae386..d039a9e 100644 --- a/rl_coach/rollout_worker.py +++ b/rl_coach/rollout_worker.py @@ -7,28 +7,16 @@ this rollout worker: - exits """ -import argparse import time import os -import json import math -from threading import Thread - -from rl_coach.base_parameters import TaskParameters -from rl_coach.coach import expand_preset +from rl_coach.base_parameters import TaskParameters, DistributedCoachSynchronizationType from rl_coach.core_types import EnvironmentSteps, RunPhase -from rl_coach.utils import short_dynamic_import -from rl_coach.memories.backend.memory_impl import construct_memory_params -from rl_coach.data_stores.data_store_impl import get_data_store, construct_data_store_params from google.protobuf import text_format from tensorflow.python.training.checkpoint_state_pb2 import CheckpointState -# Q: specify alternative distributed memory, or should this go in the preset? -# A: preset must define distributed memory to be used. we aren't going to take -# a non-distributed preset and automatically distribute it. - def has_checkpoint(checkpoint_dir): """ True if a checkpoint is present in checkpoint_dir @@ -39,6 +27,7 @@ def has_checkpoint(checkpoint_dir): return False + def wait_for_checkpoint(checkpoint_dir, data_store=None, timeout=10): """ block until there is a checkpoint in checkpoint_dir @@ -79,7 +68,7 @@ def get_latest_checkpoint(checkpoint_dir): return int(rel_path.split('_Step')[0]) -def rollout_worker(graph_manager, checkpoint_dir, data_store, num_workers, policy_type): +def rollout_worker(graph_manager, checkpoint_dir, data_store, num_workers): """ wait for first checkpoint then perform rollouts using the model """ @@ -102,7 +91,7 @@ def rollout_worker(graph_manager, checkpoint_dir, data_store, num_workers, polic new_checkpoint = get_latest_checkpoint(checkpoint_dir) - if policy_type == 'ON': + if graph_manager.agent_params.algorithm.distributed_coach_synchronization_type == DistributedCoachSynchronizationType.SYNC: while new_checkpoint < last_checkpoint + 1: if data_store: data_store.load_from_store() @@ -110,64 +99,8 @@ def rollout_worker(graph_manager, checkpoint_dir, data_store, num_workers, polic graph_manager.restore_checkpoint() - if policy_type == "OFF": - + if graph_manager.agent_params.algorithm.distributed_coach_synchronization_type == DistributedCoachSynchronizationType.ASYNC: if new_checkpoint > last_checkpoint: graph_manager.restore_checkpoint() last_checkpoint = new_checkpoint - - -def main(): - parser = argparse.ArgumentParser() - parser.add_argument('-p', '--preset', - help="(string) Name of a preset to run (class name from the 'presets' directory.)", - type=str, - required=True) - parser.add_argument('--checkpoint-dir', - help='(string) Path to a folder containing a checkpoint to restore the model from.', - type=str, - default='/checkpoint') - parser.add_argument('--memory-backend-params', - help="(string) JSON string of the memory backend params", - type=str) - parser.add_argument('--data-store-params', - help="(string) JSON string of the data store params", - type=str) - parser.add_argument('--num-workers', - help="(int) The number of workers started in this pool", - type=int, - default=1) - parser.add_argument('--policy-type', - help="(string) The type of policy: OFF/ON", - type=str, - default='OFF') - - args = parser.parse_args() - graph_manager = short_dynamic_import(expand_preset(args.preset), ignore_module_case=True) - - data_store = None - if args.memory_backend_params: - args.memory_backend_params = json.loads(args.memory_backend_params) - args.memory_backend_params['run_type'] = 'worker' - graph_manager.agent_params.memory.register_var('memory_backend_params', construct_memory_params(args.memory_backend_params)) - - if args.data_store_params: - data_store_params = construct_data_store_params(json.loads(args.data_store_params)) - data_store_params.checkpoint_dir = args.checkpoint_dir - graph_manager.data_store_params = data_store_params - data_store = get_data_store(data_store_params) - wait_for_checkpoint(checkpoint_dir=args.checkpoint_dir, data_store=data_store) - # thread = Thread(target = data_store_ckpt_load, args = [data_store]) - # thread.start() - - rollout_worker( - graph_manager=graph_manager, - checkpoint_dir=args.checkpoint_dir, - data_store=data_store, - num_workers=args.num_workers, - policy_type=args.policy_type - ) - -if __name__ == '__main__': - main() diff --git a/rl_coach/training_worker.py b/rl_coach/training_worker.py index e107ad8..3d2f9e5 100644 --- a/rl_coach/training_worker.py +++ b/rl_coach/training_worker.py @@ -1,24 +1,18 @@ """ """ -import argparse import time -import json -from threading import Thread - -from rl_coach.base_parameters import TaskParameters -from rl_coach.coach import expand_preset +from rl_coach.base_parameters import TaskParameters, DistributedCoachSynchronizationType from rl_coach import core_types -from rl_coach.utils import short_dynamic_import -from rl_coach.memories.backend.memory_impl import construct_memory_params -from rl_coach.data_stores.data_store_impl import get_data_store, construct_data_store_params + def data_store_ckpt_save(data_store): while True: data_store.save_to_store() time.sleep(10) -def training_worker(graph_manager, checkpoint_dir, policy_type): + +def training_worker(graph_manager, checkpoint_dir): """ restore a checkpoint then perform rollouts using the restored model """ @@ -49,55 +43,7 @@ def training_worker(graph_manager, checkpoint_dir, policy_type): graph_manager.evaluate(graph_manager.evaluation_steps) eval_offset += 1 - if policy_type == 'ON': + if graph_manager.agent_params.algorithm.distributed_coach_synchronization_type == DistributedCoachSynchronizationType.SYNC: graph_manager.save_checkpoint() else: graph_manager.occasionally_save_checkpoint() - - -def main(): - parser = argparse.ArgumentParser() - parser.add_argument('-p', '--preset', - help="(string) Name of a preset to run (class name from the 'presets' directory.)", - type=str, - required=True) - parser.add_argument('--checkpoint-dir', - help='(string) Path to a folder containing a checkpoint to write the model to.', - type=str, - default='/checkpoint') - parser.add_argument('--memory-backend-params', - help="(string) JSON string of the memory backend params", - type=str) - parser.add_argument('--data-store-params', - help="(string) JSON string of the data store params", - type=str) - parser.add_argument('--policy-type', - help="(string) The type of policy: OFF/ON", - type=str, - default='OFF') - args = parser.parse_args() - - graph_manager = short_dynamic_import(expand_preset(args.preset), ignore_module_case=True) - - if args.memory_backend_params: - args.memory_backend_params = json.loads(args.memory_backend_params) - args.memory_backend_params['run_type'] = 'trainer' - graph_manager.agent_params.memory.register_var('memory_backend_params', construct_memory_params(args.memory_backend_params)) - - if args.data_store_params: - data_store_params = construct_data_store_params(json.loads(args.data_store_params)) - data_store_params.checkpoint_dir = args.checkpoint_dir - graph_manager.data_store_params = data_store_params - # data_store = get_data_store(data_store_params) - # thread = Thread(target = data_store_ckpt_save, args = [data_store]) - # thread.start() - - training_worker( - graph_manager=graph_manager, - checkpoint_dir=args.checkpoint_dir, - policy_type=args.policy_type - ) - - -if __name__ == '__main__': - main() diff --git a/rl_coach/utils.py b/rl_coach/utils.py index b542f1e..76729b3 100644 --- a/rl_coach/utils.py +++ b/rl_coach/utils.py @@ -23,6 +23,7 @@ import signal import sys import threading import time +import traceback from multiprocessing import Manager from subprocess import Popen from typing import List, Tuple @@ -30,6 +31,8 @@ from typing import List, Tuple import atexit import numpy as np +from rl_coach.logger import screen + killed_processes = [] eps = np.finfo(np.float32).eps From d75df17d9731694607f21fcc5c0d309960e0618d Mon Sep 17 00:00:00 2001 From: Leo Dirac Date: Mon, 5 Nov 2018 15:25:49 -0800 Subject: [PATCH 094/162] Modifying ScreenLogger to optionally not output color codes (#56) * Modifying ScreenLogger to not output color when configured by new CLI parameter --- rl_coach/coach.py | 6 +++++ rl_coach/logger.py | 56 ++++++++++++++++++++++++++++++++++------------ 2 files changed, 48 insertions(+), 14 deletions(-) diff --git a/rl_coach/coach.py b/rl_coach/coach.py index eecfc70..48d8076 100644 --- a/rl_coach/coach.py +++ b/rl_coach/coach.py @@ -408,6 +408,9 @@ def main(): help="(flag) TensorFlow verbosity level", default=3, type=int) + parser.add_argument('--nocolor', + help="(flag) Turn off color-codes in screen logging. Ascii text only", + action='store_true') parser.add_argument('-s', '--checkpoint_save_secs', help="(int) Time in seconds between saving checkpoints of the model.", default=None, @@ -495,6 +498,9 @@ def main(): args = parse_arguments(parser) + if args.nocolor: + screen.set_use_colors(False) + graph_manager = get_graph_manager_from_args(args) if args.distributed_coach and not graph_manager.agent_params.algorithm.distributed_coach_synchronization_type: diff --git a/rl_coach/logger.py b/rl_coach/logger.py index 0a42296..7a26c07 100644 --- a/rl_coach/logger.py +++ b/rl_coach/logger.py @@ -57,8 +57,26 @@ class Colors(object): # prints to screen with a prefix identifying the origin of the print class ScreenLogger(object): - def __init__(self, name): + def __init__(self, name, use_colors=True): self.name = name + self.set_use_colors(use_colors) + + def set_use_colors(self, use_colors): + self._use_colors = use_colors + if use_colors: + self._prefix_success = Colors.GREEN + self._prefix_warning = Colors.YELLOW + self._prefix_error = Colors.RED + self._prefix_title = Colors.BG_CYAN + self._prefix_ask = Colors.BG_CYAN + self._suffix = Colors.END + else: + self._prefix_success = "" + self._prefix_warning = "!! " + self._prefix_error = "!!!! " + self._prefix_title = "## " + self._prefix_ask = "" + self._suffix = "" def separator(self): print("") @@ -68,29 +86,35 @@ class ScreenLogger(object): def log(self, data): print(data) - def log_dict(self, dict, prefix=""): - str = "{}{}{} - ".format(Colors.PURPLE, prefix, Colors.END) - for k, v in dict.items(): - str += "{}{}: {}{} ".format(Colors.BLUE, k, Colors.END, v) - print(str) + def log_dict(self, data, prefix=""): + if self._use_colors: + str = "{}{}{} - ".format(Colors.PURPLE, prefix, Colors.END) + for k, v in data.items(): + str += "{}{}: {}{} ".format(Colors.BLUE, k, Colors.END, v) + print(str) + else: + logentries = [] + for k, v in data.items(): + logentries.append("{}={}".format(k, v)) + logline = "{}> {}".format(prefix, ", ".join(logentries)) + print(logline) def log_title(self, title): - print("{}{}{}".format(Colors.BG_CYAN, title, Colors.END)) + print("{}{}{}".format(self._prefix_title, title, self._suffix)) def success(self, text): - print("{}{}{}".format(Colors.GREEN, text, Colors.END)) + print("{}{}{}".format(self._prefix_success, text, self._suffix)) def warning(self, text): - print("{}{}{}".format(Colors.YELLOW, text, Colors.END)) + print("{}{}{}".format(self._prefix_warning, text, self._suffix)) def error(self, text, crash=True): - print("{}{}{}".format(Colors.RED, text, Colors.END)) + print("{}{}{}".format(self._prefix_error, text, self._suffix)) if crash: - atexit.unregister(summarize_experiment) exit(1) def ask_input(self, title): - return input("{}{}{}".format(Colors.BG_CYAN, title, Colors.END)) + return input("{}{}{}".format(self._prefix_ask, title, self._suffix)) def ask_input_with_timeout(self, title, timeout, msg_if_timeout='Timeout expired.'): class TimeoutExpired(Exception): @@ -125,7 +149,7 @@ class ScreenLogger(object): default_answer = 'y/N' while True: - answer = input("{}{}{} ({})".format(Colors.BG_CYAN, title, Colors.END, default_answer)) + answer = input("{}{}{} ({})".format(self._prefix_ask, title, self._suffix, default_answer)) if answer == "yes" or answer == "YES" or answer == "y" or answer == "Y": return True elif answer == "no" or answer == "NO" or answer == "n" or answer == "N": @@ -140,7 +164,10 @@ class ScreenLogger(object): :param title: The new title :return: None """ - print("\x1b]2;{}\x07".format(title)) + if self._use_colors: + print("\x1b]2;{}\x07".format(title)) + else: + print("Title: %s" % title) class BaseLogger(object): @@ -397,5 +424,6 @@ def get_experiment_path(experiment_name, create_path=True): os.makedirs(experiment_path) return experiment_path + global screen screen = ScreenLogger("") From 811152126c841c1e100efb28ae5e6a5e5e329b6c Mon Sep 17 00:00:00 2001 From: Itai Caspi <30383381+itaicaspi-intel@users.noreply.github.com> Date: Tue, 6 Nov 2018 10:55:21 +0200 Subject: [PATCH 095/162] Export graph to ONNX (#61) Implements the ONNX graph exporting feature. Currently does not work for NAF, C51 and A3C_LSTM due to unsupported TF layers in the tf2onnx library. --- .../tensorflow_components/architecture.py | 41 ++++++++++- .../tensorflow_components/heads/ppo_head.py | 10 ++- rl_coach/coach.py | 12 ++++ rl_coach/graph_managers/graph_manager.py | 71 +++++++++++++------ 4 files changed, 107 insertions(+), 27 deletions(-) diff --git a/rl_coach/architectures/tensorflow_components/architecture.py b/rl_coach/architectures/tensorflow_components/architecture.py index 7c5c248..3353b52 100644 --- a/rl_coach/architectures/tensorflow_components/architecture.py +++ b/rl_coach/architectures/tensorflow_components/architecture.py @@ -14,6 +14,7 @@ # limitations under the License. # import time +import os import numpy as np import tensorflow as tf @@ -22,7 +23,7 @@ from rl_coach.architectures.architecture import Architecture from rl_coach.base_parameters import AgentParameters, DistributedTaskParameters from rl_coach.core_types import GradientClippingMethod from rl_coach.spaces import SpacesDefinition -from rl_coach.utils import force_list, squeeze_list +from rl_coach.utils import force_list, squeeze_list, start_shell_command_and_wait def variable_summaries(var): @@ -614,3 +615,41 @@ class TensorFlowArchitecture(Architecture): if self.middleware.__class__.__name__ == 'LSTMMiddleware': self.curr_rnn_c_in = self.middleware.c_init self.curr_rnn_h_in = self.middleware.h_init + + +def save_onnx_graph(input_nodes, output_nodes, checkpoint_save_dir: str) -> None: + """ + Given the input nodes and output nodes of the TF graph, save it as an onnx graph + This requires the TF graph and the weights checkpoint to be stored in the experiment directory. + It then freezes the graph (merging the graph and weights checkpoint), and converts it to ONNX. + + :param input_nodes: A list of input nodes for the TF graph + :param output_nodes: A list of output nodes for the TF graph + :param checkpoint_save_dir: The directory to save the ONNX graph to + :return: None + """ + import tf2onnx # just to verify that tf2onnx is installed + + # freeze graph + frozen_graph_path = os.path.join(checkpoint_save_dir, "frozen_graph.pb") + freeze_graph_command = [ + "python -m tensorflow.python.tools.freeze_graph", + "--input_graph={}".format(os.path.join(checkpoint_save_dir, "graphdef.pb")), + "--input_binary=true", + "--output_node_names='{}'".format(','.join([o.split(":")[0] for o in output_nodes])), + "--input_checkpoint={}".format(tf.train.latest_checkpoint(checkpoint_save_dir)), + "--output_graph={}".format(frozen_graph_path) + ] + start_shell_command_and_wait(" ".join(freeze_graph_command)) + + # convert graph to onnx + onnx_graph_path = os.path.join(checkpoint_save_dir, "model.onnx") + convert_to_onnx_command = [ + "python -m tf2onnx.convert", + "--input {}".format(frozen_graph_path), + "--inputs '{}'".format(','.join(input_nodes)), + "--outputs '{}'".format(','.join(output_nodes)), + "--output {}".format(onnx_graph_path), + "--verbose" + ] + start_shell_command_and_wait(" ".join(convert_to_onnx_command)) diff --git a/rl_coach/architectures/tensorflow_components/heads/ppo_head.py b/rl_coach/architectures/tensorflow_components/heads/ppo_head.py index 6ce7898..f15e6f2 100644 --- a/rl_coach/architectures/tensorflow_components/heads/ppo_head.py +++ b/rl_coach/architectures/tensorflow_components/heads/ppo_head.py @@ -127,11 +127,15 @@ class PPOHead(Head): self.input = [self.actions, self.old_policy_mean, self.old_policy_std] self.policy_mean = self.dense_layer(num_actions)(input_layer, name='policy_mean', kernel_initializer=normalized_columns_initializer(0.01)) - if self.is_local: + + # for local networks in distributed settings, we need to move variables we create manually to the + # tf.GraphKeys.LOCAL_VARIABLES collection, since the variable scope custom getter which is set in + # Architecture does not apply to them + if self.is_local and isinstance(self.ap.task_parameters, DistributedTaskParameters): self.policy_logstd = tf.Variable(np.zeros((1, num_actions)), dtype='float32', - collections=[tf.GraphKeys.LOCAL_VARIABLES]) + collections=[tf.GraphKeys.LOCAL_VARIABLES], name="policy_log_std") else: - self.policy_logstd = tf.Variable(np.zeros((1, num_actions)), dtype='float32') + self.policy_logstd = tf.Variable(np.zeros((1, num_actions)), dtype='float32', name="policy_log_std") self.policy_std = tf.tile(tf.exp(self.policy_logstd), [tf.shape(input_layer)[0], 1], name='policy_std') diff --git a/rl_coach/coach.py b/rl_coach/coach.py index 48d8076..c9cd304 100644 --- a/rl_coach/coach.py +++ b/rl_coach/coach.py @@ -241,6 +241,10 @@ def parse_arguments(parser: argparse.ArgumentParser) -> argparse.Namespace: # checkpoints args.checkpoint_save_dir = os.path.join(args.experiment_path, 'checkpoint') if args.checkpoint_save_secs is not None else None + if args.export_onnx_graph and not args.checkpoint_save_secs: + screen.warning("Exporting ONNX graphs requires setting the --checkpoint_save_secs flag. " + "The --export_onnx_graph will have no effect.") + return args @@ -474,6 +478,14 @@ def main(): help="(int) A seed to use for running the experiment", default=None, type=int) + parser.add_argument('-onnx', '--export_onnx_graph', + help="(flag) Export the ONNX graph to the experiment directory. " + "This will have effect only if the --checkpoint_save_secs flag is used in order to store " + "checkpoints, since the weights checkpoint are needed for the ONNX graph. " + "Keep in mind that this can cause major overhead on the experiment. " + "Exporting ONNX graphs requires manually installing the tf2onnx package " + "(https://github.com/onnx/tensorflow-onnx).", + action='store_true') parser.add_argument('-dc', '--distributed_coach', help="(flag) Use distributed Coach.", action='store_true') diff --git a/rl_coach/graph_managers/graph_manager.py b/rl_coach/graph_managers/graph_manager.py index b373c1c..7743885 100644 --- a/rl_coach/graph_managers/graph_manager.py +++ b/rl_coach/graph_managers/graph_manager.py @@ -100,6 +100,8 @@ class GraphManager(object): self.preset_validation_params = PresetValidationParameters() self.reset_required = False + self.last_checkpoint_saving_time = time.time() + # counters self.total_steps_counters = { RunPhase.HEATUP: TotalStepsCounter(), @@ -227,28 +229,47 @@ class GraphManager(object): # restore from checkpoint if given self.restore_checkpoint() - # tf.train.write_graph(tf.get_default_graph(), - # logdir=self.task_parameters.save_checkpoint_dir, - # name='graphdef.pb', - # as_text=False) - # self.save_checkpoint() - # - # output_nodes = [] - # for level in self.level_managers: - # for agent in level.agents.values(): - # for network in agent.networks.values(): - # for output in network.online_network.outputs: - # output_nodes.append(output.name.split(":")[0]) - # - # freeze_graph_command = [ - # "python -m tensorflow.python.tools.freeze_graph", - # "--input_graph={}".format(os.path.join(self.task_parameters.save_checkpoint_dir, "graphdef.pb")), - # "--input_binary=true", - # "--output_node_names='{}'".format(','.join(output_nodes)), - # "--input_checkpoint={}".format(os.path.join(self.task_parameters.save_checkpoint_dir, "0_Step-0.ckpt")), - # "--output_graph={}".format(os.path.join(self.task_parameters.save_checkpoint_dir, "frozen_graph.pb")) - # ] - # start_shell_command_and_wait(" ".join(freeze_graph_command)) + # the TF graph is static, and therefore is saved once - in the beginning of the experiment + if hasattr(self.task_parameters, 'checkpoint_save_dir') and self.task_parameters.checkpoint_save_dir: + self.save_graph() + + def save_graph(self) -> None: + """ + Save the TF graph to a protobuf description file in the experiment directory + :return: None + """ + import tensorflow as tf + + # write graph + tf.train.write_graph(tf.get_default_graph(), + logdir=self.task_parameters.checkpoint_save_dir, + name='graphdef.pb', + as_text=False) + + def save_onnx_graph(self) -> None: + """ + Save the graph as an ONNX graph. + This requires the graph and the weights checkpoint to be stored in the experiment directory. + It then freezes the graph (merging the graph and weights checkpoint), and converts it to ONNX. + :return: None + """ + + # collect input and output nodes + input_nodes = [] + output_nodes = [] + for level in self.level_managers: + for agent in level.agents.values(): + for network in agent.networks.values(): + for input_key, input in network.online_network.inputs.items(): + if not input_key.startswith("output_"): + input_nodes.append(input.name) + for output in network.online_network.outputs: + output_nodes.append(output.name) + + # TODO: make this framework agnostic + from rl_coach.architectures.tensorflow_components.architecture import save_onnx_graph + + save_onnx_graph(input_nodes, output_nodes, self.task_parameters.checkpoint_save_dir) def setup_logger(self) -> None: # dump documentation @@ -496,7 +517,7 @@ class GraphManager(object): and (self.task_parameters.task_index == 0 # distributed or self.task_parameters.task_index is None # single-worker ): - self.save_checkpoint() + self.save_checkpoint() def save_checkpoint(self): checkpoint_path = os.path.join(self.task_parameters.checkpoint_save_dir, @@ -511,6 +532,10 @@ class GraphManager(object): # this is required in order for agents to save additional information like a DND for example [manager.save_checkpoint(self.checkpoint_id) for manager in self.level_managers] + # the ONNX graph will be stored only if checkpoints are stored and the -onnx flag is used + if self.task_parameters.export_onnx_graph: + self.save_onnx_graph() + screen.log_dict( OrderedDict([ ("Saving in path", saved_checkpoint_path), From 93571306c37de65eaecbf79fa77b75bfb0b1f68b Mon Sep 17 00:00:00 2001 From: Sina Afrooze Date: Tue, 6 Nov 2018 07:39:29 -0800 Subject: [PATCH 096/162] Removed tensorflow specific code in presets (#59) * Add generic layer specification for using in presets * Modify presets to use the generic scheme --- rl_coach/architectures/layers.py | 78 ++++++++++++ .../embedders/embedder.py | 7 +- .../tensorflow_components/general_network.py | 28 +---- .../tensorflow_components/layers.py | 117 +++++++++++------- .../middlewares/middleware.py | 7 +- .../tensorflow_components/utils.py | 40 ++++++ rl_coach/presets/Atari_NStepQ.py | 2 +- rl_coach/presets/BitFlip_DQN.py | 2 +- rl_coach/presets/BitFlip_DQN_HER.py | 2 +- rl_coach/presets/CARLA_CIL.py | 33 +++-- rl_coach/presets/CartPole_PPO.py | 2 +- rl_coach/presets/ControlSuite_DDPG.py | 2 +- rl_coach/presets/Fetch_DDPG_HER_baselines.py | 2 +- rl_coach/presets/Mujoco_A3C_LSTM.py | 2 +- rl_coach/presets/Mujoco_ClippedPPO.py | 2 +- rl_coach/presets/Mujoco_DDPG.py | 2 +- rl_coach/presets/Mujoco_NAF.py | 2 +- rl_coach/presets/Mujoco_PPO.py | 2 +- rl_coach/presets/Pendulum_HAC.py | 2 +- 19 files changed, 233 insertions(+), 101 deletions(-) create mode 100644 rl_coach/architectures/layers.py create mode 100644 rl_coach/architectures/tensorflow_components/utils.py diff --git a/rl_coach/architectures/layers.py b/rl_coach/architectures/layers.py new file mode 100644 index 0000000..e295199 --- /dev/null +++ b/rl_coach/architectures/layers.py @@ -0,0 +1,78 @@ +# +# Copyright (c) 2017 Intel Corporation +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +""" +Module implementing base classes for common network layers used by preset schemes +""" + + +class Conv2d(object): + """ + Base class for framework specfic Conv2d layer + """ + def __init__(self, num_filters: int, kernel_size: int, strides: int): + self.num_filters = num_filters + self.kernel_size = kernel_size + self.strides = strides + + def __str__(self): + return "Convolution (num filters = {}, kernel size = {}, stride = {})"\ + .format(self.num_filters, self.kernel_size, self.strides) + + +class BatchnormActivationDropout(object): + """ + Base class for framework specific batchnorm->activation->dropout layer group + """ + def __init__(self, batchnorm: bool=False, activation_function: str=None, dropout_rate: float=0): + self.batchnorm = batchnorm + self.activation_function = activation_function + self.dropout_rate = dropout_rate + + def __str__(self): + result = [] + if self.batchnorm: + result += ["Batch Normalization"] + if self.activation_function: + result += ["Activation (type = {})".format(self.activation_function)] + if self.dropout_rate > 0: + result += ["Dropout (rate = {})".format(self.dropout_rate)] + return "\n".join(result) + + +class Dense(object): + """ + Base class for framework specific Dense layer + """ + def __init__(self, units: int): + self.units = units + + def __str__(self): + return "Dense (num outputs = {})".format(self.units) + + +class NoisyNetDense(object): + """ + Base class for framework specific factorized Noisy Net layer + + https://arxiv.org/abs/1706.10295. + """ + + def __init__(self, units: int): + self.units = units + self.sigma0 = 0.5 + + def __str__(self): + return "Noisy Dense (num outputs = {})".format(self.units) diff --git a/rl_coach/architectures/tensorflow_components/embedders/embedder.py b/rl_coach/architectures/tensorflow_components/embedders/embedder.py index 004c5c4..967b1ba 100644 --- a/rl_coach/architectures/tensorflow_components/embedders/embedder.py +++ b/rl_coach/architectures/tensorflow_components/embedders/embedder.py @@ -20,8 +20,7 @@ import copy import numpy as np import tensorflow as tf -from rl_coach.architectures.tensorflow_components.layers import batchnorm_activation_dropout, Dense, \ - BatchnormActivationDropout +from rl_coach.architectures.tensorflow_components.layers import BatchnormActivationDropout, convert_layer, Dense from rl_coach.base_parameters import EmbedderScheme, NetworkComponentParameters from rl_coach.core_types import InputEmbedding @@ -62,7 +61,9 @@ class InputEmbedder(object): if isinstance(self.scheme, EmbedderScheme): self.layers_params = copy.copy(self.schemes[self.scheme]) else: - self.layers_params = copy.copy(self.scheme) + # if scheme is specified directly, convert to TF layer if it's not a callable object + # NOTE: if layer object is callable, it must return a TF tensor when invoked + self.layers_params = [convert_layer(l) for l in copy.copy(self.scheme)] # we allow adding batchnorm, dropout or activation functions after each layer. # The motivation is to simplify the transition between a network with batchnorm and a network without diff --git a/rl_coach/architectures/tensorflow_components/general_network.py b/rl_coach/architectures/tensorflow_components/general_network.py index fa494b5..143cebd 100644 --- a/rl_coach/architectures/tensorflow_components/general_network.py +++ b/rl_coach/architectures/tensorflow_components/general_network.py @@ -24,6 +24,7 @@ from rl_coach.architectures.embedder_parameters import InputEmbedderParameters from rl_coach.architectures.head_parameters import HeadParameters from rl_coach.architectures.middleware_parameters import MiddlewareParameters from rl_coach.architectures.tensorflow_components.architecture import TensorFlowArchitecture +from rl_coach.architectures.tensorflow_components import utils from rl_coach.base_parameters import AgentParameters, EmbeddingMergerType from rl_coach.core_types import PredictionType from rl_coach.spaces import SpacesDefinition, PlanarMapsObservationSpace @@ -99,27 +100,6 @@ class GeneralTensorFlowNetwork(TensorFlowArchitecture): return ret_dict - @staticmethod - def get_activation_function(activation_function_string: str): - """ - Map the activation function from a string to the tensorflow framework equivalent - :param activation_function_string: the type of the activation function - :return: the tensorflow activation function - """ - activation_functions = { - 'relu': tf.nn.relu, - 'tanh': tf.nn.tanh, - 'sigmoid': tf.nn.sigmoid, - 'elu': tf.nn.elu, - 'selu': tf.nn.selu, - 'leaky_relu': tf.nn.leaky_relu, - 'none': None - } - assert activation_function_string in activation_functions.keys(), \ - "Activation function must be one of the following {}. instead it was: {}"\ - .format(activation_functions.keys(), activation_function_string) - return activation_functions[activation_function_string] - def get_input_embedder(self, input_name: str, embedder_params: InputEmbedderParameters): """ Given an input embedder parameters class, creates the input embedder and returns it @@ -144,7 +124,7 @@ class GeneralTensorFlowNetwork(TensorFlowArchitecture): embedder_path = 'rl_coach.architectures.tensorflow_components.embedders:' + mod_names[emb_type] embedder_params_copy = copy.copy(embedder_params) - embedder_params_copy.activation_function = self.get_activation_function(embedder_params.activation_function) + embedder_params_copy.activation_function = utils.get_activation_function(embedder_params.activation_function) embedder_params_copy.input_rescaling = embedder_params_copy.input_rescaling[emb_type] embedder_params_copy.input_offset = embedder_params_copy.input_offset[emb_type] embedder_params_copy.name = input_name @@ -162,7 +142,7 @@ class GeneralTensorFlowNetwork(TensorFlowArchitecture): mod_name = middleware_params.parameterized_class_name middleware_path = 'rl_coach.architectures.tensorflow_components.middlewares:' + mod_name middleware_params_copy = copy.copy(middleware_params) - middleware_params_copy.activation_function = self.get_activation_function(middleware_params.activation_function) + middleware_params_copy.activation_function = utils.get_activation_function(middleware_params.activation_function) module = dynamic_import_and_instantiate_module_from_params(middleware_params_copy, path=middleware_path) return module @@ -176,7 +156,7 @@ class GeneralTensorFlowNetwork(TensorFlowArchitecture): mod_name = head_params.parameterized_class_name head_path = 'rl_coach.architectures.tensorflow_components.heads:' + mod_name head_params_copy = copy.copy(head_params) - head_params_copy.activation_function = self.get_activation_function(head_params_copy.activation_function) + head_params_copy.activation_function = utils.get_activation_function(head_params_copy.activation_function) return dynamic_import_and_instantiate_module_from_params(head_params_copy, path=head_path, extra_kwargs={ 'agent_parameters': self.ap, 'spaces': self.spaces, 'network_name': self.network_wrapper_name, 'head_idx': head_idx, 'is_local': self.network_is_local}) diff --git a/rl_coach/architectures/tensorflow_components/layers.py b/rl_coach/architectures/tensorflow_components/layers.py index a75ed6c..f9dc356 100644 --- a/rl_coach/architectures/tensorflow_components/layers.py +++ b/rl_coach/architectures/tensorflow_components/layers.py @@ -1,9 +1,11 @@ import math -from typing import List, Union +from types import FunctionType +from typing import Any import tensorflow as tf -from rl_coach.utils import force_list +from rl_coach.architectures import layers +from rl_coach.architectures.tensorflow_components import utils def batchnorm_activation_dropout(input_layer, batchnorm, activation_function, dropout, dropout_rate, is_training, name): @@ -17,6 +19,8 @@ def batchnorm_activation_dropout(input_layer, batchnorm, activation_function, dr # activation if activation_function: + if isinstance(activation_function, str): + activation_function = utils.get_activation_function(activation_function) layers.append( activation_function(layers[-1], name="{}_activation".format(name)) ) @@ -33,11 +37,35 @@ def batchnorm_activation_dropout(input_layer, batchnorm, activation_function, dr return layers -class Conv2d(object): +# define global dictionary for storing layer type to layer implementation mapping +tf_layer_dict = dict() + + +def reg_to_tf(layer_type) -> FunctionType: + """ function decorator that registers layer implementation + :return: decorated function + """ + def reg_impl_decorator(func): + assert layer_type not in tf_layer_dict + tf_layer_dict[layer_type] = func + return func + return reg_impl_decorator + + +def convert_layer(layer): + """ + If layer is callable, return layer, otherwise convert to TF type + :param layer: layer to be converted + :return: converted layer if not callable, otherwise layer itself + """ + if callable(layer): + return layer + return tf_layer_dict[type(layer)](layer) + + +class Conv2d(layers.Conv2d): def __init__(self, num_filters: int, kernel_size: int, strides: int): - self.num_filters = num_filters - self.kernel_size = kernel_size - self.strides = strides + super(Conv2d, self).__init__(num_filters=num_filters, kernel_size=kernel_size, strides=strides) def __call__(self, input_layer, name: str=None, is_training=None): """ @@ -49,16 +77,19 @@ class Conv2d(object): return tf.layers.conv2d(input_layer, filters=self.num_filters, kernel_size=self.kernel_size, strides=self.strides, data_format='channels_last', name=name) - def __str__(self): - return "Convolution (num filters = {}, kernel size = {}, stride = {})"\ - .format(self.num_filters, self.kernel_size, self.strides) + @staticmethod + @reg_to_tf(layers.Conv2d) + def to_tf(base: layers.Conv2d): + return Conv2d( + num_filters=base.num_filters, + kernel_size=base.kernel_size, + strides=base.strides) -class BatchnormActivationDropout(object): +class BatchnormActivationDropout(layers.BatchnormActivationDropout): def __init__(self, batchnorm: bool=False, activation_function=None, dropout_rate: float=0): - self.batchnorm = batchnorm - self.activation_function = activation_function - self.dropout_rate = dropout_rate + super(BatchnormActivationDropout, self).__init__( + batchnorm=batchnorm, activation_function=activation_function, dropout_rate=dropout_rate) def __call__(self, input_layer, name: str=None, is_training=None): """ @@ -72,20 +103,18 @@ class BatchnormActivationDropout(object): dropout=self.dropout_rate > 0, dropout_rate=self.dropout_rate, is_training=is_training, name=name) - def __str__(self): - result = [] - if self.batchnorm: - result += ["Batch Normalization"] - if self.activation_function: - result += ["Activation (type = {})".format(self.activation_function.__name__)] - if self.dropout_rate > 0: - result += ["Dropout (rate = {})".format(self.dropout_rate)] - return "\n".join(result) + @staticmethod + @reg_to_tf(layers.BatchnormActivationDropout) + def to_tf(base: layers.BatchnormActivationDropout): + return BatchnormActivationDropout( + batchnorm=base.batchnorm, + activation_function=base.activation_function, + dropout_rate=base.dropout_rate) -class Dense(object): +class Dense(layers.Dense): def __init__(self, units: int): - self.units = units + super(Dense, self).__init__(units=units) def __call__(self, input_layer, name: str=None, kernel_initializer=None, activation=None, is_training=None): """ @@ -97,11 +126,13 @@ class Dense(object): return tf.layers.dense(input_layer, self.units, name=name, kernel_initializer=kernel_initializer, activation=activation) - def __str__(self): - return "Dense (num outputs = {})".format(self.units) + @staticmethod + @reg_to_tf(layers.Dense) + def to_tf(base: layers.Dense): + return Dense(units=base.units) -class NoisyNetDense(object): +class NoisyNetDense(layers.NoisyNetDense): """ A factorized Noisy Net layer @@ -109,8 +140,7 @@ class NoisyNetDense(object): """ def __init__(self, units: int): - self.units = units - self.sigma0 = 0.5 + super(NoisyNetDense, self).__init__(units=units) def __call__(self, input_layer, name: str, kernel_initializer=None, activation=None, is_training=None): """ @@ -125,6 +155,16 @@ class NoisyNetDense(object): # forward (either act or train, both for online and target networks). # A3C, on the other hand, should sample noise only when policy changes (i.e. after every t_max steps) + def _f(values): + return tf.sqrt(tf.abs(values)) * tf.sign(values) + + def _factorized_noise(inputs, outputs): + # TODO: use factorized noise only for compute intensive algos (e.g. DQN). + # lighter algos (e.g. DQN) should not use it + noise1 = _f(tf.random_normal((inputs, 1))) + noise2 = _f(tf.random_normal((1, outputs))) + return tf.matmul(noise1, noise2) + num_inputs = input_layer.get_shape()[-1].value num_outputs = self.units @@ -145,23 +185,14 @@ class NoisyNetDense(object): initializer=kernel_stddev_initializer) bias_stddev = tf.get_variable('bias_stddev', shape=(num_outputs,), initializer=kernel_stddev_initializer) - bias_noise = self.f(tf.random_normal((num_outputs,))) - weight_noise = self.factorized_noise(num_inputs, num_outputs) + bias_noise = _f(tf.random_normal((num_outputs,))) + weight_noise = _factorized_noise(num_inputs, num_outputs) bias = bias_mean + bias_stddev * bias_noise weight = weight_mean + weight_stddev * weight_noise return activation(tf.matmul(input_layer, weight) + bias) - def factorized_noise(self, inputs, outputs): - # TODO: use factorized noise only for compute intensive algos (e.g. DQN). - # lighter algos (e.g. DQN) should not use it - noise1 = self.f(tf.random_normal((inputs, 1))) - noise2 = self.f(tf.random_normal((1, outputs))) - return tf.matmul(noise1, noise2) - @staticmethod - def f(values): - return tf.sqrt(tf.abs(values)) * tf.sign(values) - - def __str__(self): - return "Noisy Dense (num outputs = {})".format(self.units) + @reg_to_tf(layers.NoisyNetDense) + def to_tf(base: layers.NoisyNetDense): + return NoisyNetDense(units=base.units) diff --git a/rl_coach/architectures/tensorflow_components/middlewares/middleware.py b/rl_coach/architectures/tensorflow_components/middlewares/middleware.py index 02376de..bb10ea9 100644 --- a/rl_coach/architectures/tensorflow_components/middlewares/middleware.py +++ b/rl_coach/architectures/tensorflow_components/middlewares/middleware.py @@ -14,10 +14,11 @@ # limitations under the License. # import copy +from typing import Union import tensorflow as tf -from rl_coach.architectures.tensorflow_components.layers import Dense, BatchnormActivationDropout +from rl_coach.architectures.tensorflow_components.layers import BatchnormActivationDropout, convert_layer, Dense from rl_coach.base_parameters import MiddlewareScheme, NetworkComponentParameters from rl_coach.core_types import MiddlewareEmbedding @@ -50,7 +51,9 @@ class Middleware(object): if isinstance(self.scheme, MiddlewareScheme): self.layers_params = copy.copy(self.schemes[self.scheme]) else: - self.layers_params = copy.copy(self.scheme) + # if scheme is specified directly, convert to TF layer if it's not a callable object + # NOTE: if layer object is callable, it must return a TF tensor when invoked + self.layers_params = [convert_layer(l) for l in copy.copy(self.scheme)] # we allow adding batchnorm, dropout or activation functions after each layer. # The motivation is to simplify the transition between a network with batchnorm and a network without diff --git a/rl_coach/architectures/tensorflow_components/utils.py b/rl_coach/architectures/tensorflow_components/utils.py new file mode 100644 index 0000000..749a0ab --- /dev/null +++ b/rl_coach/architectures/tensorflow_components/utils.py @@ -0,0 +1,40 @@ +# +# Copyright (c) 2017 Intel Corporation +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +""" +Module containing utility functions +""" +import tensorflow as tf + + +def get_activation_function(activation_function_string: str): + """ + Map the activation function from a string to the tensorflow framework equivalent + :param activation_function_string: the type of the activation function + :return: the tensorflow activation function + """ + activation_functions = { + 'relu': tf.nn.relu, + 'tanh': tf.nn.tanh, + 'sigmoid': tf.nn.sigmoid, + 'elu': tf.nn.elu, + 'selu': tf.nn.selu, + 'leaky_relu': tf.nn.leaky_relu, + 'none': None + } + assert activation_function_string in activation_functions.keys(), \ + "Activation function must be one of the following {}. instead it was: {}" \ + .format(activation_functions.keys(), activation_function_string) + return activation_functions[activation_function_string] diff --git a/rl_coach/presets/Atari_NStepQ.py b/rl_coach/presets/Atari_NStepQ.py index 6807f86..4b86d80 100644 --- a/rl_coach/presets/Atari_NStepQ.py +++ b/rl_coach/presets/Atari_NStepQ.py @@ -1,5 +1,5 @@ from rl_coach.agents.n_step_q_agent import NStepQAgentParameters -from rl_coach.architectures.tensorflow_components.layers import Conv2d, Dense +from rl_coach.architectures.layers import Conv2d, Dense from rl_coach.base_parameters import VisualizationParameters, PresetValidationParameters from rl_coach.core_types import TrainingSteps, EnvironmentEpisodes, EnvironmentSteps from rl_coach.environments.environment import SingleLevelSelection diff --git a/rl_coach/presets/BitFlip_DQN.py b/rl_coach/presets/BitFlip_DQN.py index ed849f3..5b1c0ec 100644 --- a/rl_coach/presets/BitFlip_DQN.py +++ b/rl_coach/presets/BitFlip_DQN.py @@ -1,6 +1,6 @@ from rl_coach.agents.dqn_agent import DQNAgentParameters from rl_coach.architectures.embedder_parameters import InputEmbedderParameters -from rl_coach.architectures.tensorflow_components.layers import Dense +from rl_coach.architectures.layers import Dense from rl_coach.base_parameters import VisualizationParameters, EmbedderScheme, \ PresetValidationParameters from rl_coach.core_types import TrainingSteps, EnvironmentEpisodes, EnvironmentSteps diff --git a/rl_coach/presets/BitFlip_DQN_HER.py b/rl_coach/presets/BitFlip_DQN_HER.py index 3d9c2f9..8f1d4e1 100644 --- a/rl_coach/presets/BitFlip_DQN_HER.py +++ b/rl_coach/presets/BitFlip_DQN_HER.py @@ -1,6 +1,6 @@ from rl_coach.agents.dqn_agent import DQNAgentParameters from rl_coach.architectures.embedder_parameters import InputEmbedderParameters -from rl_coach.architectures.tensorflow_components.layers import Dense +from rl_coach.architectures.layers import Dense from rl_coach.base_parameters import VisualizationParameters, EmbedderScheme, \ PresetValidationParameters from rl_coach.core_types import TrainingSteps, EnvironmentEpisodes, EnvironmentSteps diff --git a/rl_coach/presets/CARLA_CIL.py b/rl_coach/presets/CARLA_CIL.py index 8477cdf..4d5efff 100644 --- a/rl_coach/presets/CARLA_CIL.py +++ b/rl_coach/presets/CARLA_CIL.py @@ -1,7 +1,6 @@ import os import numpy as np -import tensorflow as tf # make sure you have $CARLA_ROOT/PythonClient in your PYTHONPATH from carla.driving_benchmark.experiment_suites import CoRL2017 from rl_coach.logger import screen @@ -10,7 +9,7 @@ from rl_coach.agents.cil_agent import CILAgentParameters from rl_coach.architectures.embedder_parameters import InputEmbedderParameters from rl_coach.architectures.head_parameters import RegressionHeadParameters from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters -from rl_coach.architectures.tensorflow_components.layers import Conv2d, Dense, BatchnormActivationDropout +from rl_coach.architectures.layers import Conv2d, Dense, BatchnormActivationDropout from rl_coach.base_parameters import VisualizationParameters from rl_coach.core_types import TrainingSteps, EnvironmentEpisodes, EnvironmentSteps from rl_coach.environments.carla_environment import CarlaEnvironmentParameters @@ -46,34 +45,34 @@ agent_params.network_wrappers['main'].input_embedders_parameters = { 'CameraRGB': InputEmbedderParameters( scheme=[ Conv2d(32, 5, 2), - BatchnormActivationDropout(batchnorm=True, activation_function=tf.tanh), + BatchnormActivationDropout(batchnorm=True, activation_function='tanh'), Conv2d(32, 3, 1), - BatchnormActivationDropout(batchnorm=True, activation_function=tf.tanh), + BatchnormActivationDropout(batchnorm=True, activation_function='tanh'), Conv2d(64, 3, 2), - BatchnormActivationDropout(batchnorm=True, activation_function=tf.tanh), + BatchnormActivationDropout(batchnorm=True, activation_function='tanh'), Conv2d(64, 3, 1), - BatchnormActivationDropout(batchnorm=True, activation_function=tf.tanh), + BatchnormActivationDropout(batchnorm=True, activation_function='tanh'), Conv2d(128, 3, 2), - BatchnormActivationDropout(batchnorm=True, activation_function=tf.tanh), + BatchnormActivationDropout(batchnorm=True, activation_function='tanh'), Conv2d(128, 3, 1), - BatchnormActivationDropout(batchnorm=True, activation_function=tf.tanh), + BatchnormActivationDropout(batchnorm=True, activation_function='tanh'), Conv2d(256, 3, 1), - BatchnormActivationDropout(batchnorm=True, activation_function=tf.tanh), + BatchnormActivationDropout(batchnorm=True, activation_function='tanh'), Conv2d(256, 3, 1), - BatchnormActivationDropout(batchnorm=True, activation_function=tf.tanh), + BatchnormActivationDropout(batchnorm=True, activation_function='tanh'), Dense(512), - BatchnormActivationDropout(activation_function=tf.tanh, dropout_rate=0.3), + BatchnormActivationDropout(activation_function='tanh', dropout_rate=0.3), Dense(512), - BatchnormActivationDropout(activation_function=tf.tanh, dropout_rate=0.3) + BatchnormActivationDropout(activation_function='tanh', dropout_rate=0.3) ], activation_function='none' # we define the activation function for each layer explicitly ), 'measurements': InputEmbedderParameters( scheme=[ Dense(128), - BatchnormActivationDropout(activation_function=tf.tanh, dropout_rate=0.5), + BatchnormActivationDropout(activation_function='tanh', dropout_rate=0.5), Dense(128), - BatchnormActivationDropout(activation_function=tf.tanh, dropout_rate=0.5) + BatchnormActivationDropout(activation_function='tanh', dropout_rate=0.5) ], activation_function='none' # we define the activation function for each layer explicitly ) @@ -84,7 +83,7 @@ agent_params.network_wrappers['main'].middleware_parameters = \ FCMiddlewareParameters( scheme=[ Dense(512), - BatchnormActivationDropout(activation_function=tf.tanh, dropout_rate=0.5) + BatchnormActivationDropout(activation_function='tanh', dropout_rate=0.5) ], activation_function='none' ) @@ -94,9 +93,9 @@ agent_params.network_wrappers['main'].heads_parameters = [ RegressionHeadParameters( scheme=[ Dense(256), - BatchnormActivationDropout(activation_function=tf.tanh, dropout_rate=0.5), + BatchnormActivationDropout(activation_function='tanh', dropout_rate=0.5), Dense(256), - BatchnormActivationDropout(activation_function=tf.tanh) + BatchnormActivationDropout(activation_function='tanh') ], num_output_head_copies=4 # follow lane, left, right, straight ) diff --git a/rl_coach/presets/CartPole_PPO.py b/rl_coach/presets/CartPole_PPO.py index 40e06d1..e8af4c1 100644 --- a/rl_coach/presets/CartPole_PPO.py +++ b/rl_coach/presets/CartPole_PPO.py @@ -1,5 +1,5 @@ from rl_coach.agents.clipped_ppo_agent import ClippedPPOAgentParameters -from rl_coach.architectures.tensorflow_components.layers import Dense +from rl_coach.architectures.layers import Dense from rl_coach.base_parameters import VisualizationParameters, PresetValidationParameters, DistributedCoachSynchronizationType from rl_coach.core_types import TrainingSteps, EnvironmentEpisodes, EnvironmentSteps, RunPhase from rl_coach.environments.gym_environment import GymVectorEnvironment, mujoco_v2 diff --git a/rl_coach/presets/ControlSuite_DDPG.py b/rl_coach/presets/ControlSuite_DDPG.py index 260ca52..bef6898 100644 --- a/rl_coach/presets/ControlSuite_DDPG.py +++ b/rl_coach/presets/ControlSuite_DDPG.py @@ -1,5 +1,5 @@ from rl_coach.agents.ddpg_agent import DDPGAgentParameters -from rl_coach.architectures.tensorflow_components.layers import Dense +from rl_coach.architectures.layers import Dense from rl_coach.base_parameters import VisualizationParameters, EmbedderScheme, PresetValidationParameters from rl_coach.core_types import TrainingSteps, EnvironmentEpisodes, EnvironmentSteps from rl_coach.environments.control_suite_environment import ControlSuiteEnvironmentParameters, control_suite_envs diff --git a/rl_coach/presets/Fetch_DDPG_HER_baselines.py b/rl_coach/presets/Fetch_DDPG_HER_baselines.py index d3fa643..c2f17d7 100644 --- a/rl_coach/presets/Fetch_DDPG_HER_baselines.py +++ b/rl_coach/presets/Fetch_DDPG_HER_baselines.py @@ -1,7 +1,7 @@ from rl_coach.agents.ddpg_agent import DDPGAgentParameters from rl_coach.architectures.embedder_parameters import InputEmbedderParameters from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters -from rl_coach.architectures.tensorflow_components.layers import Dense +from rl_coach.architectures.layers import Dense from rl_coach.base_parameters import VisualizationParameters, EmbedderScheme, PresetValidationParameters from rl_coach.core_types import EnvironmentEpisodes, EnvironmentSteps, TrainingSteps from rl_coach.environments.environment import SingleLevelSelection diff --git a/rl_coach/presets/Mujoco_A3C_LSTM.py b/rl_coach/presets/Mujoco_A3C_LSTM.py index 1027c01..323f28c 100644 --- a/rl_coach/presets/Mujoco_A3C_LSTM.py +++ b/rl_coach/presets/Mujoco_A3C_LSTM.py @@ -1,7 +1,7 @@ from rl_coach.agents.actor_critic_agent import ActorCriticAgentParameters from rl_coach.architectures.embedder_parameters import InputEmbedderParameters from rl_coach.architectures.middleware_parameters import LSTMMiddlewareParameters -from rl_coach.architectures.tensorflow_components.layers import Dense +from rl_coach.architectures.layers import Dense from rl_coach.base_parameters import VisualizationParameters, MiddlewareScheme, PresetValidationParameters from rl_coach.core_types import TrainingSteps, EnvironmentEpisodes, EnvironmentSteps from rl_coach.environments.environment import SingleLevelSelection diff --git a/rl_coach/presets/Mujoco_ClippedPPO.py b/rl_coach/presets/Mujoco_ClippedPPO.py index d99f7ac..ca2d662 100644 --- a/rl_coach/presets/Mujoco_ClippedPPO.py +++ b/rl_coach/presets/Mujoco_ClippedPPO.py @@ -1,5 +1,5 @@ from rl_coach.agents.clipped_ppo_agent import ClippedPPOAgentParameters -from rl_coach.architectures.tensorflow_components.layers import Dense +from rl_coach.architectures.layers import Dense from rl_coach.base_parameters import VisualizationParameters, PresetValidationParameters from rl_coach.core_types import TrainingSteps, EnvironmentEpisodes, EnvironmentSteps from rl_coach.environments.environment import SingleLevelSelection diff --git a/rl_coach/presets/Mujoco_DDPG.py b/rl_coach/presets/Mujoco_DDPG.py index 908039c..03a95a8 100644 --- a/rl_coach/presets/Mujoco_DDPG.py +++ b/rl_coach/presets/Mujoco_DDPG.py @@ -1,5 +1,5 @@ from rl_coach.agents.ddpg_agent import DDPGAgentParameters -from rl_coach.architectures.tensorflow_components.layers import Dense +from rl_coach.architectures.layers import Dense from rl_coach.base_parameters import VisualizationParameters, PresetValidationParameters, EmbedderScheme from rl_coach.core_types import EnvironmentEpisodes, EnvironmentSteps from rl_coach.environments.environment import SingleLevelSelection diff --git a/rl_coach/presets/Mujoco_NAF.py b/rl_coach/presets/Mujoco_NAF.py index 44d9262..6db8f66 100644 --- a/rl_coach/presets/Mujoco_NAF.py +++ b/rl_coach/presets/Mujoco_NAF.py @@ -1,5 +1,5 @@ from rl_coach.agents.naf_agent import NAFAgentParameters -from rl_coach.architectures.tensorflow_components.layers import Dense +from rl_coach.architectures.layers import Dense from rl_coach.base_parameters import VisualizationParameters, PresetValidationParameters from rl_coach.core_types import TrainingSteps, EnvironmentEpisodes, EnvironmentSteps, GradientClippingMethod from rl_coach.environments.environment import SingleLevelSelection diff --git a/rl_coach/presets/Mujoco_PPO.py b/rl_coach/presets/Mujoco_PPO.py index 75aae60..4eb2f72 100644 --- a/rl_coach/presets/Mujoco_PPO.py +++ b/rl_coach/presets/Mujoco_PPO.py @@ -1,5 +1,5 @@ from rl_coach.agents.ppo_agent import PPOAgentParameters -from rl_coach.architectures.tensorflow_components.layers import Dense +from rl_coach.architectures.layers import Dense from rl_coach.base_parameters import VisualizationParameters, PresetValidationParameters from rl_coach.core_types import TrainingSteps, EnvironmentEpisodes, EnvironmentSteps from rl_coach.environments.environment import SingleLevelSelection diff --git a/rl_coach/presets/Pendulum_HAC.py b/rl_coach/presets/Pendulum_HAC.py index b6fa02c..d945f34 100644 --- a/rl_coach/presets/Pendulum_HAC.py +++ b/rl_coach/presets/Pendulum_HAC.py @@ -2,7 +2,7 @@ import numpy as np from rl_coach.agents.hac_ddpg_agent import HACDDPGAgentParameters from rl_coach.architectures.embedder_parameters import InputEmbedderParameters -from rl_coach.architectures.tensorflow_components.layers import Dense +from rl_coach.architectures.layers import Dense from rl_coach.base_parameters import VisualizationParameters, EmbeddingMergerType, EmbedderScheme from rl_coach.core_types import EnvironmentEpisodes, EnvironmentSteps, TrainingSteps from rl_coach.environments.gym_environment import GymVectorEnvironment From e7a91b4dc3d85c3856a8aa429b09eb23d30beb2f Mon Sep 17 00:00:00 2001 From: Itai Caspi <30383381+itaicaspi-intel@users.noreply.github.com> Date: Wed, 7 Nov 2018 15:47:02 +0200 Subject: [PATCH 097/162] Fix cmd line arguments handling (#68) * refactoring the merging of the task parameters and the command line parameters * removing some unused command line arguments * fix for saving checkpoints when not passing through coach.py --- .../distributed_tf_utils.py | 2 +- rl_coach/base_parameters.py | 18 ++++- rl_coach/coach.py | 79 ++++++++----------- rl_coach/graph_managers/graph_manager.py | 4 + 4 files changed, 53 insertions(+), 50 deletions(-) diff --git a/rl_coach/architectures/tensorflow_components/distributed_tf_utils.py b/rl_coach/architectures/tensorflow_components/distributed_tf_utils.py index ccf3e89..2ea216d 100644 --- a/rl_coach/architectures/tensorflow_components/distributed_tf_utils.py +++ b/rl_coach/architectures/tensorflow_components/distributed_tf_utils.py @@ -94,7 +94,7 @@ def create_monitored_session(target: tf.train.Server, task_index: int, is_chief=is_chief, hooks=[], checkpoint_dir=checkpoint_dir, - checkpoint_save_secs=checkpoint_save_secs, + save_checkpoint_secs=checkpoint_save_secs, config=config ) diff --git a/rl_coach/base_parameters.py b/rl_coach/base_parameters.py index e23226e..03dbf25 100644 --- a/rl_coach/base_parameters.py +++ b/rl_coach/base_parameters.py @@ -432,14 +432,17 @@ class AgentParameters(Parameters): class TaskParameters(Parameters): def __init__(self, framework_type: Frameworks=Frameworks.tensorflow, evaluate_only: bool=False, use_cpu: bool=False, - experiment_path='/tmp', seed=None, checkpoint_save_secs=None): + experiment_path='/tmp', seed=None, checkpoint_save_secs=None, checkpoint_restore_dir=None, + checkpoint_save_dir=None): """ :param framework_type: deep learning framework type. currently only tensorflow is supported :param evaluate_only: the task will be used only for evaluating the model :param use_cpu: use the cpu for this task :param experiment_path: the path to the directory which will store all the experiment outputs - :param checkpoint_save_secs: the number of seconds between each checkpoint saving :param seed: a seed to use for the random numbers generator + :param checkpoint_save_secs: the number of seconds between each checkpoint saving + :param checkpoint_restore_dir: the directory to restore the checkpoints from + :param checkpoint_save_dir: the directory to store the checkpoints in """ self.framework_type = framework_type self.task_index = 0 # TODO: not really needed @@ -447,6 +450,8 @@ class TaskParameters(Parameters): self.use_cpu = use_cpu self.experiment_path = experiment_path self.checkpoint_save_secs = checkpoint_save_secs + self.checkpoint_restore_dir = checkpoint_restore_dir + self.checkpoint_save_dir = checkpoint_save_dir self.seed = seed @@ -454,7 +459,8 @@ class DistributedTaskParameters(TaskParameters): def __init__(self, framework_type: Frameworks, parameters_server_hosts: str, worker_hosts: str, job_type: str, task_index: int, evaluate_only: bool=False, num_tasks: int=None, num_training_tasks: int=None, use_cpu: bool=False, experiment_path=None, dnd=None, - shared_memory_scratchpad=None, seed=None): + shared_memory_scratchpad=None, seed=None, checkpoint_save_secs=None, checkpoint_restore_dir=None, + checkpoint_save_dir=None): """ :param framework_type: deep learning framework type. currently only tensorflow is supported :param evaluate_only: the task will be used only for evaluating the model @@ -469,9 +475,13 @@ class DistributedTaskParameters(TaskParameters): :param experiment_path: the path to the directory which will store all the experiment outputs :param dnd: an external DND to use for NEC. This is a workaround needed for a shared DND not using the scratchpad. :param seed: a seed to use for the random numbers generator + :param checkpoint_save_secs: the number of seconds between each checkpoint saving + :param checkpoint_restore_dir: the directory to restore the checkpoints from + :param checkpoint_save_dir: the directory to store the checkpoints in """ super().__init__(framework_type=framework_type, evaluate_only=evaluate_only, use_cpu=use_cpu, - experiment_path=experiment_path, seed=seed) + experiment_path=experiment_path, seed=seed, checkpoint_save_secs=checkpoint_save_secs, + checkpoint_restore_dir=checkpoint_restore_dir, checkpoint_save_dir=checkpoint_save_dir) self.parameters_server_hosts = parameters_server_hosts self.worker_hosts = worker_hosts self.job_type = job_type diff --git a/rl_coach/coach.py b/rl_coach/coach.py index c9cd304..19daf40 100644 --- a/rl_coach/coach.py +++ b/rl_coach/coach.py @@ -213,13 +213,10 @@ def parse_arguments(parser: argparse.ArgumentParser) -> argparse.Namespace: screen.error("The requested checkpoint folder to load from does not exist.") # no preset was given. check if the user requested to play some environment on its own - if args.preset is None and args.play: - if args.environment_type: - args.agent_type = 'Human' - else: - screen.error('When no preset is given for Coach to run, and the user requests human control over ' - 'the environment, the user is expected to input the desired environment_type and level.' - '\nAt least one of these parameters was not given.') + if args.preset is None and args.play and not args.environment_type: + screen.error('When no preset is given for Coach to run, and the user requests human control over ' + 'the environment, the user is expected to input the desired environment_type and level.' + '\nAt least one of these parameters was not given.') elif args.preset and args.play: screen.error("Both the --preset and the --play flags were set. These flags can not be used together. " "For human control, please use the --play flag together with the environment type flag (-et)") @@ -428,24 +425,8 @@ def main(): parser.add_argument('-dm', '--dump_mp4', help="(flag) Enable the mp4 saving functionality.", action='store_true') - parser.add_argument('-at', '--agent_type', - help="(string) Choose an agent type class to override on top of the selected preset. " - "If no preset is defined, a preset can be set from the command-line by combining settings " - "which are set by using --agent_type, --experiment_type, --environemnt_type", - default=None, - type=str) parser.add_argument('-et', '--environment_type', - help="(string) Choose an environment type class to override on top of the selected preset." - "If no preset is defined, a preset can be set from the command-line by combining settings " - "which are set by using --agent_type, --experiment_type, --environemnt_type", - default=None, - type=str) - parser.add_argument('-ept', '--exploration_policy_type', - help="(string) Choose an exploration policy type class to override on top of the selected " - "preset." - "If no preset is defined, a preset can be set from the command-line by combining settings " - "which are set by using --agent_type, --experiment_type, --environemnt_type" - , + help="(string) Choose an environment type class to override on top of the selected preset.", default=None, type=str) parser.add_argument('-lvl', '--level', @@ -546,13 +527,16 @@ def main(): # Single-threaded runs if args.num_workers == 1: # Start the training or evaluation - task_parameters = TaskParameters(framework_type=args.framework, - evaluate_only=args.evaluate, - experiment_path=args.experiment_path, - seed=args.seed, - use_cpu=args.use_cpu, - checkpoint_save_secs=args.checkpoint_save_secs) - task_parameters.__dict__ = add_items_to_dict(task_parameters.__dict__, args.__dict__) + task_parameters = TaskParameters( + framework_type=args.framework, + evaluate_only=args.evaluate, + experiment_path=args.experiment_path, + seed=args.seed, + use_cpu=args.use_cpu, + checkpoint_save_secs=args.checkpoint_save_secs, + checkpoint_restore_dir=args.checkpoint_restore_dir, + checkpoint_save_dir=args.checkpoint_save_dir + ) start_graph(graph_manager=graph_manager, task_parameters=task_parameters) @@ -575,19 +559,24 @@ def main(): def start_distributed_task(job_type, task_index, evaluation_worker=False, shared_memory_scratchpad=shared_memory_scratchpad): - task_parameters = DistributedTaskParameters(framework_type=args.framework, - parameters_server_hosts=ps_hosts, - worker_hosts=worker_hosts, - job_type=job_type, - task_index=task_index, - evaluate_only=evaluation_worker, - use_cpu=args.use_cpu, - num_tasks=total_tasks, # training tasks + 1 evaluation task - num_training_tasks=args.num_workers, - experiment_path=args.experiment_path, - shared_memory_scratchpad=shared_memory_scratchpad, - seed=args.seed+task_index if args.seed is not None else None) # each worker gets a different seed - task_parameters.__dict__ = add_items_to_dict(task_parameters.__dict__, args.__dict__) + task_parameters = DistributedTaskParameters( + framework_type=args.framework, + parameters_server_hosts=ps_hosts, + worker_hosts=worker_hosts, + job_type=job_type, + task_index=task_index, + evaluate_only=evaluation_worker, + use_cpu=args.use_cpu, + num_tasks=total_tasks, # training tasks + 1 evaluation task + num_training_tasks=args.num_workers, + experiment_path=args.experiment_path, + shared_memory_scratchpad=shared_memory_scratchpad, + seed=args.seed+task_index if args.seed is not None else None, # each worker gets a different seed + checkpoint_save_secs=args.checkpoint_save_secs, + checkpoint_restore_dir=args.checkpoint_restore_dir, + checkpoint_save_dir=args.checkpoint_save_dir + ) + # we assume that only the evaluation workers are rendering graph_manager.visualization_parameters.render = args.render and evaluation_worker p = Process(target=start_graph, args=(graph_manager, task_parameters)) @@ -607,7 +596,7 @@ def main(): workers.append(start_distributed_task("worker", task_index)) # evaluation worker - if args.evaluation_worker: + if args.evaluation_worker or args.render: evaluation_worker = start_distributed_task("worker", args.num_workers, evaluation_worker=True) # wait for all workers diff --git a/rl_coach/graph_managers/graph_manager.py b/rl_coach/graph_managers/graph_manager.py index 7743885..422f724 100644 --- a/rl_coach/graph_managers/graph_manager.py +++ b/rl_coach/graph_managers/graph_manager.py @@ -100,6 +100,8 @@ class GraphManager(object): self.preset_validation_params = PresetValidationParameters() self.reset_required = False + # timers + self.graph_creation_time = None self.last_checkpoint_saving_time = time.time() # counters @@ -520,6 +522,8 @@ class GraphManager(object): self.save_checkpoint() def save_checkpoint(self): + if self.task_parameters.checkpoint_save_dir is None: + self.task_parameters.checkpoint_save_dir = os.path.join(self.task_parameters.experiment_path, 'checkpoint') checkpoint_path = os.path.join(self.task_parameters.checkpoint_save_dir, "{}_Step-{}.ckpt".format( self.checkpoint_id, From 5fadb9c18e3de16cc5633175199f9e9e2c381102 Mon Sep 17 00:00:00 2001 From: Sina Afrooze Date: Wed, 7 Nov 2018 07:07:15 -0800 Subject: [PATCH 098/162] Adding mxnet components to rl_coach/architectures (#60) Adding mxnet components to rl_coach architectures. - Supports PPO and DQN - Tested with CartPole_PPO and CarPole_DQN - Normalizing filters don't work right now (see #49) and are disabled in CartPole_PPO preset - Checkpointing is disabled for MXNet --- docker/Dockerfile | 4 +- rl_coach/architectures/architecture.py | 37 +- .../mxnet_components/__init__.py | 0 .../mxnet_components/architecture.py | 405 +++++++++++ .../mxnet_components/embedders/__init__.py | 4 + .../mxnet_components/embedders/embedder.py | 71 ++ .../embedders/image_embedder.py | 76 ++ .../embedders/vector_embedder.py | 71 ++ .../mxnet_components/general_network.py | 501 +++++++++++++ .../mxnet_components/heads/__init__.py | 14 + .../mxnet_components/heads/head.py | 181 +++++ .../mxnet_components/heads/ppo_head.py | 669 ++++++++++++++++++ .../mxnet_components/heads/ppo_v_head.py | 123 ++++ .../mxnet_components/heads/q_head.py | 106 +++ .../mxnet_components/heads/v_head.py | 100 +++ .../architectures/mxnet_components/layers.py | 99 +++ .../mxnet_components/middlewares/__init__.py | 4 + .../middlewares/fc_middleware.py | 52 ++ .../middlewares/lstm_middleware.py | 80 +++ .../middlewares/middleware.py | 61 ++ .../architectures/mxnet_components/utils.py | 280 ++++++++ rl_coach/architectures/network_wrapper.py | 11 +- .../tensorflow_components/architecture.py | 23 +- rl_coach/graph_managers/graph_manager.py | 73 +- rl_coach/presets/CartPole_PPO.py | 4 +- .../mxnet_components/__init__.py | 0 .../mxnet_components/embedders/__init__.py | 0 .../embedders/test_image_embedder.py | 21 + .../embedders/test_vector_embedder.py | 22 + .../mxnet_components/heads/__init__.py | 0 .../mxnet_components/heads/test_ppo_head.py | 406 +++++++++++ .../mxnet_components/heads/test_ppo_v_head.py | 90 +++ .../mxnet_components/heads/test_q_head.py | 60 ++ .../mxnet_components/heads/test_v_head.py | 57 ++ .../mxnet_components/middlewares/__init__.py | 0 .../middlewares/test_fc_middleware.py | 22 + .../middlewares/test_lstm_middleware.py | 25 + .../mxnet_components/test_utils.py | 144 ++++ setup.py | 12 + 39 files changed, 3864 insertions(+), 44 deletions(-) create mode 100644 rl_coach/architectures/mxnet_components/__init__.py create mode 100644 rl_coach/architectures/mxnet_components/architecture.py create mode 100644 rl_coach/architectures/mxnet_components/embedders/__init__.py create mode 100644 rl_coach/architectures/mxnet_components/embedders/embedder.py create mode 100644 rl_coach/architectures/mxnet_components/embedders/image_embedder.py create mode 100644 rl_coach/architectures/mxnet_components/embedders/vector_embedder.py create mode 100644 rl_coach/architectures/mxnet_components/general_network.py create mode 100644 rl_coach/architectures/mxnet_components/heads/__init__.py create mode 100644 rl_coach/architectures/mxnet_components/heads/head.py create mode 100644 rl_coach/architectures/mxnet_components/heads/ppo_head.py create mode 100644 rl_coach/architectures/mxnet_components/heads/ppo_v_head.py create mode 100644 rl_coach/architectures/mxnet_components/heads/q_head.py create mode 100644 rl_coach/architectures/mxnet_components/heads/v_head.py create mode 100644 rl_coach/architectures/mxnet_components/layers.py create mode 100644 rl_coach/architectures/mxnet_components/middlewares/__init__.py create mode 100644 rl_coach/architectures/mxnet_components/middlewares/fc_middleware.py create mode 100644 rl_coach/architectures/mxnet_components/middlewares/lstm_middleware.py create mode 100644 rl_coach/architectures/mxnet_components/middlewares/middleware.py create mode 100644 rl_coach/architectures/mxnet_components/utils.py create mode 100644 rl_coach/tests/architectures/mxnet_components/__init__.py create mode 100644 rl_coach/tests/architectures/mxnet_components/embedders/__init__.py create mode 100644 rl_coach/tests/architectures/mxnet_components/embedders/test_image_embedder.py create mode 100644 rl_coach/tests/architectures/mxnet_components/embedders/test_vector_embedder.py create mode 100644 rl_coach/tests/architectures/mxnet_components/heads/__init__.py create mode 100644 rl_coach/tests/architectures/mxnet_components/heads/test_ppo_head.py create mode 100644 rl_coach/tests/architectures/mxnet_components/heads/test_ppo_v_head.py create mode 100644 rl_coach/tests/architectures/mxnet_components/heads/test_q_head.py create mode 100644 rl_coach/tests/architectures/mxnet_components/heads/test_v_head.py create mode 100644 rl_coach/tests/architectures/mxnet_components/middlewares/__init__.py create mode 100644 rl_coach/tests/architectures/mxnet_components/middlewares/test_fc_middleware.py create mode 100644 rl_coach/tests/architectures/mxnet_components/middlewares/test_lstm_middleware.py create mode 100644 rl_coach/tests/architectures/mxnet_components/test_utils.py diff --git a/docker/Dockerfile b/docker/Dockerfile index 1fb5dc3..1237e64 100644 --- a/docker/Dockerfile +++ b/docker/Dockerfile @@ -5,12 +5,12 @@ COPY setup.py /root/src/. COPY requirements.txt /root/src/. COPY README.md /root/src/. WORKDIR /root/src -RUN pip3 install -e . +RUN pip3 install -e .[all] # everything above here should be cached most of the time COPY . /root/src WORKDIR /root/src -RUN pip3 install -e . +RUN pip3 install -e .[all] RUN chmod 777 /root/src/docker/docker_entrypoint.sh ENTRYPOINT ["/root/src/docker/docker_entrypoint.sh"] diff --git a/rl_coach/architectures/architecture.py b/rl_coach/architectures/architecture.py index 1ae2d47..78b66cb 100644 --- a/rl_coach/architectures/architecture.py +++ b/rl_coach/architectures/architecture.py @@ -41,22 +41,41 @@ class Architecture(object): self.optimizer = None self.ap = agent_parameters - def predict(self, inputs: Dict[str, np.ndarray]) -> List[np.ndarray]: + def predict(self, + inputs: Dict[str, np.ndarray], + outputs: List[Any] = None, + squeeze_output: bool = True, + initial_feed_dict: Dict[Any, np.ndarray] = None) -> Tuple[np.ndarray, ...]: """ Given input observations, use the model to make predictions (e.g. action or value). :param inputs: current state (i.e. observations, measurements, goals, etc.) (e.g. `{'observation': numpy.ndarray}` of shape (batch_size, observation_space_size)) + :param outputs: list of outputs to return. Return all outputs if unspecified. Type of the list elements + depends on the framework backend. + :param squeeze_output: call squeeze_list on output before returning if True + :param initial_feed_dict: a dictionary of extra inputs for forward pass. :return: predictions of action or value of shape (batch_size, action_space_size) for action predictions) """ pass + @staticmethod + def parallel_predict(sess: Any, + network_input_tuples: List[Tuple['Architecture', Dict[str, np.ndarray]]]) -> \ + Tuple[np.ndarray, ...]: + """ + :param sess: active session to use for prediction + :param network_input_tuples: tuple of network and corresponding input + :return: list or tuple of outputs from all networks + """ + pass + def train_on_batch(self, inputs: Dict[str, np.ndarray], targets: List[np.ndarray], scaler: float=1., additional_fetches: list=None, - importance_weights: np.ndarray=None) -> tuple: + importance_weights: np.ndarray=None) -> Tuple[float, List[float], float, list]: """ Given a batch of inputs (e.g. states) and targets (e.g. discounted rewards), takes a training step: i.e. runs a forward pass and backward pass of the network, accumulates the gradients and applies an optimization step to @@ -118,8 +137,7 @@ class Architecture(object): targets: List[np.ndarray], additional_fetches: list=None, importance_weights: np.ndarray=None, - no_accumulation: bool=False) ->\ - Tuple[float, List[float], float, list]: + no_accumulation: bool=False) -> Tuple[float, List[float], float, list]: """ Given a batch of inputs (i.e. states) and targets (e.g. discounted rewards), computes and accumulates the gradients for model parameters. Will run forward and backward pass to compute gradients, clip the gradient @@ -142,30 +160,33 @@ class Architecture(object): calculated gradients :return: tuple of total_loss, losses, norm_unclipped_grads, fetched_tensors total_loss (float): sum of all head losses - losses (list of float): list of all losses. The order is list of target losses followed by list of regularization losses. - The specifics of losses is dependant on the network parameters (number of heads, etc.) + losses (list of float): list of all losses. The order is list of target losses followed by list of + regularization losses. The specifics of losses is dependant on the network parameters + (number of heads, etc.) norm_unclippsed_grads (float): global norm of all gradients before any gradient clipping is applied fetched_tensors: all values for additional_fetches """ pass - def apply_and_reset_gradients(self, gradients: List[np.ndarray]) -> None: + def apply_and_reset_gradients(self, gradients: List[np.ndarray], scaler: float=1.) -> None: """ Applies the given gradients to the network weights and resets the gradient accumulations. Has the same impact as calling `apply_gradients`, then `reset_accumulated_gradients`. :param gradients: gradients for the parameter weights, taken from `accumulated_gradients` property of an identical network (either self or another identical network) + :param scaler: A scaling factor that allows rescaling the gradients before applying them """ pass - def apply_gradients(self, gradients: List[np.ndarray]) -> None: + def apply_gradients(self, gradients: List[np.ndarray], scaler: float=1.) -> None: """ Applies the given gradients to the network weights. Will be performed sync or async depending on `network_parameters.async_training` :param gradients: gradients for the parameter weights, taken from `accumulated_gradients` property of an identical network (either self or another identical network) + :param scaler: A scaling factor that allows rescaling the gradients before applying them """ pass diff --git a/rl_coach/architectures/mxnet_components/__init__.py b/rl_coach/architectures/mxnet_components/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/rl_coach/architectures/mxnet_components/architecture.py b/rl_coach/architectures/mxnet_components/architecture.py new file mode 100644 index 0000000..dd860fb --- /dev/null +++ b/rl_coach/architectures/mxnet_components/architecture.py @@ -0,0 +1,405 @@ +# +# Copyright (c) 2017 Intel Corporation +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +import copy +from typing import Any, Dict, Generator, List, Tuple, Union + +import numpy as np +import mxnet as mx +from mxnet import autograd, gluon, nd +from mxnet.ndarray import NDArray + +from rl_coach.architectures.architecture import Architecture +from rl_coach.architectures.mxnet_components.heads.head import LOSS_OUT_TYPE_LOSS, LOSS_OUT_TYPE_REGULARIZATION +from rl_coach.architectures.mxnet_components import utils +from rl_coach.base_parameters import AgentParameters, DistributedTaskParameters +from rl_coach.spaces import SpacesDefinition +from rl_coach.utils import force_list, squeeze_list + + +class MxnetArchitecture(Architecture): + def __init__(self, agent_parameters: AgentParameters, spaces: SpacesDefinition, name: str= "", + global_network=None, network_is_local: bool=True, network_is_trainable: bool=False): + """ + :param agent_parameters: the agent parameters + :param spaces: the spaces definition of the agent + :param name: the name of the network + :param global_network: the global network replica that is shared between all the workers + :param network_is_local: is the network global (shared between workers) or local (dedicated to the worker) + :param network_is_trainable: is the network trainable (we can apply gradients on it) + """ + super().__init__(agent_parameters, spaces, name) + self.middleware = None + self.network_is_local = network_is_local + self.global_network = global_network + if not self.network_parameters.tensorflow_support: + raise ValueError('TensorFlow is not supported for this agent') + self.losses = [] # type: List[HeadLoss] + self.shared_accumulated_gradients = [] + self.curr_rnn_c_in = None + self.curr_rnn_h_in = None + self.gradients_wrt_inputs = [] + self.train_writer = None + self.accumulated_gradients = None + self.network_is_trainable = network_is_trainable + self.is_training = False + self.model = None # type: GeneralModel + + self.is_chief = self.ap.task_parameters.task_index == 0 + self.network_is_global = not self.network_is_local and global_network is None + self.distributed_training = self.network_is_global or self.network_is_local and global_network is not None + + self.optimizer_type = self.network_parameters.optimizer_type + if self.ap.task_parameters.seed is not None: + mx.random.seed(self.ap.task_parameters.seed) + + # Call to child class to create the model + self.construct_model() + + self.trainer = None # type: gluon.Trainer + + def __str__(self): + return self.model.summary(*self._dummy_model_inputs()) + + @property + def _model_grads(self) -> Generator[NDArray, NDArray, Any]: + """ + Creates a copy of model gradients and returns them in a list, in the same order as collect_params() + :return: a generator for model gradient values + """ + return (p.list_grad()[0].copy() for p in self.model.collect_params().values() if p.grad_req != 'null') + + def _dummy_model_inputs(self) -> Tuple[NDArray, ...]: + """ + Creates a tuple of input arrays with correct shapes that can be used for shape inference + of the model weights and for printing the summary + :return: tuple of inputs for model forward pass + """ + allowed_inputs = copy.copy(self.spaces.state.sub_spaces) + allowed_inputs["action"] = copy.copy(self.spaces.action) + allowed_inputs["goal"] = copy.copy(self.spaces.goal) + embedders = self.model.nets[0].input_embedders + inputs = tuple(nd.zeros((1,) + tuple(allowed_inputs[emb.embedder_name].shape.tolist())) for emb in embedders) + return inputs + + def construct_model(self) -> None: + """ + Construct network model. Implemented by child class. + """ + raise NotImplementedError + + def set_session(self, sess) -> None: + """ + Initializes the model parameters and creates the model trainer. + NOTEL Session for mxnet backend must be None. + :param sess: must be None + """ + assert sess is None + # FIXME Add GPU initialization + # FIXME Add initializer + self.model.collect_params().initialize(ctx=mx.cpu()) + # Hybridize model and losses + self.model.hybridize() + for l in self.losses: + l.hybridize() + + # Pass dummy data with correct shape to trigger shape inference and full parameter initialization + self.model(*self._dummy_model_inputs()) + + if self.network_is_trainable: + self.trainer = gluon.Trainer( + self.model.collect_params(), optimizer=self.optimizer, update_on_kvstore=False) + + def reset_accumulated_gradients(self) -> None: + """ + Reset model gradients as well as accumulated gradients to zero. If accumulated gradients + have not been created yet, it constructs them on CPU. + """ + # Set model gradients to zero + for p in self.model.collect_params().values(): + p.zero_grad() + # Set accumulated gradients to zero if already initialized, otherwise create a copy + if self.accumulated_gradients: + for a in self.accumulated_gradients: + a *= 0 + else: + self.accumulated_gradients = [g.copy() for g in self._model_grads] + + def accumulate_gradients(self, + inputs: Dict[str, np.ndarray], + targets: List[np.ndarray], + additional_fetches: List[Tuple[int, str]] = None, + importance_weights: np.ndarray = None, + no_accumulation: bool = False) -> Tuple[float, List[float], float, list]: + """ + Runs a forward & backward pass, clips gradients if needed and accumulates them into the accumulation + :param inputs: environment states (observation, etc.) as well extra inputs required by loss. Shape of ndarray + is (batch_size, observation_space_size) or (batch_size, observation_space_size, stack_size) + :param targets: targets required by loss (e.g. sum of discounted rewards) + :param additional_fetches: additional fetches to calculate and return. Each fetch is specified as (int, str) + tuple of head-type-index and fetch-name. The tuple is obtained from each head. + :param importance_weights: ndarray of shape (batch_size,) to multiply with batch loss. + :param no_accumulation: if True, set gradient values to the new gradients, otherwise sum with previously + calculated gradients + :return: tuple of total_loss, losses, norm_unclipped_grads, fetched_tensors + total_loss (float): sum of all head losses + losses (list of float): list of all losses. The order is list of target losses followed by list of + regularization losses. The specifics of losses is dependant on the network parameters + (number of heads, etc.) + norm_unclippsed_grads (float): global norm of all gradients before any gradient clipping is applied + fetched_tensors: all values for additional_fetches + """ + if self.accumulated_gradients is None: + self.reset_accumulated_gradients() + + embedders = [emb.embedder_name for emb in self.model.nets[0].input_embedders] + nd_inputs = tuple(nd.array(inputs[emb]) for emb in embedders) + + assert self.middleware.__class__.__name__ != 'LSTMMiddleware', "LSTM middleware not supported" + + targets = force_list(targets) + with autograd.record(): + out_per_head = utils.split_outputs_per_head(self.model(*nd_inputs), self.model.output_heads) + tgt_per_loss = utils.split_targets_per_loss(targets, self.losses) + + losses = list() + regularizations = list() + additional_fetches = [(k, None) for k in additional_fetches] + for h, h_loss, h_out, l_tgt in zip(self.model.output_heads, self.losses, out_per_head, tgt_per_loss): + l_in = utils.get_loss_agent_inputs(inputs, head_type_idx=h.head_type_idx, loss=h_loss) + # Align arguments with loss.loss_forward and convert to NDArray + l_args = utils.to_mx_ndarray(utils.align_loss_args(h_out, l_in, l_tgt, h_loss)) + # Calculate loss and all auxiliary outputs + loss_outputs = utils.loss_output_dict(utils.to_list(h_loss(*l_args)), h_loss.output_schema) + if LOSS_OUT_TYPE_LOSS in loss_outputs: + losses.extend(loss_outputs[LOSS_OUT_TYPE_LOSS]) + if LOSS_OUT_TYPE_REGULARIZATION in loss_outputs: + regularizations.extend(loss_outputs[LOSS_OUT_TYPE_REGULARIZATION]) + # Set additional fetches + for i, fetch in enumerate(additional_fetches): + head_type_idx, fetch_name = fetch[0] # fetch key is a tuple of (head_type_index, fetch_name) + if head_type_idx == h.head_type_idx: + assert fetch[1] is None # sanity check that fetch is None + additional_fetches[i] = (fetch[0], loss_outputs[fetch_name]) + + # Total loss is losses and regularization (NOTE: order is important) + total_loss_list = losses + regularizations + total_loss = nd.add_n(*total_loss_list) + + # Calculate gradients + total_loss.backward() + + assert self.optimizer_type != 'LBFGS', 'LBFGS not supported' + + # allreduce gradients from all contexts + self.trainer.allreduce_grads() + + # Calculate global norm of gradients + # FIXME global norm is returned even when not used for clipping! Is this necessary? + # FIXME global norm might be calculated twice if clipping method is global norm + norm_unclipped_grads = utils.global_norm(self._model_grads) + + # Clip gradients + if self.network_parameters.clip_gradients: + utils.clip_grad( + self._model_grads, + clip_method=self.network_parameters.gradients_clipping_method, + clip_val=self.network_parameters.clip_gradients, + inplace=True) + + # Update self.accumulated_gradients depending on no_accumulation flag + if no_accumulation: + for acc_grad, model_grad in zip(self.accumulated_gradients, self._model_grads): + acc_grad[:] = model_grad + else: + for acc_grad, model_grad in zip(self.accumulated_gradients, self._model_grads): + acc_grad += model_grad + + # result of of additional fetches + fetched_tensors = [fetch[1] for fetch in additional_fetches] + + # convert everything to numpy or scalar before returning + result = utils.asnumpy_or_asscalar((total_loss, total_loss_list, norm_unclipped_grads, fetched_tensors)) + return result + + def apply_and_reset_gradients(self, gradients: List[np.ndarray], scaler: float=1.) -> None: + """ + Applies the given gradients to the network weights and resets accumulated gradients to zero + :param gradients: The gradients to use for the update + :param scaler: A scaling factor that allows rescaling the gradients before applying them + """ + self.apply_gradients(gradients, scaler) + self.reset_accumulated_gradients() + + def apply_gradients(self, gradients: List[np.ndarray], scaler: float=1.) -> None: + """ + Applies the given gradients to the network weights + :param gradients: The gradients to use for the update + :param scaler: A scaling factor that allows rescaling the gradients before applying them. + The gradients will be MULTIPLIED by this factor + """ + assert self.optimizer_type != 'LBFGS' + + batch_size = 1 + if self.distributed_training and not self.network_parameters.async_training: + # rescale the gradients so that they average out with the gradients from the other workers + if self.network_parameters.scale_down_gradients_by_number_of_workers_for_sync_training: + batch_size = self.ap.task_parameters.num_training_tasks + + # set parameter gradients to gradients passed in + for param_grad, gradient in zip(self._model_grads, gradients): + param_grad[:] = gradient + # update gradients + self.trainer.update(batch_size=batch_size) + + def _predict(self, inputs: Dict[str, np.ndarray]) -> Tuple[NDArray, ...]: + """ + Run a forward pass of the network using the given input + :param inputs: The input dictionary for the network. Key is name of the embedder. + :return: The network output + + WARNING: must only call once per state since each call is assumed by LSTM to be a new time step. + """ + embedders = [emb.embedder_name for emb in self.model.nets[0].input_embedders] + nd_inputs = tuple(nd.array(inputs[emb]) for emb in embedders) + + assert self.middleware.__class__.__name__ != 'LSTMMiddleware' + + output = self.model(*nd_inputs) + return output + + def predict(self, + inputs: Dict[str, np.ndarray], + outputs: List[str]=None, + squeeze_output: bool=True, + initial_feed_dict: Dict[str, np.ndarray]=None) -> Tuple[np.ndarray, ...]: + """ + Run a forward pass of the network using the given input + :param inputs: The input dictionary for the network. Key is name of the embedder. + :param outputs: list of outputs to return. Return all outputs if unspecified (currently not supported) + :param squeeze_output: call squeeze_list on output if True + :param initial_feed_dict: a dictionary of extra inputs for forward pass (currently not supported) + :return: The network output + + WARNING: must only call once per state since each call is assumed by LSTM to be a new time step. + """ + assert initial_feed_dict is None, "initial_feed_dict must be None" + assert outputs is None, "outputs must be None" + + output = self._predict(inputs) + + output = tuple(o.asnumpy() for o in output) + if squeeze_output: + output = squeeze_list(output) + return output + + @staticmethod + def parallel_predict(sess: Any, + network_input_tuples: List[Tuple['MxnetArchitecture', Dict[str, np.ndarray]]]) -> \ + Tuple[np.ndarray, ...]: + """ + :param sess: active session to use for prediction (must be None for MXNet) + :param network_input_tuples: tuple of network and corresponding input + :return: tuple of outputs from all networks + """ + assert sess is None + output = list() + for net, inputs in network_input_tuples: + output += net._predict(inputs) + return tuple(o.asnumpy() for o in output) + + def train_on_batch(self, + inputs: Dict[str, np.ndarray], + targets: List[np.ndarray], + scaler: float = 1., + additional_fetches: list = None, + importance_weights: np.ndarray = None) -> Tuple[float, List[float], float, list]: + """ + Given a batch of inputs (e.g. states) and targets (e.g. discounted rewards), takes a training step: i.e. runs a + forward pass and backward pass of the network, accumulates the gradients and applies an optimization step to + update the weights. + :param inputs: environment states (observation, etc.) as well extra inputs required by loss. Shape of ndarray + is (batch_size, observation_space_size) or (batch_size, observation_space_size, stack_size) + :param targets: targets required by loss (e.g. sum of discounted rewards) + :param scaler: value to scale gradients by before optimizing network weights + :param additional_fetches: additional fetches to calculate and return. Each fetch is specified as (int, str) + tuple of head-type-index and fetch-name. The tuple is obtained from each head. + :param importance_weights: ndarray of shape (batch_size,) to multiply with batch loss. + :return: tuple of total_loss, losses, norm_unclipped_grads, fetched_tensors + total_loss (float): sum of all head losses + losses (list of float): list of all losses. The order is list of target losses followed by list + of regularization losses. The specifics of losses is dependant on the network parameters + (number of heads, etc.) + norm_unclippsed_grads (float): global norm of all gradients before any gradient clipping is applied + fetched_tensors: all values for additional_fetches + """ + loss = self.accumulate_gradients(inputs, targets, additional_fetches=additional_fetches, + importance_weights=importance_weights) + self.apply_and_reset_gradients(self.accumulated_gradients, scaler) + return loss + + def get_weights(self) -> gluon.ParameterDict: + """ + :return: a ParameterDict containing all network weights + """ + return self.model.collect_params() + + def set_weights(self, weights: gluon.ParameterDict, new_rate: float=1.0) -> None: + """ + Sets the network weights from the given ParameterDict + :param new_rate: ratio for adding new and old weight values: val=rate*weights + (1-rate)*old_weights + """ + old_weights = self.model.collect_params() + for name, p in weights.items(): + name = name[len(weights.prefix):] # Strip prefix + old_p = old_weights[old_weights.prefix + name] # Add prefix + old_p.set_data(new_rate * p._reduce() + (1 - new_rate) * old_p._reduce()) + + def get_variable_value(self, variable: Union[gluon.Parameter, NDArray]) -> np.ndarray: + """ + Get the value of a variable + :param variable: the variable + :return: the value of the variable + """ + if isinstance(variable, gluon.Parameter): + variable = variable._reduce().asnumpy() + if isinstance(variable, NDArray): + return variable.asnumpy() + return variable + + def set_variable_value(self, assign_op: callable, value: Any, placeholder=None) -> None: + """ + Updates value of a variable. + :param assign_op: a callable assign function for setting the variable + :param value: a value to set the variable to + :param placeholder: unused (placeholder in symbolic framework backends) + """ + assert callable(assign_op) + assign_op(value) + + def set_is_training(self, state: bool) -> None: + """ + Set the phase of the network between training and testing + :param state: The current state (True = Training, False = Testing) + :return: None + """ + self.is_training = state + + def reset_internal_memory(self) -> None: + """ + Reset any internal memory used by the network. For example, an LSTM internal state + :return: None + """ + assert self.middleware.__class__.__name__ != 'LSTMMiddleware', 'LSTM middleware not supported' diff --git a/rl_coach/architectures/mxnet_components/embedders/__init__.py b/rl_coach/architectures/mxnet_components/embedders/__init__.py new file mode 100644 index 0000000..eb0482f --- /dev/null +++ b/rl_coach/architectures/mxnet_components/embedders/__init__.py @@ -0,0 +1,4 @@ +from .image_embedder import ImageEmbedder +from .vector_embedder import VectorEmbedder + +__all__ = ['ImageEmbedder', 'VectorEmbedder'] diff --git a/rl_coach/architectures/mxnet_components/embedders/embedder.py b/rl_coach/architectures/mxnet_components/embedders/embedder.py new file mode 100644 index 0000000..c2b6340 --- /dev/null +++ b/rl_coach/architectures/mxnet_components/embedders/embedder.py @@ -0,0 +1,71 @@ +from typing import Union +from types import ModuleType + +import mxnet as mx +from mxnet.gluon import nn +from rl_coach.architectures.embedder_parameters import InputEmbedderParameters +from rl_coach.architectures.mxnet_components.layers import convert_layer +from rl_coach.base_parameters import EmbedderScheme + +nd_sym_type = Union[mx.nd.NDArray, mx.sym.Symbol] + + +class InputEmbedder(nn.HybridBlock): + def __init__(self, params: InputEmbedderParameters): + """ + An input embedder is the first part of the network, which takes the input from the state and produces a vector + embedding by passing it through a neural network. The embedder will mostly be input type dependent, and there + can be multiple embedders in a single network. + + :param params: parameters object containing input_clipping, input_rescaling, batchnorm, activation_function + and dropout properties. + """ + super(InputEmbedder, self).__init__() + self.embedder_name = params.name + self.input_clipping = params.input_clipping + self.scheme = params.scheme + + with self.name_scope(): + self.net = nn.HybridSequential() + if isinstance(self.scheme, EmbedderScheme): + blocks = self.schemes[self.scheme] + else: + # if scheme is specified directly, convert to MX layer if it's not a callable object + # NOTE: if layer object is callable, it must return a gluon block when invoked + blocks = [convert_layer(l) for l in self.scheme] + for block in blocks: + self.net.add(block()) + if params.batchnorm: + self.net.add(nn.BatchNorm()) + if params.activation_function: + self.net.add(nn.Activation(params.activation_function)) + if params.dropout: + self.net.add(nn.Dropout(rate=params.dropout)) + + @property + def schemes(self) -> dict: + """ + Schemes are the pre-defined network architectures of various depths and complexities that can be used for the + InputEmbedder. Should be implemented in child classes, and are used to create Block when InputEmbedder is + initialised. + + :return: dictionary of schemes, with key of type EmbedderScheme enum and value being list of mxnet.gluon.Block. + """ + raise NotImplementedError("Inheriting embedder must define schemes matching its allowed default " + "configurations.") + + def hybrid_forward(self, F: ModuleType, x: nd_sym_type, *args, **kwargs) -> nd_sym_type: + """ + Used for forward pass through embedder network. + + :param F: backend api, either `mxnet.nd` or `mxnet.sym` (if block has been hybridized). + :param x: environment state, where first dimension is batch_size, then dimensions are data type dependent. + :return: embedding of environment state, where shape is (batch_size, channels). + """ + # `input_rescaling` and `input_offset` set on inheriting embedder + x = x / self.input_rescaling + x = x - self.input_offset + if self.input_clipping is not None: + x.clip(a_min=self.input_clipping[0], a_max=self.input_clipping[1]) + x = self.net(x) + return x.flatten() diff --git a/rl_coach/architectures/mxnet_components/embedders/image_embedder.py b/rl_coach/architectures/mxnet_components/embedders/image_embedder.py new file mode 100644 index 0000000..36842d8 --- /dev/null +++ b/rl_coach/architectures/mxnet_components/embedders/image_embedder.py @@ -0,0 +1,76 @@ +from typing import Union +from types import ModuleType + +import mxnet as mx +from rl_coach.architectures.embedder_parameters import InputEmbedderParameters +from rl_coach.architectures.mxnet_components.embedders.embedder import InputEmbedder +from rl_coach.architectures.mxnet_components.layers import Conv2d +from rl_coach.base_parameters import EmbedderScheme + +nd_sym_type = Union[mx.nd.NDArray, mx.sym.Symbol] + + +class ImageEmbedder(InputEmbedder): + def __init__(self, params: InputEmbedderParameters): + """ + An image embedder is an input embedder that takes an image input from the state and produces a vector + embedding by passing it through a neural network. + + :param params: parameters object containing input_clipping, input_rescaling, batchnorm, activation_function + and dropout properties. + """ + super(ImageEmbedder, self).__init__(params) + self.input_rescaling = params.input_rescaling['image'] + self.input_offset = params.input_offset['image'] + + @property + def schemes(self) -> dict: + """ + Schemes are the pre-defined network architectures of various depths and complexities that can be used. Are used + to create Block when ImageEmbedder is initialised. + + :return: dictionary of schemes, with key of type EmbedderScheme enum and value being list of mxnet.gluon.Block. + """ + return { + EmbedderScheme.Empty: + [], + + EmbedderScheme.Shallow: + [ + Conv2d(num_filters=32, kernel_size=8, strides=4) + ], + + # Use for Atari DQN + EmbedderScheme.Medium: + [ + Conv2d(num_filters=32, kernel_size=8, strides=4), + Conv2d(num_filters=64, kernel_size=4, strides=2), + Conv2d(num_filters=64, kernel_size=3, strides=1) + ], + + # Use for Carla + EmbedderScheme.Deep: + [ + Conv2d(num_filters=32, kernel_size=5, strides=2), + Conv2d(num_filters=32, kernel_size=3, strides=1), + Conv2d(num_filters=64, kernel_size=3, strides=2), + Conv2d(num_filters=64, kernel_size=3, strides=1), + Conv2d(num_filters=128, kernel_size=3, strides=2), + Conv2d(num_filters=128, kernel_size=3, strides=1), + Conv2d(num_filters=256, kernel_size=3, strides=2), + Conv2d(num_filters=256, kernel_size=3, strides=1) + ] + } + + def hybrid_forward(self, F: ModuleType, x: nd_sym_type, *args, **kwargs) -> nd_sym_type: + """ + Used for forward pass through embedder network. + + :param F: backend api, either `mxnet.nd` or `mxnet.sym` (if block has been hybridized). + :param x: image representing environment state, of shape (batch_size, in_channels, height, width). + :return: embedding of environment state, of shape (batch_size, channels). + """ + if len(x.shape) != 4 and self.scheme != EmbedderScheme.Empty: + raise ValueError("Image embedders expect the input size to have 4 dimensions. The given size is: {}" + .format(x.shape)) + return super(ImageEmbedder, self).hybrid_forward(F, x, *args, **kwargs) diff --git a/rl_coach/architectures/mxnet_components/embedders/vector_embedder.py b/rl_coach/architectures/mxnet_components/embedders/vector_embedder.py new file mode 100644 index 0000000..4a36357 --- /dev/null +++ b/rl_coach/architectures/mxnet_components/embedders/vector_embedder.py @@ -0,0 +1,71 @@ +from typing import Union +from types import ModuleType + +import mxnet as mx +from mxnet import nd, sym +from rl_coach.architectures.embedder_parameters import InputEmbedderParameters +from rl_coach.architectures.mxnet_components.embedders.embedder import InputEmbedder +from rl_coach.architectures.mxnet_components.layers import Dense +from rl_coach.base_parameters import EmbedderScheme + +nd_sym_type = Union[mx.nd.NDArray, mx.sym.Symbol] + + +class VectorEmbedder(InputEmbedder): + def __init__(self, params: InputEmbedderParameters): + """ + An vector embedder is an input embedder that takes an vector input from the state and produces a vector + embedding by passing it through a neural network. + + :param params: parameters object containing input_clipping, input_rescaling, batchnorm, activation_function + and dropout properties. + """ + super(VectorEmbedder, self).__init__(params) + self.input_rescaling = params.input_rescaling['vector'] + self.input_offset = params.input_offset['vector'] + + @property + def schemes(self): + """ + Schemes are the pre-defined network architectures of various depths and complexities that can be used. Are used + to create Block when VectorEmbedder is initialised. + + :return: dictionary of schemes, with key of type EmbedderScheme enum and value being list of mxnet.gluon.Block. + """ + return { + EmbedderScheme.Empty: + [], + + EmbedderScheme.Shallow: + [ + Dense(units=128) + ], + + # Use for DQN + EmbedderScheme.Medium: + [ + Dense(units=256) + ], + + # Use for Carla + EmbedderScheme.Deep: + [ + Dense(units=128), + Dense(units=128), + Dense(units=128) + ] + } + + def hybrid_forward(self, F: ModuleType, x: nd_sym_type, *args, **kwargs) -> nd_sym_type: + """ + Used for forward pass through embedder network. + + :param F: backend api, either `nd` or `sym` (if block has been hybridized). + :type F: nd or sym + :param x: vector representing environment state, of shape (batch_size, in_channels). + :return: embedding of environment state, of shape (batch_size, channels). + """ + if isinstance(x, nd.NDArray) and len(x.shape) != 2 and self.scheme != EmbedderScheme.Empty: + raise ValueError("Vector embedders expect the input size to have 2 dimensions. The given size is: {}" + .format(x.shape)) + return super(VectorEmbedder, self).hybrid_forward(F, x, *args, **kwargs) diff --git a/rl_coach/architectures/mxnet_components/general_network.py b/rl_coach/architectures/mxnet_components/general_network.py new file mode 100644 index 0000000..bb1b176 --- /dev/null +++ b/rl_coach/architectures/mxnet_components/general_network.py @@ -0,0 +1,501 @@ +# +# Copyright (c) 2017 Intel Corporation +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import copy +from itertools import chain +from typing import List, Tuple, Union +from types import ModuleType + +import numpy as np +import mxnet as mx +from mxnet import nd, sym +from mxnet.gluon import HybridBlock +from mxnet.ndarray import NDArray +from mxnet.symbol import Symbol + +from rl_coach.base_parameters import NetworkParameters +from rl_coach.architectures.embedder_parameters import InputEmbedderParameters +from rl_coach.architectures.head_parameters import HeadParameters, PPOHeadParameters +from rl_coach.architectures.head_parameters import PPOVHeadParameters, VHeadParameters, QHeadParameters +from rl_coach.architectures.middleware_parameters import MiddlewareParameters +from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters, LSTMMiddlewareParameters +from rl_coach.architectures.mxnet_components.architecture import MxnetArchitecture +from rl_coach.architectures.mxnet_components.embedders import ImageEmbedder, VectorEmbedder +from rl_coach.architectures.mxnet_components.heads import Head, HeadLoss, PPOHead, PPOVHead, VHead, QHead +from rl_coach.architectures.mxnet_components.middlewares import FCMiddleware, LSTMMiddleware +from rl_coach.architectures.mxnet_components import utils +from rl_coach.base_parameters import AgentParameters, EmbeddingMergerType +from rl_coach.spaces import SpacesDefinition, PlanarMapsObservationSpace + + +class GeneralMxnetNetwork(MxnetArchitecture): + """ + A generalized version of all possible networks implemented using mxnet. + """ + def __init__(self, + agent_parameters: AgentParameters, + spaces: SpacesDefinition, + name: str, + global_network=None, + network_is_local: bool=True, + network_is_trainable: bool=False): + """ + :param agent_parameters: the agent parameters + :param spaces: the spaces definition of the agent + :param name: the name of the network + :param global_network: the global network replica that is shared between all the workers + :param network_is_local: is the network global (shared between workers) or local (dedicated to the worker) + :param network_is_trainable: is the network trainable (we can apply gradients on it) + """ + self.network_wrapper_name = name.split('/')[0] + self.network_parameters = agent_parameters.network_wrappers[self.network_wrapper_name] + if self.network_parameters.use_separate_networks_per_head: + self.num_heads_per_network = 1 + self.num_networks = len(self.network_parameters.heads_parameters) + else: + self.num_heads_per_network = len(self.network_parameters.heads_parameters) + self.num_networks = 1 + + super().__init__(agent_parameters, spaces, name, global_network, + network_is_local, network_is_trainable) + + def construct_model(self): + # validate the configuration + if len(self.network_parameters.input_embedders_parameters) == 0: + raise ValueError("At least one input type should be defined") + + if len(self.network_parameters.heads_parameters) == 0: + raise ValueError("At least one output type should be defined") + + if self.network_parameters.middleware_parameters is None: + raise ValueError("Exactly one middleware type should be defined") + + self.model = GeneralModel( + num_networks=self.num_networks, + num_heads_per_network=self.num_heads_per_network, + network_is_local=self.network_is_local, + network_name=self.network_wrapper_name, + agent_parameters=self.ap, + network_parameters=self.network_parameters, + spaces=self.spaces) + + self.losses = self.model.losses() + + # Learning rate + lr_scheduler = None + if self.network_parameters.learning_rate_decay_rate != 0: + lr_scheduler = mx.lr_scheduler.FactorScheduler( + step=self.network_parameters.learning_rate_decay_steps, + factor=self.network_parameters.learning_rate_decay_rate) + + # Optimizer + # FIXME Does this code for distributed training make sense? + if self.distributed_training and self.network_is_local and self.network_parameters.shared_optimizer: + # distributed training + is a local network + optimizer shared -> take the global optimizer + self.optimizer = self.global_network.optimizer + elif (self.distributed_training and self.network_is_local and not self.network_parameters.shared_optimizer)\ + or self.network_parameters.shared_optimizer or not self.distributed_training: + + if self.network_parameters.optimizer_type == 'Adam': + self.optimizer = mx.optimizer.Adam( + learning_rate=self.network_parameters.learning_rate, + beta1=self.network_parameters.adam_optimizer_beta1, + beta2=self.network_parameters.adam_optimizer_beta2, + epsilon=self.network_parameters.optimizer_epsilon, + lr_scheduler=lr_scheduler) + elif self.network_parameters.optimizer_type == 'RMSProp': + self.optimizer = mx.optimizer.RMSProp( + learning_rate=self.network_parameters.learning_rate, + gamma1=self.network_parameters.rms_prop_optimizer_decay, + epsilon=self.network_parameters.optimizer_epsilon, + lr_scheduler=lr_scheduler) + elif self.network_parameters.optimizer_type == 'LBFGS': + raise NotImplementedError('LBFGS optimizer not implemented') + else: + raise Exception("{} is not a valid optimizer type".format(self.network_parameters.optimizer_type)) + + @property + def output_heads(self): + return self.model.output_heads + + +def _get_activation(activation_function_string: str): + """ + Map the activation function from a string to the mxnet framework equivalent + :param activation_function_string: the type of the activation function + :return: mxnet activation function string + """ + return utils.get_mxnet_activation_name(activation_function_string) + + +def _sanitize_activation(params: Union[InputEmbedderParameters, MiddlewareParameters, HeadParameters]) ->\ + Union[InputEmbedderParameters, MiddlewareParameters, HeadParameters]: + """ + Change activation function to the mxnet specific value + :param params: any parameter that has activation_function property + :return: copy of params with activation function correctly set + """ + params_copy = copy.copy(params) + params_copy.activation_function = _get_activation(params.activation_function) + return params_copy + + +def _get_input_embedder(spaces: SpacesDefinition, + input_name: str, + embedder_params: InputEmbedderParameters) -> ModuleType: + """ + Given an input embedder parameters class, creates the input embedder and returns it + :param input_name: the name of the input to the embedder (used for retrieving the shape). The input should + be a value within the state or the action. + :param embedder_params: the parameters of the class of the embedder + :return: the embedder instance + """ + allowed_inputs = copy.copy(spaces.state.sub_spaces) + allowed_inputs["action"] = copy.copy(spaces.action) + allowed_inputs["goal"] = copy.copy(spaces.goal) + + if input_name not in allowed_inputs.keys(): + raise ValueError("The key for the input embedder ({}) must match one of the following keys: {}" + .format(input_name, allowed_inputs.keys())) + + type = "vector" + if isinstance(allowed_inputs[input_name], PlanarMapsObservationSpace): + type = "image" + + def sanitize_params(params: InputEmbedderParameters): + params_copy = _sanitize_activation(params) + # params_copy.input_rescaling = params_copy.input_rescaling[type] + # params_copy.input_offset = params_copy.input_offset[type] + params_copy.name = input_name + return params_copy + + embedder_params = sanitize_params(embedder_params) + if type == 'vector': + module = VectorEmbedder(embedder_params) + elif type == 'image': + module = ImageEmbedder(embedder_params) + else: + raise KeyError('Unsupported embedder type: {}'.format(type)) + return module + + +def _get_middleware(middleware_params: MiddlewareParameters) -> ModuleType: + """ + Given a middleware type, creates the middleware and returns it + :param middleware_params: the paramaeters of the middleware class + :return: the middleware instance + """ + middleware_params = _sanitize_activation(middleware_params) + if isinstance(middleware_params, FCMiddlewareParameters): + module = FCMiddleware(middleware_params) + elif isinstance(middleware_params, LSTMMiddlewareParameters): + module = LSTMMiddleware(middleware_params) + else: + raise KeyError('Unsupported middleware type: {}'.format(type(middleware_params))) + + return module + + +def _get_output_head( + head_params: HeadParameters, + head_idx: int, + head_type_index: int, + agent_params: AgentParameters, + spaces: SpacesDefinition, + network_name: str, + is_local: bool) -> Head: + """ + Given a head type, creates the head and returns it + :param head_params: the parameters of the head to create + :param head_idx: the head index + :param head_type_index: the head type index (same index if head_param.num_output_head_copies>0) + :param agent_params: agent parameters + :param spaces: state and action space definitions + :param network_name: name of the network + :param is_local: + :return: head block + """ + head_params = _sanitize_activation(head_params) + if isinstance(head_params, PPOHeadParameters): + module = PPOHead( + agent_parameters=agent_params, + spaces=spaces, + network_name=network_name, + head_type_idx=head_type_index, + loss_weight=head_params.loss_weight, + is_local=is_local, + activation_function=head_params.activation_function, + dense_layer=head_params.dense_layer) + elif isinstance(head_params, VHeadParameters): + module = VHead( + agent_parameters=agent_params, + spaces=spaces, + network_name=network_name, + head_type_idx=head_type_index, + loss_weight=head_params.loss_weight, + is_local=is_local, + activation_function=head_params.activation_function, + dense_layer=head_params.dense_layer) + elif isinstance(head_params, PPOVHeadParameters): + module = PPOVHead( + agent_parameters=agent_params, + spaces=spaces, + network_name=network_name, + head_type_idx=head_type_index, + loss_weight=head_params.loss_weight, + is_local=is_local, + activation_function=head_params.activation_function, + dense_layer=head_params.dense_layer) + elif isinstance(head_params, QHeadParameters): + module = QHead( + agent_parameters=agent_params, + spaces=spaces, + network_name=network_name, + head_type_idx=head_type_index, + loss_weight=head_params.loss_weight, + is_local=is_local, + activation_function=head_params.activation_function, + dense_layer=head_params.dense_layer) + else: + raise KeyError('Unsupported head type: {}'.format(type(head_params))) + + return module + + +class ScaledGradHead(HybridBlock): + """ + Wrapper block for applying gradient scaling to input before feeding the head network + """ + def __init__(self, + head_index: int, + head_type_index: int, + network_name: str, + spaces: SpacesDefinition, + network_is_local: bool, + agent_params: AgentParameters, + head_params: HeadParameters) -> None: + """ + :param head_idx: the head index + :param head_type_index: the head type index (same index if head_param.num_output_head_copies>0) + :param network_name: name of the network + :param spaces: state and action space definitions + :param network_is_local: whether network is local + :param agent_params: agent parameters + :param head_params: head parameters + """ + super(ScaledGradHead, self).__init__() + + head_params = _sanitize_activation(head_params) + with self.name_scope(): + self.head = _get_output_head( + head_params=head_params, + head_idx=head_index, + head_type_index=head_type_index, + agent_params=agent_params, + spaces=spaces, + network_name=network_name, + is_local=network_is_local) + self.gradient_rescaler = self.params.get_constant( + name='gradient_rescaler', + value=np.array([float(head_params.rescale_gradient_from_head_by_factor)])) + # self.gradient_rescaler = self.params.get( + # name='gradient_rescaler', + # shape=(1,), + # init=mx.init.Constant(float(head_params.rescale_gradient_from_head_by_factor))) + + def hybrid_forward(self, + F: ModuleType, + x: Union[NDArray, Symbol], + gradient_rescaler: Union[NDArray, Symbol]) -> Tuple[Union[NDArray, Symbol], ...]: + """ Overrides gluon.HybridBlock.hybrid_forward + :param nd or sym F: ndarray or symbol module + :param x: head input + :param gradient_rescaler: gradient rescaler for partial blocking of gradient + :return: head output + """ + grad_scaled_x = F.broadcast_mul((1 - gradient_rescaler), F.BlockGrad(x)) + F.broadcast_mul(gradient_rescaler, x) + out = self.head(grad_scaled_x) + return out + + +class SingleModel(HybridBlock): + """ + Block that connects a single embedder, with middleware and one to multiple heads + """ + def __init__(self, + network_is_local: bool, + network_name: str, + agent_parameters: AgentParameters, + in_emb_param_dict: {str: InputEmbedderParameters}, + embedding_merger_type: EmbeddingMergerType, + middleware_param: MiddlewareParameters, + head_param_list: [HeadParameters], + head_type_idx_start: int, + spaces: SpacesDefinition, + *args, **kwargs): + """ + :param network_is_local: True if network is local + :param network_name: name of the network + :param agent_parameters: agent parameters + :param in_emb_param_dict: dictionary of embedder name to embedding parameters + :param embedding_merger_type: type of merging output of embedders: concatenate or sum + :param middleware_param: middleware parameters + :param head_param_list: list of head parameters, one per head type + :param head_type_idx_start: start index for head type index counting + :param spaces: state and action space definition + """ + super(SingleModel, self).__init__(*args, **kwargs) + + self._embedding_merger_type = embedding_merger_type + self._input_embedders = list() # type: List[HybridBlock] + self._output_heads = list() # type: List[ScaledGradHead] + + with self.name_scope(): + for input_name in sorted(in_emb_param_dict): + input_type = in_emb_param_dict[input_name] + input_embedder = _get_input_embedder(spaces, input_name, input_type) + self.register_child(input_embedder) + self._input_embedders.append(input_embedder) + + self.middleware = _get_middleware(middleware_param) + + for i, head_param in enumerate(head_param_list): + for head_copy_idx in range(head_param.num_output_head_copies): + # create output head and add it to the output heads list + output_head = ScaledGradHead( + head_index=(head_type_idx_start + i) * head_param.num_output_head_copies + head_copy_idx, + head_type_index=head_type_idx_start + i, + network_name=network_name, + spaces=spaces, + network_is_local=network_is_local, + agent_params=agent_parameters, + head_params=head_param) + self.register_child(output_head) + self._output_heads.append(output_head) + + def hybrid_forward(self, F, *inputs: Union[NDArray, Symbol]) -> Tuple[Union[NDArray, Symbol], ...]: + """ Overrides gluon.HybridBlock.hybrid_forward + :param nd or sym F: ndarray or symbol block + :param inputs: model inputs, one for each embedder + :return: head outputs in a tuple + """ + # Input Embeddings + state_embedding = list() + for input, embedder in zip(inputs, self._input_embedders): + state_embedding.append(embedder(input)) + + # Merger + if len(state_embedding) == 1: + state_embedding = state_embedding[0] + else: + if self._embedding_merger_type == EmbeddingMergerType.Concat: + state_embedding = F.concat(*state_embedding, dim=1, name='merger') # NC or NCHW layout + elif self._embedding_merger_type == EmbeddingMergerType.Sum: + state_embedding = F.add_n(*state_embedding, name='merger') + + # Middleware + state_embedding = self.middleware(state_embedding) + + # Head + outputs = tuple() + for head in self._output_heads: + outputs += (head(state_embedding),) + + return outputs + + @property + def input_embedders(self) -> List[HybridBlock]: + """ + :return: list of input embedders + """ + return self._input_embedders + + @property + def output_heads(self) -> List[Head]: + """ + :return: list of output heads + """ + return [h.head for h in self._output_heads] + + +class GeneralModel(HybridBlock): + """ + Block that creates multiple single models + """ + def __init__(self, + num_networks: int, + num_heads_per_network: int, + network_is_local: bool, + network_name: str, + agent_parameters: AgentParameters, + network_parameters: NetworkParameters, + spaces: SpacesDefinition, + *args, **kwargs): + """ + :param num_networks: number of networks to create + :param num_heads_per_network: number of heads per network to create + :param network_is_local: True if network is local + :param network_name: name of the network + :param agent_parameters: agent parameters + :param network_parameters: network parameters + :param spaces: state and action space definitions + """ + super(GeneralModel, self).__init__(*args, **kwargs) + + with self.name_scope(): + self.nets = list() + for network_idx in range(num_networks): + head_type_idx_start = network_idx * num_heads_per_network + head_type_idx_end = head_type_idx_start + num_heads_per_network + net = SingleModel( + head_type_idx_start=head_type_idx_start, + network_name=network_name, + network_is_local=network_is_local, + agent_parameters=agent_parameters, + in_emb_param_dict=network_parameters.input_embedders_parameters, + embedding_merger_type=network_parameters.embedding_merger_type, + middleware_param=network_parameters.middleware_parameters, + head_param_list=network_parameters.heads_parameters[head_type_idx_start:head_type_idx_end], + spaces=spaces) + self.register_child(net) + self.nets.append(net) + + def hybrid_forward(self, F, *inputs): + """ Overrides gluon.HybridBlock.hybrid_forward + :param nd or sym F: ndarray or symbol block + :param inputs: model inputs, one for each embedder. Passed to all networks. + :return: head outputs in a tuple + """ + outputs = tuple() + for net in self.nets: + out = net(*inputs) + outputs += out + return outputs + + @property + def output_heads(self) -> List[Head]: + """ Return all heads in a single list + Note: There is a one-to-one mapping between output_heads and losses + :return: list of heads + """ + return list(chain.from_iterable(net.output_heads for net in self.nets)) + + def losses(self) -> List[HeadLoss]: + """ Construct loss blocks for network training + Note: There is a one-to-one mapping between output_heads and losses + :return: list of loss blocks + """ + return [h.loss() for net in self.nets for h in net.output_heads] diff --git a/rl_coach/architectures/mxnet_components/heads/__init__.py b/rl_coach/architectures/mxnet_components/heads/__init__.py new file mode 100644 index 0000000..47c1878 --- /dev/null +++ b/rl_coach/architectures/mxnet_components/heads/__init__.py @@ -0,0 +1,14 @@ +from .head import Head, HeadLoss +from .q_head import QHead +from .ppo_head import PPOHead +from .ppo_v_head import PPOVHead +from .v_head import VHead + +__all__ = [ + 'Head', + 'HeadLoss', + 'QHead', + 'PPOHead', + 'PPOVHead', + 'VHead' +] diff --git a/rl_coach/architectures/mxnet_components/heads/head.py b/rl_coach/architectures/mxnet_components/heads/head.py new file mode 100644 index 0000000..4a83152 --- /dev/null +++ b/rl_coach/architectures/mxnet_components/heads/head.py @@ -0,0 +1,181 @@ +from typing import Dict, List, Union, Tuple + +from mxnet.gluon import nn, loss +from mxnet.ndarray import NDArray +from mxnet.symbol import Symbol +from rl_coach.base_parameters import AgentParameters +from rl_coach.spaces import SpacesDefinition + + +LOSS_OUT_TYPE_LOSS = 'loss' +LOSS_OUT_TYPE_REGULARIZATION = 'regularization' + + +class LossInputSchema(object): + """ + Helper class to contain schema for loss hybrid_forward input + """ + def __init__(self, head_outputs: List[str], agent_inputs: List[str], targets: List[str]): + """ + :param head_outputs: list of argument names in hybrid_forward that are outputs of the head. + The order and number MUST MATCH the output from the head. + :param agent_inputs: list of argument names in hybrid_forward that are inputs from the agent. + The order and number MUST MATCH `output__` for this head. + :param targets: list of argument names in hybrid_forward that are targets for the loss. + The order and number MUST MATCH targets passed from the agent. + """ + self._head_outputs = head_outputs + self._agent_inputs = agent_inputs + self._targets = targets + + @property + def head_outputs(self): + return self._head_outputs + + @property + def agent_inputs(self): + return self._agent_inputs + + @property + def targets(self): + return self._targets + + +class HeadLoss(loss.Loss): + """ + ABC for loss functions of each head. Child class must implement input_schema() and loss_forward() + """ + def __init__(self, *args, **kwargs): + super(HeadLoss, self).__init__(*args, **kwargs) + self._output_schema = None # type: List[str] + + @property + def input_schema(self) -> LossInputSchema: + """ + :return: schema for input of hybrid_forward. Read docstring for LossInputSchema for details. + """ + raise NotImplementedError + + @property + def output_schema(self) -> List[str]: + """ + :return: schema for output of hybrid_forward. Must contain 'loss' and 'regularization' keys at least once. + The order and total number must match that of returned values from the loss. 'loss' and 'regularization' + are special keys. Any other string is treated as auxiliary outputs and must include match auxiliary + fetch names returned by the head. + """ + return self._output_schema + + def forward(self, *args): + """ + Override forward() so that number of outputs can be checked against the schema + """ + outputs = super(HeadLoss, self).forward(*args) + if isinstance(outputs, tuple) or isinstance(outputs, list): + num_outputs = len(outputs) + else: + assert isinstance(outputs, NDArray) or isinstance(outputs, Symbol) + num_outputs = 1 + assert num_outputs == len(self.output_schema), "Number of outputs don't match schema ({} != {})".format( + num_outputs, len(self.output_schema)) + return outputs + + def _loss_output(self, outputs: List[Tuple[Union[NDArray, Symbol], str]]): + """ + Must be called on the output from hybrid_forward(). + Saves the returned output as the schema and returns output values in a list + :return: list of output values + """ + output_schema = [o[1] for o in outputs] + assert self._output_schema is None or self._output_schema == output_schema + self._output_schema = output_schema + return tuple(o[0] for o in outputs) + + def hybrid_forward(self, F, x, *args, **kwargs): + """ + Passes the cal to loss_forward() and constructs output schema from its output by calling loss_output() + """ + return self._loss_output(self.loss_forward(F, x, *args, **kwargs)) + + def loss_forward(self, F, x, *args, **kwargs) -> List[Tuple[Union[NDArray, Symbol], str]]: + """ + Similar to hybrid_forward, but returns list of (NDArray, type_str) + """ + raise NotImplementedError + + +class Head(nn.HybridBlock): + def __init__(self, agent_parameters: AgentParameters, spaces: SpacesDefinition, + network_name: str, head_type_idx: int=0, loss_weight: float=1., is_local: bool=True, + activation_function: str='relu', dense_layer: None=None): + """ + A head is the final part of the network. It takes the embedding from the middleware embedder and passes it + through a neural network to produce the output of the network. There can be multiple heads in a network, and + each one has an assigned loss function. The heads are algorithm dependent. + + :param agent_parameters: containing algorithm parameters such as clip_likelihood_ratio_using_epsilon + and beta_entropy. + :param spaces: containing action spaces used for defining size of network output. + :param network_name: name of head network. currently unused. + :param head_type_idx: index of head network. currently unused. + :param loss_weight: scalar used to adjust relative weight of loss (if using this loss with others). + :param is_local: flag to denote if network is local. currently unused. + :param activation_function: activation function to use between layers. currently unused. + :param dense_layer: type of dense layer to use in network. currently unused. + """ + super(Head, self).__init__() + self.head_type_idx = head_type_idx + self.network_name = network_name + self.loss_weight = loss_weight + self.is_local = is_local + self.ap = agent_parameters + self.spaces = spaces + self.return_type = None + self.activation_function = activation_function + self.dense_layer = dense_layer + self._num_outputs = None + + def loss(self) -> HeadLoss: + """ + Returns loss block to be used for specific head implementation. + + :return: loss block (can be called as function) for outputs returned by the head network. + """ + raise NotImplementedError() + + @property + def num_outputs(self): + """ Returns number of outputs that forward() call will return + + :return: + """ + assert self._num_outputs is not None, 'must call forward() once to configure number of outputs' + return self._num_outputs + + def forward(self, *args): + """ + Override forward() so that number of outputs can be automatically set + """ + outputs = super(Head, self).forward(*args) + if isinstance(outputs, tuple): + num_outputs = len(outputs) + else: + assert isinstance(outputs, NDArray) or isinstance(outputs, Symbol) + num_outputs = 1 + if self._num_outputs is None: + self._num_outputs = num_outputs + else: + assert self._num_outputs == num_outputs, 'Number of outputs cannot change ({} != {})'.format( + self._num_outputs, num_outputs) + assert self._num_outputs == len(self.loss().input_schema.head_outputs) + return outputs + + def hybrid_forward(self, F, x, *args, **kwargs): + """ + Used for forward pass through head network. + + :param F: backend api, either `mxnet.nd` or `mxnet.sym` (if block has been hybridized). + :param x: middleware state representation, of shape (batch_size, in_channels). + :return: final output of network, that will be used in loss calculations. + """ + raise NotImplementedError() diff --git a/rl_coach/architectures/mxnet_components/heads/ppo_head.py b/rl_coach/architectures/mxnet_components/heads/ppo_head.py new file mode 100644 index 0000000..01b2192 --- /dev/null +++ b/rl_coach/architectures/mxnet_components/heads/ppo_head.py @@ -0,0 +1,669 @@ +from typing import List, Tuple, Union +from types import ModuleType + +import math +import mxnet as mx +from mxnet.gluon import nn +from rl_coach.base_parameters import AgentParameters +from rl_coach.core_types import ActionProbabilities +from rl_coach.spaces import SpacesDefinition, BoxActionSpace, DiscreteActionSpace +from rl_coach.utils import eps +from rl_coach.architectures.mxnet_components.heads.head import Head, HeadLoss, LossInputSchema +from rl_coach.architectures.mxnet_components.heads.head import LOSS_OUT_TYPE_LOSS, LOSS_OUT_TYPE_REGULARIZATION +from rl_coach.architectures.mxnet_components.utils import hybrid_clip + + +LOSS_OUT_TYPE_KL = 'kl_divergence' +LOSS_OUT_TYPE_ENTROPY = 'entropy' +LOSS_OUT_TYPE_LIKELIHOOD_RATIO = 'likelihood_ratio' +LOSS_OUT_TYPE_CLIPPED_LIKELIHOOD_RATIO = 'clipped_likelihood_ratio' + +nd_sym_type = Union[mx.nd.NDArray, mx.sym.Symbol] + + +class MultivariateNormalDist: + def __init__(self, + num_var: int, + mean: nd_sym_type, + sigma: nd_sym_type, + F: ModuleType=mx.nd) -> None: + """ + Distribution object for Multivariate Normal. Works with batches. + Optionally works with batches and time steps, but be consistent in usage: i.e. if using time_step, + mean, sigma and data for log_prob must all include a time_step dimension. + + :param num_var: number of variables in distribution + :param mean: mean for each variable, + of shape (num_var) or + of shape (batch_size, num_var) or + of shape (batch_size, time_step, num_var). + :param sigma: covariance matrix, + of shape (num_var, num_var) or + of shape (batch_size, num_var, num_var) or + of shape (batch_size, time_step, num_var, num_var). + :param (mx.nd or mx.sym) F: backend api (mx.sym if block has been hybridized). + """ + self.num_var = num_var + self.mean = mean + self.sigma = sigma + self.F = F + + def inverse_using_cholesky(self, matrix: nd_sym_type) -> nd_sym_type: + """ + Calculate inverses for a batch of matrices using Cholesky decomposition method. + + :param matrix: matrix (or matrices) to invert, + of shape (num_var, num_var) or + of shape (batch_size, num_var, num_var) or + of shape (batch_size, time_step, num_var, num_var). + :return: inverted matrix (or matrices), + of shape (num_var, num_var) or + of shape (batch_size, num_var, num_var) or + of shape (batch_size, time_step, num_var, num_var). + """ + cholesky_factor = self.F.linalg.potrf(matrix) + return self.F.linalg.potri(cholesky_factor) + + def log_det(self, matrix: nd_sym_type) -> nd_sym_type: + """ + Calculate log of the determinant for a batch of matrices using Cholesky decomposition method. + + :param matrix: matrix (or matrices) to invert, + of shape (num_var, num_var) or + of shape (batch_size, num_var, num_var) or + of shape (batch_size, time_step, num_var, num_var). + :return: inverted matrix (or matrices), + of shape (num_var, num_var) or + of shape (batch_size, num_var, num_var) or + of shape (batch_size, time_step, num_var, num_var). + """ + cholesky_factor = self.F.linalg.potrf(matrix) + return 2 * self.F.linalg.sumlogdiag(cholesky_factor) + + def log_prob(self, x: nd_sym_type) -> nd_sym_type: + """ + Calculate the log probability of data given the current distribution. + + See http://www.notenoughthoughts.net/posts/normal-log-likelihood-gradient.html + and https://discuss.mxnet.io/t/multivariate-gaussian-log-density-operator/1169/7 + + :param x: input data, + of shape (num_var) or + of shape (batch_size, num_var) or + of shape (batch_size, time_step, num_var). + :return: log_probability, + of shape (1) or + of shape (batch_size) or + of shape (batch_size, time_step). + """ + a = (self.num_var / 2) * math.log(2 * math.pi) + log_det_sigma = self.log_det(self.sigma) + b = (1 / 2) * log_det_sigma + sigma_inv = self.inverse_using_cholesky(self.sigma) + # deviation from mean, and dev_t is equivalent to transpose on last two dims. + dev = (x - self.mean).expand_dims(-1) + dev_t = (x - self.mean).expand_dims(-2) + + # since batch_dot only works with ndarrays with ndim of 3, + # and we could have ndarrays with ndim of 4, + # we flatten batch_size and time_step into single dim. + dev_flat = dev.reshape(shape=(-1, 0, 0), reverse=1) + sigma_inv_flat = sigma_inv.reshape(shape=(-1, 0, 0), reverse=1) + dev_t_flat = dev_t.reshape(shape=(-1, 0, 0), reverse=1) + c = (1 / 2) * self.F.batch_dot(self.F.batch_dot(dev_t_flat, sigma_inv_flat), dev_flat) + # and now reshape back to (batch_size, time_step) if required. + c = c.reshape_like(b) + + log_likelihood = -a - b - c + return log_likelihood + + def entropy(self) -> nd_sym_type: + """ + Calculate entropy of current distribution. + + See http://www.nowozin.net/sebastian/blog/the-entropy-of-a-normal-distribution.html + :return: entropy, + of shape (1) or + of shape (batch_size) or + of shape (batch_size, time_step). + """ + # todo: check if differential entropy is correct + log_det_sigma = self.log_det(self.sigma) + return (self.num_var / 2) + ((self.num_var / 2) * math.log(2 * math.pi)) + ((1 / 2) * log_det_sigma) + + def kl_div(self, alt_dist) -> nd_sym_type: + """ + Calculated KL-Divergence with another MultivariateNormalDist distribution + See https://en.wikipedia.org/wiki/Kullback%E2%80%93Leibler_divergence + Specifically https://wikimedia.org/api/rest_v1/media/math/render/svg/a3bf3b4917bd1fcb8be48d6d6139e2e387bdc7d3 + + :param alt_dist: alternative distribution used for kl divergence calculation + :type alt_dist: MultivariateNormalDist + :return: KL-Divergence, of shape (1,) + """ + sigma_a_inv = self.F.linalg.potri(self.F.linalg.potrf(self.sigma)) + sigma_b_inv = self.F.linalg.potri(self.F.linalg.potrf(alt_dist.sigma)) + term1a = mx.nd.batch_dot(sigma_b_inv, self.sigma) + # sum of diagonal for batch of matrices + term1 = (self.F.eye(self.num_var).broadcast_like(term1a) * term1a).sum(axis=-1).sum(axis=-1) + mean_diff = (alt_dist.mean - self.mean).expand_dims(-1) + mean_diff_t = (alt_dist.mean - self.mean).expand_dims(-2) + term2 = self.F.batch_dot(self.F.batch_dot(mean_diff_t, sigma_b_inv), mean_diff).reshape_like(term1) + term3 = (2 * self.F.linalg.sumlogdiag(self.F.linalg.potrf(alt_dist.sigma))) -\ + (2 * self.F.linalg.sumlogdiag(self.F.linalg.potrf(self.sigma))) + return 0.5 * (term1 + term2 - self.num_var + term3) + + + +class CategoricalDist: + def __init__(self, n_classes: int, probs: nd_sym_type, F: ModuleType=mx.nd) -> None: + """ + Distribution object for Categorical data. + Optionally works with batches and time steps, but be consistent in usage: i.e. if using time_step, + mean, sigma and data for log_prob must all include a time_step dimension. + + :param n_classes: number of classes in distribution + :param probs: probabilities for each class, + of shape (n_classes), + of shape (batch_size, n_classes) or + of shape (batch_size, time_step, n_classes) + :param (mx.nd or mx.sym) F: backend api (mx.sym if block has been hybridized). + """ + self.n_classes = n_classes + self.probs = probs + self.F = F + + + def log_prob(self, actions: nd_sym_type) -> nd_sym_type: + """ + Calculate the log probability of data given the current distribution. + + :param actions: actions, with int8 data type, + of shape (1) if probs was (n_classes), + of shape (batch_size) if probs was (batch_size, n_classes) and + of shape (batch_size, time_step) if probs was (batch_size, time_step, n_classes) + :return: log_probability, + of shape (1) if probs was (n_classes), + of shape (batch_size) if probs was (batch_size, n_classes) and + of shape (batch_size, time_step) if probs was (batch_size, time_step, n_classes) + """ + action_mask = actions.one_hot(depth=self.n_classes) + action_probs = (self.probs * action_mask).sum(axis=-1) + return action_probs.log() + + def entropy(self) -> nd_sym_type: + """ + Calculate entropy of current distribution. + + :return: entropy, + of shape (1) if probs was (n_classes), + of shape (batch_size) if probs was (batch_size, n_classes) and + of shape (batch_size, time_step) if probs was (batch_size, time_step, n_classes) + """ + # todo: look into numerical stability + return -(self.probs.log()*self.probs).sum(axis=-1) + + def kl_div(self, alt_dist) -> nd_sym_type: + """ + Calculated KL-Divergence with another Categorical distribution + + :param alt_dist: alternative distribution used for kl divergence calculation + :type alt_dist: CategoricalDist + :return: KL-Divergence + """ + logits_a = self.probs.clip(a_min=eps, a_max=1 - eps).log() + logits_b = alt_dist.probs.clip(a_min=eps, a_max=1 - eps).log() + t = self.probs * (logits_a - logits_b) + t = self.F.where(condition=(alt_dist.probs == 0), x=self.F.ones_like(alt_dist.probs) * math.inf, y=t) + t = self.F.where(condition=(self.probs == 0), x=self.F.zeros_like(self.probs), y=t) + return t.sum(axis=-1) + + +class DiscretePPOHead(nn.HybridBlock): + def __init__(self, num_actions: int) -> None: + """ + Head block for Discrete Proximal Policy Optimization, to calculate probabilities for each action given + middleware representation of the environment state. + + :param num_actions: number of actions in action space. + """ + super(DiscretePPOHead, self).__init__() + with self.name_scope(): + self.dense = nn.Dense(units=num_actions, flatten=False) + + def hybrid_forward(self, F: ModuleType, x: nd_sym_type) -> nd_sym_type: + """ + Used for forward pass through head network. + + :param (mx.nd or mx.sym) F: backend api (mx.sym if block has been hybridized). + :param x: middleware state representation, + of shape (batch_size, in_channels) or + of shape (batch_size, time_step, in_channels). + :return: batch of probabilities for each action, + of shape (batch_size, num_actions) or + of shape (batch_size, time_step, num_actions). + """ + policy_values = self.dense(x) + policy_probs = F.softmax(policy_values) + return policy_probs + + +class ContinuousPPOHead(nn.HybridBlock): + def __init__(self, num_actions: int) -> None: + """ + Head block for Continuous Proximal Policy Optimization, to calculate probabilities for each action given + middleware representation of the environment state. + + :param num_actions: number of actions in action space. + """ + super(ContinuousPPOHead, self).__init__() + with self.name_scope(): + # todo: change initialization strategy + self.dense = nn.Dense(units=num_actions, flatten=False) + # all samples (across batch, and time step) share the same covariance, which is learnt, + # but since we assume the action probability variables are independent, + # only the diagonal entries of the covariance matrix are specified. + self.log_std = self.params.get('log_std', + shape=num_actions, + init=mx.init.Zero(), + allow_deferred_init=True) + # todo: is_local? + + def hybrid_forward(self, F: ModuleType, x: nd_sym_type, log_std: nd_sym_type) -> List[nd_sym_type]: + """ + Used for forward pass through head network. + + :param (mx.nd or mx.sym) F: backend api (mx.sym if block has been hybridized). + :param x: middleware state representation, + of shape (batch_size, in_channels) or + of shape (batch_size, time_step, in_channels). + :return: batch of probabilities for each action, + of shape (batch_size, action_mean) or + of shape (batch_size, time_step, action_mean). + """ + policy_means = self.dense(x) + policy_std = log_std.exp() + return [policy_means, policy_std] + + +class ClippedPPOLossDiscrete(HeadLoss): + def __init__(self, + num_actions: int, + clip_likelihood_ratio_using_epsilon: float, + beta: float=0, + use_kl_regularization: bool=False, + initial_kl_coefficient: float=1, + kl_cutoff: float=0, + high_kl_penalty_coefficient: float=1, + weight: float=1, + batch_axis: int=0) -> None: + """ + Loss for discrete version of Clipped PPO. + + :param num_actions: number of actions in action space. + :param clip_likelihood_ratio_using_epsilon: epsilon to use for likelihood ratio clipping. + :param beta: loss coefficient applied to entropy + :param use_kl_regularization: option to add kl divergence loss + :param initial_kl_coefficient: loss coefficient applied kl divergence loss (also see high_kl_penalty_coefficient). + :param kl_cutoff: threshold for using high_kl_penalty_coefficient + :param high_kl_penalty_coefficient: loss coefficient applied to kv divergence above kl_cutoff + :param weight: scalar used to adjust relative weight of loss (if using this loss with others). + :param batch_axis: axis used for mini-batch (default is 0) and excluded from loss aggregation. + """ + super(ClippedPPOLossDiscrete, self).__init__(weight=weight, batch_axis=batch_axis) + self.weight = weight + self.num_actions = num_actions + self.clip_likelihood_ratio_using_epsilon = clip_likelihood_ratio_using_epsilon + self.beta = beta + self.use_kl_regularization = use_kl_regularization + self.initial_kl_coefficient = initial_kl_coefficient if self.use_kl_regularization else 0.0 + self.kl_coefficient = self.params.get('kl_coefficient', + shape=(1,), + init=mx.init.Constant([initial_kl_coefficient,]), + differentiable=False) + self.kl_cutoff = kl_cutoff + self.high_kl_penalty_coefficient = high_kl_penalty_coefficient + + @property + def input_schema(self) -> LossInputSchema: + return LossInputSchema( + head_outputs=['new_policy_probs'], + agent_inputs=['actions', 'old_policy_probs', 'clip_param_rescaler'], + targets=['advantages'] + ) + + def loss_forward(self, + F: ModuleType, + new_policy_probs: nd_sym_type, + actions: nd_sym_type, + old_policy_probs: nd_sym_type, + clip_param_rescaler: nd_sym_type, + advantages: nd_sym_type, + kl_coefficient: nd_sym_type) -> List[Tuple[nd_sym_type, str]]: + """ + Used for forward pass through loss computations. + Works with batches of data, and optionally time_steps, but be consistent in usage: i.e. if using time_step, + new_policy_probs, old_policy_probs, actions and advantages all must include a time_step dimension. + + NOTE: order of input arguments MUST NOT CHANGE because it matches the order + parameters are passed in ppo_agent:train_network() + + :param (mx.nd or mx.sym) F: backend api (mx.sym if block has been hybridized). + :param new_policy_probs: action probabilities predicted by DiscretePPOHead network, + of shape (batch_size, num_actions) or + of shape (batch_size, time_step, num_actions). + :param old_policy_probs: action probabilities for previous policy, + of shape (batch_size, num_actions) or + of shape (batch_size, time_step, num_actions). + :param actions: true actions taken during rollout, + of shape (batch_size) or + of shape (batch_size, time_step). + :param clip_param_rescaler: scales epsilon to use for likelihood ratio clipping. + :param advantages: change in state value after taking action (a.k.a advantage) + of shape (batch_size) or + of shape (batch_size, time_step). + :param kl_coefficient: loss coefficient applied kl divergence loss (also see high_kl_penalty_coefficient). + :return: loss, of shape (batch_size). + """ + + old_policy_dist = CategoricalDist(self.num_actions, old_policy_probs, F=F) + action_probs_wrt_old_policy = old_policy_dist.log_prob(actions) + + new_policy_dist = CategoricalDist(self.num_actions, new_policy_probs, F=F) + action_probs_wrt_new_policy = new_policy_dist.log_prob(actions) + + entropy_loss = - self.beta * new_policy_dist.entropy().mean() + + if self.use_kl_regularization: + kl_div = old_policy_dist.kl_div(new_policy_dist).mean() + weighted_kl_div = kl_coefficient * kl_div + high_kl_div = F.stack(F.zeros_like(kl_div), kl_div - self.kl_cutoff).max().square() + weighted_high_kl_div = self.high_kl_penalty_coefficient * high_kl_div + kl_div_loss = weighted_kl_div + weighted_high_kl_div + else: + kl_div_loss = F.zeros(shape=(1,)) + + # working with log probs, so minus first, then exponential (same as division) + likelihood_ratio = (action_probs_wrt_new_policy - action_probs_wrt_old_policy).exp() + + if self.clip_likelihood_ratio_using_epsilon is not None: + # clipping of likelihood ratio + min_value = 1 - self.clip_likelihood_ratio_using_epsilon * clip_param_rescaler + max_value = 1 + self.clip_likelihood_ratio_using_epsilon * clip_param_rescaler + + # can't use F.clip (with variable clipping bounds), hence custom implementation + clipped_likelihood_ratio = hybrid_clip(F, likelihood_ratio, clip_lower=min_value, clip_upper=max_value) + + # lower bound of original, and clipped versions or each scaled advantage + # element-wise min between the two ndarrays + unclipped_scaled_advantages = likelihood_ratio * advantages + clipped_scaled_advantages = clipped_likelihood_ratio * advantages + scaled_advantages = F.stack(unclipped_scaled_advantages, clipped_scaled_advantages).min(axis=0) + else: + scaled_advantages = likelihood_ratio * advantages + clipped_likelihood_ratio = F.zeros_like(likelihood_ratio) + + # for each batch, calculate expectation of scaled_advantages across time steps, + # but want code to work with data without time step too, so reshape to add timestep if doesn't exist. + scaled_advantages_w_time = scaled_advantages.reshape(shape=(0, -1)) + expected_scaled_advantages = scaled_advantages_w_time.mean(axis=1) + # want to maximize expected_scaled_advantages, add minus so can minimize. + surrogate_loss = (-expected_scaled_advantages * self.weight).mean() + + return [ + (surrogate_loss, LOSS_OUT_TYPE_LOSS), + (entropy_loss + kl_div_loss, LOSS_OUT_TYPE_REGULARIZATION), + (kl_div_loss, LOSS_OUT_TYPE_KL), + (entropy_loss, LOSS_OUT_TYPE_ENTROPY), + (likelihood_ratio, LOSS_OUT_TYPE_LIKELIHOOD_RATIO), + (clipped_likelihood_ratio, LOSS_OUT_TYPE_CLIPPED_LIKELIHOOD_RATIO) + ] + + +class ClippedPPOLossContinuous(HeadLoss): + def __init__(self, + num_actions: int, + clip_likelihood_ratio_using_epsilon: float, + beta: float=0, + use_kl_regularization: bool=False, + initial_kl_coefficient: float=1, + kl_cutoff: float=0, + high_kl_penalty_coefficient: float=1, + weight: float=1, + batch_axis: int=0): + """ + Loss for continuous version of Clipped PPO. + + :param num_actions: number of actions in action space. + :param clip_likelihood_ratio_using_epsilon: epsilon to use for likelihood ratio clipping. + :param beta: loss coefficient applied to entropy + :param batch_axis: axis used for mini-batch (default is 0) and excluded from loss aggregation. + :param use_kl_regularization: option to add kl divergence loss + :param initial_kl_coefficient: initial loss coefficient applied kl divergence loss (also see high_kl_penalty_coefficient). + :param kl_cutoff: threshold for using high_kl_penalty_coefficient + :param high_kl_penalty_coefficient: loss coefficient applied to kv divergence above kl_cutoff + :param weight: scalar used to adjust relative weight of loss (if using this loss with others). + :param batch_axis: axis used for mini-batch (default is 0) and excluded from loss aggregation. + """ + super(ClippedPPOLossContinuous, self).__init__(weight=weight, batch_axis=batch_axis) + self.weight = weight + self.num_actions = num_actions + self.clip_likelihood_ratio_using_epsilon = clip_likelihood_ratio_using_epsilon + self.beta = beta + self.use_kl_regularization = use_kl_regularization + self.initial_kl_coefficient = initial_kl_coefficient if self.use_kl_regularization else 0.0 + self.kl_coefficient = self.params.get('kl_coefficient', + shape=(1,), + init=mx.init.Constant([initial_kl_coefficient,]), + differentiable=False) + self.kl_cutoff = kl_cutoff + self.high_kl_penalty_coefficient = high_kl_penalty_coefficient + + @property + def input_schema(self) -> LossInputSchema: + return LossInputSchema( + head_outputs=['new_policy_means','new_policy_stds'], + agent_inputs=['actions', 'old_policy_means', 'old_policy_stds', 'clip_param_rescaler'], + targets=['advantages'] + ) + + def loss_forward(self, + F: ModuleType, + new_policy_means: nd_sym_type, + new_policy_stds: nd_sym_type, + actions: nd_sym_type, + old_policy_means: nd_sym_type, + old_policy_stds: nd_sym_type, + clip_param_rescaler: nd_sym_type, + advantages: nd_sym_type, + kl_coefficient: nd_sym_type) -> List[Tuple[nd_sym_type, str]]: + """ + Used for forward pass through loss computations. + Works with batches of data, and optionally time_steps, but be consistent in usage: i.e. if using time_step, + new_policy_means, old_policy_means, actions and advantages all must include a time_step dimension. + + :param (mx.nd or mx.sym) F: backend api (mx.sym if block has been hybridized). + :param new_policy_means: action means predicted by MultivariateNormalDist network, + of shape (batch_size, num_actions) or + of shape (batch_size, time_step, num_actions). + :param new_policy_stds: action standard deviation returned by head, + of shape (batch_size, num_actions) or + of shape (batch_size, time_step, num_actions). + :param actions: true actions taken during rollout, + of shape (batch_size) or + of shape (batch_size, time_step). + :param old_policy_means: action means for previous policy, + of shape (batch_size, num_actions) or + of shape (batch_size, time_step, num_actions). + :param old_policy_stds: action standard deviation returned by head previously, + of shape (batch_size, num_actions) or + of shape (batch_size, time_step, num_actions). + :param clip_param_rescaler: scales epsilon to use for likelihood ratio clipping. + :param advantages: change in state value after taking action (a.k.a advantage) + of shape (batch_size) or + of shape (batch_size, time_step). + :param kl_coefficient: loss coefficient applied kl divergence loss (also see high_kl_penalty_coefficient). + :return: loss, of shape (batch_size). + """ + old_var = old_policy_stds ** 2 + # sets diagonal in (batch size and time step) covariance matrices + old_covar = mx.nd.eye(N=self.num_actions) * (old_var + eps).broadcast_like(old_policy_means).expand_dims(-2) + old_policy_dist = MultivariateNormalDist(self.num_actions, old_policy_means, old_covar, F=F) + action_probs_wrt_old_policy = old_policy_dist.log_prob(actions) + + new_var = new_policy_stds ** 2 + # sets diagonal in (batch size and time step) covariance matrices + new_covar = mx.nd.eye(N=self.num_actions) * (new_var + eps).broadcast_like(new_policy_means).expand_dims(-2) + new_policy_dist = MultivariateNormalDist(self.num_actions, new_policy_means, new_covar, F=F) + action_probs_wrt_new_policy = new_policy_dist.log_prob(actions) + + entropy_loss = - self.beta * new_policy_dist.entropy().mean() + + if self.use_kl_regularization: + kl_div = old_policy_dist.kl_div(new_policy_dist).mean() + weighted_kl_div = kl_coefficient * kl_div + high_kl_div = F.stack(F.zeros_like(kl_div), kl_div - self.kl_cutoff).max().square() + weighted_high_kl_div = self.high_kl_penalty_coefficient * high_kl_div + kl_div_loss = weighted_kl_div + weighted_high_kl_div + else: + kl_div_loss = F.zeros(shape=(1,)) + + # working with log probs, so minus first, then exponential (same as division) + likelihood_ratio = (action_probs_wrt_new_policy - action_probs_wrt_old_policy).exp() + + if self.clip_likelihood_ratio_using_epsilon is not None: + # clipping of likelihood ratio + min_value = 1 - self.clip_likelihood_ratio_using_epsilon * clip_param_rescaler + max_value = 1 + self.clip_likelihood_ratio_using_epsilon * clip_param_rescaler + + # can't use F.clip (with variable clipping bounds), hence custom implementation + clipped_likelihood_ratio = hybrid_clip(F, likelihood_ratio, clip_lower=min_value, clip_upper=max_value) + + # lower bound of original, and clipped versions or each scaled advantage + # element-wise min between the two ndarrays + unclipped_scaled_advantages = likelihood_ratio * advantages + clipped_scaled_advantages = clipped_likelihood_ratio * advantages + scaled_advantages = F.stack(unclipped_scaled_advantages, clipped_scaled_advantages).min(axis=0) + else: + scaled_advantages = likelihood_ratio * advantages + clipped_likelihood_ratio = F.zeros_like(likelihood_ratio) + + # for each batch, calculate expectation of scaled_advantages across time steps, + # but want code to work with data without time step too, so reshape to add timestep if doesn't exist. + scaled_advantages_w_time = scaled_advantages.reshape(shape=(0, -1)) + expected_scaled_advantages = scaled_advantages_w_time.mean(axis=1) + # want to maximize expected_scaled_advantages, add minus so can minimize. + surrogate_loss = (-expected_scaled_advantages * self.weight).mean() + + return [ + (surrogate_loss, LOSS_OUT_TYPE_LOSS), + (entropy_loss + kl_div_loss, LOSS_OUT_TYPE_REGULARIZATION), + (kl_div_loss, LOSS_OUT_TYPE_KL), + (entropy_loss, LOSS_OUT_TYPE_ENTROPY), + (likelihood_ratio, LOSS_OUT_TYPE_LIKELIHOOD_RATIO), + (clipped_likelihood_ratio, LOSS_OUT_TYPE_CLIPPED_LIKELIHOOD_RATIO) + ] + + +class PPOHead(Head): + def __init__(self, + agent_parameters: AgentParameters, + spaces: SpacesDefinition, + network_name: str, + head_type_idx: int=0, + loss_weight: float=1., + is_local: bool=True, + activation_function: str='tanh', + dense_layer: None=None) -> None: + """ + Head block for Proximal Policy Optimization, to calculate probabilities for each action given middleware + representation of the environment state. + + :param agent_parameters: containing algorithm parameters such as clip_likelihood_ratio_using_epsilon + and beta_entropy. + :param spaces: containing action spaces used for defining size of network output. + :param network_name: name of head network. currently unused. + :param head_type_idx: index of head network. currently unused. + :param loss_weight: scalar used to adjust relative weight of loss (if using this loss with others). + :param is_local: flag to denote if network is local. currently unused. + :param activation_function: activation function to use between layers. currently unused. + :param dense_layer: type of dense layer to use in network. currently unused. + """ + super().__init__(agent_parameters, spaces, network_name, head_type_idx, loss_weight, is_local, activation_function, + dense_layer=dense_layer) + self.return_type = ActionProbabilities + + self.clip_likelihood_ratio_using_epsilon = agent_parameters.algorithm.clip_likelihood_ratio_using_epsilon + self.beta = agent_parameters.algorithm.beta_entropy + self.use_kl_regularization = agent_parameters.algorithm.use_kl_regularization + if self.use_kl_regularization: + self.initial_kl_coefficient = agent_parameters.algorithm.initial_kl_coefficient + self.kl_cutoff = 2 * agent_parameters.algorithm.target_kl_divergence + self.high_kl_penalty_coefficient = agent_parameters.algorithm.high_kl_penalty_coefficient + else: + self.initial_kl_coefficient, self.kl_cutoff, self.high_kl_penalty_coefficient = (None, None, None) + self._loss = [] + + if isinstance(self.spaces.action, DiscreteActionSpace): + self.net = DiscretePPOHead(num_actions=len(self.spaces.action.actions)) + elif isinstance(self.spaces.action, BoxActionSpace): + self.net = ContinuousPPOHead(num_actions=len(self.spaces.action.actions)) + else: + raise ValueError("Only discrete or continuous action spaces are supported for PPO.") + + def hybrid_forward(self, + F: ModuleType, + x: nd_sym_type) -> nd_sym_type: + """ + :param (mx.nd or mx.sym) F: backend api (mx.sym if block has been hybridized). + :param x: middleware embedding + :return: policy parameters/probabilities + """ + return self.net(x) + + def loss(self) -> mx.gluon.loss.Loss: + """ + Specifies loss block to be used for this policy head. + + :return: loss block (can be called as function) for action probabilities returned by this policy network. + """ + if isinstance(self.spaces.action, DiscreteActionSpace): + loss = ClippedPPOLossDiscrete(len(self.spaces.action.actions), + self.clip_likelihood_ratio_using_epsilon, + self.beta, + self.use_kl_regularization, self.initial_kl_coefficient, + self.kl_cutoff, self.high_kl_penalty_coefficient, + self.loss_weight) + elif isinstance(self.spaces.action, BoxActionSpace): + loss = ClippedPPOLossContinuous(len(self.spaces.action.actions), + self.clip_likelihood_ratio_using_epsilon, + self.beta, + self.use_kl_regularization, self.initial_kl_coefficient, + self.kl_cutoff, self.high_kl_penalty_coefficient, + self.loss_weight) + else: + raise ValueError("Only discrete or continuous action spaces are supported for PPO.") + loss.initialize() + # set a property so can assign_kl_coefficient in future, + # make a list, otherwise it would be added as a child of Head Block (due to type check) + self._loss = [loss] + return loss + + @property + def kl_divergence(self): + return self.head_type_idx, LOSS_OUT_TYPE_KL + + @property + def entropy(self): + return self.head_type_idx, LOSS_OUT_TYPE_ENTROPY + + @property + def likelihood_ratio(self): + return self.head_type_idx, LOSS_OUT_TYPE_LIKELIHOOD_RATIO + + @property + def clipped_likelihood_ratio(self): + return self.head_type_idx, LOSS_OUT_TYPE_CLIPPED_LIKELIHOOD_RATIO + + def assign_kl_coefficient(self, kl_coefficient: float) -> None: + self._loss[0].kl_coefficient.set_data(mx.nd.array((kl_coefficient,))) \ No newline at end of file diff --git a/rl_coach/architectures/mxnet_components/heads/ppo_v_head.py b/rl_coach/architectures/mxnet_components/heads/ppo_v_head.py new file mode 100644 index 0000000..5512b90 --- /dev/null +++ b/rl_coach/architectures/mxnet_components/heads/ppo_v_head.py @@ -0,0 +1,123 @@ +from typing import List, Tuple, Union +from types import ModuleType + +import mxnet as mx +from mxnet.gluon import nn +from rl_coach.architectures.mxnet_components.heads.head import Head, HeadLoss, LossInputSchema +from rl_coach.architectures.mxnet_components.heads.head import LOSS_OUT_TYPE_LOSS +from rl_coach.base_parameters import AgentParameters +from rl_coach.core_types import ActionProbabilities +from rl_coach.spaces import SpacesDefinition + +nd_sym_type = Union[mx.nd.NDArray, mx.sym.Symbol] + + +class PPOVHeadLoss(HeadLoss): + def __init__(self, clip_likelihood_ratio_using_epsilon: float, weight: float=1, batch_axis: int=0) -> None: + """ + Loss for PPO Value network. + Schulman implemented this extension in OpenAI baselines for PPO2 + See https://github.com/openai/baselines/blob/master/baselines/ppo2/ppo2.py#L72 + + :param clip_likelihood_ratio_using_epsilon: epsilon to use for likelihood ratio clipping. + :param weight: scalar used to adjust relative weight of loss (if using this loss with others). + :param batch_axis: axis used for mini-batch (default is 0) and excluded from loss aggregation. + """ + super(PPOVHeadLoss, self).__init__(weight=weight, batch_axis=batch_axis) + self.weight = weight + self.clip_likelihood_ratio_using_epsilon = clip_likelihood_ratio_using_epsilon + + @property + def input_schema(self) -> LossInputSchema: + return LossInputSchema( + head_outputs=['new_policy_values'], + agent_inputs=['old_policy_values'], + targets=['target_values'] + ) + + def loss_forward(self, + F: ModuleType, + new_policy_values: nd_sym_type, + old_policy_values: nd_sym_type, + target_values: nd_sym_type) -> List[Tuple[nd_sym_type, str]]: + """ + Used for forward pass through loss computations. + Calculates two losses (L2 and a clipped difference L2 loss) and takes the maximum of the two. + Works with batches of data, and optionally time_steps, but be consistent in usage: i.e. if using time_step, + new_policy_values, old_policy_values and target_values all must include a time_step dimension. + + :param (mx.nd or mx.sym) F: backend api (mx.sym if block has been hybridized). + :param new_policy_values: values predicted by PPOVHead network, + of shape (batch_size) or + of shape (batch_size, time_step). + :param old_policy_values: values predicted by old value network, + of shape (batch_size) or + of shape (batch_size, time_step). + :param target_values: actual state values, + of shape (batch_size) or + of shape (batch_size, time_step). + :return: loss, of shape (batch_size). + """ + # L2 loss + value_loss_1 = (new_policy_values - target_values).square() + # Clipped difference L2 loss + diff = new_policy_values - old_policy_values + clipped_diff = diff.clip(a_min=-self.clip_likelihood_ratio_using_epsilon, + a_max=self.clip_likelihood_ratio_using_epsilon) + value_loss_2 = (old_policy_values + clipped_diff - target_values).square() + # Maximum of the two losses, element-wise maximum. + value_loss_max = mx.nd.stack(value_loss_1, value_loss_2).max(axis=0) + # Aggregate over temporal axis, adding if doesn't exist (hense reshape) + value_loss_max_w_time = value_loss_max.reshape(shape=(0, -1)) + value_loss = value_loss_max_w_time.mean(axis=1) + # Weight the loss (and average over samples of batch) + value_loss_weighted = value_loss.mean() * self.weight + return [(value_loss_weighted, LOSS_OUT_TYPE_LOSS)] + + +class PPOVHead(Head): + def __init__(self, + agent_parameters: AgentParameters, + spaces: SpacesDefinition, + network_name: str, + head_type_idx: int=0, + loss_weight: float=1., + is_local: bool = True, + activation_function: str='relu', + dense_layer: None=None) -> None: + """ + PPO Value Head for predicting state values. + + :param agent_parameters: containing algorithm parameters, but currently unused. + :param spaces: containing action spaces, but currently unused. + :param network_name: name of head network. currently unused. + :param head_type_idx: index of head network. currently unused. + :param loss_weight: scalar used to adjust relative weight of loss (if using this loss with others). + :param is_local: flag to denote if network is local. currently unused. + :param activation_function: activation function to use between layers. currently unused. + :param dense_layer: type of dense layer to use in network. currently unused. + """ + super(PPOVHead, self).__init__(agent_parameters, spaces, network_name, head_type_idx, loss_weight, is_local, + activation_function, dense_layer=dense_layer) + self.clip_likelihood_ratio_using_epsilon = agent_parameters.algorithm.clip_likelihood_ratio_using_epsilon + self.return_type = ActionProbabilities + with self.name_scope(): + self.dense = nn.Dense(units=1) + + def hybrid_forward(self, F: ModuleType, x: nd_sym_type) -> nd_sym_type: + """ + Used for forward pass through value head network. + + :param (mx.nd or mx.sym) F: backend api (mx.sym if block has been hybridized). + :param x: middleware state representation, of shape (batch_size, in_channels). + :return: final value output of network, of shape (batch_size). + """ + return self.dense(x).squeeze() + + def loss(self) -> mx.gluon.loss.Loss: + """ + Specifies loss block to be used for specific value head implementation. + + :return: loss block (can be called as function) for outputs returned by the value head network. + """ + return PPOVHeadLoss(self.clip_likelihood_ratio_using_epsilon, weight=self.loss_weight) diff --git a/rl_coach/architectures/mxnet_components/heads/q_head.py b/rl_coach/architectures/mxnet_components/heads/q_head.py new file mode 100644 index 0000000..88e107f --- /dev/null +++ b/rl_coach/architectures/mxnet_components/heads/q_head.py @@ -0,0 +1,106 @@ +from typing import Union, List, Tuple +from types import ModuleType + +import mxnet as mx +from mxnet.gluon.loss import Loss, HuberLoss, L2Loss +from mxnet.gluon import nn +from rl_coach.architectures.mxnet_components.heads.head import Head, HeadLoss, LossInputSchema +from rl_coach.architectures.mxnet_components.heads.head import LOSS_OUT_TYPE_LOSS +from rl_coach.base_parameters import AgentParameters +from rl_coach.core_types import QActionStateValue +from rl_coach.spaces import SpacesDefinition, BoxActionSpace, DiscreteActionSpace + +nd_sym_type = Union[mx.nd.NDArray, mx.sym.Symbol] + + +class QHeadLoss(HeadLoss): + def __init__(self, loss_type: Loss=L2Loss, weight: float=1, batch_axis: int=0) -> None: + """ + Loss for Q-Value Head. + + :param loss_type: loss function with default of mean squared error (i.e. L2Loss). + :param weight: scalar used to adjust relative weight of loss (if using this loss with others). + :param batch_axis: axis used for mini-batch (default is 0) and excluded from loss aggregation. + """ + super(QHeadLoss, self).__init__(weight=weight, batch_axis=batch_axis) + with self.name_scope(): + self.loss_fn = loss_type(weight=weight, batch_axis=batch_axis) + + @property + def input_schema(self) -> LossInputSchema: + return LossInputSchema( + head_outputs=['pred'], + agent_inputs=[], + targets=['target'] + ) + + def loss_forward(self, + F: ModuleType, + pred: nd_sym_type, + target: nd_sym_type) -> List[Tuple[nd_sym_type, str]]: + """ + Used for forward pass through loss computations. + + :param F: backend api, either `mxnet.nd` or `mxnet.sym` (if block has been hybridized). + :param pred: state-action q-values predicted by QHead network, of shape (batch_size, num_actions). + :param target: actual state-action q-values, of shape (batch_size, num_actions). + :return: loss, of shape (batch_size). + """ + loss = self.loss_fn(pred, target).mean() + return [(loss, LOSS_OUT_TYPE_LOSS)] + + +class QHead(Head): + def __init__(self, + agent_parameters: AgentParameters, + spaces: SpacesDefinition, + network_name: str, + head_type_idx: int=0, + loss_weight: float=1., + is_local: bool=True, + activation_function: str='relu', + dense_layer: None=None, + loss_type: Union[HuberLoss, L2Loss]=L2Loss) -> None: + """ + Q-Value Head for predicting state-action Q-Values. + + :param agent_parameters: containing algorithm parameters, but currently unused. + :param spaces: containing action spaces used for defining size of network output. + :param network_name: name of head network. currently unused. + :param head_type_idx: index of head network. currently unused. + :param loss_weight: scalar used to adjust relative weight of loss (if using this loss with others). + :param is_local: flag to denote if network is local. currently unused. + :param activation_function: activation function to use between layers. currently unused. + :param dense_layer: type of dense layer to use in network. currently unused. + :param loss_type: loss function to use. + """ + super(QHead, self).__init__(agent_parameters, spaces, network_name, head_type_idx, loss_weight, + is_local, activation_function, dense_layer) + if isinstance(self.spaces.action, BoxActionSpace): + self.num_actions = 1 + elif isinstance(self.spaces.action, DiscreteActionSpace): + self.num_actions = len(self.spaces.action.actions) + self.return_type = QActionStateValue + assert (loss_type == L2Loss) or (loss_type == HuberLoss), "Only expecting L2Loss or HuberLoss." + self.loss_type = loss_type + + with self.name_scope(): + self.dense = nn.Dense(units=self.num_actions) + + def loss(self) -> Loss: + """ + Specifies loss block to be used for specific value head implementation. + + :return: loss block (can be called as function) for outputs returned by the head network. + """ + return QHeadLoss(loss_type=self.loss_type, weight=self.loss_weight) + + def hybrid_forward(self, F: ModuleType, x: nd_sym_type) -> nd_sym_type: + """ + Used for forward pass through Q-Value head network. + + :param F: backend api, either `mxnet.nd` or `mxnet.sym` (if block has been hybridized). + :param x: middleware state representation, of shape (batch_size, in_channels). + :return: predicted state-action q-values, of shape (batch_size, num_actions). + """ + return self.dense(x) diff --git a/rl_coach/architectures/mxnet_components/heads/v_head.py b/rl_coach/architectures/mxnet_components/heads/v_head.py new file mode 100644 index 0000000..a04cafd --- /dev/null +++ b/rl_coach/architectures/mxnet_components/heads/v_head.py @@ -0,0 +1,100 @@ +from typing import Union, List, Tuple +from types import ModuleType + +import mxnet as mx +from mxnet.gluon.loss import Loss, HuberLoss, L2Loss +from mxnet.gluon import nn +from rl_coach.architectures.mxnet_components.heads.head import Head, HeadLoss, LossInputSchema +from rl_coach.architectures.mxnet_components.heads.head import LOSS_OUT_TYPE_LOSS +from rl_coach.base_parameters import AgentParameters +from rl_coach.core_types import VStateValue +from rl_coach.spaces import SpacesDefinition + +nd_sym_type = Union[mx.nd.NDArray, mx.sym.Symbol] + + +class VHeadLoss(HeadLoss): + def __init__(self, loss_type: Loss=L2Loss, weight: float=1, batch_axis: int=0) -> None: + """ + Loss for Value Head. + + :param loss_type: loss function with default of mean squared error (i.e. L2Loss). + :param weight: scalar used to adjust relative weight of loss (if using this loss with others). + :param batch_axis: axis used for mini-batch (default is 0) and excluded from loss aggregation. + """ + super(VHeadLoss, self).__init__(weight=weight, batch_axis=batch_axis) + with self.name_scope(): + self.loss_fn = loss_type(weight=weight, batch_axis=batch_axis) + + @property + def input_schema(self) -> LossInputSchema: + return LossInputSchema( + head_outputs=['pred'], + agent_inputs=[], + targets=['target'] + ) + + def loss_forward(self, + F: ModuleType, + pred: nd_sym_type, + target: nd_sym_type) -> List[Tuple[nd_sym_type, str]]: + """ + Used for forward pass through loss computations. + + :param F: backend api, either `mxnet.nd` or `mxnet.sym` (if block has been hybridized). + :param pred: state values predicted by VHead network, of shape (batch_size). + :param target: actual state values, of shape (batch_size). + :return: loss, of shape (batch_size). + """ + loss = self.loss_fn(pred, target).mean() + return [(loss, LOSS_OUT_TYPE_LOSS)] + + +class VHead(Head): + def __init__(self, + agent_parameters: AgentParameters, + spaces: SpacesDefinition, + network_name: str, + head_type_idx: int=0, + loss_weight: float=1., + is_local: bool=True, + activation_function: str='relu', + dense_layer: None=None, + loss_type: Union[HuberLoss, L2Loss]=L2Loss): + """ + Value Head for predicting state values. + :param agent_parameters: containing algorithm parameters, but currently unused. + :param spaces: containing action spaces, but currently unused. + :param network_name: name of head network. currently unused. + :param head_type_idx: index of head network. currently unused. + :param loss_weight: scalar used to adjust relative weight of loss (if using this loss with others). + :param is_local: flag to denote if network is local. currently unused. + :param activation_function: activation function to use between layers. currently unused. + :param dense_layer: type of dense layer to use in network. currently unused. + :param loss_type: loss function with default of mean squared error (i.e. L2Loss), or alternatively HuberLoss. + """ + super(VHead, self).__init__(agent_parameters, spaces, network_name, head_type_idx, loss_weight, + is_local, activation_function, dense_layer) + assert (loss_type == L2Loss) or (loss_type == HuberLoss), "Only expecting L2Loss or HuberLoss." + self.loss_type = loss_type + self.return_type = VStateValue + with self.name_scope(): + self.dense = nn.Dense(units=1) + + def loss(self) -> Loss: + """ + Specifies loss block to be used for specific value head implementation. + + :return: loss block (can be called as function) for outputs returned by the head network. + """ + return VHeadLoss(loss_type=self.loss_type, weight=self.loss_weight) + + def hybrid_forward(self, F: ModuleType, x: nd_sym_type) -> nd_sym_type: + """ + Used for forward pass through value head network. + + :param F: backend api, either `mxnet.nd` or `mxnet.sym` (if block has been hybridized). + :param x: middleware state representation, of shape (batch_size, in_channels). + :return: final output of value network, of shape (batch_size). + """ + return self.dense(x).squeeze() diff --git a/rl_coach/architectures/mxnet_components/layers.py b/rl_coach/architectures/mxnet_components/layers.py new file mode 100644 index 0000000..233d225 --- /dev/null +++ b/rl_coach/architectures/mxnet_components/layers.py @@ -0,0 +1,99 @@ +""" +Module implementing basic layers in mxnet +""" + +from types import FunctionType + +from mxnet.gluon import nn + +from rl_coach.architectures import layers +from rl_coach.architectures.mxnet_components import utils + + +# define global dictionary for storing layer type to layer implementation mapping +mx_layer_dict = dict() + + +def reg_to_mx(layer_type) -> FunctionType: + """ function decorator that registers layer implementation + :return: decorated function + """ + def reg_impl_decorator(func): + assert layer_type not in mx_layer_dict + mx_layer_dict[layer_type] = func + return func + return reg_impl_decorator + + +def convert_layer(layer): + """ + If layer is callable, return layer, otherwise convert to MX type + :param layer: layer to be converted + :return: converted layer if not callable, otherwise layer itself + """ + if callable(layer): + return layer + return mx_layer_dict[type(layer)](layer) + + +class Conv2d(layers.Conv2d): + def __init__(self, num_filters: int, kernel_size: int, strides: int): + super(Conv2d, self).__init__(num_filters=num_filters, kernel_size=kernel_size, strides=strides) + + def __call__(self) -> nn.Conv2D: + """ + returns a conv2d block + :return: conv2d block + """ + return nn.Conv2D(channels=self.num_filters, kernel_size=self.kernel_size, strides=self.strides) + + @staticmethod + @reg_to_mx(layers.Conv2d) + def to_mx(base: layers.Conv2d): + return Conv2d(num_filters=base.num_filters, kernel_size=base.kernel_size, strides=base.strides) + + +class BatchnormActivationDropout(layers.BatchnormActivationDropout): + def __init__(self, batchnorm: bool=False, activation_function=None, dropout_rate: float=0): + super(BatchnormActivationDropout, self).__init__( + batchnorm=batchnorm, activation_function=activation_function, dropout_rate=dropout_rate) + + def __call__(self): + """ + returns a list of mxnet batchnorm, activation and dropout layers + :return: batchnorm, activation and dropout layers + """ + block = nn.HybridSequential() + if self.batchnorm: + block.add(nn.BatchNorm()) + if self.activation_function: + block.add(nn.Activation(activation=utils.get_mxnet_activation_name(self.activation_function))) + if self.dropout_rate: + block.add(nn.Dropout(self.dropout_rate)) + return block + + @staticmethod + @reg_to_mx(layers.BatchnormActivationDropout) + def to_mx(base: layers.BatchnormActivationDropout): + return BatchnormActivationDropout( + batchnorm=base.batchnorm, + activation_function=base.activation_function, + dropout_rate=base.dropout_rate) + + +class Dense(layers.Dense): + def __init__(self, units: int): + super(Dense, self).__init__(units=units) + + def __call__(self): + """ + returns a mxnet dense layer + :return: dense layer + """ + # Set flatten to False for consistent behavior with tf.layers.dense + return nn.Dense(self.units, flatten=False) + + @staticmethod + @reg_to_mx(layers.Dense) + def to_mx(base: layers.Dense): + return Dense(units=base.units) diff --git a/rl_coach/architectures/mxnet_components/middlewares/__init__.py b/rl_coach/architectures/mxnet_components/middlewares/__init__.py new file mode 100644 index 0000000..3647dfa --- /dev/null +++ b/rl_coach/architectures/mxnet_components/middlewares/__init__.py @@ -0,0 +1,4 @@ +from .fc_middleware import FCMiddleware +from .lstm_middleware import LSTMMiddleware + +__all__ = ["FCMiddleware", "LSTMMiddleware"] \ No newline at end of file diff --git a/rl_coach/architectures/mxnet_components/middlewares/fc_middleware.py b/rl_coach/architectures/mxnet_components/middlewares/fc_middleware.py new file mode 100644 index 0000000..de3377f --- /dev/null +++ b/rl_coach/architectures/mxnet_components/middlewares/fc_middleware.py @@ -0,0 +1,52 @@ +""" +Module that defines the fully-connected middleware class +""" + +from rl_coach.architectures.mxnet_components.layers import Dense +from rl_coach.architectures.mxnet_components.middlewares.middleware import Middleware +from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters +from rl_coach.base_parameters import MiddlewareScheme + + +class FCMiddleware(Middleware): + def __init__(self, params: FCMiddlewareParameters): + """ + FCMiddleware or Fully-Connected Middleware can be used in the middle part of the network. It takes the + embeddings from the input embedders, after they were aggregated in some method (for example, concatenation) + and passes it through a neural network which can be customizable but shared between the heads of the network. + + :param params: parameters object containing batchnorm, activation_function and dropout properties. + """ + super(FCMiddleware, self).__init__(params) + + @property + def schemes(self) -> dict: + """ + Schemes are the pre-defined network architectures of various depths and complexities that can be used for the + Middleware. Are used to create Block when FCMiddleware is initialised. + + :return: dictionary of schemes, with key of type MiddlewareScheme enum and value being list of mxnet.gluon.Block. + """ + return { + MiddlewareScheme.Empty: + [], + + # Use for PPO + MiddlewareScheme.Shallow: + [ + Dense(units=64) + ], + + # Use for DQN + MiddlewareScheme.Medium: + [ + Dense(units=512) + ], + + MiddlewareScheme.Deep: + [ + Dense(units=128), + Dense(units=128), + Dense(units=128) + ] + } diff --git a/rl_coach/architectures/mxnet_components/middlewares/lstm_middleware.py b/rl_coach/architectures/mxnet_components/middlewares/lstm_middleware.py new file mode 100644 index 0000000..b8316d4 --- /dev/null +++ b/rl_coach/architectures/mxnet_components/middlewares/lstm_middleware.py @@ -0,0 +1,80 @@ +""" +Module that defines the LSTM middleware class +""" + +from typing import Union +from types import ModuleType + +import mxnet as mx +from mxnet.gluon import rnn +from rl_coach.architectures.mxnet_components.layers import Dense +from rl_coach.architectures.mxnet_components.middlewares.middleware import Middleware +from rl_coach.architectures.middleware_parameters import LSTMMiddlewareParameters +from rl_coach.base_parameters import MiddlewareScheme + +nd_sym_type = Union[mx.nd.NDArray, mx.sym.Symbol] + + +class LSTMMiddleware(Middleware): + def __init__(self, params: LSTMMiddlewareParameters): + """ + LSTMMiddleware or Long Short Term Memory Middleware can be used in the middle part of the network. It takes the + embeddings from the input embedders, after they were aggregated in some method (for example, concatenation) + and passes it through a neural network which can be customizable but shared between the heads of the network. + + :param params: parameters object containing batchnorm, activation_function, dropout and + number_of_lstm_cells properties. + """ + super(LSTMMiddleware, self).__init__(params) + self.number_of_lstm_cells = params.number_of_lstm_cells + with self.name_scope(): + self.lstm = rnn.LSTM(hidden_size=self.number_of_lstm_cells) + + @property + def schemes(self) -> dict: + """ + Schemes are the pre-defined network architectures of various depths and complexities that can be used for the + Middleware. Are used to create Block when LSTMMiddleware is initialised, and are applied before the LSTM. + + :return: dictionary of schemes, with key of type MiddlewareScheme enum and value being list of mxnet.gluon.Block. + """ + return { + MiddlewareScheme.Empty: + [], + + # Use for PPO + MiddlewareScheme.Shallow: + [ + Dense(units=64) + ], + + # Use for DQN + MiddlewareScheme.Medium: + [ + Dense(units=512) + ], + + MiddlewareScheme.Deep: + [ + Dense(units=128), + Dense(units=128), + Dense(units=128) + ] + } + + def hybrid_forward(self, + F: ModuleType, + x: nd_sym_type, + *args, **kwargs) -> nd_sym_type: + """ + Used for forward pass through LSTM middleware network. + Applies dense layers from selected scheme before passing result to LSTM layer. + + :param F: backend api, either `mxnet.nd` or `mxnet.sym` (if block has been hybridized). + :param x: state embedding, of shape (batch_size, in_channels). + :return: state middleware embedding, where shape is (batch_size, channels). + """ + x_ntc = x.reshape(shape=(0, 0, -1)) + emb_ntc = super(LSTMMiddleware, self).hybrid_forward(F, x_ntc, *args, **kwargs) + emb_tnc = emb_ntc.transpose(axes=(1, 0, 2)) + return self.lstm(emb_tnc) diff --git a/rl_coach/architectures/mxnet_components/middlewares/middleware.py b/rl_coach/architectures/mxnet_components/middlewares/middleware.py new file mode 100644 index 0000000..8b9db01 --- /dev/null +++ b/rl_coach/architectures/mxnet_components/middlewares/middleware.py @@ -0,0 +1,61 @@ +from typing import Union +from types import ModuleType + +import mxnet as mx +from mxnet.gluon import nn +from rl_coach.architectures.middleware_parameters import MiddlewareParameters +from rl_coach.architectures.mxnet_components.layers import convert_layer +from rl_coach.base_parameters import MiddlewareScheme + +nd_sym_type = Union[mx.nd.NDArray, mx.sym.Symbol] + + +class Middleware(nn.HybridBlock): + def __init__(self, params: MiddlewareParameters): + """ + Middleware is the middle part of the network. It takes the embeddings from the input embedders, + after they were aggregated in some method (for example, concatenation) and passes it through a neural network + which can be customizable but shared between the heads of the network. + + :param params: parameters object containing batchnorm, activation_function and dropout properties. + """ + super(Middleware, self).__init__() + self.scheme = params.scheme + + with self.name_scope(): + self.net = nn.HybridSequential() + if isinstance(self.scheme, MiddlewareScheme): + blocks = self.schemes[self.scheme] + else: + # if scheme is specified directly, convert to MX layer if it's not a callable object + # NOTE: if layer object is callable, it must return a gluon block when invoked + blocks = [convert_layer(l) for l in self.scheme] + for block in blocks: + self.net.add(block()) + if params.batchnorm: + self.net.add(nn.BatchNorm()) + if params.activation_function: + self.net.add(nn.Activation(params.activation_function)) + if params.dropout: + self.net.add(nn.Dropout(rate=params.dropout)) + + @property + def schemes(self) -> dict: + """ + Schemes are the pre-defined network architectures of various depths and complexities that can be used for the + Middleware. Should be implemented in child classes, and are used to create Block when Middleware is initialised. + + :return: dictionary of schemes, with key of type MiddlewareScheme enum and value being list of mxnet.gluon.Block. + """ + raise NotImplementedError("Inheriting embedder must define schemes matching its allowed default " + "configurations.") + + def hybrid_forward(self, F: ModuleType, x: nd_sym_type, *args, **kwargs) -> nd_sym_type: + """ + Used for forward pass through middleware network. + + :param F: backend api, either `mxnet.nd` or `mxnet.sym` (if block has been hybridized). + :param x: state embedding, of shape (batch_size, in_channels). + :return: state middleware embedding, where shape is (batch_size, channels). + """ + return self.net(x) diff --git a/rl_coach/architectures/mxnet_components/utils.py b/rl_coach/architectures/mxnet_components/utils.py new file mode 100644 index 0000000..5f1659c --- /dev/null +++ b/rl_coach/architectures/mxnet_components/utils.py @@ -0,0 +1,280 @@ +""" +Module defining utility functions +""" +import inspect +from typing import Any, Dict, Generator, Iterable, List, Tuple, Union +from types import ModuleType + +import mxnet as mx +from mxnet import nd +from mxnet.ndarray import NDArray +import numpy as np + +from rl_coach.core_types import GradientClippingMethod + +nd_sym_type = Union[mx.nd.NDArray, mx.sym.Symbol] + + +def to_mx_ndarray(data: Union[list, tuple, np.ndarray, NDArray, int, float]) ->\ + Union[List[NDArray], Tuple[NDArray], NDArray]: + """ + Convert data to mx.nd.NDArray. Data can be a list or tuple of np.ndarray, int, or float or + it can be np.ndarray, int, or float + :param data: input data to be converted + :return: converted output data + """ + if isinstance(data, list): + data = [to_mx_ndarray(d) for d in data] + elif isinstance(data, tuple): + data = tuple(to_mx_ndarray(d) for d in data) + elif isinstance(data, np.ndarray): + data = nd.array(data) + elif isinstance(data, NDArray): + pass + elif isinstance(data, int) or isinstance(data, float): + data = nd.array([data]) + else: + raise TypeError('Unsupported data type: {}'.format(type(data))) + return data + + +def asnumpy_or_asscalar(data: Union[NDArray, list, tuple]) -> Union[np.ndarray, np.number, list, tuple]: + """ + Convert NDArray (or list or tuple of NDArray) to numpy. If shape is (1,), then convert to scalar instead. + NOTE: This behavior is consistent with tensorflow + :param data: NDArray or list or tuple of NDArray + :return: data converted to numpy ndarray or to numpy scalar + """ + if isinstance(data, list): + data = [asnumpy_or_asscalar(d) for d in data] + elif isinstance(data, tuple): + data = tuple(asnumpy_or_asscalar(d) for d in data) + elif isinstance(data, NDArray): + data = data.asscalar() if data.shape == (1,) else data.asnumpy() + else: + raise TypeError('Unsupported data type: {}'.format(type(data))) + return data + + +def global_norm(arrays: Union[Generator[NDArray, NDArray, NDArray], List[NDArray], Tuple[NDArray]]) -> NDArray: + """ + Calculate global norm on list or tuple of NDArrays using this formula: + `global_norm = sqrt(sum([l2norm(p)**2 for p in parameters]))` + + :param arrays: list or tuple of parameters to calculate global norm on + :return: single-value NDArray + """ + def _norm(array): + if array.stype == 'default': + x = array.reshape((-1,)) + return nd.dot(x, x) + return array.norm().square() + + total_norm = nd.add_n(*[_norm(arr) for arr in arrays]) + total_norm = nd.sqrt(total_norm) + return total_norm + + +def split_outputs_per_head(outputs: Tuple[NDArray], heads: list) -> List[List[NDArray]]: + """ + Split outputs into outputs per head + :param outputs: list of all outputs + :param heads: list of all heads + :return: list of outputs for each head + """ + head_outputs = [] + for h in heads: + head_outputs.append(list(outputs[:h.num_outputs])) + outputs = outputs[h.num_outputs:] + assert len(outputs) == 0 + return head_outputs + + +def split_targets_per_loss(targets: list, losses: list) -> List[list]: + """ + Splits targets into targets per loss + :param targets: list of all targets (typically numpy ndarray) + :param losses: list of all losses + :return: list of targets for each loss + """ + loss_targets = list() + for l in losses: + loss_data_len = len(l.input_schema.targets) + assert len(targets) >= loss_data_len, "Data length doesn't match schema" + loss_targets.append(targets[:loss_data_len]) + targets = targets[loss_data_len:] + assert len(targets) == 0 + return loss_targets + + +def get_loss_agent_inputs(inputs: Dict[str, np.ndarray], head_type_idx: int, loss: Any) -> List[np.ndarray]: + """ + Collects all inputs with prefix 'output__' and matches them against agent_inputs in loss input schema. + :param inputs: list of all agent inputs + :param head_type_idx: head-type index of the corresponding head + :param loss: corresponding loss + :return: list of agent inputs for this loss. This list matches the length in loss input schema. + """ + loss_inputs = list() + for k in sorted(inputs.keys()): + if k.startswith('output_{}_'.format(head_type_idx)): + loss_inputs.append(inputs[k]) + # Enforce that number of inputs for head_type are the same as agent_inputs specified by loss input_schema + assert len(loss_inputs) == len(loss.input_schema.agent_inputs), "agent_input length doesn't match schema" + return loss_inputs + + +def align_loss_args( + head_outputs: List[NDArray], + agent_inputs: List[np.ndarray], + targets: List[np.ndarray], + loss: Any) -> List[np.ndarray]: + """ + Creates a list of arguments from head_outputs, agent_inputs, and targets aligned with parameters of + loss.loss_forward() based on their name in loss input_schema + :param head_outputs: list of all head_outputs for this loss + :param agent_inputs: list of all agent_inputs for this loss + :param targets: list of all targets for this loss + :param loss: corresponding loss + :return: list of arguments in correct order to be passed to loss + """ + arg_list = list() + schema = loss.input_schema + assert len(schema.head_outputs) == len(head_outputs) + assert len(schema.agent_inputs) == len(agent_inputs) + assert len(schema.targets) == len(targets) + + prev_found = True + for arg_name in inspect.getfullargspec(loss.loss_forward).args[2:]: # First two args are self and F + found = False + for schema_list, data in [(schema.head_outputs, head_outputs), + (schema.agent_inputs, agent_inputs), + (schema.targets, targets)]: + try: + arg_list.append(data[schema_list.index(arg_name)]) + found = True + break + except ValueError: + continue + assert not found or prev_found, "missing arguments detected!" + prev_found = found + return arg_list + + +def to_tuple(data: Union[tuple, list, Any]): + """ + If input is list, it is converted to tuple. If it's tuple, it is returned untouched. Otherwise + returns a single-element tuple of the data. + :return: tuple-ified data + """ + if isinstance(data, tuple): + pass + elif isinstance(data, list): + data = tuple(data) + else: + data = (data,) + return data + + +def to_list(data: Union[tuple, list, Any]): + """ + If input is tuple, it is converted to list. If it's list, it is returned untouched. Otherwise + returns a single-element list of the data. + :return: list-ified data + """ + if isinstance(data, list): + pass + elif isinstance(data, tuple): + data = list(data) + else: + data = [data] + return data + + +def loss_output_dict(output: List[NDArray], schema: List[str]) -> Dict[str, List[NDArray]]: + """ + Creates a dictionary for loss output based on the output schema. If two output values have the same + type string in the schema they are concatenated in the same dicrionary item. + :param output: list of output values + :param schema: list of type-strings for output values + :return: dictionary of keyword to list of NDArrays + """ + assert len(output) == len(schema) + output_dict = dict() + for name, val in zip(schema, output): + if name in output_dict: + output_dict[name].append(val) + else: + output_dict[name] = [val] + return output_dict + + +def clip_grad( + grads: Union[Generator[NDArray, NDArray, NDArray], List[NDArray], Tuple[NDArray]], + clip_method: GradientClippingMethod, + clip_val: float, + inplace=True) -> List[NDArray]: + """ + Clip gradient values inplace + :param grads: gradients to be clipped + :param clip_method: clipping method + :param clip_val: clipping value. Interpreted differently depending on clipping method. + :param inplace: modify grads if True, otherwise create NDArrays + :return: clipped gradients + """ + output = list(grads) if inplace else list(nd.empty(g.shape) for g in grads) + if clip_method == GradientClippingMethod.ClipByGlobalNorm: + norm_unclipped_grads = global_norm(grads) + scale = clip_val / (norm_unclipped_grads.asscalar() + 1e-8) # todo: use branching operators? + if scale < 1.0: + for g, o in zip(grads, output): + nd.broadcast_mul(g, nd.array([scale]), out=o) + elif clip_method == GradientClippingMethod.ClipByValue: + for g, o in zip(grads, output): + g.clip(-clip_val, clip_val, out=o) + elif clip_method == GradientClippingMethod.ClipByNorm: + for g, o in zip(grads, output): + nd.broadcast_mul(g, nd.minimum(1.0, clip_val / (g.norm() + 1e-8)), out=o) + else: + raise KeyError('Unsupported gradient clipping method') + return output + + +def hybrid_clip(F: ModuleType, x: nd_sym_type, clip_lower: nd_sym_type, clip_upper: nd_sym_type) -> nd_sym_type: + """ + Apply clipping to input x between clip_lower and clip_upper. + Added because F.clip doesn't support clipping bounds that are mx.nd.NDArray or mx.sym.Symbol. + + :param F: backend api, either `mxnet.nd` or `mxnet.sym` (if block has been hybridized). + :param x: input data + :param clip_lower: lower bound used for clipping, should be of shape (1,) + :param clip_upper: upper bound used for clipping, should be of shape (1,) + :return: clipped data + """ + x_clip_lower = clip_lower.broadcast_like(x) + x_clip_upper = clip_upper.broadcast_like(x) + x_clipped = F.stack(x, x_clip_lower, axis=0).max(axis=0) + x_clipped = F.stack(x_clipped, x_clip_upper, axis=0).min(axis=0) + return x_clipped + + +def get_mxnet_activation_name(activation_name: str): + """ + Convert coach activation name to mxnet specific activation name + :param activation_name: name of the activation inc coach + :return: name of the activation in mxnet + """ + activation_functions = { + 'relu': 'relu', + 'tanh': 'tanh', + 'sigmoid': 'sigmoid', + # FIXME Add other activations + # 'elu': tf.nn.elu, + 'selu': 'softrelu', + # 'leaky_relu': tf.nn.leaky_relu, + 'none': None + } + assert activation_name in activation_functions, \ + "Activation function must be one of the following {}. instead it was: {}".format( + activation_functions.keys(), activation_name) + return activation_functions[activation_name] diff --git a/rl_coach/architectures/network_wrapper.py b/rl_coach/architectures/network_wrapper.py index 042c93c..b9decee 100644 --- a/rl_coach/architectures/network_wrapper.py +++ b/rl_coach/architectures/network_wrapper.py @@ -183,16 +183,7 @@ class NetworkWrapper(object): target_network or global_network) and the second element is the inputs :return: the outputs of all the networks in the same order as the inputs were given """ - feed_dict = {} - fetches = [] - - for idx, (network, input) in enumerate(network_input_tuples): - feed_dict.update(network.create_feed_dict(input)) - fetches += network.outputs - - outputs = self.sess.run(fetches, feed_dict) - - return outputs + return type(self.online_network).parallel_predict(self.sess, network_input_tuples) def get_local_variables(self): """ diff --git a/rl_coach/architectures/tensorflow_components/architecture.py b/rl_coach/architectures/tensorflow_components/architecture.py index 3353b52..8e7deae 100644 --- a/rl_coach/architectures/tensorflow_components/architecture.py +++ b/rl_coach/architectures/tensorflow_components/architecture.py @@ -13,8 +13,9 @@ # See the License for the specific language governing permissions and # limitations under the License. # -import time import os +import time +from typing import Any, List, Tuple, Dict import numpy as np import tensorflow as tf @@ -544,6 +545,26 @@ class TensorFlowArchitecture(Architecture): output = squeeze_list(output) return output + @staticmethod + def parallel_predict(sess: Any, + network_input_tuples: List[Tuple['TensorFlowArchitecture', Dict[str, np.ndarray]]]) ->\ + List[np.ndarray]: + """ + :param sess: active session to use for prediction + :param network_input_tuples: tuple of network and corresponding input + :return: list of outputs from all networks + """ + feed_dict = {} + fetches = [] + + for network, input in network_input_tuples: + feed_dict.update(network.create_feed_dict(input)) + fetches += network.outputs + + outputs = sess.run(fetches, feed_dict) + + return outputs + def train_on_batch(self, inputs, targets, scaler=1., additional_fetches=None, importance_weights=None): """ Given a batch of examples and targets, runs a forward pass & backward pass and then applies the gradients diff --git a/rl_coach/graph_managers/graph_manager.py b/rl_coach/graph_managers/graph_manager.py index 422f724..778e4ed 100644 --- a/rl_coach/graph_managers/graph_manager.py +++ b/rl_coach/graph_managers/graph_manager.py @@ -22,7 +22,7 @@ from distutils.dir_util import copy_tree, remove_tree from typing import List, Tuple import contextlib -from rl_coach.base_parameters import iterable_to_items, TaskParameters, DistributedTaskParameters, \ +from rl_coach.base_parameters import iterable_to_items, TaskParameters, DistributedTaskParameters, Frameworks, \ VisualizationParameters, \ Parameters, PresetValidationParameters from rl_coach.core_types import TotalStepsCounter, RunPhase, PlayingStepsType, TrainingSteps, EnvironmentEpisodes, \ @@ -161,7 +161,8 @@ class GraphManager(object): """ raise NotImplementedError("") - def create_worker_or_parameters_server(self, task_parameters: DistributedTaskParameters): + @staticmethod + def _create_worker_or_parameters_server_tf(task_parameters: DistributedTaskParameters): import tensorflow as tf config = tf.ConfigProto() config.allow_soft_placement = True # allow placing ops on cpu if they are not fit for gpu @@ -170,7 +171,8 @@ class GraphManager(object): config.intra_op_parallelism_threads = 1 config.inter_op_parallelism_threads = 1 - from rl_coach.architectures.tensorflow_components.distributed_tf_utils import create_and_start_parameters_server, \ + from rl_coach.architectures.tensorflow_components.distributed_tf_utils import \ + create_and_start_parameters_server, \ create_cluster_spec, create_worker_server_and_device # create cluster spec @@ -190,7 +192,16 @@ class GraphManager(object): raise ValueError("The job type should be either ps or worker and not {}" .format(task_parameters.job_type)) - def create_session(self, task_parameters: DistributedTaskParameters): + @staticmethod + def create_worker_or_parameters_server(task_parameters: DistributedTaskParameters): + if task_parameters.framework_type == Frameworks.tensorflow: + GraphManager._create_worker_or_parameters_server_tf(task_parameters) + elif task_parameters.framework_type == Frameworks.mxnet: + raise NotImplementedError('Distributed training not implemented for MXNet') + else: + raise ValueError('Invalid framework {}'.format(task_parameters.framework_type)) + + def _create_session_tf(self, task_parameters: TaskParameters): import tensorflow as tf config = tf.ConfigProto() config.allow_soft_placement = True # allow placing ops on cpu if they are not fit for gpu @@ -235,6 +246,15 @@ class GraphManager(object): if hasattr(self.task_parameters, 'checkpoint_save_dir') and self.task_parameters.checkpoint_save_dir: self.save_graph() + def create_session(self, task_parameters: TaskParameters): + if task_parameters.framework_type == Frameworks.tensorflow: + self._create_session_tf(task_parameters) + elif task_parameters.framework_type == Frameworks.mxnet: + self.set_session(sess=None) # Initialize all modules + # TODO add checkpoint loading + else: + raise ValueError('Invalid framework {}'.format(task_parameters.framework_type)) + def save_graph(self) -> None: """ Save the TF graph to a protobuf description file in the experiment directory @@ -490,27 +510,35 @@ class GraphManager(object): self.train_and_act(self.steps_between_evaluation_periods) self.evaluate(self.evaluation_steps) + def _restore_checkpoint_tf(self, checkpoint_dir: str): + import tensorflow as tf + checkpoint = tf.train.get_checkpoint_state(checkpoint_dir) + screen.log_title("Loading checkpoint: {}".format(checkpoint.model_checkpoint_path)) + variables = {} + for var_name, _ in tf.contrib.framework.list_variables(checkpoint_dir): + # Load the variable + var = tf.contrib.framework.load_variable(checkpoint_dir, var_name) + + # Set the new name + new_name = var_name + new_name = new_name.replace('global/', 'online/') + variables[new_name] = var + + for v in self.variables_to_restore: + self.sess.run(v.assign(variables[v.name.split(':')[0]])) + def restore_checkpoint(self): self.verify_graph_was_created() # TODO: find better way to load checkpoints that were saved with a global network into the online network if hasattr(self.task_parameters, 'checkpoint_restore_dir') and self.task_parameters.checkpoint_restore_dir: - import tensorflow as tf - checkpoint_dir = self.task_parameters.checkpoint_restore_dir - checkpoint = tf.train.get_checkpoint_state(checkpoint_dir) - screen.log_title("Loading checkpoint: {}".format(checkpoint.model_checkpoint_path)) - variables = {} - for var_name, _ in tf.contrib.framework.list_variables(checkpoint_dir): - # Load the variable - var = tf.contrib.framework.load_variable(checkpoint_dir, var_name) - - # Set the new name - new_name = var_name - new_name = new_name.replace('global/', 'online/') - variables[new_name] = var - - for v in self.variables_to_restore: - self.sess.run(v.assign(variables[v.name.split(':')[0]])) + if self.task_parameters.framework_type == Frameworks.tensorflow: + self._restore_checkpoint_tf(self.task_parameters.checkpoint_restore_dir) + elif self.task_parameters.framework_type == Frameworks.mxnet: + # TODO implement checkpoint restore + pass + else: + raise ValueError('Invalid framework {}'.format(self.task_parameters.framework_type)) def occasionally_save_checkpoint(self): # only the chief process saves checkpoints @@ -529,7 +557,10 @@ class GraphManager(object): self.checkpoint_id, self.total_steps_counters[RunPhase.TRAIN][EnvironmentSteps])) if not isinstance(self.task_parameters, DistributedTaskParameters): - saved_checkpoint_path = self.checkpoint_saver.save(self.sess, checkpoint_path) + if self.checkpoint_saver is not None: + saved_checkpoint_path = self.checkpoint_saver.save(self.sess, checkpoint_path) + else: + saved_checkpoint_path = "" else: saved_checkpoint_path = checkpoint_path diff --git a/rl_coach/presets/CartPole_PPO.py b/rl_coach/presets/CartPole_PPO.py index e8af4c1..0c13abb 100644 --- a/rl_coach/presets/CartPole_PPO.py +++ b/rl_coach/presets/CartPole_PPO.py @@ -49,8 +49,8 @@ agent_params.algorithm.distributed_coach_synchronization_type = DistributedCoach agent_params.exploration = EGreedyParameters() agent_params.exploration.epsilon_schedule = LinearSchedule(1.0, 0.01, 10000) -agent_params.pre_network_filter.add_observation_filter('observation', 'normalize_observation', - ObservationNormalizationFilter(name='normalize_observation')) +# agent_params.pre_network_filter.add_observation_filter('observation', 'normalize_observation', +# ObservationNormalizationFilter(name='normalize_observation')) ############### # Environment # diff --git a/rl_coach/tests/architectures/mxnet_components/__init__.py b/rl_coach/tests/architectures/mxnet_components/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/rl_coach/tests/architectures/mxnet_components/embedders/__init__.py b/rl_coach/tests/architectures/mxnet_components/embedders/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/rl_coach/tests/architectures/mxnet_components/embedders/test_image_embedder.py b/rl_coach/tests/architectures/mxnet_components/embedders/test_image_embedder.py new file mode 100644 index 0000000..0e7f9da --- /dev/null +++ b/rl_coach/tests/architectures/mxnet_components/embedders/test_image_embedder.py @@ -0,0 +1,21 @@ +import mxnet as mx +import os +import pytest +import sys +sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(__file__)))) + + +from rl_coach.base_parameters import EmbedderScheme +from rl_coach.architectures.embedder_parameters import InputEmbedderParameters +from rl_coach.architectures.mxnet_components.embedders.image_embedder import ImageEmbedder + + +@pytest.mark.unit_test +def test_image_embedder(): + params = InputEmbedderParameters(scheme=EmbedderScheme.Medium) + emb = ImageEmbedder(params=params) + emb.initialize() + input_data = mx.nd.random.uniform(low=0, high=1, shape=(10, 3, 244, 244)) + output = emb(input_data) + assert len(output.shape) == 2 # since last block was flatten + assert output.shape[0] == 10 # since batch_size is 10 diff --git a/rl_coach/tests/architectures/mxnet_components/embedders/test_vector_embedder.py b/rl_coach/tests/architectures/mxnet_components/embedders/test_vector_embedder.py new file mode 100644 index 0000000..8d51840 --- /dev/null +++ b/rl_coach/tests/architectures/mxnet_components/embedders/test_vector_embedder.py @@ -0,0 +1,22 @@ +import mxnet as mx +import os +import pytest +import sys +sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(__file__)))) + + +from rl_coach.architectures.embedder_parameters import InputEmbedderParameters +from rl_coach.architectures.mxnet_components.embedders.vector_embedder import VectorEmbedder +from rl_coach.base_parameters import EmbedderScheme + + +@pytest.mark.unit_test +def test_vector_embedder(): + params = InputEmbedderParameters(scheme=EmbedderScheme.Medium) + emb = VectorEmbedder(params=params) + emb.initialize() + input_data = mx.nd.random.uniform(low=0, high=255, shape=(10, 100)) + output = emb(input_data) + assert len(output.shape) == 2 # since last block was flatten + assert output.shape[0] == 10 # since batch_size is 10 + assert output.shape[1] == 256 # since last dense layer has 256 units diff --git a/rl_coach/tests/architectures/mxnet_components/heads/__init__.py b/rl_coach/tests/architectures/mxnet_components/heads/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/rl_coach/tests/architectures/mxnet_components/heads/test_ppo_head.py b/rl_coach/tests/architectures/mxnet_components/heads/test_ppo_head.py new file mode 100644 index 0000000..9814925 --- /dev/null +++ b/rl_coach/tests/architectures/mxnet_components/heads/test_ppo_head.py @@ -0,0 +1,406 @@ +import mxnet as mx +import numpy as np +import os +import pytest +from scipy import stats as sp_stats +import sys +sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(__file__)))) + + +from rl_coach.architectures.head_parameters import PPOHeadParameters +from rl_coach.architectures.mxnet_components.heads.ppo_head import CategoricalDist, MultivariateNormalDist,\ + DiscretePPOHead, ClippedPPOLossDiscrete, ClippedPPOLossContinuous, PPOHead +from rl_coach.agents.clipped_ppo_agent import ClippedPPOAlgorithmParameters, ClippedPPOAgentParameters +from rl_coach.spaces import SpacesDefinition, DiscreteActionSpace + + +@pytest.mark.unit_test +def test_multivariate_normal_dist_shape(): + num_var = 2 + means = mx.nd.array((0, 1)) + covar = mx.nd.array(((1, 0),(0, 0.5))) + data = mx.nd.array((0.5, 0.8)) + policy_dist = MultivariateNormalDist(num_var, means, covar) + log_probs = policy_dist.log_prob(data) + assert log_probs.ndim == 1 + assert log_probs.shape[0] == 1 + + +@pytest.mark.unit_test +def test_multivariate_normal_dist_batch_shape(): + num_var = 2 + batch_size = 3 + means = mx.nd.random.uniform(shape=(batch_size, num_var)) + # create batch of covariance matrices only defined on diagonal + std = mx.nd.array((1, 0.5)).broadcast_like(means).expand_dims(-2) + covar = mx.nd.eye(N=num_var) * std + data = mx.nd.random.uniform(shape=(batch_size, num_var)) + policy_dist = MultivariateNormalDist(num_var, means, covar) + log_probs = policy_dist.log_prob(data) + assert log_probs.ndim == 1 + assert log_probs.shape[0] == batch_size + + +@pytest.mark.unit_test +def test_multivariate_normal_dist_batch_time_shape(): + num_var = 2 + batch_size = 3 + time_steps = 4 + means = mx.nd.random.uniform(shape=(batch_size, time_steps, num_var)) + # create batch (per time step) of covariance matrices only defined on diagonal + std = mx.nd.array((1, 0.5)).broadcast_like(means).expand_dims(-2) + covar = mx.nd.eye(N=num_var) * std + data = mx.nd.random.uniform(shape=(batch_size, time_steps, num_var)) + policy_dist = MultivariateNormalDist(num_var, means, covar) + log_probs = policy_dist.log_prob(data) + assert log_probs.ndim == 2 + assert log_probs.shape[0] == batch_size + assert log_probs.shape[1] == time_steps + + +@pytest.mark.unit_test +def test_multivariate_normal_dist_kl_div(): + n_classes = 2 + dist_a = MultivariateNormalDist(num_var=n_classes, + mean = mx.nd.array([0.2, 0.8]).expand_dims(0), + sigma = mx.nd.array([[1, 0.5], [0.5, 0.5]]).expand_dims(0)) + dist_b = MultivariateNormalDist(num_var=n_classes, + mean = mx.nd.array([0.3, 0.7]).expand_dims(0), + sigma = mx.nd.array([[1, 0.2], [0.2, 0.5]]).expand_dims(0)) + + actual = dist_a.kl_div(dist_b).asnumpy() + np.testing.assert_almost_equal(actual=actual, desired=0.195100128) + + +@pytest.mark.unit_test +def test_multivariate_normal_dist_kl_div_batch(): + n_classes = 2 + dist_a = MultivariateNormalDist(num_var=n_classes, + mean = mx.nd.array([[0.2, 0.8], + [0.2, 0.8]]), + sigma = mx.nd.array([[[1, 0.5], [0.5, 0.5]], + [[1, 0.5], [0.5, 0.5]]])) + dist_b = MultivariateNormalDist(num_var=n_classes, + mean = mx.nd.array([[0.3, 0.7], + [0.3, 0.7]]), + sigma = mx.nd.array([[[1, 0.2], [0.2, 0.5]], + [[1, 0.2], [0.2, 0.5]]])) + + actual = dist_a.kl_div(dist_b).asnumpy() + np.testing.assert_almost_equal(actual=actual, desired=[0.195100128, 0.195100128]) + + +@pytest.mark.unit_test +def test_categorical_dist_shape(): + num_actions = 2 + # actions taken, of shape (batch_size, time_steps) + actions = mx.nd.array((1,)) + # action probabilities, of shape (batch_size, time_steps, num_actions) + policy_probs = mx.nd.array((0.8, 0.2)) + policy_dist = CategoricalDist(num_actions, policy_probs) + action_probs = policy_dist.log_prob(actions) + assert action_probs.ndim == 1 + assert action_probs.shape[0] == 1 + + +@pytest.mark.unit_test +def test_categorical_dist_batch_shape(): + batch_size = 3 + num_actions = 2 + # actions taken, of shape (batch_size, time_steps) + actions = mx.nd.array((0, 1, 0)) + # action probabilities, of shape (batch_size, time_steps, num_actions) + policy_probs = mx.nd.array(((0.8, 0.2), (0.5, 0.5), (0.5, 0.5))) + policy_dist = CategoricalDist(num_actions, policy_probs) + action_probs = policy_dist.log_prob(actions) + assert action_probs.ndim == 1 + assert action_probs.shape[0] == batch_size + + +@pytest.mark.unit_test +def test_categorical_dist_batch_time_shape(): + batch_size = 3 + time_steps = 4 + num_actions = 2 + # actions taken, of shape (batch_size, time_steps) + actions = mx.nd.array(((0, 1, 0, 0), + (1, 1, 0, 0), + (0, 0, 0, 0))) + # action probabilities, of shape (batch_size, time_steps, num_actions) + policy_probs = mx.nd.array((((0.8, 0.2), (0.5, 0.5), (0.5, 0.5), (0.5, 0.5)), + ((0.5, 0.5), (0.5, 0.5), (0.5, 0.5), (0.5, 0.5)), + ((0.5, 0.5), (0.5, 0.5), (0.5, 0.5), (0.5, 0.5)))) + policy_dist = CategoricalDist(num_actions, policy_probs) + action_probs = policy_dist.log_prob(actions) + assert action_probs.ndim == 2 + assert action_probs.shape[0] == batch_size + assert action_probs.shape[1] == time_steps + + +@pytest.mark.unit_test +def test_categorical_dist_batch(): + n_classes = 2 + probs = mx.nd.array(((0.8, 0.2), + (0.7, 0.3), + (0.5, 0.5))) + + dist = CategoricalDist(n_classes, probs) + # check log_prob + actions = mx.nd.array((0, 1, 0)) + manual_log_prob = np.array((-0.22314353, -1.20397282, -0.69314718)) + np.testing.assert_almost_equal(actual=dist.log_prob(actions).asnumpy(), desired=manual_log_prob) + # check entropy + sp_entropy = np.array([sp_stats.entropy(pk=(0.8, 0.2)), + sp_stats.entropy(pk=(0.7, 0.3)), + sp_stats.entropy(pk=(0.5, 0.5))]) + np.testing.assert_almost_equal(actual=dist.entropy().asnumpy(), desired=sp_entropy) + + +@pytest.mark.unit_test +def test_categorical_dist_kl_div(): + n_classes = 3 + dist_a = CategoricalDist(n_classes=n_classes, probs=mx.nd.array([0.4, 0.2, 0.4])) + dist_b = CategoricalDist(n_classes=n_classes, probs=mx.nd.array([0.3, 0.4, 0.3])) + dist_c = CategoricalDist(n_classes=n_classes, probs=mx.nd.array([0.2, 0.6, 0.2])) + dist_d = CategoricalDist(n_classes=n_classes, probs=mx.nd.array([0.0, 1.0, 0.0])) + np.testing.assert_almost_equal(actual=dist_a.kl_div(dist_b).asnumpy(), desired=0.09151624) + np.testing.assert_almost_equal(actual=dist_a.kl_div(dist_c).asnumpy(), desired=0.33479536) + np.testing.assert_almost_equal(actual=dist_c.kl_div(dist_a).asnumpy(), desired=0.38190854) + np.testing.assert_almost_equal(actual=dist_a.kl_div(dist_d).asnumpy(), desired=np.nan) + np.testing.assert_almost_equal(actual=dist_d.kl_div(dist_a).asnumpy(), desired=1.60943782) + + +@pytest.mark.unit_test +def test_categorical_dist_kl_div_batch(): + n_classes = 3 + dist_a = CategoricalDist(n_classes=n_classes, probs=mx.nd.array([[0.4, 0.2, 0.4], + [0.4, 0.2, 0.4], + [0.4, 0.2, 0.4]])) + dist_b = CategoricalDist(n_classes=n_classes, probs=mx.nd.array([[0.3, 0.4, 0.3], + [0.3, 0.4, 0.3], + [0.3, 0.4, 0.3]])) + actual = dist_a.kl_div(dist_b).asnumpy() + np.testing.assert_almost_equal(actual=actual, desired=[0.09151624, 0.09151624, 0.09151624]) + + +@pytest.mark.unit_test +def test_clipped_ppo_loss_continuous_batch(): + # check lower loss for policy with better probabilities: + # i.e. higher probability on high advantage actions, low probability on low advantage actions. + loss_fn = ClippedPPOLossContinuous(num_actions=2, + clip_likelihood_ratio_using_epsilon=0.2) + loss_fn.initialize() + # actual actions taken, of shape (batch_size) + actions = mx.nd.array(((0.5, -0.5), (0.2, 0.3), (0.4, 2.0))) + # advantages from taking action, of shape (batch_size) + advantages = mx.nd.array((2, -2, 1)) + # action probabilities, of shape (batch_size, num_actions) + old_policy_means = mx.nd.array(((1, 0), (0, 0), (-1, 0))) + new_policy_means_worse = mx.nd.array(((2, 0), (0, 0), (-1, 0))) + new_policy_means_better = mx.nd.array(((0.5, 0), (0, 0), (-1, 0))) + + policy_stds = mx.nd.array(((1, 1), (1, 1), (1, 1))) + clip_param_rescaler = mx.nd.array((1,)) + + loss_worse = loss_fn(new_policy_means_worse, policy_stds, + actions, old_policy_means, policy_stds, + clip_param_rescaler, advantages) + loss_better = loss_fn(new_policy_means_better, policy_stds, + actions, old_policy_means, policy_stds, + clip_param_rescaler, advantages) + + assert len(loss_worse) == 6 # (LOSS, REGULARIZATION, KL, ENTROPY, LIKELIHOOD_RATIO, CLIPPED_LIKELIHOOD_RATIO) + loss_worse_val = loss_worse[0] + assert loss_worse_val.ndim == 1 + assert loss_worse_val.shape[0] == 1 + assert len(loss_better) == 6 # (LOSS, REGULARIZATION, KL, ENTROPY, LIKELIHOOD_RATIO, CLIPPED_LIKELIHOOD_RATIO) + loss_better_val = loss_better[0] + assert loss_better_val.ndim == 1 + assert loss_better_val.shape[0] == 1 + assert loss_worse_val > loss_better_val + + +@pytest.mark.unit_test +def test_clipped_ppo_loss_discrete_batch(): + # check lower loss for policy with better probabilities: + # i.e. higher probability on high advantage actions, low probability on low advantage actions. + loss_fn = ClippedPPOLossDiscrete(num_actions=2, + clip_likelihood_ratio_using_epsilon=None, + use_kl_regularization=True, + initial_kl_coefficient=1) + loss_fn.initialize() + + # actual actions taken, of shape (batch_size) + actions = mx.nd.array((0, 1, 0)) + # advantages from taking action, of shape (batch_size) + advantages = mx.nd.array((-2, 2, 1)) + # action probabilities, of shape (batch_size, num_actions) + old_policy_probs = mx.nd.array(((0.7, 0.3), (0.2, 0.8), (0.4, 0.6))) + new_policy_probs_worse = mx.nd.array(((0.9, 0.1), (0.2, 0.8), (0.4, 0.6))) + new_policy_probs_better = mx.nd.array(((0.5, 0.5), (0.2, 0.8), (0.4, 0.6))) + + clip_param_rescaler = mx.nd.array((1,)) + + loss_worse = loss_fn(new_policy_probs_worse, actions, old_policy_probs, clip_param_rescaler, advantages) + loss_better = loss_fn(new_policy_probs_better, actions, old_policy_probs, clip_param_rescaler, advantages) + + assert len(loss_worse) == 6 # (LOSS, REGULARIZATION, KL, ENTROPY, LIKELIHOOD_RATIO, CLIPPED_LIKELIHOOD_RATIO) + lw_loss, lw_reg, lw_kl, lw_ent, lw_lr, lw_clip_lr = loss_worse + assert lw_loss.ndim == 1 + assert lw_loss.shape[0] == 1 + assert len(loss_better) == 6 # (LOSS, REGULARIZATION, KL, ENTROPY, LIKELIHOOD_RATIO, CLIPPED_LIKELIHOOD_RATIO) + lb_loss, lb_reg, lb_kl, lb_ent, lb_lr, lb_clip_lr = loss_better + assert lb_loss.ndim == 1 + assert lb_loss.shape[0] == 1 + assert lw_loss > lb_loss + assert lw_kl > lb_kl + + +@pytest.mark.unit_test +def test_clipped_ppo_loss_discrete_batch_kl_div(): + # check lower loss for policy with better probabilities: + # i.e. higher probability on high advantage actions, low probability on low advantage actions. + loss_fn = ClippedPPOLossDiscrete(num_actions=2, + clip_likelihood_ratio_using_epsilon=None, + use_kl_regularization=True, + initial_kl_coefficient=0.5) + loss_fn.initialize() + + # actual actions taken, of shape (batch_size) + actions = mx.nd.array((0, 1, 0)) + # advantages from taking action, of shape (batch_size) + advantages = mx.nd.array((-2, 2, 1)) + # action probabilities, of shape (batch_size, num_actions) + old_policy_probs = mx.nd.array(((0.7, 0.3), (0.2, 0.8), (0.4, 0.6))) + new_policy_probs_worse = mx.nd.array(((0.9, 0.1), (0.2, 0.8), (0.4, 0.6))) + new_policy_probs_better = mx.nd.array(((0.5, 0.5), (0.2, 0.8), (0.4, 0.6))) + + clip_param_rescaler = mx.nd.array((1,)) + + loss_worse = loss_fn(new_policy_probs_worse, actions, old_policy_probs, clip_param_rescaler, advantages) + loss_better = loss_fn(new_policy_probs_better, actions, old_policy_probs, clip_param_rescaler, advantages) + + assert len(loss_worse) == 6 # (LOSS, REGULARIZATION, KL, ENTROPY, LIKELIHOOD_RATIO, CLIPPED_LIKELIHOOD_RATIO) + lw_loss, lw_reg, lw_kl, lw_ent, lw_lr, lw_clip_lr = loss_worse + assert lw_kl.ndim == 1 + assert lw_kl.shape[0] == 1 + assert len(loss_better) == 6 # (LOSS, REGULARIZATION, KL, ENTROPY, LIKELIHOOD_RATIO, CLIPPED_LIKELIHOOD_RATIO) + lb_loss, lb_reg, lb_kl, lb_ent, lb_lr, lb_clip_lr = loss_better + assert lb_kl.ndim == 1 + assert lb_kl.shape[0] == 1 + assert lw_kl > lb_kl + assert lw_reg > lb_reg + + +@pytest.mark.unit_test +def test_clipped_ppo_loss_discrete_batch_time(): + batch_size = 3 + time_steps = 4 + num_actions = 2 + + # actions taken, of shape (batch_size, time_steps) + actions = mx.nd.array(((0, 1, 0, 0), + (1, 1, 0, 0), + (0, 0, 0, 0))) + # advantages from taking action, of shape (batch_size, time_steps) + advantages = mx.nd.array(((-2, 2, 1, 0), + (-1, 1, 0, 1), + (-1, 0, 1, 0))) + # action probabilities, of shape (batch_size, num_actions) + old_policy_probs = mx.nd.array((((0.8, 0.2), (0.5, 0.5), (0.5, 0.5), (0.5, 0.5)), + ((0.5, 0.5), (0.5, 0.5), (0.5, 0.5), (0.5, 0.5)), + ((0.5, 0.5), (0.5, 0.5), (0.5, 0.5), (0.5, 0.5)))) + new_policy_probs_worse = mx.nd.array((((0.9, 0.1), (0.5, 0.5), (0.5, 0.5), (0.5, 0.5)), + ((0.5, 0.5), (0.5, 0.5), (0.5, 0.5), (0.5, 0.5)), + ((0.5, 0.5), (0.5, 0.5), (0.5, 0.5), (0.5, 0.5)))) + new_policy_probs_better = mx.nd.array((((0.2, 0.8), (0.5, 0.5), (0.5, 0.5), (0.5, 0.5)), + ((0.5, 0.5), (0.5, 0.5), (0.5, 0.5), (0.5, 0.5)), + ((0.5, 0.5), (0.5, 0.5), (0.5, 0.5), (0.5, 0.5)))) + + # check lower loss for policy with better probabilities: + # i.e. higher probability on high advantage actions, low probability on low advantage actions. + loss_fn = ClippedPPOLossDiscrete(num_actions=num_actions, + clip_likelihood_ratio_using_epsilon=0.2) + loss_fn.initialize() + + clip_param_rescaler = mx.nd.array((1,)) + + loss_worse = loss_fn(new_policy_probs_worse, actions, old_policy_probs, clip_param_rescaler, advantages) + loss_better = loss_fn(new_policy_probs_better, actions, old_policy_probs, clip_param_rescaler, advantages) + + assert len(loss_worse) == 6 # (LOSS, REGULARIZATION, KL, ENTROPY, LIKELIHOOD_RATIO, CLIPPED_LIKELIHOOD_RATIO) + loss_worse_val = loss_worse[0] + assert loss_worse_val.ndim == 1 + assert loss_worse_val.shape[0] == 1 + assert len(loss_better) == 6 # (LOSS, REGULARIZATION, KL, ENTROPY, LIKELIHOOD_RATIO, CLIPPED_LIKELIHOOD_RATIO) + loss_better_val = loss_better[0] + assert loss_better_val.ndim == 1 + assert loss_better_val.shape[0] == 1 + assert loss_worse_val > loss_better_val + + +@pytest.mark.unit_test +def test_clipped_ppo_loss_discrete_weight(): + actions = mx.nd.array((0, 1, 0)) + advantages = mx.nd.array((-2, 2, 1)) + old_policy_probs = mx.nd.array(((0.7, 0.3), (0.2, 0.8), (0.4, 0.6))) + new_policy_probs = mx.nd.array(((0.9, 0.1), (0.2, 0.8), (0.4, 0.6))) + + clip_param_rescaler = mx.nd.array((1,)) + loss_fn = ClippedPPOLossDiscrete(num_actions=2, + clip_likelihood_ratio_using_epsilon=0.2) + loss_fn.initialize() + loss = loss_fn(new_policy_probs, actions, old_policy_probs, clip_param_rescaler, advantages) + loss_fn_weighted = ClippedPPOLossDiscrete(num_actions=2, + clip_likelihood_ratio_using_epsilon=0.2, + weight=0.5) + loss_fn_weighted.initialize() + loss_weighted = loss_fn_weighted(new_policy_probs, actions, old_policy_probs, clip_param_rescaler, advantages) + assert loss[0] == loss_weighted[0] * 2 + + +@pytest.mark.unit_test +def test_clipped_ppo_loss_discrete_hybridize(): + loss_fn = ClippedPPOLossDiscrete(num_actions=2, + clip_likelihood_ratio_using_epsilon=0.2) + loss_fn.initialize() + loss_fn.hybridize() + actions = mx.nd.array((0, 1, 0)) + advantages = mx.nd.array((-2, 2, 1)) + old_policy_probs = mx.nd.array(((0.7, 0.3), (0.2, 0.8), (0.4, 0.6))) + new_policy_probs = mx.nd.array(((0.9, 0.1), (0.2, 0.8), (0.4, 0.6))) + clip_param_rescaler = mx.nd.array((1,)) + + loss = loss_fn(new_policy_probs, actions, old_policy_probs, clip_param_rescaler, advantages) + assert loss[0] == mx.nd.array((-0.142857153,)) + + +@pytest.mark.unit_test +def test_discrete_ppo_head(): + head = DiscretePPOHead(num_actions=2) + head.initialize() + middleware_data = mx.nd.random.uniform(shape=(10, 100)) + probs = head(middleware_data) + assert probs.ndim == 2 # (batch_size, num_actions) + assert probs.shape[0] == 10 # since batch_size is 10 + assert probs.shape[1] == 2 # since num_actions is 2 + + +@pytest.mark.unit_test +def test_ppo_head(): + agent_parameters = ClippedPPOAgentParameters() + num_actions = 5 + action_space = DiscreteActionSpace(num_actions=num_actions) + spaces = SpacesDefinition(state=None, goal=None, action=action_space, reward=None) + head = PPOHead(agent_parameters=agent_parameters, + spaces=spaces, + network_name="test_ppo_head") + + head.initialize() + + batch_size = 15 + middleware_data = mx.nd.random.uniform(shape=(batch_size, 100)) + probs = head(middleware_data) + assert probs.ndim == 2 # (batch_size, num_actions) + assert probs.shape[0] == batch_size + assert probs.shape[1] == num_actions diff --git a/rl_coach/tests/architectures/mxnet_components/heads/test_ppo_v_head.py b/rl_coach/tests/architectures/mxnet_components/heads/test_ppo_v_head.py new file mode 100644 index 0000000..abd7016 --- /dev/null +++ b/rl_coach/tests/architectures/mxnet_components/heads/test_ppo_v_head.py @@ -0,0 +1,90 @@ +import mxnet as mx +import os +import pytest +import sys +sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(__file__)))) + + +from rl_coach.architectures.mxnet_components.heads.ppo_v_head import PPOVHead, PPOVHeadLoss +from rl_coach.agents.clipped_ppo_agent import ClippedPPOAlgorithmParameters, ClippedPPOAgentParameters +from rl_coach.spaces import SpacesDefinition, DiscreteActionSpace + + +@pytest.mark.unit_test +def test_ppo_v_head_loss_batch(): + loss_fn = PPOVHeadLoss(clip_likelihood_ratio_using_epsilon=0.1) + total_return = mx.nd.array((5, -3, 0)) + old_policy_values = mx.nd.array((3, -1, -1)) + new_policy_values_worse = mx.nd.array((2, 0, -1)) + new_policy_values_better = mx.nd.array((4, -2, -1)) + + loss_worse = loss_fn(new_policy_values_worse, old_policy_values, total_return) + loss_better = loss_fn(new_policy_values_better, old_policy_values, total_return) + + assert len(loss_worse) == 1 # (LOSS) + loss_worse_val = loss_worse[0] + assert loss_worse_val.ndim == 1 + assert loss_worse_val.shape[0] == 1 + assert len(loss_better) == 1 # (LOSS) + loss_better_val = loss_better[0] + assert loss_better_val.ndim == 1 + assert loss_better_val.shape[0] == 1 + assert loss_worse_val > loss_better_val + + +@pytest.mark.unit_test +def test_ppo_v_head_loss_batch_time(): + loss_fn = PPOVHeadLoss(clip_likelihood_ratio_using_epsilon=0.1) + total_return = mx.nd.array(((3, 1, 1, 0), + (1, 0, 0, 1), + (3, 0, 1, 0))) + old_policy_values = mx.nd.array(((2, 1, 1, 0), + (1, 0, 0, 1), + (0, 0, 1, 0))) + new_policy_values_worse = mx.nd.array(((2, 1, 1, 0), + (1, 0, 0, 1), + (2, 0, 1, 0))) + new_policy_values_better = mx.nd.array(((3, 1, 1, 0), + (1, 0, 0, 1), + (2, 0, 1, 0))) + + loss_worse = loss_fn(new_policy_values_worse, old_policy_values, total_return) + loss_better = loss_fn(new_policy_values_better, old_policy_values, total_return) + + assert len(loss_worse) == 1 # (LOSS) + loss_worse_val = loss_worse[0] + assert loss_worse_val.ndim == 1 + assert loss_worse_val.shape[0] == 1 + assert len(loss_better) == 1 # (LOSS) + loss_better_val = loss_better[0] + assert loss_better_val.ndim == 1 + assert loss_better_val.shape[0] == 1 + assert loss_worse_val > loss_better_val + + +@pytest.mark.unit_test +def test_ppo_v_head_loss_weight(): + total_return = mx.nd.array((5, -3, 0)) + old_policy_values = mx.nd.array((3, -1, -1)) + new_policy_values = mx.nd.array((4, -2, -1)) + loss_fn = PPOVHeadLoss(clip_likelihood_ratio_using_epsilon=0.2, weight=1) + loss = loss_fn(new_policy_values, old_policy_values, total_return) + loss_fn_weighted = PPOVHeadLoss(clip_likelihood_ratio_using_epsilon=0.2, weight=0.5) + loss_weighted = loss_fn_weighted(new_policy_values, old_policy_values, total_return) + assert loss[0].sum() == loss_weighted[0].sum() * 2 + + +@pytest.mark.unit_test +def test_ppo_v_head(): + agent_parameters = ClippedPPOAgentParameters() + action_space = DiscreteActionSpace(num_actions=5) + spaces = SpacesDefinition(state=None, goal=None, action=action_space, reward=None) + value_net = PPOVHead(agent_parameters=agent_parameters, + spaces=spaces, + network_name="test_ppo_v_head") + value_net.initialize() + batch_size = 15 + middleware_data = mx.nd.random.uniform(shape=(batch_size, 100)) + values = value_net(middleware_data) + assert values.ndim == 1 # (batch_size) + assert values.shape[0] == batch_size diff --git a/rl_coach/tests/architectures/mxnet_components/heads/test_q_head.py b/rl_coach/tests/architectures/mxnet_components/heads/test_q_head.py new file mode 100644 index 0000000..542b3b4 --- /dev/null +++ b/rl_coach/tests/architectures/mxnet_components/heads/test_q_head.py @@ -0,0 +1,60 @@ +import mxnet as mx +import os +import pytest +import sys +sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(__file__)))) + + +from rl_coach.architectures.mxnet_components.heads.q_head import QHead, QHeadLoss +from rl_coach.agents.clipped_ppo_agent import ClippedPPOAgentParameters +from rl_coach.spaces import SpacesDefinition, DiscreteActionSpace + + + +@pytest.mark.unit_test +def test_q_head_loss(): + loss_fn = QHeadLoss() + # example with batch_size of 3, and num_actions of 2 + target_q_values = mx.nd.array(((3, 5), (-1, -2), (0, 2))) + pred_q_values_worse = mx.nd.array(((6, 5), (-1, -2), (0, 2))) + pred_q_values_better = mx.nd.array(((4, 5), (-2, -2), (1, 2))) + loss_worse = loss_fn(pred_q_values_worse, target_q_values) + loss_better = loss_fn(pred_q_values_better, target_q_values) + assert len(loss_worse) == 1 # (LOSS) + loss_worse_val = loss_worse[0] + assert loss_worse_val.ndim == 1 + assert loss_worse_val.shape[0] == 1 + assert len(loss_better) == 1 # (LOSS) + loss_better_val = loss_better[0] + assert loss_better_val.ndim == 1 + assert loss_better_val.shape[0] == 1 + assert loss_worse_val > loss_better_val + + +@pytest.mark.unit_test +def test_v_head_loss_weight(): + target_q_values = mx.nd.array(((3, 5), (-1, -2), (0, 2))) + pred_q_values = mx.nd.array(((4, 5), (-2, -2), (1, 2))) + loss_fn = QHeadLoss() + loss = loss_fn(pred_q_values, target_q_values) + loss_fn_weighted = QHeadLoss(weight=0.5) + loss_weighted = loss_fn_weighted(pred_q_values, target_q_values) + assert loss[0] == loss_weighted[0]*2 + + +@pytest.mark.unit_test +def test_ppo_v_head(): + agent_parameters = ClippedPPOAgentParameters() + num_actions = 5 + action_space = DiscreteActionSpace(num_actions=num_actions) + spaces = SpacesDefinition(state=None, goal=None, action=action_space, reward=None) + value_net = QHead(agent_parameters=agent_parameters, + spaces=spaces, + network_name="test_q_head") + value_net.initialize() + batch_size = 15 + middleware_data = mx.nd.random.uniform(shape=(batch_size, 100)) + values = value_net(middleware_data) + assert values.ndim == 2 # (batch_size, num_actions) + assert values.shape[0] == batch_size + assert values.shape[1] == num_actions \ No newline at end of file diff --git a/rl_coach/tests/architectures/mxnet_components/heads/test_v_head.py b/rl_coach/tests/architectures/mxnet_components/heads/test_v_head.py new file mode 100644 index 0000000..271d661 --- /dev/null +++ b/rl_coach/tests/architectures/mxnet_components/heads/test_v_head.py @@ -0,0 +1,57 @@ +import mxnet as mx +import os +import pytest +import sys +sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(__file__)))) + + +from rl_coach.architectures.mxnet_components.heads.v_head import VHead, VHeadLoss +from rl_coach.agents.clipped_ppo_agent import ClippedPPOAlgorithmParameters, ClippedPPOAgentParameters +from rl_coach.spaces import SpacesDefinition, DiscreteActionSpace + + + +@pytest.mark.unit_test +def test_v_head_loss(): + loss_fn = VHeadLoss() + target_values = mx.nd.array((3, -1, 0)) + pred_values_worse = mx.nd.array((0, 0, 1)) + pred_values_better = mx.nd.array((2, -1, 0)) + loss_worse = loss_fn(pred_values_worse, target_values) + loss_better = loss_fn(pred_values_better, target_values) + assert len(loss_worse) == 1 # (LOSS) + loss_worse_val = loss_worse[0] + assert loss_worse_val.ndim == 1 + assert loss_worse_val.shape[0] == 1 + assert len(loss_better) == 1 # (LOSS) + loss_better_val = loss_better[0] + assert loss_better_val.ndim == 1 + assert loss_better_val.shape[0] == 1 + assert loss_worse_val > loss_better_val + + +@pytest.mark.unit_test +def test_v_head_loss_weight(): + target_values = mx.nd.array((3, -1, 0)) + pred_values = mx.nd.array((0, 0, 1)) + loss_fn = VHeadLoss() + loss = loss_fn(pred_values, target_values) + loss_fn_weighted = VHeadLoss(weight=0.5) + loss_weighted = loss_fn_weighted(pred_values, target_values) + assert loss[0] == loss_weighted[0]*2 + + +@pytest.mark.unit_test +def test_ppo_v_head(): + agent_parameters = ClippedPPOAgentParameters() + action_space = DiscreteActionSpace(num_actions=5) + spaces = SpacesDefinition(state=None, goal=None, action=action_space, reward=None) + value_net = VHead(agent_parameters=agent_parameters, + spaces=spaces, + network_name="test_v_head") + value_net.initialize() + batch_size = 15 + middleware_data = mx.nd.random.uniform(shape=(batch_size, 100)) + values = value_net(middleware_data) + assert values.ndim == 1 # (batch_size) + assert values.shape[0] == batch_size \ No newline at end of file diff --git a/rl_coach/tests/architectures/mxnet_components/middlewares/__init__.py b/rl_coach/tests/architectures/mxnet_components/middlewares/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/rl_coach/tests/architectures/mxnet_components/middlewares/test_fc_middleware.py b/rl_coach/tests/architectures/mxnet_components/middlewares/test_fc_middleware.py new file mode 100644 index 0000000..3a0807b --- /dev/null +++ b/rl_coach/tests/architectures/mxnet_components/middlewares/test_fc_middleware.py @@ -0,0 +1,22 @@ +import mxnet as mx +import os +import pytest +import sys +sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(__file__)))) + + +from rl_coach.base_parameters import MiddlewareScheme +from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters +from rl_coach.architectures.mxnet_components.middlewares.fc_middleware import FCMiddleware + + +@pytest.mark.unit_test +def test_fc_middleware(): + params = FCMiddlewareParameters(scheme=MiddlewareScheme.Medium) + mid = FCMiddleware(params=params) + mid.initialize() + embedded_data = mx.nd.random.uniform(low=0, high=1, shape=(10, 100)) + output = mid(embedded_data) + assert output.ndim == 2 # since last block was flatten + assert output.shape[0] == 10 # since batch_size is 10 + assert output.shape[1] == 512 # since last layer of middleware (middle scheme) had 512 units diff --git a/rl_coach/tests/architectures/mxnet_components/middlewares/test_lstm_middleware.py b/rl_coach/tests/architectures/mxnet_components/middlewares/test_lstm_middleware.py new file mode 100644 index 0000000..076932c --- /dev/null +++ b/rl_coach/tests/architectures/mxnet_components/middlewares/test_lstm_middleware.py @@ -0,0 +1,25 @@ +import mxnet as mx +import os +import pytest +import sys +sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(__file__)))) + + +from rl_coach.base_parameters import MiddlewareScheme +from rl_coach.architectures.middleware_parameters import LSTMMiddlewareParameters +from rl_coach.architectures.mxnet_components.middlewares.lstm_middleware import LSTMMiddleware + + +@pytest.mark.unit_test +def test_lstm_middleware(): + params = LSTMMiddlewareParameters(number_of_lstm_cells=25, scheme=MiddlewareScheme.Medium) + mid = LSTMMiddleware(params=params) + mid.initialize() + # NTC + embedded_data = mx.nd.random.uniform(low=0, high=1, shape=(10, 15, 20)) + # NTC -> TNC + output = mid(embedded_data) + assert output.ndim == 3 # since last block was flatten + assert output.shape[0] == 15 # since t is 15 + assert output.shape[1] == 10 # since batch_size is 10 + assert output.shape[2] == 25 # since number_of_lstm_cells is 25 diff --git a/rl_coach/tests/architectures/mxnet_components/test_utils.py b/rl_coach/tests/architectures/mxnet_components/test_utils.py new file mode 100644 index 0000000..0af729a --- /dev/null +++ b/rl_coach/tests/architectures/mxnet_components/test_utils.py @@ -0,0 +1,144 @@ +import pytest + +import mxnet as mx +from mxnet import nd +import numpy as np + +from rl_coach.architectures.mxnet_components.utils import * + + +@pytest.mark.unit_test +def test_to_mx_ndarray(): + # scalar + assert to_mx_ndarray(1.2) == nd.array([1.2]) + # list of one scalar + assert to_mx_ndarray([1.2]) == [nd.array([1.2])] + # list of multiple scalars + assert to_mx_ndarray([1.2, 3.4]) == [nd.array([1.2]), nd.array([3.4])] + # list of lists of scalars + assert to_mx_ndarray([[1.2], [3.4]]) == [[nd.array([1.2])], [nd.array([3.4])]] + # numpy + assert np.array_equal(to_mx_ndarray(np.array([[1.2], [3.4]])).asnumpy(), nd.array([[1.2], [3.4]]).asnumpy()) + # tuple + assert to_mx_ndarray(((1.2,), (3.4,))) == ((nd.array([1.2]),), (nd.array([3.4]),)) + + +@pytest.mark.unit_test +def test_asnumpy_or_asscalar(): + # scalar float32 + assert asnumpy_or_asscalar(nd.array([1.2])) == np.float32(1.2) + # scalar int32 + assert asnumpy_or_asscalar(nd.array([2], dtype=np.int32)) == np.int32(2) + # list of one scalar + assert asnumpy_or_asscalar([nd.array([1.2])]) == [np.float32(1.2)] + # list of multiple scalars + assert asnumpy_or_asscalar([nd.array([1.2]), nd.array([3.4])]) == [np.float32([1.2]), np.float32([3.4])] + # list of lists of scalars + assert asnumpy_or_asscalar([[nd.array([1.2])], [nd.array([3.4])]]) == [[np.float32([1.2])], [np.float32([3.4])]] + # tensor + assert np.array_equal(asnumpy_or_asscalar(nd.array([[1.2], [3.4]])), np.array([[1.2], [3.4]], dtype=np.float32)) + # tuple + assert (asnumpy_or_asscalar(((nd.array([1.2]),), (nd.array([3.4]),))) == + ((np.array([1.2], dtype=np.float32),), (np.array([3.4], dtype=np.float32),))) + + +@pytest.mark.unit_test +def test_global_norm(): + data = list() + for i in range(1, 6): + data.append(np.ones((i * 10, i * 10)) * i) + gnorm = np.asscalar(np.sqrt(sum([np.sum(np.square(d)) for d in data]))) + assert np.isclose(gnorm, global_norm([nd.array(d) for d in data]).asscalar()) + + +@pytest.mark.unit_test +def test_split_outputs_per_head(): + class TestHead: + def __init__(self, num_outputs): + self.num_outputs = num_outputs + + assert split_outputs_per_head((1, 2, 3, 4), [TestHead(2), TestHead(1), TestHead(1)]) == [[1, 2], [3], [4]] + + +class DummySchema: + def __init__(self, num_head_outputs, num_agent_inputs, num_targets): + self.head_outputs = ['head_output_{}'.format(i) for i in range(num_head_outputs)] + self.agent_inputs = ['agent_input_{}'.format(i) for i in range(num_agent_inputs)] + self.targets = ['target_{}'.format(i) for i in range(num_targets)] + + +class DummyLoss: + def __init__(self, num_head_outputs, num_agent_inputs, num_targets): + self.input_schema = DummySchema(num_head_outputs, num_agent_inputs, num_targets) + + +@pytest.mark.unit_test +def test_split_targets_per_loss(): + assert split_targets_per_loss([1, 2, 3, 4], + [DummyLoss(10, 100, 2), DummyLoss(20, 200, 1), DummyLoss(30, 300, 1)]) == \ + [[1, 2], [3], [4]] + + +@pytest.mark.unit_test +def test_get_loss_agent_inputs(): + input_dict = {'output_0_0': [1, 2], 'output_0_1': [3, 4], 'output_1_0': [5]} + assert get_loss_agent_inputs(input_dict, 0, DummyLoss(10, 2, 100)) == [[1, 2], [3, 4]] + assert get_loss_agent_inputs(input_dict, 1, DummyLoss(20, 1, 200)) == [[5]] + + +@pytest.mark.unit_test +def test_align_loss_args(): + class TestLossFwd(DummyLoss): + def __init__(self, num_targets, num_agent_inputs, num_head_outputs): + super(TestLossFwd, self).__init__(num_targets, num_agent_inputs, num_head_outputs) + + def loss_forward(self, F, head_output_2, head_output_1, agent_input_2, target_0, agent_input_1, param1, param2): + pass + + assert align_loss_args([1, 2, 3], [4, 5, 6, 7], [8, 9], TestLossFwd(3, 4, 2)) == [3, 2, 6, 8, 5] + + +@pytest.mark.unit_test +def test_to_tuple(): + assert to_tuple(123) == (123,) + assert to_tuple((1, 2, 3)) == (1, 2, 3) + assert to_tuple([1, 2, 3]) == (1, 2, 3) + + +@pytest.mark.unit_test +def test_to_list(): + assert to_list(123) == [123] + assert to_list((1, 2, 3)) == [1, 2, 3] + assert to_list([1, 2, 3]) == [1, 2, 3] + + +@pytest.mark.unit_test +def test_loss_output_dict(): + assert loss_output_dict([1, 2, 3], ['loss', 'loss', 'reg']) == {'loss': [1, 2], 'reg': [3]} + + +@pytest.mark.unit_test +def test_clip_grad(): + a = np.array([1, 2, -3]) + b = np.array([4, 5, -6]) + clip = 2 + gscale = np.minimum(1.0, clip / np.sqrt(np.sum(np.square(a)) + np.sum(np.square(b)))) + for lhs, rhs in zip(clip_grad([nd.array(a), nd.array(b)], GradientClippingMethod.ClipByGlobalNorm, clip_val=clip), + [a, b]): + assert np.allclose(lhs.asnumpy(), rhs * gscale) + for lhs, rhs in zip(clip_grad([nd.array(a), nd.array(b)], GradientClippingMethod.ClipByValue, clip_val=clip), + [a, b]): + assert np.allclose(lhs.asnumpy(), np.clip(rhs, -clip, clip)) + for lhs, rhs in zip(clip_grad([nd.array(a), nd.array(b)], GradientClippingMethod.ClipByNorm, clip_val=clip), + [a, b]): + scale = np.minimum(1.0, clip / np.sqrt(np.sum(np.square(rhs)))) + assert np.allclose(lhs.asnumpy(), rhs * scale) + + +@pytest.mark.unit_test +def test_hybrid_clip(): + x = mx.nd.array((0.5, 1.5, 2.5)) + a = mx.nd.array((1,)) + b = mx.nd.array((2,)) + clipped = hybrid_clip(F=mx.nd, x=x, clip_lower=a, clip_upper=b) + assert (np.isclose(a= clipped.asnumpy(), b=(1, 1.5, 2))).all() diff --git a/setup.py b/setup.py index e77cd70..70790b2 100644 --- a/setup.py +++ b/setup.py @@ -68,6 +68,17 @@ if not using_GPU: else: install_requires.append('tensorflow-gpu==1.9.0') +# Framework-specific dependencies. +extras = { + 'mxnet': ['mxnet-cu90mkl>=1.3.0'] +} + +all_deps = [] +for group_name in extras: + all_deps += extras[group_name] +extras['all'] = all_deps + + setup( name='rl-coach', version='0.10.0', @@ -78,6 +89,7 @@ setup( packages=find_packages(), python_requires=">=3.5.*", install_requires=install_requires, + extras_require=extras, package_data={'rl_coach': ['dashboard_components/*.css', 'environments/doom/*.cfg', 'environments/doom/*.wad', From 35c477c922454b1163a9908fb85e49f004114c68 Mon Sep 17 00:00:00 2001 From: Itai Caspi <30383381+itaicaspi-intel@users.noreply.github.com> Date: Wed, 7 Nov 2018 17:08:10 +0200 Subject: [PATCH 099/162] allowing grayscale observations in gym (#66) * allowing grayscale observations in gym --- rl_coach/environments/gym_environment.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/rl_coach/environments/gym_environment.py b/rl_coach/environments/gym_environment.py index ad97961..956185c 100644 --- a/rl_coach/environments/gym_environment.py +++ b/rl_coach/environments/gym_environment.py @@ -267,8 +267,9 @@ class GymEnvironment(Environment): state_space = self.env.observation_space.spaces for observation_space_name, observation_space in state_space.items(): - if len(observation_space.shape) == 3 and observation_space.shape[-1] == 3: - # we assume gym has image observations which are RGB and where their values are within 0-255 + if len(observation_space.shape) == 3: + # we assume gym has image observations (with arbitrary number of channels) where their values are + # within 0-255, and where the channel dimension is the last dimension self.state_space[observation_space_name] = ImageObservationSpace( shape=np.array(observation_space.shape), high=255, From 49dea39d34a562a46daa562051a66846326f66f4 Mon Sep 17 00:00:00 2001 From: Gal Leibovich Date: Wed, 7 Nov 2018 18:33:08 +0200 Subject: [PATCH 100/162] N-step returns for rainbow (#67) * n_step returns for rainbow * Rename CartPole_PPO -> CartPole_ClippedPPO --- rl_coach/agents/agent.py | 9 +- rl_coach/agents/categorical_dqn_agent.py | 36 ++++-- rl_coach/agents/clipped_ppo_agent.py | 8 +- rl_coach/agents/mmc_agent.py | 4 +- rl_coach/agents/nec_agent.py | 8 +- rl_coach/agents/pal_agent.py | 3 +- rl_coach/agents/policy_gradients_agent.py | 2 +- rl_coach/agents/policy_optimization_agent.py | 6 +- rl_coach/agents/ppo_agent.py | 7 +- rl_coach/agents/rainbow_dqn_agent.py | 37 +++--- rl_coach/base_parameters.py | 3 + rl_coach/core_types.py | 107 +++++++++++------- .../episodic/episodic_experience_replay.py | 16 ++- .../episodic_hindsight_experience_replay.py | 2 +- .../prioritized_experience_replay.py | 28 +++-- ...CartPole_PPO.py => CartPole_ClippedPPO.py} | 2 +- .../test_prioritized_experience_replay.py | 8 +- .../memories/test_single_episode_buffer.py | 4 +- 18 files changed, 173 insertions(+), 117 deletions(-) rename rl_coach/presets/{CartPole_PPO.py => CartPole_ClippedPPO.py} (98%) diff --git a/rl_coach/agents/agent.py b/rl_coach/agents/agent.py index 0aa258a..e3b116c 100644 --- a/rl_coach/agents/agent.py +++ b/rl_coach/agents/agent.py @@ -116,7 +116,6 @@ class Agent(AgentInterface): self.output_filter.set_device(device) self.pre_network_filter.set_device(device) - # initialize all internal variables self._phase = RunPhase.HEATUP self.total_shaped_reward_in_current_episode = 0 @@ -143,7 +142,7 @@ class Agent(AgentInterface): self.accumulated_shaped_rewards_across_evaluation_episodes = 0 self.num_successes_across_evaluation_episodes = 0 self.num_evaluation_episodes_completed = 0 - self.current_episode_buffer = Episode(discount=self.ap.algorithm.discount) + self.current_episode_buffer = Episode(discount=self.ap.algorithm.discount, n_step=self.ap.algorithm.n_step) # TODO: add agents observation rendering for debugging purposes (not the same as the environment rendering) # environment parameters @@ -452,10 +451,10 @@ class Agent(AgentInterface): :return: None """ self.current_episode_buffer.is_complete = True - self.current_episode_buffer.update_returns() + self.current_episode_buffer.update_transitions_rewards_and_bootstrap_data() for transition in self.current_episode_buffer.transitions: - self.discounted_return.add_sample(transition.total_return) + self.discounted_return.add_sample(transition.n_step_discounted_rewards) if self.phase != RunPhase.TEST or self.ap.task_parameters.evaluate_only: self.current_episode += 1 @@ -497,7 +496,7 @@ class Agent(AgentInterface): self.curr_state = {} self.current_episode_steps_counter = 0 self.episode_running_info = {} - self.current_episode_buffer = Episode(discount=self.ap.algorithm.discount) + self.current_episode_buffer = Episode(discount=self.ap.algorithm.discount, n_step=self.ap.algorithm.n_step) if self.exploration_policy: self.exploration_policy.reset() self.input_filter.reset() diff --git a/rl_coach/agents/categorical_dqn_agent.py b/rl_coach/agents/categorical_dqn_agent.py index 24a610b..bca506a 100644 --- a/rl_coach/agents/categorical_dqn_agent.py +++ b/rl_coach/agents/categorical_dqn_agent.py @@ -17,14 +17,11 @@ from typing import Union import numpy as np - from rl_coach.agents.dqn_agent import DQNNetworkParameters, DQNAlgorithmParameters, DQNAgentParameters from rl_coach.agents.value_optimization_agent import ValueOptimizationAgent from rl_coach.architectures.head_parameters import CategoricalQHeadParameters -from rl_coach.base_parameters import AgentParameters from rl_coach.core_types import StateType from rl_coach.exploration_policies.e_greedy import EGreedyParameters -from rl_coach.memories.non_episodic.experience_replay import ExperienceReplayParameters from rl_coach.memories.non_episodic.prioritized_experience_replay import PrioritizedExperienceReplay from rl_coach.schedules import LinearSchedule @@ -85,28 +82,47 @@ class CategoricalDQNAgent(ValueOptimizationAgent): # for the action we actually took, the error is calculated by the atoms distribution # for all other actions, the error is 0 - distributed_q_st_plus_1, TD_targets = self.networks['main'].parallel_prediction([ + distributional_q_st_plus_1, TD_targets = self.networks['main'].parallel_prediction([ (self.networks['main'].target_network, batch.next_states(network_keys)), (self.networks['main'].online_network, batch.states(network_keys)) ]) - # only update the action that we have actually done in this transition - target_actions = np.argmax(self.distribution_prediction_to_q_values(distributed_q_st_plus_1), axis=1) + # select the optimal actions for the next state + target_actions = np.argmax(self.distribution_prediction_to_q_values(distributional_q_st_plus_1), axis=1) m = np.zeros((self.ap.network_wrappers['main'].batch_size, self.z_values.size)) batches = np.arange(self.ap.network_wrappers['main'].batch_size) + + # an alternative to the for loop. 3.7x perf improvement vs. the same code done with for looping. + # only 10% speedup overall - leaving commented out as the code is not as clear. + + # tzj_ = np.fmax(np.fmin(batch.rewards() + (1.0 - batch.game_overs()) * self.ap.algorithm.discount * + # np.transpose(np.repeat(self.z_values[np.newaxis, :], batch.size, axis=0), (1, 0)), + # self.z_values[-1]), + # self.z_values[0]) + # + # bj_ = (tzj_ - self.z_values[0]) / (self.z_values[1] - self.z_values[0]) + # u_ = (np.ceil(bj_)).astype(int) + # l_ = (np.floor(bj_)).astype(int) + # m_ = np.zeros((self.ap.network_wrappers['main'].batch_size, self.z_values.size)) + # np.add.at(m_, [batches, l_], + # np.transpose(distributional_q_st_plus_1[batches, target_actions], (1, 0)) * (u_ - bj_)) + # np.add.at(m_, [batches, u_], + # np.transpose(distributional_q_st_plus_1[batches, target_actions], (1, 0)) * (bj_ - l_)) + for j in range(self.z_values.size): tzj = np.fmax(np.fmin(batch.rewards() + (1.0 - batch.game_overs()) * self.ap.algorithm.discount * self.z_values[j], - self.z_values[self.z_values.size - 1]), + self.z_values[-1]), self.z_values[0]) bj = (tzj - self.z_values[0])/(self.z_values[1] - self.z_values[0]) u = (np.ceil(bj)).astype(int) l = (np.floor(bj)).astype(int) - m[batches, l] = m[batches, l] + (distributed_q_st_plus_1[batches, target_actions, j] * (u - bj)) - m[batches, u] = m[batches, u] + (distributed_q_st_plus_1[batches, target_actions, j] * (bj - l)) + m[batches, l] += (distributional_q_st_plus_1[batches, target_actions, j] * (u - bj)) + m[batches, u] += (distributional_q_st_plus_1[batches, target_actions, j] * (bj - l)) # total_loss = cross entropy between actual result above and predicted result for the given action + # only update the action that we have actually done in this transition TD_targets[batches, batch.actions()] = m # update errors in prioritized replay buffer @@ -120,7 +136,7 @@ class CategoricalDQNAgent(ValueOptimizationAgent): # TODO: fix this spaghetti code if isinstance(self.memory, PrioritizedExperienceReplay): errors = losses[0][np.arange(batch.size), batch.actions()] - self.memory.update_priorities(batch.info('idx'), errors) + self.call_memory('update_priorities', (batch.info('idx'), errors)) return total_loss, losses, unclipped_grads diff --git a/rl_coach/agents/clipped_ppo_agent.py b/rl_coach/agents/clipped_ppo_agent.py index 080525f..c581736 100644 --- a/rl_coach/agents/clipped_ppo_agent.py +++ b/rl_coach/agents/clipped_ppo_agent.py @@ -116,8 +116,10 @@ class ClippedPPOAgent(ActorCriticAgent): # calculate advantages advantages = [] value_targets = [] + total_returns = batch.n_step_discounted_rewards() + if self.policy_gradient_rescaler == PolicyGradientRescaler.A_VALUE: - advantages = batch.total_returns() - current_state_values + advantages = total_returns - current_state_values elif self.policy_gradient_rescaler == PolicyGradientRescaler.GAE: # get bootstraps episode_start_idx = 0 @@ -181,11 +183,13 @@ class ClippedPPOAgent(ActorCriticAgent): result = self.networks['main'].target_network.predict({k: v[start:end] for k, v in batch.states(network_keys).items()}) old_policy_distribution = result[1:] + total_returns = batch.n_step_discounted_rewards(expand_dims=True) + # calculate gradients and apply on both the local policy network and on the global policy network if self.ap.algorithm.estimate_state_value_using_gae: value_targets = np.expand_dims(gae_based_value_targets, -1) else: - value_targets = batch.total_returns(expand_dims=True)[start:end] + value_targets = total_returns[start:end] inputs = copy.copy({k: v[start:end] for k, v in batch.states(network_keys).items()}) inputs['output_1_0'] = actions diff --git a/rl_coach/agents/mmc_agent.py b/rl_coach/agents/mmc_agent.py index 3ce23e1..964d922 100644 --- a/rl_coach/agents/mmc_agent.py +++ b/rl_coach/agents/mmc_agent.py @@ -58,11 +58,13 @@ class MixedMonteCarloAgent(ValueOptimizationAgent): (self.networks['main'].online_network, batch.states(network_keys)) ]) + total_returns = batch.n_step_discounted_rewards() + for i in range(self.ap.network_wrappers['main'].batch_size): one_step_target = batch.rewards()[i] + \ (1.0 - batch.game_overs()[i]) * self.ap.algorithm.discount * \ q_st_plus_1[i][selected_actions[i]] - monte_carlo_target = batch.total_returns()[i] + monte_carlo_target = total_returns()[i] TD_targets[i, batch.actions()[i]] = (1 - self.mixing_rate) * one_step_target + \ self.mixing_rate * monte_carlo_target diff --git a/rl_coach/agents/nec_agent.py b/rl_coach/agents/nec_agent.py index 6f168bb..1ba8abe 100644 --- a/rl_coach/agents/nec_agent.py +++ b/rl_coach/agents/nec_agent.py @@ -98,10 +98,10 @@ class NECAgent(ValueOptimizationAgent): network_keys = self.ap.network_wrappers['main'].input_embedders_parameters.keys() TD_targets = self.networks['main'].online_network.predict(batch.states(network_keys)) - + bootstrapped_return_from_old_policy = batch.n_step_discounted_rewards() # only update the action that we have actually done in this transition for i in range(self.ap.network_wrappers['main'].batch_size): - TD_targets[i, batch.actions()[i]] = batch.total_returns()[i] + TD_targets[i, batch.actions()[i]] = bootstrapped_return_from_old_policy[i] # set the gradients to fetch for the DND update fetches = [] @@ -165,10 +165,10 @@ class NECAgent(ValueOptimizationAgent): episode = self.call_memory('get_last_complete_episode') if episode is not None and self.phase != RunPhase.TEST: assert len(self.current_episode_state_embeddings) == episode.length() - returns = episode.get_transitions_attribute('total_return') + discounted_rewards = episode.get_transitions_attribute('n_step_discounted_rewards') actions = episode.get_transitions_attribute('action') self.networks['main'].online_network.output_heads[0].DND.add(self.current_episode_state_embeddings, - actions, returns) + actions, discounted_rewards) def save_checkpoint(self, checkpoint_id): with open(os.path.join(self.ap.task_parameters.checkpoint_save_dir, str(checkpoint_id) + '.dnd'), 'wb') as f: diff --git a/rl_coach/agents/pal_agent.py b/rl_coach/agents/pal_agent.py index cb928e7..cba983c 100644 --- a/rl_coach/agents/pal_agent.py +++ b/rl_coach/agents/pal_agent.py @@ -70,6 +70,7 @@ class PALAgent(ValueOptimizationAgent): # calculate TD error TD_targets = np.copy(q_st_online) + total_returns = batch.n_step_discounted_rewards() for i in range(self.ap.network_wrappers['main'].batch_size): TD_targets[i, batch.actions()[i]] = batch.rewards()[i] + \ (1.0 - batch.game_overs()[i]) * self.ap.algorithm.discount * \ @@ -83,7 +84,7 @@ class PALAgent(ValueOptimizationAgent): TD_targets[i, batch.actions()[i]] -= self.alpha * advantage_learning_update # mixing monte carlo updates - monte_carlo_target = batch.total_returns()[i] + monte_carlo_target = total_returns[i] TD_targets[i, batch.actions()[i]] = (1 - self.monte_carlo_mixing_rate) * TD_targets[i, batch.actions()[i]] \ + self.monte_carlo_mixing_rate * monte_carlo_target diff --git a/rl_coach/agents/policy_gradients_agent.py b/rl_coach/agents/policy_gradients_agent.py index 7db5fd8..95ff617 100644 --- a/rl_coach/agents/policy_gradients_agent.py +++ b/rl_coach/agents/policy_gradients_agent.py @@ -74,7 +74,7 @@ class PolicyGradientsAgent(PolicyOptimizationAgent): # batch contains a list of episodes to learn from network_keys = self.ap.network_wrappers['main'].input_embedders_parameters.keys() - total_returns = batch.total_returns() + total_returns = batch.n_step_discounted_rewards() for i in reversed(range(batch.size)): if self.policy_gradient_rescaler == PolicyGradientRescaler.TOTAL_RETURN: total_returns[i] = total_returns[0] diff --git a/rl_coach/agents/policy_optimization_agent.py b/rl_coach/agents/policy_optimization_agent.py index 18e81cb..390edcd 100644 --- a/rl_coach/agents/policy_optimization_agent.py +++ b/rl_coach/agents/policy_optimization_agent.py @@ -73,11 +73,11 @@ class PolicyOptimizationAgent(Agent): episode_discounted_returns = [] for i in range(episode.length()): transition = episode.get_transition(i) - episode_discounted_returns.append(transition.total_return) + episode_discounted_returns.append(transition.n_step_discounted_rewards) self.num_episodes_where_step_has_been_seen[i] += 1 self.mean_return_over_multiple_episodes[i] -= self.mean_return_over_multiple_episodes[i] / \ self.num_episodes_where_step_has_been_seen[i] - self.mean_return_over_multiple_episodes[i] += transition.total_return / \ + self.mean_return_over_multiple_episodes[i] += transition.n_step_discounted_rewards / \ self.num_episodes_where_step_has_been_seen[i] self.mean_discounted_return = np.mean(episode_discounted_returns) self.std_discounted_return = np.std(episode_discounted_returns) @@ -97,7 +97,7 @@ class PolicyOptimizationAgent(Agent): network.set_is_training(True) # we need to update the returns of the episode until now - episode.update_returns() + episode.update_transitions_rewards_and_bootstrap_data() # get t_max transitions or less if the we got to a terminal state # will be used for both actor-critic and vanilla PG. diff --git a/rl_coach/agents/ppo_agent.py b/rl_coach/agents/ppo_agent.py index 83b6fc4..d455caa 100644 --- a/rl_coach/agents/ppo_agent.py +++ b/rl_coach/agents/ppo_agent.py @@ -112,11 +112,11 @@ class PPOAgent(ActorCriticAgent): # current_states_with_timestep = self.concat_state_and_timestep(batch) current_state_values = self.networks['critic'].online_network.predict(batch.states(network_keys)).squeeze() - + total_returns = batch.n_step_discounted_rewards() # calculate advantages advantages = [] if self.policy_gradient_rescaler == PolicyGradientRescaler.A_VALUE: - advantages = batch.total_returns() - current_state_values + advantages = total_returns - current_state_values elif self.policy_gradient_rescaler == PolicyGradientRescaler.GAE: # get bootstraps episode_start_idx = 0 @@ -155,6 +155,7 @@ class PPOAgent(ActorCriticAgent): # current_states_with_timestep = self.concat_state_and_timestep(dataset) mix_fraction = self.ap.algorithm.value_targets_mix_fraction + total_returns = batch.n_step_discounted_rewards(True) for j in range(epochs): curr_batch_size = batch.size if self.networks['critic'].online_network.optimizer_type != 'LBFGS': @@ -165,7 +166,7 @@ class PPOAgent(ActorCriticAgent): k: v[i * curr_batch_size:(i + 1) * curr_batch_size] for k, v in batch.states(network_keys).items() } - total_return_batch = batch.total_returns(True)[i * curr_batch_size:(i + 1) * curr_batch_size] + total_return_batch = total_returns[i * curr_batch_size:(i + 1) * curr_batch_size] old_policy_values = force_list(self.networks['critic'].target_network.predict( current_states_batch).squeeze()) if self.networks['critic'].online_network.optimizer_type != 'LBFGS': diff --git a/rl_coach/agents/rainbow_dqn_agent.py b/rl_coach/agents/rainbow_dqn_agent.py index 609ea0b..446a870 100644 --- a/rl_coach/agents/rainbow_dqn_agent.py +++ b/rl_coach/agents/rainbow_dqn_agent.py @@ -39,23 +39,17 @@ class RainbowDQNNetworkParameters(DQNNetworkParameters): class RainbowDQNAlgorithmParameters(CategoricalDQNAlgorithmParameters): def __init__(self): super().__init__() + self.n_step = 3 - -class RainbowDQNExplorationParameters(ParameterNoiseParameters): - def __init__(self, agent_params): - super().__init__(agent_params) - - -class RainbowDQNMemoryParameters(PrioritizedExperienceReplayParameters): - def __init__(self): - super().__init__() + # needed for n-step updates to work. i.e. waiting for a full episode to be closed before storing each transition + self.store_transitions_only_when_episodes_are_terminated = True class RainbowDQNAgentParameters(CategoricalDQNAgentParameters): def __init__(self): super().__init__() self.algorithm = RainbowDQNAlgorithmParameters() - self.exploration = RainbowDQNExplorationParameters(self) + self.exploration = ParameterNoiseParameters(self) self.memory = PrioritizedExperienceReplayParameters() self.network_wrappers = {"main": RainbowDQNNetworkParameters()} @@ -65,15 +59,13 @@ class RainbowDQNAgentParameters(CategoricalDQNAgentParameters): # Rainbow Deep Q Network - https://arxiv.org/abs/1710.02298 -# Agent implementation is WIP. Currently is composed of: +# Agent implementation is composed of: # 1. NoisyNets # 2. C51 # 3. Prioritized ER # 4. DDQN # 5. Dueling DQN -# -# still missing: -# 1. N-Step +# 6. N-step returns class RainbowDQNAgent(CategoricalDQNAgent): def __init__(self, agent_parameters, parent: Union['LevelManager', 'CompositeAgent']=None): @@ -87,7 +79,7 @@ class RainbowDQNAgent(CategoricalDQNAgent): # for the action we actually took, the error is calculated by the atoms distribution # for all other actions, the error is 0 - distributed_q_st_plus_1, TD_targets = self.networks['main'].parallel_prediction([ + distributional_q_st_plus_n, TD_targets = self.networks['main'].parallel_prediction([ (self.networks['main'].target_network, batch.next_states(network_keys)), (self.networks['main'].online_network, batch.states(network_keys)) ]) @@ -98,15 +90,16 @@ class RainbowDQNAgent(CategoricalDQNAgent): batches = np.arange(self.ap.network_wrappers['main'].batch_size) for j in range(self.z_values.size): - tzj = np.fmax(np.fmin(batch.rewards() + - (1.0 - batch.game_overs()) * self.ap.algorithm.discount * self.z_values[j], - self.z_values[self.z_values.size - 1]), - self.z_values[0]) + # we use batch.info('should_bootstrap_next_state') instead of (1 - batch.game_overs()) since with n-step, + # we will not bootstrap for the last n-step transitions in the episode + tzj = np.fmax(np.fmin(batch.n_step_discounted_rewards() + batch.info('should_bootstrap_next_state') * + (self.ap.algorithm.discount ** self.ap.algorithm.n_step) * self.z_values[j], + self.z_values[-1]), self.z_values[0]) bj = (tzj - self.z_values[0])/(self.z_values[1] - self.z_values[0]) u = (np.ceil(bj)).astype(int) l = (np.floor(bj)).astype(int) - m[batches, l] = m[batches, l] + (distributed_q_st_plus_1[batches, target_actions, j] * (u - bj)) - m[batches, u] = m[batches, u] + (distributed_q_st_plus_1[batches, target_actions, j] * (bj - l)) + m[batches, l] += (distributional_q_st_plus_n[batches, target_actions, j] * (u - bj)) + m[batches, u] += (distributional_q_st_plus_n[batches, target_actions, j] * (bj - l)) # total_loss = cross entropy between actual result above and predicted result for the given action TD_targets[batches, batch.actions()] = m @@ -122,7 +115,7 @@ class RainbowDQNAgent(CategoricalDQNAgent): # TODO: fix this spaghetti code if isinstance(self.memory, PrioritizedExperienceReplay): errors = losses[0][np.arange(batch.size), batch.actions()] - self.memory.update_priorities(batch.info('idx'), errors) + self.call_memory('update_priorities', (batch.info('idx'), errors)) return total_loss, losses, unclipped_grads diff --git a/rl_coach/base_parameters.py b/rl_coach/base_parameters.py index 03dbf25..03ed774 100644 --- a/rl_coach/base_parameters.py +++ b/rl_coach/base_parameters.py @@ -166,6 +166,9 @@ class AlgorithmParameters(Parameters): # intrinsic reward self.scale_external_reward_by_intrinsic_reward_value = False + # n-step returns + self.n_step = -1 # calculate the total return (no bootstrap, by default) + # Distributed Coach params self.distributed_coach_synchronization_type = None diff --git a/rl_coach/core_types.py b/rl_coach/core_types.py index 6d6bfe4..5180bc1 100644 --- a/rl_coach/core_types.py +++ b/rl_coach/core_types.py @@ -125,6 +125,7 @@ class Middleware_LSTM_Embedding(MiddlewareEmbedding): class Measurements(PredictionType): pass + PlayingStepsType = Union[EnvironmentSteps, EnvironmentEpisodes, Frames] @@ -162,7 +163,7 @@ class Transition(object): self._state = self.state = state self._action = self.action = action self._reward = self.reward = reward - self._total_return = self.total_return = None + self._n_step_discounted_rewards = self.n_step_discounted_rewards = None if not next_state: next_state = state self._next_state = self._next_state = next_state @@ -207,15 +208,15 @@ class Transition(object): self._reward = val @property - def total_return(self): - if self._total_return is None: - raise Exception("The total_return was not filled by any of the modules between the environment and the " - "agent. Make sure that you are using an episodic experience replay.") - return self._total_return + def n_step_discounted_rewards(self): + if self._n_step_discounted_rewards is None: + raise Exception("The n_step_discounted_rewards were not filled by any of the modules between the " + "environment and the agent. Make sure that you are using an episodic experience replay.") + return self._n_step_discounted_rewards - @total_return.setter - def total_return(self, val): - self._total_return = val + @n_step_discounted_rewards.setter + def n_step_discounted_rewards(self, val): + self._n_step_discounted_rewards = val @property def game_over(self): @@ -322,6 +323,7 @@ class ActionInfo(object): """ Action info is a class that holds an action and various additional information details about it """ + def __init__(self, action: ActionType, action_probability: float=0, action_value: float=0., state_value: float=0., max_action_value: float=None, action_intrinsic_reward: float=0): @@ -359,7 +361,7 @@ class Batch(object): self._states = {} self._actions = None self._rewards = None - self._total_returns = None + self._n_step_discounted_rewards = None self._game_overs = None self._next_states = {} self._goals = None @@ -380,8 +382,8 @@ class Batch(object): self._actions = self._actions[start:end] if self._rewards is not None: self._rewards = self._rewards[start:end] - if self._total_returns is not None: - self._total_returns = self._total_returns[start:end] + if self._n_step_discounted_rewards is not None: + self._n_step_discounted_rewards = self._n_step_discounted_rewards[start:end] if self._game_overs is not None: self._game_overs = self._game_overs[start:end] for k, v in self._next_states.items(): @@ -402,7 +404,7 @@ class Batch(object): self._states = {} self._actions = None self._rewards = None - self._total_returns = None + self._n_step_discounted_rewards = None self._game_overs = None self._next_states = {} self._goals = None @@ -471,18 +473,20 @@ class Batch(object): return np.expand_dims(self._rewards, -1) return self._rewards - def total_returns(self, expand_dims=False) -> np.ndarray: + def n_step_discounted_rewards(self, expand_dims=False) -> np.ndarray: """ - if the total_returns were not converted to a batch before, extract them to a batch and then return the batch - if the total return was not filled, this will raise an exception + if the n_step_discounted_rewards were not converted to a batch before, extract them to a batch and then return + the batch + if the n step discounted rewards were not filled, this will raise an exception :param expand_dims: add an extra dimension to the total_returns batch :return: a numpy array containing all the total return values of the batch """ - if self._total_returns is None: - self._total_returns = np.array([transition.total_return for transition in self.transitions]) + if self._n_step_discounted_rewards is None: + self._n_step_discounted_rewards = np.array([transition.n_step_discounted_rewards for transition in + self.transitions]) if expand_dims: - return np.expand_dims(self._total_returns, -1) - return self._total_returns + return np.expand_dims(self._n_step_discounted_rewards, -1) + return self._n_step_discounted_rewards def game_overs(self, expand_dims=False) -> np.ndarray: """ @@ -510,7 +514,8 @@ class Batch(object): # addition to the current_state, so that all the inputs of the network will be filled) for key in set(fetches).intersection(self.transitions[0].next_state.keys()): if key not in self._next_states.keys(): - self._next_states[key] = np.array([np.array(transition.next_state[key]) for transition in self.transitions]) + self._next_states[key] = np.array( + [np.array(transition.next_state[key]) for transition in self.transitions]) if expand_dims: next_states[key] = np.expand_dims(self._next_states[key], -1) else: @@ -530,6 +535,16 @@ class Batch(object): return np.expand_dims(self._goals, -1) return self._goals + def info_as_list(self, key) -> list: + """ + get the info and store it internally as a list, if wasn't stored before. return it as a list + :param expand_dims: add an extra dimension to the info batch + :return: a list containing all the info values of the batch corresponding to the given key + """ + if key not in self._info.keys(): + self._info[key] = [transition.info[key] for transition in self.transitions] + return self._info[key] + def info(self, key, expand_dims=False) -> np.ndarray: """ if the given info dictionary key was not converted to a batch before, extract it to a batch and then return the @@ -537,11 +552,11 @@ class Batch(object): :param expand_dims: add an extra dimension to the info batch :return: a numpy array containing all the info values of the batch corresponding to the given key """ - if key not in self._info.keys(): - self._info[key] = np.array([transition.info[key] for transition in self.transitions]) + info_list = self.info_as_list(key) + if expand_dims: - return np.expand_dims(self._info[key], -1) - return self._info[key] + return np.expand_dims(info_list, -1) + return np.array(info_list) @property def size(self) -> int: @@ -572,6 +587,7 @@ class TotalStepsCounter(object): """ A wrapper around a dictionary counting different StepMethods steps done. """ + def __init__(self): self.counters = { EnvironmentEpisodes: 0, @@ -619,7 +635,6 @@ class Episode(object): """ self.transitions = [] # a num_transitions x num_transitions table with the n step return in the n'th row - self.returns_table = None self._length = 0 self.discount = discount self.bootstrap_total_return_from_old_policy = bootstrap_total_return_from_old_policy @@ -650,28 +665,48 @@ class Episode(object): def get_first_transition(self): return self.get_transition(0) if self.length() > 0 else None - def update_returns(self): + def update_discounted_rewards(self): if self.n_step == -1 or self.n_step > self.length(): curr_n_step = self.length() else: curr_n_step = self.n_step + rewards = np.array([t.reward for t in self.transitions]) rewards = rewards.astype('float') - total_return = rewards.copy() + discounted_rewards = rewards.copy() current_discount = self.discount for i in range(1, curr_n_step): - total_return += current_discount * np.pad(rewards[i:], (0, i), 'constant', constant_values=0) + discounted_rewards += current_discount * np.pad(rewards[i:], (0, i), 'constant', constant_values=0) current_discount *= self.discount # calculate the bootstrapped returns if self.bootstrap_total_return_from_old_policy: bootstraps = np.array([np.squeeze(t.info['max_action_value']) for t in self.transitions[curr_n_step:]]) - bootstrapped_return = total_return + current_discount * np.pad(bootstraps, (0, curr_n_step), 'constant', - constant_values=0) - total_return = bootstrapped_return + bootstrapped_return = discounted_rewards + current_discount * np.pad(bootstraps, (0, curr_n_step), + 'constant', constant_values=0) + discounted_rewards = bootstrapped_return for transition_idx in range(self.length()): - self.transitions[transition_idx].total_return = total_return[transition_idx] + self.transitions[transition_idx].n_step_discounted_rewards = discounted_rewards[transition_idx] + + def update_transitions_rewards_and_bootstrap_data(self): + if not isinstance(self.n_step, int) or (self.n_step < 1 and self.n_step != -1): + raise ValueError("n-step should be an integer with value >= 1, or set to -1 for always setting to episode" + " length.") + elif self.n_step > 1: + curr_n_step = self.n_step if self.n_step < self.length() else self.length() + + for idx, transition in enumerate(self.transitions): + next_n_step_transition_idx = (idx + curr_n_step) + if next_n_step_transition_idx < len(self.transitions): + # next state will now point to the n-step next state + transition.next_state = self.transitions[next_n_step_transition_idx].state + transition.info['should_bootstrap_next_state'] = True + else: + transition.next_state = self.transitions[-1].next_state + transition.info['should_bootstrap_next_state'] = False + + self.update_discounted_rewards() def update_actions_probabilities(self): probability_product = 1 @@ -681,12 +716,6 @@ class Episode(object): for transition_idx, transition in enumerate(self.transitions): transition.info['probability_product'] = probability_product - def get_returns_table(self): - return self.returns_table - - def get_returns(self): - return self.get_transitions_attribute('total_return') - def get_transitions_attribute(self, attribute_name): if len(self.transitions) > 0 and hasattr(self.transitions[0], attribute_name): return [getattr(t, attribute_name) for t in self.transitions] diff --git a/rl_coach/memories/episodic/episodic_experience_replay.py b/rl_coach/memories/episodic/episodic_experience_replay.py index e3d2eb2..2f4b393 100644 --- a/rl_coach/memories/episodic/episodic_experience_replay.py +++ b/rl_coach/memories/episodic/episodic_experience_replay.py @@ -27,6 +27,7 @@ class EpisodicExperienceReplayParameters(MemoryParameters): def __init__(self): super().__init__() self.max_size = (MemoryGranularity.Transitions, 1000000) + self.n_step = -1 @property def path(self): @@ -39,12 +40,13 @@ class EpisodicExperienceReplay(Memory): calculations of total return and other values that depend on the sequential behavior of the transitions in the episode. """ - def __init__(self, max_size: Tuple[MemoryGranularity, int]): + def __init__(self, max_size: Tuple[MemoryGranularity, int]=(MemoryGranularity.Transitions, 1000000), n_step=-1): """ :param max_size: the maximum number of transitions or episodes to hold in the memory """ super().__init__(max_size) - self._buffer = [Episode()] # list of episodes + self.n_step = n_step + self._buffer = [Episode(n_step=self.n_step)] # list of episodes self.transitions = [] self._length = 1 # the episodic replay buffer starts with a single empty episode self._num_transitions = 0 @@ -109,7 +111,7 @@ class EpisodicExperienceReplay(Memory): self._remove_episode(0) def _update_episode(self, episode: Episode) -> None: - episode.update_returns() + episode.update_transitions_rewards_and_bootstrap_data() def verify_last_episode_is_closed(self) -> None: """ @@ -138,7 +140,7 @@ class EpisodicExperienceReplay(Memory): self._length += 1 # create a new Episode for the next transitions to be placed into - self._buffer.append(Episode()) + self._buffer.append(Episode(n_step=self.n_step)) # if update episode adds to the buffer, a new Episode needs to be ready first # it would be better if this were less state full @@ -158,12 +160,14 @@ class EpisodicExperienceReplay(Memory): :param transition: a transition to store :return: None """ + # Calling super.store() so that in case a memory backend is used, the memory backend can store this transition. super().store(transition) + self.reader_writer_lock.lock_writing_and_reading() if len(self._buffer) == 0: - self._buffer.append(Episode()) + self._buffer.append(Episode(n_step=self.n_step)) last_episode = self._buffer[-1] last_episode.insert(transition) self.transitions.append(transition) @@ -284,7 +288,7 @@ class EpisodicExperienceReplay(Memory): self.reader_writer_lock.lock_writing_and_reading() self.transitions = [] - self._buffer = [Episode()] + self._buffer = [Episode(n_step=self.n_step)] self._length = 1 self._num_transitions = 0 self._num_transitions_in_complete_episodes = 0 diff --git a/rl_coach/memories/episodic/episodic_hindsight_experience_replay.py b/rl_coach/memories/episodic/episodic_hindsight_experience_replay.py index c30f451..69468a1 100644 --- a/rl_coach/memories/episodic/episodic_hindsight_experience_replay.py +++ b/rl_coach/memories/episodic/episodic_hindsight_experience_replay.py @@ -139,7 +139,7 @@ class EpisodicHindsightExperienceReplay(EpisodicExperienceReplay): hindsight_transition.reward, hindsight_transition.game_over = \ self.goals_space.get_reward_for_goal_and_state(goal, hindsight_transition.next_state) - hindsight_transition.total_return = None + hindsight_transition.n_step_discounted_rewards = None episode.insert(hindsight_transition) super().store_episode(episode) diff --git a/rl_coach/memories/non_episodic/prioritized_experience_replay.py b/rl_coach/memories/non_episodic/prioritized_experience_replay.py index 544df48..120a738 100644 --- a/rl_coach/memories/non_episodic/prioritized_experience_replay.py +++ b/rl_coach/memories/non_episodic/prioritized_experience_replay.py @@ -128,14 +128,14 @@ class SegmentTree(object): self.tree[node_idx] = new_val self._propagate(node_idx) - def get(self, val: float) -> Tuple[int, float, Any]: + def get_element_by_partial_sum(self, val: float) -> Tuple[int, float, Any]: """ Given a value between 0 and the tree sum, return the object which this value is in it's range. For example, if we have 3 leaves: 10, 20, 30, and val=35, this will return the 3rd leaf, by accumulating leaves by their order until getting to 35. This allows sampling leaves according to their proportional probability. :param val: a value within the range 0 and the tree sum - :return: the index of the resulting leaf in the tree, it's probability and + :return: the index of the resulting leaf in the tree, its probability and the object itself """ node_idx = self._retrieve(0, val) @@ -237,12 +237,12 @@ class PrioritizedExperienceReplay(ExperienceReplay): # sample a batch for i in range(size): - start_probability = segment_size * i - end_probability = segment_size * (i + 1) + segment_start = segment_size * i + segment_end = segment_size * (i + 1) # sample leaf and calculate its weight - val = random.uniform(start_probability, end_probability) - leaf_idx, priority, transition = self.sum_tree.get(val) + val = random.uniform(segment_start, segment_end) + leaf_idx, priority, transition = self.sum_tree.get_element_by_partial_sum(val) priority /= self.sum_tree.total_value() # P(j) = p^a / sum(p^a) weight = (self.num_transitions() * priority) ** -self.beta.current_value # (N * P(j)) ^ -beta normalized_weight = weight / max_weight # wj = ((N * P(j)) ^ -beta) / max wi @@ -261,7 +261,7 @@ class PrioritizedExperienceReplay(ExperienceReplay): self.reader_writer_lock.release_writing() return batch - def store(self, transition: Transition) -> None: + def store(self, transition: Transition, lock=True) -> None: """ Store a new transition in the memory. :param transition: a transition to store @@ -270,7 +270,8 @@ class PrioritizedExperienceReplay(ExperienceReplay): # Calling super.store() so that in case a memory backend is used, the memory backend can store this transition. super().store(transition) - self.reader_writer_lock.lock_writing_and_reading() + if lock: + self.reader_writer_lock.lock_writing_and_reading() transition_priority = self.maximal_priority self.sum_tree.add(transition_priority ** self.alpha, transition) @@ -278,18 +279,21 @@ class PrioritizedExperienceReplay(ExperienceReplay): self.max_tree.add(transition_priority, transition) super().store(transition, False) - self.reader_writer_lock.release_writing_and_reading() + if lock: + self.reader_writer_lock.release_writing_and_reading() - def clean(self) -> None: + def clean(self, lock=True) -> None: """ Clean the memory by removing all the episodes :return: None """ - self.reader_writer_lock.lock_writing_and_reading() + if lock: + self.reader_writer_lock.lock_writing_and_reading() super().clean(lock=False) self.sum_tree = SegmentTree(self.power_of_2_size, SegmentTree.Operation.SUM) self.min_tree = SegmentTree(self.power_of_2_size, SegmentTree.Operation.MIN) self.max_tree = SegmentTree(self.power_of_2_size, SegmentTree.Operation.MAX) - self.reader_writer_lock.release_writing_and_reading() + if lock: + self.reader_writer_lock.release_writing_and_reading() diff --git a/rl_coach/presets/CartPole_PPO.py b/rl_coach/presets/CartPole_ClippedPPO.py similarity index 98% rename from rl_coach/presets/CartPole_PPO.py rename to rl_coach/presets/CartPole_ClippedPPO.py index 0c13abb..7c4d3c1 100644 --- a/rl_coach/presets/CartPole_PPO.py +++ b/rl_coach/presets/CartPole_ClippedPPO.py @@ -63,7 +63,7 @@ env_params = GymVectorEnvironment(level='CartPole-v0') preset_validation_params = PresetValidationParameters() preset_validation_params.test = True preset_validation_params.min_reward_threshold = 150 -preset_validation_params.max_episodes_to_achieve_reward = 250 +preset_validation_params.max_episodes_to_achieve_reward = 400 graph_manager = BasicRLGraphManager(agent_params=agent_params, env_params=env_params, schedule_params=schedule_params, vis_params=VisualizationParameters(), diff --git a/rl_coach/tests/memories/test_prioritized_experience_replay.py b/rl_coach/tests/memories/test_prioritized_experience_replay.py index 020c4a1..51f1a61 100644 --- a/rl_coach/tests/memories/test_prioritized_experience_replay.py +++ b/rl_coach/tests/memories/test_prioritized_experience_replay.py @@ -26,10 +26,10 @@ def test_sum_tree(): sum_tree.add(5, "5") assert sum_tree.total_value() == 20 - assert sum_tree.get(2) == (0, 2.5, '2.5') - assert sum_tree.get(3) == (1, 5.0, '5') - assert sum_tree.get(10) == (2, 5.0, '5') - assert sum_tree.get(13) == (3, 7.5, '7.5') + assert sum_tree.get_element_by_partial_sum(2) == (0, 2.5, '2.5') + assert sum_tree.get_element_by_partial_sum(3) == (1, 5.0, '5') + assert sum_tree.get_element_by_partial_sum(10) == (2, 5.0, '5') + assert sum_tree.get_element_by_partial_sum(13) == (3, 7.5, '7.5') sum_tree.update(2, 10) assert sum_tree.__str__() == "[25.]\n[ 7.5 17.5]\n[ 2.5 5. 10. 7.5]\n" diff --git a/rl_coach/tests/memories/test_single_episode_buffer.py b/rl_coach/tests/memories/test_single_episode_buffer.py index c2f12ab..84e1e26 100644 --- a/rl_coach/tests/memories/test_single_episode_buffer.py +++ b/rl_coach/tests/memories/test_single_episode_buffer.py @@ -41,8 +41,8 @@ def test_store_and_get(buffer: SingleEpisodeBuffer): # check that the episode is valid episode = buffer.get(0) assert episode.length() == 2 - assert episode.get_transition(0).total_return == 1 + 0.99 - assert episode.get_transition(1).total_return == 1 + assert episode.get_transition(0).n_step_discounted_rewards == 1 + 0.99 + assert episode.get_transition(1).n_step_discounted_rewards == 1 assert buffer.mean_reward() == 1 # only one episode in the replay buffer From 8f0415b4ccca80c4e6202ba19e37dbe1142e0e11 Mon Sep 17 00:00:00 2001 From: Leo Dirac Date: Wed, 7 Nov 2018 11:01:12 -0800 Subject: [PATCH 101/162] Tweak additional_simulator_parameters for easier configuration and better error logging. (#69) --- rl_coach/environments/gym_environment.py | 12 +++++++----- 1 file changed, 7 insertions(+), 5 deletions(-) diff --git a/rl_coach/environments/gym_environment.py b/rl_coach/environments/gym_environment.py index 956185c..569c537 100644 --- a/rl_coach/environments/gym_environment.py +++ b/rl_coach/environments/gym_environment.py @@ -63,7 +63,7 @@ class GymEnvironmentParameters(EnvironmentParameters): super().__init__(level=level) self.random_initialization_steps = 0 self.max_over_num_frames = 1 - self.additional_simulator_parameters = None + self.additional_simulator_parameters = {} @property def path(self): @@ -178,7 +178,7 @@ class MaxOverFramesAndFrameskipEnvWrapper(gym.Wrapper): # Environment class GymEnvironment(Environment): def __init__(self, level: LevelSelection, frame_skip: int, visualization_parameters: VisualizationParameters, - additional_simulator_parameters: Dict[str, Any] = None, seed: Union[None, int]=None, + additional_simulator_parameters: Dict[str, Any] = {}, seed: Union[None, int]=None, human_control: bool=False, custom_reward_threshold: Union[int, float]=None, random_initialization_steps: int=1, max_over_num_frames: int=1, **kwargs): super().__init__(level, seed, frame_skip, human_control, custom_reward_threshold, @@ -218,10 +218,12 @@ class GymEnvironment(Environment): env_class = gym.envs.registration.load(self.env_id) # instantiate the environment - if self.additional_simulator_parameters: + try: self.env = env_class(**self.additional_simulator_parameters) - else: - self.env = env_class() + except: + screen.error("Failed to instantiate Gym environment class %s with arguments %s" % + (env_class, self.additional_simulator_parameters), crash=False) + raise else: self.env = gym.make(self.env_id) From 83e0b09a6a6d08b6ac6896418bded853355c55ee Mon Sep 17 00:00:00 2001 From: Itai Caspi <30383381+itaicaspi-intel@users.noreply.github.com> Date: Thu, 8 Nov 2018 12:52:42 +0200 Subject: [PATCH 102/162] adding the missing export_onnx_graph parameter to task parameters (#73) --- .../tensorflow_components/heads/ppo_head.py | 2 +- rl_coach/base_parameters.py | 10 +++++++--- rl_coach/coach.py | 6 ++++-- 3 files changed, 12 insertions(+), 6 deletions(-) diff --git a/rl_coach/architectures/tensorflow_components/heads/ppo_head.py b/rl_coach/architectures/tensorflow_components/heads/ppo_head.py index f15e6f2..2dacaea 100644 --- a/rl_coach/architectures/tensorflow_components/heads/ppo_head.py +++ b/rl_coach/architectures/tensorflow_components/heads/ppo_head.py @@ -19,7 +19,7 @@ import tensorflow as tf from rl_coach.architectures.tensorflow_components.layers import Dense from rl_coach.architectures.tensorflow_components.heads.head import Head, normalized_columns_initializer -from rl_coach.base_parameters import AgentParameters +from rl_coach.base_parameters import AgentParameters, DistributedTaskParameters from rl_coach.core_types import ActionProbabilities from rl_coach.spaces import BoxActionSpace, DiscreteActionSpace from rl_coach.spaces import SpacesDefinition diff --git a/rl_coach/base_parameters.py b/rl_coach/base_parameters.py index 03ed774..2aa09c9 100644 --- a/rl_coach/base_parameters.py +++ b/rl_coach/base_parameters.py @@ -436,7 +436,7 @@ class AgentParameters(Parameters): class TaskParameters(Parameters): def __init__(self, framework_type: Frameworks=Frameworks.tensorflow, evaluate_only: bool=False, use_cpu: bool=False, experiment_path='/tmp', seed=None, checkpoint_save_secs=None, checkpoint_restore_dir=None, - checkpoint_save_dir=None): + checkpoint_save_dir=None, export_onnx_graph: bool=False): """ :param framework_type: deep learning framework type. currently only tensorflow is supported :param evaluate_only: the task will be used only for evaluating the model @@ -446,6 +446,7 @@ class TaskParameters(Parameters): :param checkpoint_save_secs: the number of seconds between each checkpoint saving :param checkpoint_restore_dir: the directory to restore the checkpoints from :param checkpoint_save_dir: the directory to store the checkpoints in + :param export_onnx_graph: If set to True, this will export an onnx graph each time a checkpoint is saved """ self.framework_type = framework_type self.task_index = 0 # TODO: not really needed @@ -456,6 +457,7 @@ class TaskParameters(Parameters): self.checkpoint_restore_dir = checkpoint_restore_dir self.checkpoint_save_dir = checkpoint_save_dir self.seed = seed + self.export_onnx_graph = export_onnx_graph class DistributedTaskParameters(TaskParameters): @@ -463,7 +465,7 @@ class DistributedTaskParameters(TaskParameters): task_index: int, evaluate_only: bool=False, num_tasks: int=None, num_training_tasks: int=None, use_cpu: bool=False, experiment_path=None, dnd=None, shared_memory_scratchpad=None, seed=None, checkpoint_save_secs=None, checkpoint_restore_dir=None, - checkpoint_save_dir=None): + checkpoint_save_dir=None, export_onnx_graph: bool=False): """ :param framework_type: deep learning framework type. currently only tensorflow is supported :param evaluate_only: the task will be used only for evaluating the model @@ -481,10 +483,12 @@ class DistributedTaskParameters(TaskParameters): :param checkpoint_save_secs: the number of seconds between each checkpoint saving :param checkpoint_restore_dir: the directory to restore the checkpoints from :param checkpoint_save_dir: the directory to store the checkpoints in + :param export_onnx_graph: If set to True, this will export an onnx graph each time a checkpoint is saved """ super().__init__(framework_type=framework_type, evaluate_only=evaluate_only, use_cpu=use_cpu, experiment_path=experiment_path, seed=seed, checkpoint_save_secs=checkpoint_save_secs, - checkpoint_restore_dir=checkpoint_restore_dir, checkpoint_save_dir=checkpoint_save_dir) + checkpoint_restore_dir=checkpoint_restore_dir, checkpoint_save_dir=checkpoint_save_dir, + export_onnx_graph=export_onnx_graph) self.parameters_server_hosts = parameters_server_hosts self.worker_hosts = worker_hosts self.job_type = job_type diff --git a/rl_coach/coach.py b/rl_coach/coach.py index 19daf40..f750d2d 100644 --- a/rl_coach/coach.py +++ b/rl_coach/coach.py @@ -535,7 +535,8 @@ def main(): use_cpu=args.use_cpu, checkpoint_save_secs=args.checkpoint_save_secs, checkpoint_restore_dir=args.checkpoint_restore_dir, - checkpoint_save_dir=args.checkpoint_save_dir + checkpoint_save_dir=args.checkpoint_save_dir, + export_onnx_graph=args.export_onnx_graph ) start_graph(graph_manager=graph_manager, task_parameters=task_parameters) @@ -574,7 +575,8 @@ def main(): seed=args.seed+task_index if args.seed is not None else None, # each worker gets a different seed checkpoint_save_secs=args.checkpoint_save_secs, checkpoint_restore_dir=args.checkpoint_restore_dir, - checkpoint_save_dir=args.checkpoint_save_dir + checkpoint_save_dir=args.checkpoint_save_dir, + export_onnx_graph=args.export_onnx_graph ) # we assume that only the evaluation workers are rendering From 389c65cbbea933b253882d5d5581ecf4a54774fa Mon Sep 17 00:00:00 2001 From: Itai Caspi <30383381+itaicaspi-intel@users.noreply.github.com> Date: Thu, 8 Nov 2018 16:52:48 +0200 Subject: [PATCH 103/162] fix for a bug in distributed training that was introduced lately (#75) --- rl_coach/graph_managers/graph_manager.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/rl_coach/graph_managers/graph_manager.py b/rl_coach/graph_managers/graph_manager.py index 778e4ed..b20a12c 100644 --- a/rl_coach/graph_managers/graph_manager.py +++ b/rl_coach/graph_managers/graph_manager.py @@ -195,7 +195,7 @@ class GraphManager(object): @staticmethod def create_worker_or_parameters_server(task_parameters: DistributedTaskParameters): if task_parameters.framework_type == Frameworks.tensorflow: - GraphManager._create_worker_or_parameters_server_tf(task_parameters) + return GraphManager._create_worker_or_parameters_server_tf(task_parameters) elif task_parameters.framework_type == Frameworks.mxnet: raise NotImplementedError('Distributed training not implemented for MXNet') else: From 3a0a1159e978925f03215e5794f0780d6e56671d Mon Sep 17 00:00:00 2001 From: Itai Caspi <30383381+itaicaspi-intel@users.noreply.github.com> Date: Thu, 8 Nov 2018 16:53:47 +0200 Subject: [PATCH 104/162] fixing the dropout rate code (#72) addresses issue #53 --- rl_coach/architectures/embedder_parameters.py | 6 +++--- rl_coach/architectures/middleware_parameters.py | 12 ++++++------ .../mxnet_components/embedders/embedder.py | 4 ++-- .../mxnet_components/middlewares/middleware.py | 4 ++-- .../tensorflow_components/embedders/embedder.py | 7 +++---- .../embedders/image_embedder.py | 4 ++-- .../embedders/vector_embedder.py | 6 +++--- .../architectures/tensorflow_components/layers.py | 6 +++--- .../middlewares/fc_middleware.py | 5 +++-- .../middlewares/lstm_middleware.py | 5 +++-- .../tensorflow_components/middlewares/middleware.py | 7 +++---- 11 files changed, 33 insertions(+), 33 deletions(-) diff --git a/rl_coach/architectures/embedder_parameters.py b/rl_coach/architectures/embedder_parameters.py index 2731a52..2973a3a 100644 --- a/rl_coach/architectures/embedder_parameters.py +++ b/rl_coach/architectures/embedder_parameters.py @@ -21,13 +21,13 @@ from rl_coach.base_parameters import EmbedderScheme, NetworkComponentParameters class InputEmbedderParameters(NetworkComponentParameters): def __init__(self, activation_function: str='relu', scheme: Union[List, EmbedderScheme]=EmbedderScheme.Medium, - batchnorm: bool=False, dropout=False, name: str='embedder', input_rescaling=None, input_offset=None, - input_clipping=None, dense_layer=None, is_training=False): + batchnorm: bool=False, dropout_rate: float=0.0, name: str='embedder', input_rescaling=None, + input_offset=None, input_clipping=None, dense_layer=None, is_training=False): super().__init__(dense_layer=dense_layer) self.activation_function = activation_function self.scheme = scheme self.batchnorm = batchnorm - self.dropout = dropout + self.dropout_rate = dropout_rate if input_rescaling is None: input_rescaling = {'image': 255.0, 'vector': 1.0} diff --git a/rl_coach/architectures/middleware_parameters.py b/rl_coach/architectures/middleware_parameters.py index 711ec06..40533cd 100644 --- a/rl_coach/architectures/middleware_parameters.py +++ b/rl_coach/architectures/middleware_parameters.py @@ -22,12 +22,12 @@ from rl_coach.base_parameters import MiddlewareScheme, NetworkComponentParameter class MiddlewareParameters(NetworkComponentParameters): def __init__(self, parameterized_class_name: str, activation_function: str='relu', scheme: Union[List, MiddlewareScheme]=MiddlewareScheme.Medium, - batchnorm: bool=False, dropout: bool=False, name='middleware', dense_layer=None, is_training=False): + batchnorm: bool=False, dropout_rate: float=0.0, name='middleware', dense_layer=None, is_training=False): super().__init__(dense_layer=dense_layer) self.activation_function = activation_function self.scheme = scheme self.batchnorm = batchnorm - self.dropout = dropout + self.dropout_rate = dropout_rate self.name = name self.is_training = is_training self.parameterized_class_name = parameterized_class_name @@ -36,19 +36,19 @@ class MiddlewareParameters(NetworkComponentParameters): class FCMiddlewareParameters(MiddlewareParameters): def __init__(self, activation_function='relu', scheme: Union[List, MiddlewareScheme] = MiddlewareScheme.Medium, - batchnorm: bool = False, dropout: bool = False, + batchnorm: bool = False, dropout_rate: float = 0.0, name="middleware_fc_embedder", dense_layer=None, is_training=False): super().__init__(parameterized_class_name="FCMiddleware", activation_function=activation_function, - scheme=scheme, batchnorm=batchnorm, dropout=dropout, name=name, dense_layer=dense_layer, + scheme=scheme, batchnorm=batchnorm, dropout_rate=dropout_rate, name=name, dense_layer=dense_layer, is_training=is_training) class LSTMMiddlewareParameters(MiddlewareParameters): def __init__(self, activation_function='relu', number_of_lstm_cells=256, scheme: MiddlewareScheme = MiddlewareScheme.Medium, - batchnorm: bool = False, dropout: bool = False, + batchnorm: bool = False, dropout_rate: float = 0.0, name="middleware_lstm_embedder", dense_layer=None, is_training=False): super().__init__(parameterized_class_name="LSTMMiddleware", activation_function=activation_function, - scheme=scheme, batchnorm=batchnorm, dropout=dropout, name=name, dense_layer=dense_layer, + scheme=scheme, batchnorm=batchnorm, dropout_rate=dropout_rate, name=name, dense_layer=dense_layer, is_training=is_training) self.number_of_lstm_cells = number_of_lstm_cells \ No newline at end of file diff --git a/rl_coach/architectures/mxnet_components/embedders/embedder.py b/rl_coach/architectures/mxnet_components/embedders/embedder.py index c2b6340..7a92855 100644 --- a/rl_coach/architectures/mxnet_components/embedders/embedder.py +++ b/rl_coach/architectures/mxnet_components/embedders/embedder.py @@ -39,8 +39,8 @@ class InputEmbedder(nn.HybridBlock): self.net.add(nn.BatchNorm()) if params.activation_function: self.net.add(nn.Activation(params.activation_function)) - if params.dropout: - self.net.add(nn.Dropout(rate=params.dropout)) + if params.dropout_rate: + self.net.add(nn.Dropout(rate=params.dropout_rate)) @property def schemes(self) -> dict: diff --git a/rl_coach/architectures/mxnet_components/middlewares/middleware.py b/rl_coach/architectures/mxnet_components/middlewares/middleware.py index 8b9db01..dd31b38 100644 --- a/rl_coach/architectures/mxnet_components/middlewares/middleware.py +++ b/rl_coach/architectures/mxnet_components/middlewares/middleware.py @@ -36,8 +36,8 @@ class Middleware(nn.HybridBlock): self.net.add(nn.BatchNorm()) if params.activation_function: self.net.add(nn.Activation(params.activation_function)) - if params.dropout: - self.net.add(nn.Dropout(rate=params.dropout)) + if params.dropout_rate: + self.net.add(nn.Dropout(rate=params.dropout_rate)) @property def schemes(self) -> dict: diff --git a/rl_coach/architectures/tensorflow_components/embedders/embedder.py b/rl_coach/architectures/tensorflow_components/embedders/embedder.py index 967b1ba..8a7e7ff 100644 --- a/rl_coach/architectures/tensorflow_components/embedders/embedder.py +++ b/rl_coach/architectures/tensorflow_components/embedders/embedder.py @@ -34,15 +34,14 @@ class InputEmbedder(object): can be multiple embedders in a single network """ def __init__(self, input_size: List[int], activation_function=tf.nn.relu, - scheme: EmbedderScheme=None, batchnorm: bool=False, dropout: bool=False, + scheme: EmbedderScheme=None, batchnorm: bool=False, dropout_rate: float=0.0, name: str= "embedder", input_rescaling=1.0, input_offset=0.0, input_clipping=None, dense_layer=Dense, is_training=False): self.name = name self.input_size = input_size self.activation_function = activation_function self.batchnorm = batchnorm - self.dropout = dropout - self.dropout_rate = 0 + self.dropout_rate = dropout_rate self.input = None self.output = None self.scheme = scheme @@ -68,7 +67,7 @@ class InputEmbedder(object): # we allow adding batchnorm, dropout or activation functions after each layer. # The motivation is to simplify the transition between a network with batchnorm and a network without # batchnorm to a single flag (the same applies to activation function and dropout) - if self.batchnorm or self.activation_function or self.dropout: + if self.batchnorm or self.activation_function or self.dropout_rate > 0: for layer_idx in reversed(range(len(self.layers_params))): self.layers_params.insert(layer_idx+1, BatchnormActivationDropout(batchnorm=self.batchnorm, diff --git a/rl_coach/architectures/tensorflow_components/embedders/image_embedder.py b/rl_coach/architectures/tensorflow_components/embedders/image_embedder.py index b28d7f9..b05ec8e 100644 --- a/rl_coach/architectures/tensorflow_components/embedders/image_embedder.py +++ b/rl_coach/architectures/tensorflow_components/embedders/image_embedder.py @@ -32,10 +32,10 @@ class ImageEmbedder(InputEmbedder): """ def __init__(self, input_size: List[int], activation_function=tf.nn.relu, - scheme: EmbedderScheme=EmbedderScheme.Medium, batchnorm: bool=False, dropout: bool=False, + scheme: EmbedderScheme=EmbedderScheme.Medium, batchnorm: bool=False, dropout_rate: float=0.0, name: str= "embedder", input_rescaling: float=255.0, input_offset: float=0.0, input_clipping=None, dense_layer=Dense, is_training=False): - super().__init__(input_size, activation_function, scheme, batchnorm, dropout, name, input_rescaling, + super().__init__(input_size, activation_function, scheme, batchnorm, dropout_rate, name, input_rescaling, input_offset, input_clipping, dense_layer=dense_layer, is_training=is_training) self.return_type = InputImageEmbedding if len(input_size) != 3 and scheme != EmbedderScheme.Empty: diff --git a/rl_coach/architectures/tensorflow_components/embedders/vector_embedder.py b/rl_coach/architectures/tensorflow_components/embedders/vector_embedder.py index 625aab7..60b728d 100644 --- a/rl_coach/architectures/tensorflow_components/embedders/vector_embedder.py +++ b/rl_coach/architectures/tensorflow_components/embedders/vector_embedder.py @@ -31,10 +31,10 @@ class VectorEmbedder(InputEmbedder): """ def __init__(self, input_size: List[int], activation_function=tf.nn.relu, - scheme: EmbedderScheme=EmbedderScheme.Medium, batchnorm: bool=False, dropout: bool=False, - name: str= "embedder", input_rescaling: float=1.0, input_offset:float=0.0, input_clipping=None, + scheme: EmbedderScheme=EmbedderScheme.Medium, batchnorm: bool=False, dropout_rate: float=0.0, + name: str= "embedder", input_rescaling: float=1.0, input_offset: float=0.0, input_clipping=None, dense_layer=Dense, is_training=False): - super().__init__(input_size, activation_function, scheme, batchnorm, dropout, name, + super().__init__(input_size, activation_function, scheme, batchnorm, dropout_rate, name, input_rescaling, input_offset, input_clipping, dense_layer=dense_layer, is_training=is_training) diff --git a/rl_coach/architectures/tensorflow_components/layers.py b/rl_coach/architectures/tensorflow_components/layers.py index f9dc356..1156937 100644 --- a/rl_coach/architectures/tensorflow_components/layers.py +++ b/rl_coach/architectures/tensorflow_components/layers.py @@ -8,7 +8,7 @@ from rl_coach.architectures import layers from rl_coach.architectures.tensorflow_components import utils -def batchnorm_activation_dropout(input_layer, batchnorm, activation_function, dropout, dropout_rate, is_training, name): +def batchnorm_activation_dropout(input_layer, batchnorm, activation_function, dropout_rate, is_training, name): layers = [input_layer] # batchnorm @@ -26,7 +26,7 @@ def batchnorm_activation_dropout(input_layer, batchnorm, activation_function, dr ) # dropout - if dropout: + if dropout_rate > 0: layers.append( tf.layers.dropout(layers[-1], dropout_rate, name="{}_dropout".format(name), training=is_training) ) @@ -100,7 +100,7 @@ class BatchnormActivationDropout(layers.BatchnormActivationDropout): """ return batchnorm_activation_dropout(input_layer, batchnorm=self.batchnorm, activation_function=self.activation_function, - dropout=self.dropout_rate > 0, dropout_rate=self.dropout_rate, + dropout_rate=self.dropout_rate, is_training=is_training, name=name) @staticmethod diff --git a/rl_coach/architectures/tensorflow_components/middlewares/fc_middleware.py b/rl_coach/architectures/tensorflow_components/middlewares/fc_middleware.py index f85db82..816674a 100644 --- a/rl_coach/architectures/tensorflow_components/middlewares/fc_middleware.py +++ b/rl_coach/architectures/tensorflow_components/middlewares/fc_middleware.py @@ -27,10 +27,11 @@ from rl_coach.utils import force_list class FCMiddleware(Middleware): def __init__(self, activation_function=tf.nn.relu, scheme: MiddlewareScheme = MiddlewareScheme.Medium, - batchnorm: bool = False, dropout: bool = False, + batchnorm: bool = False, dropout_rate: float = 0.0, name="middleware_fc_embedder", dense_layer=Dense, is_training=False): super().__init__(activation_function=activation_function, batchnorm=batchnorm, - dropout=dropout, scheme=scheme, name=name, dense_layer=dense_layer, is_training=is_training) + dropout_rate=dropout_rate, scheme=scheme, name=name, dense_layer=dense_layer, + is_training=is_training) self.return_type = Middleware_FC_Embedding self.layers = [] diff --git a/rl_coach/architectures/tensorflow_components/middlewares/lstm_middleware.py b/rl_coach/architectures/tensorflow_components/middlewares/lstm_middleware.py index 7c4a1b0..6b7f97d 100644 --- a/rl_coach/architectures/tensorflow_components/middlewares/lstm_middleware.py +++ b/rl_coach/architectures/tensorflow_components/middlewares/lstm_middleware.py @@ -28,10 +28,11 @@ from rl_coach.utils import force_list class LSTMMiddleware(Middleware): def __init__(self, activation_function=tf.nn.relu, number_of_lstm_cells: int=256, scheme: MiddlewareScheme = MiddlewareScheme.Medium, - batchnorm: bool = False, dropout: bool = False, + batchnorm: bool = False, dropout_rate: float = 0.0, name="middleware_lstm_embedder", dense_layer=Dense, is_training=False): super().__init__(activation_function=activation_function, batchnorm=batchnorm, - dropout=dropout, scheme=scheme, name=name, dense_layer=dense_layer, is_training=is_training) + dropout_rate=dropout_rate, scheme=scheme, name=name, dense_layer=dense_layer, + is_training=is_training) self.return_type = Middleware_LSTM_Embedding self.number_of_lstm_cells = number_of_lstm_cells self.layers = [] diff --git a/rl_coach/architectures/tensorflow_components/middlewares/middleware.py b/rl_coach/architectures/tensorflow_components/middlewares/middleware.py index bb10ea9..17c6a2f 100644 --- a/rl_coach/architectures/tensorflow_components/middlewares/middleware.py +++ b/rl_coach/architectures/tensorflow_components/middlewares/middleware.py @@ -31,15 +31,14 @@ class Middleware(object): """ def __init__(self, activation_function=tf.nn.relu, scheme: MiddlewareScheme = MiddlewareScheme.Medium, - batchnorm: bool = False, dropout: bool = False, name="middleware_embedder", dense_layer=Dense, + batchnorm: bool = False, dropout_rate: float = 0.0, name="middleware_embedder", dense_layer=Dense, is_training=False): self.name = name self.input = None self.output = None self.activation_function = activation_function self.batchnorm = batchnorm - self.dropout = dropout - self.dropout_rate = 0 + self.dropout_rate = dropout_rate self.scheme = scheme self.return_type = MiddlewareEmbedding self.dense_layer = dense_layer @@ -58,7 +57,7 @@ class Middleware(object): # we allow adding batchnorm, dropout or activation functions after each layer. # The motivation is to simplify the transition between a network with batchnorm and a network without # batchnorm to a single flag (the same applies to activation function and dropout) - if self.batchnorm or self.activation_function or self.dropout: + if self.batchnorm or self.activation_function or self.dropout_rate > 0: for layer_idx in reversed(range(len(self.layers_params))): self.layers_params.insert(layer_idx+1, BatchnormActivationDropout(batchnorm=self.batchnorm, From 3fd433ffabdaf18581ec16a3d9fae5985b740872 Mon Sep 17 00:00:00 2001 From: Itai Caspi <30383381+itaicaspi-intel@users.noreply.github.com> Date: Fri, 9 Nov 2018 18:17:04 +0200 Subject: [PATCH 105/162] fix ddpg head (#78) --- .../tensorflow_components/heads/ddpg_actor_head.py | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/rl_coach/architectures/tensorflow_components/heads/ddpg_actor_head.py b/rl_coach/architectures/tensorflow_components/heads/ddpg_actor_head.py index 6b3112a..45545b4 100644 --- a/rl_coach/architectures/tensorflow_components/heads/ddpg_actor_head.py +++ b/rl_coach/architectures/tensorflow_components/heads/ddpg_actor_head.py @@ -46,9 +46,12 @@ class DDPGActor(Head): def _build_module(self, input_layer): # mean pre_activation_policy_values_mean = self.dense_layer(self.num_actions)(input_layer, name='fc_mean') - policy_values_mean = batchnorm_activation_dropout(pre_activation_policy_values_mean, self.batchnorm, - self.activation_function, - False, 0, is_training=False, name="BatchnormActivationDropout_0")[-1] + policy_values_mean = batchnorm_activation_dropout(input_layer=pre_activation_policy_values_mean, + batchnorm=self.batchnorm, + activation_function=self.activation_function, + dropout_rate=0, + is_training=False, + name="BatchnormActivationDropout_0")[-1] self.policy_mean = tf.multiply(policy_values_mean, self.output_scale, name='output_mean') if self.is_local: From 2804a7c24f7eecf3dccd6ff3fdc347ebb5887dd2 Mon Sep 17 00:00:00 2001 From: Leo Dirac Date: Sat, 10 Nov 2018 12:10:19 -0800 Subject: [PATCH 106/162] Refactor launcher to be object-oriented (#63) * Import of annoy library uses failed_import mechanism. --- rl_coach/coach.py | 763 ++++++++++-------- .../differentiable_neural_dictionary.py | 9 +- 2 files changed, 417 insertions(+), 355 deletions(-) diff --git a/rl_coach/coach.py b/rl_coach/coach.py index f750d2d..3c5c1bd 100644 --- a/rl_coach/coach.py +++ b/rl_coach/coach.py @@ -51,200 +51,6 @@ if len(set(failed_imports)) > 0: screen.warning("Warning: failed to import the following packages - {}".format(', '.join(set(failed_imports)))) -def get_graph_manager_from_args(args: argparse.Namespace) -> 'GraphManager': - """ - Return the graph manager according to the command line arguments given by the user - :param args: the arguments given by the user - :return: the updated graph manager - """ - - graph_manager = None - - # if a preset was given we will load the graph manager for the preset - if args.preset is not None: - graph_manager = short_dynamic_import(args.preset, ignore_module_case=True) - - # for human play we need to create a custom graph manager - if args.play: - env_params = short_dynamic_import(args.environment_type, ignore_module_case=True)() - env_params.human_control = True - schedule_params = HumanPlayScheduleParameters() - graph_manager = BasicRLGraphManager(HumanAgentParameters(), env_params, schedule_params, VisualizationParameters()) - - # Set framework - # Note: Some graph managers (e.g. HAC preset) create multiple agents and the attribute is called agents_params - if hasattr(graph_manager, 'agent_params'): - for network_parameters in graph_manager.agent_params.network_wrappers.values(): - network_parameters.framework = args.framework - elif hasattr(graph_manager, 'agents_params'): - for ap in graph_manager.agents_params: - for network_parameters in ap.network_wrappers.values(): - network_parameters.framework = args.framework - - if args.level: - if isinstance(graph_manager.env_params.level, SingleLevelSelection): - graph_manager.env_params.level.select(args.level) - else: - graph_manager.env_params.level = args.level - - # set the seed for the environment - if args.seed is not None: - graph_manager.env_params.seed = args.seed - - # visualization - graph_manager.visualization_parameters.dump_gifs = graph_manager.visualization_parameters.dump_gifs or args.dump_gifs - graph_manager.visualization_parameters.dump_mp4 = graph_manager.visualization_parameters.dump_mp4 or args.dump_mp4 - graph_manager.visualization_parameters.render = args.render - graph_manager.visualization_parameters.tensorboard = args.tensorboard - graph_manager.visualization_parameters.print_networks_summary = args.print_networks_summary - - # update the custom parameters - if args.custom_parameter is not None: - unstripped_key_value_pairs = [pair.split('=') for pair in args.custom_parameter.split(';')] - stripped_key_value_pairs = [tuple([pair[0].strip(), pair[1].strip()]) for pair in - unstripped_key_value_pairs if len(pair) == 2] - - # load custom parameters into run_dict - for key, value in stripped_key_value_pairs: - exec("graph_manager.{}={}".format(key, value)) - - return graph_manager - - -def display_all_presets_and_exit(): - # list available presets - screen.log_title("Available Presets:") - for preset in sorted(list_all_presets()): - print(preset) - sys.exit(0) - - -def expand_preset(preset): - if preset.lower() in [p.lower() for p in list_all_presets()]: - preset = "{}.py:graph_manager".format(os.path.join(get_base_dir(), 'presets', preset)) - else: - preset = "{}".format(preset) - # if a graph manager variable was not specified, try the default of :graph_manager - if len(preset.split(":")) == 1: - preset += ":graph_manager" - - # verify that the preset exists - preset_path = preset.split(":")[0] - if not os.path.exists(preset_path): - screen.error("The given preset ({}) cannot be found.".format(preset)) - - # verify that the preset can be instantiated - try: - short_dynamic_import(preset, ignore_module_case=True) - except TypeError as e: - traceback.print_exc() - screen.error('Internal Error: ' + str(e) + "\n\nThe given preset ({}) cannot be instantiated." - .format(preset)) - - return preset - - -def parse_arguments(parser: argparse.ArgumentParser) -> argparse.Namespace: - """ - Parse the arguments that the user entered - :param parser: the argparse command line parser - :return: the parsed arguments - """ - args = parser.parse_args() - - # if no arg is given - if len(sys.argv) == 1: - parser.print_help() - sys.exit(0) - - # list available presets - if args.list: - display_all_presets_and_exit() - - # Read args from config file for distributed Coach. - if args.distributed_coach and args.distributed_coach_run_type == RunType.ORCHESTRATOR: - coach_config = ConfigParser({ - 'image': '', - 'memory_backend': 'redispubsub', - 'data_store': 's3', - 's3_end_point': 's3.amazonaws.com', - 's3_bucket_name': '', - 's3_creds_file': '' - }) - try: - coach_config.read(args.distributed_coach_config_path) - args.image = coach_config.get('coach', 'image') - args.memory_backend = coach_config.get('coach', 'memory_backend') - args.data_store = coach_config.get('coach', 'data_store') - args.s3_end_point = coach_config.get('coach', 's3_end_point') - args.s3_bucket_name = coach_config.get('coach', 's3_bucket_name') - args.s3_creds_file = coach_config.get('coach', 's3_creds_file') - except Error as e: - screen.error("Error when reading distributed Coach config file: {}".format(e)) - - if args.image == '': - screen.error("Image cannot be empty.") - - data_store_choices = ['s3'] - if args.data_store not in data_store_choices: - screen.warning("{} data store is unsupported.".format(args.data_store)) - screen.error("Supported data stores are {}.".format(data_store_choices)) - - memory_backend_choices = ['redispubsub'] - if args.memory_backend not in memory_backend_choices: - screen.warning("{} memory backend is not supported.".format(args.memory_backend)) - screen.error("Supported memory backends are {}.".format(memory_backend_choices)) - - if args.s3_bucket_name == '': - screen.error("S3 bucket name cannot be empty.") - - if args.s3_creds_file == '': - args.s3_creds_file = None - - if args.play and args.distributed_coach: - screen.error("Playing is not supported in distributed Coach.") - - # replace a short preset name with the full path - if args.preset is not None: - args.preset = expand_preset(args.preset) - - # validate the checkpoints args - if args.checkpoint_restore_dir is not None and not os.path.exists(args.checkpoint_restore_dir): - screen.error("The requested checkpoint folder to load from does not exist.") - - # no preset was given. check if the user requested to play some environment on its own - if args.preset is None and args.play and not args.environment_type: - screen.error('When no preset is given for Coach to run, and the user requests human control over ' - 'the environment, the user is expected to input the desired environment_type and level.' - '\nAt least one of these parameters was not given.') - elif args.preset and args.play: - screen.error("Both the --preset and the --play flags were set. These flags can not be used together. " - "For human control, please use the --play flag together with the environment type flag (-et)") - elif args.preset is None and not args.play: - screen.error("Please choose a preset using the -p flag or use the --play flag together with choosing an " - "environment type (-et) in order to play the game.") - - # get experiment name and path - args.experiment_name = logger.get_experiment_name(args.experiment_name) - args.experiment_path = logger.get_experiment_path(args.experiment_name) - - if args.play and args.num_workers > 1: - screen.warning("Playing the game as a human is only available with a single worker. " - "The number of workers will be reduced to 1") - args.num_workers = 1 - - args.framework = Frameworks[args.framework.lower()] - - # checkpoints - args.checkpoint_save_dir = os.path.join(args.experiment_path, 'checkpoint') if args.checkpoint_save_secs is not None else None - - if args.export_onnx_graph and not args.checkpoint_save_secs: - screen.warning("Exporting ONNX graphs requires setting the --checkpoint_save_secs flag. " - "The --export_onnx_graph will have no effect.") - - return args - - def add_items_to_dict(target_dict, source_dict): updated_task_parameters = copy.copy(source_dict) updated_task_parameters.update(target_dict) @@ -263,6 +69,10 @@ def open_dashboard(experiment_path): def start_graph(graph_manager: 'GraphManager', task_parameters: 'TaskParameters'): + """ + Runs the graph_manager using the configured task_parameters. + This stand-alone method is a convenience for multiprocessing. + """ graph_manager.create_graph(task_parameters) # let the adventure begin @@ -360,172 +170,419 @@ def handle_distributed_coach_orchestrator(graph_manager, args): orchestrator.undeploy() -def main(): - parser = argparse.ArgumentParser() - parser.add_argument('-p', '--preset', - help="(string) Name of a preset to run (class name from the 'presets' directory.)", - default=None, - type=str) - parser.add_argument('-l', '--list', - help="(flag) List all available presets", - action='store_true') - parser.add_argument('-e', '--experiment_name', - help="(string) Experiment name to be used to store the results.", - default='', - type=str) - parser.add_argument('-r', '--render', - help="(flag) Render environment", - action='store_true') - parser.add_argument('-f', '--framework', - help="(string) Neural network framework. Available values: tensorflow", - default='tensorflow', - type=str) - parser.add_argument('-n', '--num_workers', - help="(int) Number of workers for multi-process based agents, e.g. A3C", - default=1, - type=int) - parser.add_argument('-c', '--use_cpu', - help="(flag) Use only the cpu for training. If a GPU is not available, this flag will have no " - "effect and the CPU will be used either way.", - action='store_true') - parser.add_argument('-ew', '--evaluation_worker', - help="(int) If multiple workers are used, add an evaluation worker as well which will " - "evaluate asynchronously and independently during the training. NOTE: this worker will " - "ignore the evaluation settings in the preset's ScheduleParams.", - action='store_true') - parser.add_argument('--play', - help="(flag) Play as a human by controlling the game with the keyboard. " - "This option will save a replay buffer with the game play.", - action='store_true') - parser.add_argument('--evaluate', - help="(flag) Run evaluation only. This is a convenient way to disable " - "training in order to evaluate an existing checkpoint.", - action='store_true') - parser.add_argument('-v', '--verbosity', - help="(flag) Sets the verbosity level of Coach print outs. Can be either low or high.", - default="low", - type=str) - parser.add_argument('-tfv', '--tf_verbosity', - help="(flag) TensorFlow verbosity level", - default=3, - type=int) - parser.add_argument('--nocolor', - help="(flag) Turn off color-codes in screen logging. Ascii text only", - action='store_true') - parser.add_argument('-s', '--checkpoint_save_secs', - help="(int) Time in seconds between saving checkpoints of the model.", - default=None, - type=int) - parser.add_argument('-crd', '--checkpoint_restore_dir', - help='(string) Path to a folder containing a checkpoint to restore the model from.', - type=str) - parser.add_argument('-dg', '--dump_gifs', - help="(flag) Enable the gif saving functionality.", - action='store_true') - parser.add_argument('-dm', '--dump_mp4', - help="(flag) Enable the mp4 saving functionality.", - action='store_true') - parser.add_argument('-et', '--environment_type', - help="(string) Choose an environment type class to override on top of the selected preset.", - default=None, - type=str) - parser.add_argument('-lvl', '--level', - help="(string) Choose the level that will be played in the environment that was selected." - "This value will override the level parameter in the environment class." - , - default=None, - type=str) - parser.add_argument('-cp', '--custom_parameter', - help="(string) Semicolon separated parameters used to override specific parameters on top of" - " the selected preset (or on top of the command-line assembled one). " - "Whenever a parameter value is a string, it should be inputted as '\\\"string\\\"'. " - "For ex.: " - "\"visualization.render=False; num_training_iterations=500; optimizer='rmsprop'\"", - default=None, - type=str) - parser.add_argument('--print_networks_summary', - help="(flag) Print network summary to stdout", - action='store_true') - parser.add_argument('-tb', '--tensorboard', - help="(flag) When using the TensorFlow backend, enable TensorBoard log dumps. ", - action='store_true') - parser.add_argument('-ns', '--no_summary', - help="(flag) Prevent Coach from printing a summary and asking questions at the end of runs", - action='store_true') - parser.add_argument('-d', '--open_dashboard', - help="(flag) Open dashboard with the experiment when the run starts", - action='store_true') - parser.add_argument('--seed', - help="(int) A seed to use for running the experiment", - default=None, - type=int) - parser.add_argument('-onnx', '--export_onnx_graph', - help="(flag) Export the ONNX graph to the experiment directory. " - "This will have effect only if the --checkpoint_save_secs flag is used in order to store " - "checkpoints, since the weights checkpoint are needed for the ONNX graph. " - "Keep in mind that this can cause major overhead on the experiment. " - "Exporting ONNX graphs requires manually installing the tf2onnx package " - "(https://github.com/onnx/tensorflow-onnx).", - action='store_true') - parser.add_argument('-dc', '--distributed_coach', - help="(flag) Use distributed Coach.", - action='store_true') - parser.add_argument('-dcp', '--distributed_coach_config_path', - help="(string) Path to config file when using distributed rollout workers." - "Only distributed Coach parameters should be provided through this config file." - "Rest of the parameters are provided using Coach command line options." - "Used only with --distributed_coach flag." - "Ignored if --distributed_coach flag is not used.", - type=str) - parser.add_argument('--memory_backend_params', - help=argparse.SUPPRESS, - type=str) - parser.add_argument('--data_store_params', - help=argparse.SUPPRESS, - type=str) - parser.add_argument('--distributed_coach_run_type', - help=argparse.SUPPRESS, - type=RunType, - default=RunType.ORCHESTRATOR, - choices=list(RunType)) +class CoachLauncher(object): + """ + This class is responsible for gathering all user-specified configuration options, parsing them, + instantiating a GraphManager and then starting that GraphManager with either improve() or evaluate(). + This class is also responsible for launching multiple processes. + It is structured so that it can be sub-classed to provide alternate mechanisms to configure and launch + Coach jobs. - args = parse_arguments(parser) + The key entry-point for this class is the .launch() method which is expected to be called from __main__ + and handle absolutely everything for a job. + """ - if args.nocolor: - screen.set_use_colors(False) + def launch(self): + """ + Main entry point for the class, and the standard way to run coach from the command line. + Parses command-line arguments through argparse, instantiates a GraphManager and then runs it. + """ + parser = self.get_argument_parser() + args = self.get_config_args(parser) + graph_manager = self.get_graph_manager_from_args(args) + self.run_graph_manager(graph_manager, args) - graph_manager = get_graph_manager_from_args(args) + + def get_graph_manager_from_args(self, args: argparse.Namespace) -> 'GraphManager': + """ + Return the graph manager according to the command line arguments given by the user. + :param args: the arguments given by the user + :return: the graph manager, not bound to task_parameters yet. + """ + graph_manager = None - if args.distributed_coach and not graph_manager.agent_params.algorithm.distributed_coach_synchronization_type: - screen.error("{} preset is not supported using distributed Coach.".format(args.preset)) + # if a preset was given we will load the graph manager for the preset + if args.preset is not None: + graph_manager = short_dynamic_import(args.preset, ignore_module_case=True) - # Intel optimized TF seems to run significantly faster when limiting to a single OMP thread. - # This will not affect GPU runs. - os.environ["OMP_NUM_THREADS"] = "1" + # for human play we need to create a custom graph manager + if args.play: + env_params = short_dynamic_import(args.environment_type, ignore_module_case=True)() + env_params.human_control = True + schedule_params = HumanPlayScheduleParameters() + graph_manager = BasicRLGraphManager(HumanAgentParameters(), env_params, schedule_params, VisualizationParameters()) - # turn TF debug prints off - if args.framework == Frameworks.tensorflow: - os.environ['TF_CPP_MIN_LOG_LEVEL'] = str(args.tf_verbosity) + # Set framework + # Note: Some graph managers (e.g. HAC preset) create multiple agents and the attribute is called agents_params + if hasattr(graph_manager, 'agent_params'): + for network_parameters in graph_manager.agent_params.network_wrappers.values(): + network_parameters.framework = args.framework + elif hasattr(graph_manager, 'agents_params'): + for ap in graph_manager.agents_params: + for network_parameters in ap.network_wrappers.values(): + network_parameters.framework = args.framework - # turn off the summary at the end of the run if necessary - if not args.no_summary and not args.distributed_coach: - atexit.register(logger.summarize_experiment) - screen.change_terminal_title(args.experiment_name) + if args.level: + if isinstance(graph_manager.env_params.level, SingleLevelSelection): + graph_manager.env_params.level.select(args.level) + else: + graph_manager.env_params.level = args.level - # open dashboard - if args.open_dashboard: - open_dashboard(args.experiment_path) + # set the seed for the environment + if args.seed is not None: + graph_manager.env_params.seed = args.seed - if args.distributed_coach and args.distributed_coach_run_type != RunType.ORCHESTRATOR: - handle_distributed_coach_tasks(graph_manager, args) - return + # visualization + graph_manager.visualization_parameters.dump_gifs = graph_manager.visualization_parameters.dump_gifs or args.dump_gifs + graph_manager.visualization_parameters.dump_mp4 = graph_manager.visualization_parameters.dump_mp4 or args.dump_mp4 + graph_manager.visualization_parameters.render = args.render + graph_manager.visualization_parameters.tensorboard = args.tensorboard + graph_manager.visualization_parameters.print_networks_summary = args.print_networks_summary - if args.distributed_coach and args.distributed_coach_run_type == RunType.ORCHESTRATOR: - handle_distributed_coach_orchestrator(graph_manager, args) - return + # update the custom parameters + if args.custom_parameter is not None: + unstripped_key_value_pairs = [pair.split('=') for pair in args.custom_parameter.split(';')] + stripped_key_value_pairs = [tuple([pair[0].strip(), pair[1].strip()]) for pair in + unstripped_key_value_pairs if len(pair) == 2] - # Single-threaded runs - if args.num_workers == 1: + # load custom parameters into run_dict + for key, value in stripped_key_value_pairs: + exec("graph_manager.{}={}".format(key, value)) + + return graph_manager + + + def display_all_presets_and_exit(self): + # list available presets + screen.log_title("Available Presets:") + for preset in sorted(list_all_presets()): + print(preset) + sys.exit(0) + + + def expand_preset(self, preset): + """ + Replace a short preset name with the full python path, and verify that it can be imported. + """ + if preset.lower() in [p.lower() for p in list_all_presets()]: + preset = "{}.py:graph_manager".format(os.path.join(get_base_dir(), 'presets', preset)) + else: + preset = "{}".format(preset) + # if a graph manager variable was not specified, try the default of :graph_manager + if len(preset.split(":")) == 1: + preset += ":graph_manager" + + # verify that the preset exists + preset_path = preset.split(":")[0] + if not os.path.exists(preset_path): + screen.error("The given preset ({}) cannot be found.".format(preset)) + + # verify that the preset can be instantiated + try: + short_dynamic_import(preset, ignore_module_case=True) + except TypeError as e: + traceback.print_exc() + screen.error('Internal Error: ' + str(e) + "\n\nThe given preset ({}) cannot be instantiated." + .format(preset)) + + return preset + + + def get_config_args(self, parser: argparse.ArgumentParser) -> argparse.Namespace: + """ + Returns a Namespace object with all the user-specified configuration options needed to launch. + This implementation uses argparse to take arguments from the CLI, but this can be over-ridden by + another method that gets its configuration from elsewhere. An equivalent method however must + return an identically structured Namespace object, which conforms to the structure defined by + get_argument_parser. + + This method parses the arguments that the user entered, does some basic validation, and + modification of user-specified values in short form to be more explicit. + + :param parser: a parser object which implicitly defines the format of the Namespace that + is expected to be returned. + :return: the parsed arguments as a Namespace + """ + args = parser.parse_args() + + if args.nocolor: + screen.set_use_colors(False) + + # if no arg is given + if len(sys.argv) == 1: + parser.print_help() + sys.exit(0) + + # list available presets + if args.list: + self.display_all_presets_and_exit() + + + # Read args from config file for distributed Coach. + if args.distributed_coach and args.distributed_coach_run_type == RunType.ORCHESTRATOR: + coach_config = ConfigParser({ + 'image': '', + 'memory_backend': 'redispubsub', + 'data_store': 's3', + 's3_end_point': 's3.amazonaws.com', + 's3_bucket_name': '', + 's3_creds_file': '' + }) + try: + coach_config.read(args.distributed_coach_config_path) + args.image = coach_config.get('coach', 'image') + args.memory_backend = coach_config.get('coach', 'memory_backend') + args.data_store = coach_config.get('coach', 'data_store') + args.s3_end_point = coach_config.get('coach', 's3_end_point') + args.s3_bucket_name = coach_config.get('coach', 's3_bucket_name') + args.s3_creds_file = coach_config.get('coach', 's3_creds_file') + except Error as e: + screen.error("Error when reading distributed Coach config file: {}".format(e)) + + if args.image == '': + screen.error("Image cannot be empty.") + + data_store_choices = ['s3'] + if args.data_store not in data_store_choices: + screen.warning("{} data store is unsupported.".format(args.data_store)) + screen.error("Supported data stores are {}.".format(data_store_choices)) + + memory_backend_choices = ['redispubsub'] + if args.memory_backend not in memory_backend_choices: + screen.warning("{} memory backend is not supported.".format(args.memory_backend)) + screen.error("Supported memory backends are {}.".format(memory_backend_choices)) + + if args.s3_bucket_name == '': + screen.error("S3 bucket name cannot be empty.") + + if args.s3_creds_file == '': + args.s3_creds_file = None + + if args.play and args.distributed_coach: + screen.error("Playing is not supported in distributed Coach.") + + # replace a short preset name with the full path + if args.preset is not None: + args.preset = self.expand_preset(args.preset) + + # validate the checkpoints args + if args.checkpoint_restore_dir is not None and not os.path.exists(args.checkpoint_restore_dir): + screen.error("The requested checkpoint folder to load from does not exist.") + + # no preset was given. check if the user requested to play some environment on its own + if args.preset is None and args.play and not args.environment_type: + screen.error('When no preset is given for Coach to run, and the user requests human control over ' + 'the environment, the user is expected to input the desired environment_type and level.' + '\nAt least one of these parameters was not given.') + elif args.preset and args.play: + screen.error("Both the --preset and the --play flags were set. These flags can not be used together. " + "For human control, please use the --play flag together with the environment type flag (-et)") + elif args.preset is None and not args.play: + screen.error("Please choose a preset using the -p flag or use the --play flag together with choosing an " + "environment type (-et) in order to play the game.") + + # get experiment name and path + args.experiment_name = logger.get_experiment_name(args.experiment_name) + args.experiment_path = logger.get_experiment_path(args.experiment_name) + + if args.play and args.num_workers > 1: + screen.warning("Playing the game as a human is only available with a single worker. " + "The number of workers will be reduced to 1") + args.num_workers = 1 + + args.framework = Frameworks[args.framework.lower()] + + # checkpoints + args.checkpoint_save_dir = os.path.join(args.experiment_path, 'checkpoint') if args.checkpoint_save_secs is not None else None + + if args.export_onnx_graph and not args.checkpoint_save_secs: + screen.warning("Exporting ONNX graphs requires setting the --checkpoint_save_secs flag. " + "The --export_onnx_graph will have no effect.") + + return args + + + def get_argument_parser(self) -> argparse.ArgumentParser: + """ + This returns an ArgumentParser object which defines the set of options that customers are expected to supply in order + to launch a coach job. + """ + parser = argparse.ArgumentParser() + parser.add_argument('-p', '--preset', + help="(string) Name of a preset to run (class name from the 'presets' directory.)", + default=None, + type=str) + parser.add_argument('-l', '--list', + help="(flag) List all available presets", + action='store_true') + parser.add_argument('-e', '--experiment_name', + help="(string) Experiment name to be used to store the results.", + default='', + type=str) + parser.add_argument('-r', '--render', + help="(flag) Render environment", + action='store_true') + parser.add_argument('-f', '--framework', + help="(string) Neural network framework. Available values: tensorflow", + default='tensorflow', + type=str) + parser.add_argument('-n', '--num_workers', + help="(int) Number of workers for multi-process based agents, e.g. A3C", + default=1, + type=int) + parser.add_argument('-c', '--use_cpu', + help="(flag) Use only the cpu for training. If a GPU is not available, this flag will have no " + "effect and the CPU will be used either way.", + action='store_true') + parser.add_argument('-ew', '--evaluation_worker', + help="(int) If multiple workers are used, add an evaluation worker as well which will " + "evaluate asynchronously and independently during the training. NOTE: this worker will " + "ignore the evaluation settings in the preset's ScheduleParams.", + action='store_true') + parser.add_argument('--play', + help="(flag) Play as a human by controlling the game with the keyboard. " + "This option will save a replay buffer with the game play.", + action='store_true') + parser.add_argument('--evaluate', + help="(flag) Run evaluation only. This is a convenient way to disable " + "training in order to evaluate an existing checkpoint.", + action='store_true') + parser.add_argument('-v', '--verbosity', + help="(flag) Sets the verbosity level of Coach print outs. Can be either low or high.", + default="low", + type=str) + parser.add_argument('-tfv', '--tf_verbosity', + help="(flag) TensorFlow verbosity level", + default=3, + type=int) + parser.add_argument('--nocolor', + help="(flag) Turn off color-codes in screen logging. Ascii text only", + action='store_true') + parser.add_argument('-s', '--checkpoint_save_secs', + help="(int) Time in seconds between saving checkpoints of the model.", + default=None, + type=int) + parser.add_argument('-crd', '--checkpoint_restore_dir', + help='(string) Path to a folder containing a checkpoint to restore the model from.', + type=str) + parser.add_argument('-dg', '--dump_gifs', + help="(flag) Enable the gif saving functionality.", + action='store_true') + parser.add_argument('-dm', '--dump_mp4', + help="(flag) Enable the mp4 saving functionality.", + action='store_true') + parser.add_argument('-et', '--environment_type', + help="(string) Choose an environment type class to override on top of the selected preset.", + default=None, + type=str) + parser.add_argument('-ept', '--exploration_policy_type', + help="(string) Choose an exploration policy type class to override on top of the selected " + "preset." + "If no preset is defined, a preset can be set from the command-line by combining settings " + "which are set by using --agent_type, --experiment_type, --environemnt_type" + , + default=None, + type=str) + parser.add_argument('-lvl', '--level', + help="(string) Choose the level that will be played in the environment that was selected." + "This value will override the level parameter in the environment class." + , + default=None, + type=str) + parser.add_argument('-cp', '--custom_parameter', + help="(string) Semicolon separated parameters used to override specific parameters on top of" + " the selected preset (or on top of the command-line assembled one). " + "Whenever a parameter value is a string, it should be inputted as '\\\"string\\\"'. " + "For ex.: " + "\"visualization.render=False; num_training_iterations=500; optimizer='rmsprop'\"", + default=None, + type=str) + parser.add_argument('--print_networks_summary', + help="(flag) Print network summary to stdout", + action='store_true') + parser.add_argument('-tb', '--tensorboard', + help="(flag) When using the TensorFlow backend, enable TensorBoard log dumps. ", + action='store_true') + parser.add_argument('-ns', '--no_summary', + help="(flag) Prevent Coach from printing a summary and asking questions at the end of runs", + action='store_true') + parser.add_argument('-d', '--open_dashboard', + help="(flag) Open dashboard with the experiment when the run starts", + action='store_true') + parser.add_argument('--seed', + help="(int) A seed to use for running the experiment", + default=None, + type=int) + parser.add_argument('-onnx', '--export_onnx_graph', + help="(flag) Export the ONNX graph to the experiment directory. " + "This will have effect only if the --checkpoint_save_secs flag is used in order to store " + "checkpoints, since the weights checkpoint are needed for the ONNX graph. " + "Keep in mind that this can cause major overhead on the experiment. " + "Exporting ONNX graphs requires manually installing the tf2onnx package " + "(https://github.com/onnx/tensorflow-onnx).", + action='store_true') + parser.add_argument('-dc', '--distributed_coach', + help="(flag) Use distributed Coach.", + action='store_true') + parser.add_argument('-dcp', '--distributed_coach_config_path', + help="(string) Path to config file when using distributed rollout workers." + "Only distributed Coach parameters should be provided through this config file." + "Rest of the parameters are provided using Coach command line options." + "Used only with --distributed_coach flag." + "Ignored if --distributed_coach flag is not used.", + type=str) + parser.add_argument('--memory_backend_params', + help=argparse.SUPPRESS, + type=str) + parser.add_argument('--data_store_params', + help=argparse.SUPPRESS, + type=str) + parser.add_argument('--distributed_coach_run_type', + help=argparse.SUPPRESS, + type=RunType, + default=RunType.ORCHESTRATOR, + choices=list(RunType)) + + return parser + + + def run_graph_manager(self, graph_manager: 'GraphManager', args: argparse.Namespace): + if args.distributed_coach and not graph_manager.agent_params.algorithm.distributed_coach_synchronization_type: + screen.error("{} algorithm is not supported using distributed Coach.".format(graph_manager.agent_params.algorithm)) + + # Intel optimized TF seems to run significantly faster when limiting to a single OMP thread. + # This will not affect GPU runs. + os.environ["OMP_NUM_THREADS"] = "1" + + # turn TF debug prints off + if args.framework == Frameworks.tensorflow: + os.environ['TF_CPP_MIN_LOG_LEVEL'] = str(args.tf_verbosity) + + # turn off the summary at the end of the run if necessary + if not args.no_summary and not args.distributed_coach: + atexit.register(logger.summarize_experiment) + screen.change_terminal_title(args.experiment_name) + + # open dashboard + if args.open_dashboard: + open_dashboard(args.experiment_path) + + if args.distributed_coach and args.distributed_coach_run_type != RunType.ORCHESTRATOR: + handle_distributed_coach_tasks(graph_manager, args) + return + + if args.distributed_coach and args.distributed_coach_run_type == RunType.ORCHESTRATOR: + handle_distributed_coach_orchestrator(graph_manager, args) + return + + # Single-threaded runs + if args.num_workers == 1: + self.start_single_threaded(graph_manager, args) + else: + self.start_multi_threaded(graph_manager, args) + + + def start_single_threaded(self, graph_manager: 'GraphManager', args: argparse.Namespace): # Start the training or evaluation task_parameters = TaskParameters( framework_type=args.framework, @@ -541,8 +598,8 @@ def main(): start_graph(graph_manager=graph_manager, task_parameters=task_parameters) - # Multi-threaded runs - else: + + def start_multi_threaded(self, graph_manager: 'GraphManager', args: argparse.Namespace): total_tasks = args.num_workers if args.evaluation_worker: total_tasks += 1 @@ -578,7 +635,6 @@ def main(): checkpoint_save_dir=args.checkpoint_save_dir, export_onnx_graph=args.export_onnx_graph ) - # we assume that only the evaluation workers are rendering graph_manager.visualization_parameters.render = args.render and evaluation_worker p = Process(target=start_graph, args=(graph_manager, task_parameters)) @@ -608,4 +664,5 @@ def main(): if __name__ == "__main__": - main() + launcher = CoachLauncher() + launcher.launch() diff --git a/rl_coach/memories/non_episodic/differentiable_neural_dictionary.py b/rl_coach/memories/non_episodic/differentiable_neural_dictionary.py index 19d4364..456bc96 100644 --- a/rl_coach/memories/non_episodic/differentiable_neural_dictionary.py +++ b/rl_coach/memories/non_episodic/differentiable_neural_dictionary.py @@ -18,7 +18,12 @@ import os import pickle import numpy as np -from annoy import AnnoyIndex +try: + import annoy + from annoy import AnnoyIndex +except ImportError: + from rl_coach.logger import failed_imports + failed_imports.append("annoy") class AnnoyDictionary(object): @@ -283,4 +288,4 @@ def load_dnd(model_dir): DND.dicts[a].index.build(50) - return DND \ No newline at end of file + return DND From 0fe583186edcc6f86cd0976eeb4dcc6ad7f75453 Mon Sep 17 00:00:00 2001 From: Itai Caspi <30383381+itaicaspi-intel@users.noreply.github.com> Date: Mon, 12 Nov 2018 20:26:49 +0200 Subject: [PATCH 107/162] fixing the coach entrypoint after adding the CoachLauncher abstraction (#92) --- rl_coach/coach.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/rl_coach/coach.py b/rl_coach/coach.py index 3c5c1bd..00e8b97 100644 --- a/rl_coach/coach.py +++ b/rl_coach/coach.py @@ -663,6 +663,10 @@ class CoachLauncher(object): evaluation_worker.terminate() -if __name__ == "__main__": +def main(): launcher = CoachLauncher() launcher.launch() + + +if __name__ == "__main__": + main() From 875d6ef017acbefbbbe28a2159430310aa8c7467 Mon Sep 17 00:00:00 2001 From: Ajay Deshpande Date: Mon, 12 Nov 2018 15:03:43 -0800 Subject: [PATCH 108/162] Adding target reward and target sucess (#58) * Adding target reward * Adding target successs * Addressing comments * Using custom_reward_threshold and target_success_rate * Adding exit message * Moving success rate to environment * Making target_success_rate optional --- rl_coach/agents/agent.py | 6 +- rl_coach/base_parameters.py | 2 +- rl_coach/data_stores/data_store.py | 8 ++ rl_coach/data_stores/nfs_data_store.py | 14 ++-- rl_coach/data_stores/s3_data_store.py | 22 +++-- rl_coach/environments/carla_environment.py | 9 ++- .../environments/control_suite_environment.py | 9 ++- rl_coach/environments/doom_environment.py | 11 ++- rl_coach/environments/environment.py | 10 ++- rl_coach/environments/gym_environment.py | 11 ++- .../environments/starcraft2_environment.py | 13 ++- rl_coach/graph_managers/graph_manager.py | 21 ++++- rl_coach/level_manager.py | 3 + .../orchestrators/kubernetes_orchestrator.py | 80 ++++++++++--------- rl_coach/presets/CartPole_ClippedPPO.py | 4 + rl_coach/rollout_worker.py | 10 +++ rl_coach/training_worker.py | 3 +- 17 files changed, 162 insertions(+), 74 deletions(-) diff --git a/rl_coach/agents/agent.py b/rl_coach/agents/agent.py index e3b116c..9b4fef6 100644 --- a/rl_coach/agents/agent.py +++ b/rl_coach/agents/agent.py @@ -842,7 +842,5 @@ class Agent(AgentInterface): for network in self.networks.values(): network.sync() - - - - + def get_success_rate(self) -> float: + return self.num_successes_across_evaluation_episodes / self.num_evaluation_episodes_completed diff --git a/rl_coach/base_parameters.py b/rl_coach/base_parameters.py index 2aa09c9..78b5460 100644 --- a/rl_coach/base_parameters.py +++ b/rl_coach/base_parameters.py @@ -168,7 +168,7 @@ class AlgorithmParameters(Parameters): # n-step returns self.n_step = -1 # calculate the total return (no bootstrap, by default) - + # Distributed Coach params self.distributed_coach_synchronization_type = None diff --git a/rl_coach/data_stores/data_store.py b/rl_coach/data_stores/data_store.py index 03718e1..b9e19ea 100644 --- a/rl_coach/data_stores/data_store.py +++ b/rl_coach/data_stores/data_store.py @@ -1,4 +1,6 @@ +from enum import Enum + class DataStoreParameters(object): def __init__(self, store_type, orchestrator_type, orchestrator_params): @@ -6,6 +8,7 @@ class DataStoreParameters(object): self.orchestrator_type = orchestrator_type self.orchestrator_params = orchestrator_params + class DataStore(object): def __init__(self, params: DataStoreParameters): pass @@ -24,3 +27,8 @@ class DataStore(object): def load_from_store(self): pass + + +class SyncFiles(Enum): + FINISHED = ".finished" + LOCKFILE = ".lock" diff --git a/rl_coach/data_stores/nfs_data_store.py b/rl_coach/data_stores/nfs_data_store.py index 375d917..ba2e057 100644 --- a/rl_coach/data_stores/nfs_data_store.py +++ b/rl_coach/data_stores/nfs_data_store.py @@ -58,7 +58,7 @@ class NFSDataStore(DataStore): pass def deploy_k8s_nfs(self) -> bool: - name = "nfs-server" + name = "nfs-server-{}".format(uuid.uuid4()) container = k8sclient.V1Container( name=name, image="k8s.gcr.io/volume-nfs:0.8", @@ -83,7 +83,7 @@ class NFSDataStore(DataStore): security_context=k8sclient.V1SecurityContext(privileged=True) ) template = k8sclient.V1PodTemplateSpec( - metadata=k8sclient.V1ObjectMeta(labels={'app': 'nfs-server'}), + metadata=k8sclient.V1ObjectMeta(labels={'app': name}), spec=k8sclient.V1PodSpec( containers=[container], volumes=[k8sclient.V1Volume( @@ -96,14 +96,14 @@ class NFSDataStore(DataStore): replicas=1, template=template, selector=k8sclient.V1LabelSelector( - match_labels={'app': 'nfs-server'} + match_labels={'app': name} ) ) deployment = k8sclient.V1Deployment( api_version='apps/v1', kind='Deployment', - metadata=k8sclient.V1ObjectMeta(name=name, labels={'app': 'nfs-server'}), + metadata=k8sclient.V1ObjectMeta(name=name, labels={'app': name}), spec=deployment_spec ) @@ -117,7 +117,7 @@ class NFSDataStore(DataStore): k8s_core_v1_api_client = k8sclient.CoreV1Api() - svc_name = "nfs-service" + svc_name = "nfs-service-{}".format(uuid.uuid4()) service = k8sclient.V1Service( api_version='v1', kind='Service', @@ -145,7 +145,7 @@ class NFSDataStore(DataStore): return True def create_k8s_nfs_resources(self) -> bool: - pv_name = "nfs-ckpt-pv" + pv_name = "nfs-ckpt-pv-{}".format(uuid.uuid4()) persistent_volume = k8sclient.V1PersistentVolume( api_version="v1", kind="PersistentVolume", @@ -171,7 +171,7 @@ class NFSDataStore(DataStore): print("Got exception: %s\n while creating the NFS PV", e) return False - pvc_name = "nfs-ckpt-pvc" + pvc_name = "nfs-ckpt-pvc-{}".format(uuid.uuid4()) persistent_volume_claim = k8sclient.V1PersistentVolumeClaim( api_version="v1", kind="PersistentVolumeClaim", diff --git a/rl_coach/data_stores/s3_data_store.py b/rl_coach/data_stores/s3_data_store.py index 7d40f8b..11f3fe1 100644 --- a/rl_coach/data_stores/s3_data_store.py +++ b/rl_coach/data_stores/s3_data_store.py @@ -5,6 +5,7 @@ from minio.error import ResponseError from configparser import ConfigParser, Error from google.protobuf import text_format from tensorflow.python.training.checkpoint_state_pb2 import CheckpointState +from rl_coach.data_stores.data_store import SyncFiles import os import time @@ -20,7 +21,6 @@ class S3DataStoreParameters(DataStoreParameters): self.end_point = end_point self.bucket_name = bucket_name self.checkpoint_dir = checkpoint_dir - self.lock_file = ".lock" class S3DataStore(DataStore): @@ -52,9 +52,9 @@ class S3DataStore(DataStore): def save_to_store(self): try: - self.mc.remove_object(self.params.bucket_name, self.params.lock_file) + self.mc.remove_object(self.params.bucket_name, SyncFiles.LOCKFILE.value) - self.mc.put_object(self.params.bucket_name, self.params.lock_file, io.BytesIO(b''), 0) + self.mc.put_object(self.params.bucket_name, SyncFiles.LOCKFILE.value, io.BytesIO(b''), 0) checkpoint_file = None for root, dirs, files in os.walk(self.params.checkpoint_dir): @@ -70,7 +70,7 @@ class S3DataStore(DataStore): rel_name = os.path.relpath(abs_name, self.params.checkpoint_dir) self.mc.fput_object(self.params.bucket_name, rel_name, abs_name) - self.mc.remove_object(self.params.bucket_name, self.params.lock_file) + self.mc.remove_object(self.params.bucket_name, SyncFiles.LOCKFILE.value) except ResponseError as e: print("Got exception: %s\n while saving to S3", e) @@ -80,7 +80,7 @@ class S3DataStore(DataStore): filename = os.path.abspath(os.path.join(self.params.checkpoint_dir, "checkpoint")) while True: - objects = self.mc.list_objects_v2(self.params.bucket_name, self.params.lock_file) + objects = self.mc.list_objects_v2(self.params.bucket_name, SyncFiles.LOCKFILE.value) if next(objects, None) is None: try: @@ -90,6 +90,18 @@ class S3DataStore(DataStore): break time.sleep(10) + # Check if there's a finished file + objects = self.mc.list_objects_v2(self.params.bucket_name, SyncFiles.FINISHED.value) + + if next(objects, None) is not None: + try: + self.mc.fget_object( + self.params.bucket_name, SyncFiles.FINISHED.value, + os.path.abspath(os.path.join(self.params.checkpoint_dir, SyncFiles.FINISHED.value)) + ) + except Exception as e: + pass + ckpt = CheckpointState() if os.path.exists(filename): contents = open(filename, 'r').read() diff --git a/rl_coach/environments/carla_environment.py b/rl_coach/environments/carla_environment.py index 21963a8..397998b 100644 --- a/rl_coach/environments/carla_environment.py +++ b/rl_coach/environments/carla_environment.py @@ -133,8 +133,8 @@ class CarlaEnvironment(Environment): allow_braking: bool, quality: CarlaEnvironmentParameters.Quality, cameras: List[CameraTypes], weather_id: List[int], experiment_path: str, separate_actions_for_throttle_and_brake: bool, - num_speedup_steps: int, max_speed: float, **kwargs): - super().__init__(level, seed, frame_skip, human_control, custom_reward_threshold, visualization_parameters) + num_speedup_steps: int, max_speed: float, target_success_rate: float = 1.0, **kwargs): + super().__init__(level, seed, frame_skip, human_control, custom_reward_threshold, visualization_parameters, target_success_rate) # server configuration self.server_height = server_height @@ -261,6 +261,8 @@ class CarlaEnvironment(Environment): image = self.get_rendered_image() self.renderer.create_screen(image.shape[1], image.shape[0]) + self.target_success_rate = target_success_rate + def _add_cameras(self, settings, cameras, camera_width, camera_height): # add a front facing camera if CameraTypes.FRONT in cameras: @@ -461,3 +463,6 @@ class CarlaEnvironment(Environment): image = [self.state[camera.name] for camera in self.scene.sensors] image = np.vstack(image) return image + + def get_target_success_rate(self) -> float: + return self.target_success_rate diff --git a/rl_coach/environments/control_suite_environment.py b/rl_coach/environments/control_suite_environment.py index 27f3db7..a5667e9 100644 --- a/rl_coach/environments/control_suite_environment.py +++ b/rl_coach/environments/control_suite_environment.py @@ -66,10 +66,10 @@ control_suite_envs = {':'.join(env): ':'.join(env) for env in suite.BENCHMARKING # Environment class ControlSuiteEnvironment(Environment): def __init__(self, level: LevelSelection, frame_skip: int, visualization_parameters: VisualizationParameters, - seed: Union[None, int]=None, human_control: bool=False, + target_success_rate: float=1.0, seed: Union[None, int]=None, human_control: bool=False, observation_type: ObservationType=ObservationType.Measurements, custom_reward_threshold: Union[int, float]=None, **kwargs): - super().__init__(level, seed, frame_skip, human_control, custom_reward_threshold, visualization_parameters) + super().__init__(level, seed, frame_skip, human_control, custom_reward_threshold, visualization_parameters, target_success_rate) self.observation_type = observation_type @@ -126,6 +126,8 @@ class ControlSuiteEnvironment(Environment): if not self.native_rendering: self.renderer.create_screen(image.shape[1]*scale, image.shape[0]*scale) + self.target_success_rate = target_success_rate + def _update_state(self): self.state = {} @@ -160,3 +162,6 @@ class ControlSuiteEnvironment(Environment): def get_rendered_image(self): return self.env.physics.render(camera_id=0) + + def get_target_success_rate(self) -> float: + return self.target_success_rate \ No newline at end of file diff --git a/rl_coach/environments/doom_environment.py b/rl_coach/environments/doom_environment.py index 437968b..d4269ba 100644 --- a/rl_coach/environments/doom_environment.py +++ b/rl_coach/environments/doom_environment.py @@ -1,5 +1,5 @@ # -# Copyright (c) 2017 Intel Corporation +# Copyright (c) 2017 Intel Corporation # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -124,8 +124,8 @@ class DoomEnvironment(Environment): def __init__(self, level: LevelSelection, seed: int, frame_skip: int, human_control: bool, custom_reward_threshold: Union[int, float], visualization_parameters: VisualizationParameters, - cameras: List[CameraTypes], **kwargs): - super().__init__(level, seed, frame_skip, human_control, custom_reward_threshold, visualization_parameters) + cameras: List[CameraTypes], target_success_rate: float=1.0, **kwargs): + super().__init__(level, seed, frame_skip, human_control, custom_reward_threshold, visualization_parameters, target_success_rate) self.cameras = cameras @@ -196,6 +196,8 @@ class DoomEnvironment(Environment): image = self.get_rendered_image() self.renderer.create_screen(image.shape[1], image.shape[0]) + self.target_success_rate = target_success_rate + def _update_state(self): # extract all data from the current state state = self.game.get_state() @@ -227,3 +229,6 @@ class DoomEnvironment(Environment): image = [self.state[camera.value[0]] for camera in self.cameras] image = np.vstack(image) return image + + def get_target_success_rate(self) -> float: + return self.target_success_rate diff --git a/rl_coach/environments/environment.py b/rl_coach/environments/environment.py index 295c168..841549a 100644 --- a/rl_coach/environments/environment.py +++ b/rl_coach/environments/environment.py @@ -103,6 +103,9 @@ class EnvironmentParameters(Parameters): self.default_output_filter = None self.experiment_path = None + # Set target reward and target_success if present + self.target_success_rate = 1.0 + @property def path(self): return 'rl_coach.environments.environment:Environment' @@ -111,7 +114,7 @@ class EnvironmentParameters(Parameters): class Environment(EnvironmentInterface): def __init__(self, level: LevelSelection, seed: int, frame_skip: int, human_control: bool, custom_reward_threshold: Union[int, float], visualization_parameters: VisualizationParameters, - **kwargs): + target_success_rate: float=1.0, **kwargs): """ :param level: The environment level. Each environment can have multiple levels :param seed: a seed for the random number generator of the environment @@ -166,6 +169,9 @@ class Environment(EnvironmentInterface): if not self.native_rendering: self.renderer = Renderer() + # Set target reward and target_success if present + self.target_success_rate = target_success_rate + @property def action_space(self) -> Union[List[ActionSpace], ActionSpace]: """ @@ -469,3 +475,5 @@ class Environment(EnvironmentInterface): """ return np.transpose(self.state['observation'], [1, 2, 0]) + def get_target_success_rate(self) -> float: + return self.target_success_rate diff --git a/rl_coach/environments/gym_environment.py b/rl_coach/environments/gym_environment.py index 569c537..ff3df5c 100644 --- a/rl_coach/environments/gym_environment.py +++ b/rl_coach/environments/gym_environment.py @@ -178,11 +178,11 @@ class MaxOverFramesAndFrameskipEnvWrapper(gym.Wrapper): # Environment class GymEnvironment(Environment): def __init__(self, level: LevelSelection, frame_skip: int, visualization_parameters: VisualizationParameters, - additional_simulator_parameters: Dict[str, Any] = {}, seed: Union[None, int]=None, + target_success_rate: float=1.0, additional_simulator_parameters: Dict[str, Any] = {}, seed: Union[None, int]=None, human_control: bool=False, custom_reward_threshold: Union[int, float]=None, random_initialization_steps: int=1, max_over_num_frames: int=1, **kwargs): super().__init__(level, seed, frame_skip, human_control, custom_reward_threshold, - visualization_parameters) + visualization_parameters, target_success_rate) self.random_initialization_steps = random_initialization_steps self.max_over_num_frames = max_over_num_frames @@ -221,7 +221,7 @@ class GymEnvironment(Environment): try: self.env = env_class(**self.additional_simulator_parameters) except: - screen.error("Failed to instantiate Gym environment class %s with arguments %s" % + screen.error("Failed to instantiate Gym environment class %s with arguments %s" % (env_class, self.additional_simulator_parameters), crash=False) raise else: @@ -337,6 +337,8 @@ class GymEnvironment(Environment): self.reward_success_threshold = self.env.spec.reward_threshold self.reward_space = RewardSpace(1, reward_success_threshold=self.reward_success_threshold) + self.target_success_rate = target_success_rate + def _wrap_state(self, state): if not isinstance(self.env.observation_space, gym.spaces.Dict): return {'observation': state} @@ -434,3 +436,6 @@ class GymEnvironment(Environment): if self.is_mujoco_env: self._set_mujoco_camera(0) return image + + def get_target_success_rate(self) -> float: + return self.target_success_rate diff --git a/rl_coach/environments/starcraft2_environment.py b/rl_coach/environments/starcraft2_environment.py index 69a5f98..87747d7 100644 --- a/rl_coach/environments/starcraft2_environment.py +++ b/rl_coach/environments/starcraft2_environment.py @@ -107,14 +107,14 @@ class StarCraft2EnvironmentParameters(EnvironmentParameters): # Environment class StarCraft2Environment(Environment): def __init__(self, level: LevelSelection, frame_skip: int, visualization_parameters: VisualizationParameters, - seed: Union[None, int]=None, human_control: bool=False, + target_success_rate: float=1.0, seed: Union[None, int]=None, human_control: bool=False, custom_reward_threshold: Union[int, float]=None, screen_size: int=84, minimap_size: int=64, feature_minimap_maps_to_use: List=range(7), feature_screen_maps_to_use: List=range(17), observation_type: StarcraftObservationType=StarcraftObservationType.Features, disable_fog: bool=False, auto_select_all_army: bool=True, use_full_action_space: bool=False, **kwargs): - super().__init__(level, seed, frame_skip, human_control, custom_reward_threshold, visualization_parameters) + super().__init__(level, seed, frame_skip, human_control, custom_reward_threshold, visualization_parameters, target_success_rate) self.screen_size = screen_size self.minimap_size = minimap_size @@ -163,11 +163,11 @@ class StarCraft2Environment(Environment): """ feature_screen: [height_map, visibility_map, creep, power, player_id, player_relative, unit_type, selected, - unit_hit_points, unit_hit_points_ratio, unit_energy, unit_energy_ratio, unit_shields, + unit_hit_points, unit_hit_points_ratio, unit_energy, unit_energy_ratio, unit_shields, unit_shields_ratio, unit_density, unit_density_aa, effects] feature_minimap: [height_map, visibility_map, creep, camera, player_id, player_relative, selecte d] - player: [player_id, minerals, vespene, food_cap, food_army, food_workers, idle_worker_dount, + player: [player_id, minerals, vespene, food_cap, food_army, food_workers, idle_worker_dount, army_count, warp_gate_count, larva_count] """ self.screen_shape = np.array(self.env.observation_spec()[0]['feature_screen']) @@ -192,6 +192,8 @@ class StarCraft2Environment(Environment): self.action_space = BoxActionSpace(2, 0, self.screen_size - 1, ["X-Axis, Y-Axis"], default_action=np.array([self.screen_size/2, self.screen_size/2])) + self.target_success_rate = target_success_rate + def _update_state(self): timestep = 0 self.screen = self.last_result[timestep].observation.feature_screen @@ -244,3 +246,6 @@ class StarCraft2Environment(Environment): self.env._run_config.replay_dir = experiment_path self.env.save_replay('replays') super().dump_video_of_last_episode() + + def get_target_success_rate(self): + return self.target_success_rate diff --git a/rl_coach/graph_managers/graph_manager.py b/rl_coach/graph_managers/graph_manager.py index b20a12c..0259941 100644 --- a/rl_coach/graph_managers/graph_manager.py +++ b/rl_coach/graph_managers/graph_manager.py @@ -34,6 +34,7 @@ from rl_coach.logger import screen, Logger from rl_coach.utils import set_cpu, start_shell_command_and_wait from rl_coach.data_stores.data_store_impl import get_data_store from rl_coach.orchestrators.kubernetes_orchestrator import RunType +from rl_coach.data_stores.data_store import SyncFiles class ScheduleParameters(Parameters): @@ -458,12 +459,12 @@ class GraphManager(object): """ [manager.sync() for manager in self.level_managers] - def evaluate(self, steps: PlayingStepsType, keep_networks_in_sync: bool=False) -> None: + def evaluate(self, steps: PlayingStepsType, keep_networks_in_sync: bool=False) -> bool: """ Perform evaluation for several steps :param steps: the number of steps as a tuple of steps time and steps count :param keep_networks_in_sync: sync the network parameters with the global network before each episode - :return: None + :return: bool, True if the target reward and target success has been reached """ self.verify_graph_was_created() @@ -478,6 +479,16 @@ class GraphManager(object): while self.current_step_counter < count_end: self.act(EnvironmentEpisodes(1)) self.sync() + if self.should_stop(): + if self.task_parameters.checkpoint_save_dir: + open(os.path.join(self.task_parameters.checkpoint_save_dir, SyncFiles.FINISHED.value), 'w').close() + if hasattr(self, 'data_store_params'): + data_store = get_data_store(self.data_store_params) + data_store.save_to_store() + + screen.success("Reached required success rate. Exiting.") + return True + return False def improve(self): """ @@ -508,7 +519,8 @@ class GraphManager(object): count_end = self.total_steps_counters[RunPhase.TRAIN] + self.improve_steps while self.total_steps_counters[RunPhase.TRAIN] < count_end: self.train_and_act(self.steps_between_evaluation_periods) - self.evaluate(self.evaluation_steps) + if self.evaluate(self.evaluation_steps): + break def _restore_checkpoint_tf(self, checkpoint_dir: str): import tensorflow as tf @@ -609,3 +621,6 @@ class GraphManager(object): def should_train(self) -> bool: return any([manager.should_train() for manager in self.level_managers]) + + def should_stop(self) -> bool: + return all([manager.should_stop() for manager in self.level_managers]) diff --git a/rl_coach/level_manager.py b/rl_coach/level_manager.py index 7697bf5..962fa20 100644 --- a/rl_coach/level_manager.py +++ b/rl_coach/level_manager.py @@ -263,3 +263,6 @@ class LevelManager(EnvironmentInterface): def should_train(self) -> bool: return any([agent._should_train_helper() for agent in self.agents.values()]) + + def should_stop(self) -> bool: + return all([agent.get_success_rate() >= self.environment.get_target_success_rate() for agent in self.agents.values()]) diff --git a/rl_coach/orchestrators/kubernetes_orchestrator.py b/rl_coach/orchestrators/kubernetes_orchestrator.py index d2afb4d..318c9f8 100644 --- a/rl_coach/orchestrators/kubernetes_orchestrator.py +++ b/rl_coach/orchestrators/kubernetes_orchestrator.py @@ -79,6 +79,7 @@ class Kubernetes(Deploy): self.memory_backend = get_memory_backend(self.params.memory_backend_parameters) self.params.data_store_params.orchestrator_params = {'namespace': self.params.namespace} + self.params.data_store_params.namespace = self.params.namespace self.data_store = get_data_store(self.params.data_store_params) if self.params.data_store_params.store_type == "s3": @@ -137,7 +138,8 @@ class Kubernetes(Deploy): volumes=[k8sclient.V1Volume( name="nfs-pvc", persistent_volume_claim=self.nfs_pvc - )] + )], + restart_policy='OnFailure' ), ) else: @@ -155,32 +157,30 @@ class Kubernetes(Deploy): template = k8sclient.V1PodTemplateSpec( metadata=k8sclient.V1ObjectMeta(labels={'app': name}), spec=k8sclient.V1PodSpec( - containers=[container] + containers=[container], + restart_policy='OnFailure' ), ) - deployment_spec = k8sclient.V1DeploymentSpec( - replicas=trainer_params.num_replicas, - template=template, - selector=k8sclient.V1LabelSelector( - match_labels={'app': name} - ) + job_spec = k8sclient.V1JobSpec( + completions=1, + template=template ) - deployment = k8sclient.V1Deployment( - api_version='apps/v1', - kind='Deployment', + job = k8sclient.V1Job( + api_version="batch/v1", + kind="Job", metadata=k8sclient.V1ObjectMeta(name=name), - spec=deployment_spec + spec=job_spec ) - api_client = k8sclient.AppsV1Api() + api_client = k8sclient.BatchV1Api() try: - api_client.create_namespaced_deployment(self.params.namespace, deployment) - trainer_params.orchestration_params['deployment_name'] = name + api_client.create_namespaced_job(self.params.namespace, job) + trainer_params.orchestration_params['job_name'] = name return True except k8sclient.rest.ApiException as e: - print("Got exception: %s\n while creating deployment", e) + print("Got exception: %s\n while creating job", e) return False def deploy_worker(self): @@ -217,6 +217,7 @@ class Kubernetes(Deploy): name="nfs-pvc", persistent_volume_claim=self.nfs_pvc )], + restart_policy='OnFailure' ), ) else: @@ -234,31 +235,31 @@ class Kubernetes(Deploy): template = k8sclient.V1PodTemplateSpec( metadata=k8sclient.V1ObjectMeta(labels={'app': name}), spec=k8sclient.V1PodSpec( - containers=[container] + containers=[container], + restart_policy='OnFailure' ) ) - deployment_spec = k8sclient.V1DeploymentSpec( - replicas=worker_params.num_replicas, - template=template, - selector=k8sclient.V1LabelSelector( - match_labels={'app': name} - ) - ) - deployment = k8sclient.V1Deployment( - api_version='apps/v1', - kind="Deployment", - metadata=k8sclient.V1ObjectMeta(name=name), - spec=deployment_spec + job_spec = k8sclient.V1JobSpec( + completions=worker_params.num_replicas, + parallelism=worker_params.num_replicas, + template=template ) - api_client = k8sclient.AppsV1Api() + job = k8sclient.V1Job( + api_version="batch/v1", + kind="Job", + metadata=k8sclient.V1ObjectMeta(name=name), + spec=job_spec + ) + + api_client = k8sclient.BatchV1Api() try: - api_client.create_namespaced_deployment(self.params.namespace, deployment) - worker_params.orchestration_params['deployment_name'] = name + api_client.create_namespaced_job(self.params.namespace, job) + worker_params.orchestration_params['job_name'] = name return True except k8sclient.rest.ApiException as e: - print("Got exception: %s\n while creating deployment", e) + print("Got exception: %s\n while creating Job", e) return False def worker_logs(self): @@ -273,7 +274,7 @@ class Kubernetes(Deploy): pod = None try: pods = api_client.list_namespaced_pod(self.params.namespace, label_selector='app={}'.format( - trainer_params.orchestration_params['deployment_name'] + trainer_params.orchestration_params['job_name'] )) pod = pods.items[0] @@ -324,17 +325,20 @@ class Kubernetes(Deploy): def undeploy(self): trainer_params = self.params.run_type_params.get(str(RunType.TRAINER), None) - api_client = k8sclient.AppsV1Api() - delete_options = k8sclient.V1DeleteOptions() + api_client = k8sclient.BatchV1Api() + delete_options = k8sclient.V1DeleteOptions( + propagation_policy="Foreground" + ) + if trainer_params: try: - api_client.delete_namespaced_deployment(trainer_params.orchestration_params['deployment_name'], self.params.namespace, delete_options) + api_client.delete_namespaced_job(trainer_params.orchestration_params['job_name'], self.params.namespace, delete_options) except k8sclient.rest.ApiException as e: print("Got exception: %s\n while deleting trainer", e) worker_params = self.params.run_type_params.get(str(RunType.ROLLOUT_WORKER), None) if worker_params: try: - api_client.delete_namespaced_deployment(worker_params.orchestration_params['deployment_name'], self.params.namespace, delete_options) + api_client.delete_namespaced_job(worker_params.orchestration_params['job_name'], self.params.namespace, delete_options) except k8sclient.rest.ApiException as e: print("Got exception: %s\n while deleting workers", e) self.memory_backend.undeploy() diff --git a/rl_coach/presets/CartPole_ClippedPPO.py b/rl_coach/presets/CartPole_ClippedPPO.py index 7c4d3c1..f400478 100644 --- a/rl_coach/presets/CartPole_ClippedPPO.py +++ b/rl_coach/presets/CartPole_ClippedPPO.py @@ -56,6 +56,10 @@ agent_params.exploration.epsilon_schedule = LinearSchedule(1.0, 0.01, 10000) # Environment # ############### env_params = GymVectorEnvironment(level='CartPole-v0') +env_params.custom_reward_threshold = 200 +# Set the target success +env_params.target_success_rate = 1.0 + ######## # Test # diff --git a/rl_coach/rollout_worker.py b/rl_coach/rollout_worker.py index d039a9e..184ccdc 100644 --- a/rl_coach/rollout_worker.py +++ b/rl_coach/rollout_worker.py @@ -15,6 +15,7 @@ from rl_coach.base_parameters import TaskParameters, DistributedCoachSynchroniza from rl_coach.core_types import EnvironmentSteps, RunPhase from google.protobuf import text_format from tensorflow.python.training.checkpoint_state_pb2 import CheckpointState +from rl_coach.data_stores.data_store import SyncFiles def has_checkpoint(checkpoint_dir): @@ -68,6 +69,10 @@ def get_latest_checkpoint(checkpoint_dir): return int(rel_path.split('_Step')[0]) +def should_stop(checkpoint_dir): + return os.path.exists(os.path.join(checkpoint_dir, SyncFiles.FINISHED.value)) + + def rollout_worker(graph_manager, checkpoint_dir, data_store, num_workers): """ wait for first checkpoint then perform rollouts using the model @@ -87,12 +92,17 @@ def rollout_worker(graph_manager, checkpoint_dir, data_store, num_workers): for i in range(int(graph_manager.improve_steps.num_steps/act_steps)): + if should_stop(checkpoint_dir): + break + graph_manager.act(EnvironmentSteps(num_steps=act_steps)) new_checkpoint = get_latest_checkpoint(checkpoint_dir) if graph_manager.agent_params.algorithm.distributed_coach_synchronization_type == DistributedCoachSynchronizationType.SYNC: while new_checkpoint < last_checkpoint + 1: + if should_stop(checkpoint_dir): + break if data_store: data_store.load_from_store() new_checkpoint = get_latest_checkpoint(checkpoint_dir) diff --git a/rl_coach/training_worker.py b/rl_coach/training_worker.py index 3d2f9e5..fbb5640 100644 --- a/rl_coach/training_worker.py +++ b/rl_coach/training_worker.py @@ -40,8 +40,9 @@ def training_worker(graph_manager, checkpoint_dir): graph_manager.phase = core_types.RunPhase.UNDEFINED if steps * graph_manager.agent_params.algorithm.num_consecutive_playing_steps.num_steps > graph_manager.steps_between_evaluation_periods.num_steps * eval_offset: - graph_manager.evaluate(graph_manager.evaluation_steps) eval_offset += 1 + if graph_manager.evaluate(graph_manager.evaluation_steps): + break if graph_manager.agent_params.algorithm.distributed_coach_synchronization_type == DistributedCoachSynchronizationType.SYNC: graph_manager.save_checkpoint() From a849c17e46ea7188a7b6229c9f68417803e730ad Mon Sep 17 00:00:00 2001 From: Balaji Subramaniam Date: Tue, 13 Nov 2018 09:17:38 -0800 Subject: [PATCH 109/162] Enable distributed SharedRunningStats (#81) - Use Redis pub/sub for updating SharedRunningStats. --- rl_coach/agents/agent.py | 11 ++-- rl_coach/agents/ppo_agent.py | 2 +- .../tensorflow_components/shared_variables.py | 53 ++++++++++++++++--- rl_coach/filters/filter.py | 13 ++--- .../observation_normalization_filter.py | 5 +- .../reward/reward_normalization_filter.py | 5 +- rl_coach/presets/CartPole_ClippedPPO.py | 3 -- rl_coach/presets/CartPole_DQN.py | 2 +- rl_coach/presets/Mujoco_ClippedPPO.py | 4 +- rl_coach/presets/Mujoco_PPO.py | 5 +- 10 files changed, 76 insertions(+), 27 deletions(-) diff --git a/rl_coach/agents/agent.py b/rl_coach/agents/agent.py index 9b4fef6..c26282a 100644 --- a/rl_coach/agents/agent.py +++ b/rl_coach/agents/agent.py @@ -112,9 +112,14 @@ class Agent(AgentInterface): self.output_filter = self.ap.output_filter self.pre_network_filter = self.ap.pre_network_filter device = self.replicated_device if self.replicated_device else self.worker_device - self.input_filter.set_device(device) - self.output_filter.set_device(device) - self.pre_network_filter.set_device(device) + if hasattr(self.ap.memory, 'memory_backend_params') and self.ap.algorithm.distributed_coach_synchronization_type: + self.input_filter.set_device(device, memory_backend_params=self.ap.memory.memory_backend_params) + self.output_filter.set_device(device, memory_backend_params=self.ap.memory.memory_backend_params) + self.pre_network_filter.set_device(device, memory_backend_params=self.ap.memory.memory_backend_params) + else: + self.input_filter.set_device(device) + self.output_filter.set_device(device) + self.pre_network_filter.set_device(device) # initialize all internal variables self._phase = RunPhase.HEATUP diff --git a/rl_coach/agents/ppo_agent.py b/rl_coach/agents/ppo_agent.py index d455caa..64539e9 100644 --- a/rl_coach/agents/ppo_agent.py +++ b/rl_coach/agents/ppo_agent.py @@ -310,7 +310,7 @@ class PPOAgent(ActorCriticAgent): # clean memory self.call_memory('clean') - def _should_train_helper(self): + def _should_train_helper(self, wait_for_full_episode=True): return super()._should_train_helper(True) def train(self): diff --git a/rl_coach/architectures/tensorflow_components/shared_variables.py b/rl_coach/architectures/tensorflow_components/shared_variables.py index 1a3289c..8748885 100644 --- a/rl_coach/architectures/tensorflow_components/shared_variables.py +++ b/rl_coach/architectures/tensorflow_components/shared_variables.py @@ -1,5 +1,5 @@ # -# Copyright (c) 2017 Intel Corporation +# Copyright (c) 2017 Intel Corporation # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -15,11 +15,16 @@ # import numpy as np +import pickle +import redis import tensorflow as tf +import threading + +from rl_coach.memories.backend.memory_impl import get_memory_backend class SharedRunningStats(object): - def __init__(self, replicated_device=None, epsilon=1e-2, name="", create_ops=True): + def __init__(self, replicated_device=None, epsilon=1e-2, name="", create_ops=True, pubsub_params=None): self.sess = None self.name = name self.replicated_device = replicated_device @@ -28,6 +33,13 @@ class SharedRunningStats(object): if create_ops: with tf.device(replicated_device): self.create_ops() + self.pubsub = None + if pubsub_params: + self.channel = "channel-srs-{}".format(self.name) + self.pubsub = get_memory_backend(pubsub_params) + subscribe_thread = SharedRunningStatsSubscribe(self) + subscribe_thread.daemon = True + subscribe_thread.start() def create_ops(self, shape=[1], clip_values=None): self.clip_values = clip_values @@ -74,13 +86,20 @@ class SharedRunningStats(object): self.sess = sess def push(self, x): + if self.pubsub: + self.pubsub.redis_connection.publish(self.channel, pickle.dumps(x)) + return + + self.push_val(x) + + def push_val(self, x): x = x.astype('float64') self.sess.run([self._inc_sum, self._inc_sum_squared, self._inc_count], - feed_dict={ - self.new_sum: x.sum(axis=0).ravel(), - self.new_sum_squared: np.square(x).sum(axis=0).ravel(), - self.newcount: np.array(len(x), dtype='float64') - }) + feed_dict={ + self.new_sum: x.sum(axis=0).ravel(), + self.new_sum_squared: np.square(x).sum(axis=0).ravel(), + self.newcount: np.array(len(x), dtype='float64') + }) if self._shape is None: self._shape = x.shape @@ -119,3 +138,23 @@ class SharedRunningStats(object): return self.sess.run(self.clipped_obs, feed_dict={self.raw_obs: batch}) else: return self.sess.run(self.normalized_obs, feed_dict={self.raw_obs: batch}) + + +class SharedRunningStatsSubscribe(threading.Thread): + def __init__(self, shared_running_stats): + super().__init__() + self.shared_running_stats = shared_running_stats + self.redis_address = self.shared_running_stats.pubsub.params.redis_address + self.redis_port = self.shared_running_stats.pubsub.params.redis_port + self.redis_connection = redis.Redis(self.redis_address, self.redis_port) + self.pubsub = self.redis_connection.pubsub() + self.channel = self.shared_running_stats.channel + self.pubsub.subscribe(self.channel) + + def run(self): + for message in self.pubsub.listen(): + try: + obj = pickle.loads(message['data']) + self.shared_running_stats.push_val(obj) + except Exception: + continue diff --git a/rl_coach/filters/filter.py b/rl_coach/filters/filter.py index 35d7e7c..705aa64 100644 --- a/rl_coach/filters/filter.py +++ b/rl_coach/filters/filter.py @@ -46,10 +46,11 @@ class Filter(object): """ raise NotImplementedError("") - def set_device(self, device) -> None: + def set_device(self, device, memory_backend_params=None) -> None: """ An optional function that allows the filter to get the device if it is required to use tensorflow ops :param device: the device to use + :param memory_backend_params: parameters associated with the memory backend :return: None """ pass @@ -84,13 +85,13 @@ class OutputFilter(Filter): duplicate.i_am_a_reference_filter = False return duplicate - def set_device(self, device) -> None: + def set_device(self, device, memory_backend_params=None) -> None: """ An optional function that allows the filter to get the device if it is required to use tensorflow ops :param device: the device to use :return: None """ - [f.set_device(device) for f in self.action_filters.values()] + [f.set_device(device, memory_backend_params) for f in self.action_filters.values()] def set_session(self, sess) -> None: """ @@ -225,14 +226,14 @@ class InputFilter(Filter): duplicate.i_am_a_reference_filter = False return duplicate - def set_device(self, device) -> None: + def set_device(self, device, memory_backend_params=None) -> None: """ An optional function that allows the filter to get the device if it is required to use tensorflow ops :param device: the device to use :return: None """ - [f.set_device(device) for f in self.reward_filters.values()] - [[f.set_device(device) for f in filters.values()] for filters in self.observation_filters.values()] + [f.set_device(device, memory_backend_params) for f in self.reward_filters.values()] + [[f.set_device(device, memory_backend_params) for f in filters.values()] for filters in self.observation_filters.values()] def set_session(self, sess) -> None: """ diff --git a/rl_coach/filters/observation/observation_normalization_filter.py b/rl_coach/filters/observation/observation_normalization_filter.py index 178036d..21be759 100644 --- a/rl_coach/filters/observation/observation_normalization_filter.py +++ b/rl_coach/filters/observation/observation_normalization_filter.py @@ -41,13 +41,14 @@ class ObservationNormalizationFilter(ObservationFilter): self.supports_batching = True self.observation_space = None - def set_device(self, device) -> None: + def set_device(self, device, memory_backend_params=None) -> None: """ An optional function that allows the filter to get the device if it is required to use tensorflow ops :param device: the device to use :return: None """ - self.running_observation_stats = SharedRunningStats(device, name=self.name, create_ops=False) + self.running_observation_stats = SharedRunningStats(device, name=self.name, create_ops=False, + pubsub_params=memory_backend_params) def set_session(self, sess) -> None: """ diff --git a/rl_coach/filters/reward/reward_normalization_filter.py b/rl_coach/filters/reward/reward_normalization_filter.py index fa33a4e..cd46995 100644 --- a/rl_coach/filters/reward/reward_normalization_filter.py +++ b/rl_coach/filters/reward/reward_normalization_filter.py @@ -38,13 +38,14 @@ class RewardNormalizationFilter(RewardFilter): self.clip_max = clip_max self.running_rewards_stats = None - def set_device(self, device) -> None: + def set_device(self, device, memory_backend_params=None) -> None: """ An optional function that allows the filter to get the device if it is required to use tensorflow ops :param device: the device to use :return: None """ - self.running_rewards_stats = SharedRunningStats(device, name='rewards_stats') + self.running_rewards_stats = SharedRunningStats(device, name='rewards_stats', + pubsub_params=memory_backend_params) def set_session(self, sess) -> None: """ diff --git a/rl_coach/presets/CartPole_ClippedPPO.py b/rl_coach/presets/CartPole_ClippedPPO.py index f400478..35dfbda 100644 --- a/rl_coach/presets/CartPole_ClippedPPO.py +++ b/rl_coach/presets/CartPole_ClippedPPO.py @@ -5,7 +5,6 @@ from rl_coach.core_types import TrainingSteps, EnvironmentEpisodes, EnvironmentS from rl_coach.environments.gym_environment import GymVectorEnvironment, mujoco_v2 from rl_coach.exploration_policies.additive_noise import AdditiveNoiseParameters from rl_coach.exploration_policies.e_greedy import EGreedyParameters -from rl_coach.filters.observation.observation_normalization_filter import ObservationNormalizationFilter from rl_coach.graph_managers.basic_rl_graph_manager import BasicRLGraphManager from rl_coach.graph_managers.graph_manager import ScheduleParameters from rl_coach.schedules import LinearSchedule @@ -49,8 +48,6 @@ agent_params.algorithm.distributed_coach_synchronization_type = DistributedCoach agent_params.exploration = EGreedyParameters() agent_params.exploration.epsilon_schedule = LinearSchedule(1.0, 0.01, 10000) -# agent_params.pre_network_filter.add_observation_filter('observation', 'normalize_observation', -# ObservationNormalizationFilter(name='normalize_observation')) ############### # Environment # diff --git a/rl_coach/presets/CartPole_DQN.py b/rl_coach/presets/CartPole_DQN.py index ba4472f..02a38d7 100644 --- a/rl_coach/presets/CartPole_DQN.py +++ b/rl_coach/presets/CartPole_DQN.py @@ -1,5 +1,5 @@ from rl_coach.agents.dqn_agent import DQNAgentParameters -from rl_coach.base_parameters import VisualizationParameters, PresetValidationParameters, DistributedCoachSynchronizationType +from rl_coach.base_parameters import VisualizationParameters, PresetValidationParameters from rl_coach.core_types import TrainingSteps, EnvironmentEpisodes, EnvironmentSteps from rl_coach.environments.gym_environment import GymVectorEnvironment from rl_coach.graph_managers.basic_rl_graph_manager import BasicRLGraphManager diff --git a/rl_coach/presets/Mujoco_ClippedPPO.py b/rl_coach/presets/Mujoco_ClippedPPO.py index ca2d662..d7ec89c 100644 --- a/rl_coach/presets/Mujoco_ClippedPPO.py +++ b/rl_coach/presets/Mujoco_ClippedPPO.py @@ -1,6 +1,6 @@ from rl_coach.agents.clipped_ppo_agent import ClippedPPOAgentParameters from rl_coach.architectures.layers import Dense -from rl_coach.base_parameters import VisualizationParameters, PresetValidationParameters +from rl_coach.base_parameters import VisualizationParameters, PresetValidationParameters, DistributedCoachSynchronizationType from rl_coach.core_types import TrainingSteps, EnvironmentEpisodes, EnvironmentSteps from rl_coach.environments.environment import SingleLevelSelection from rl_coach.environments.gym_environment import GymVectorEnvironment, mujoco_v2 @@ -43,6 +43,8 @@ agent_params.algorithm.gae_lambda = 0.95 agent_params.algorithm.discount = 0.99 agent_params.algorithm.optimization_epochs = 10 agent_params.algorithm.estimate_state_value_using_gae = True +# Distributed Coach synchronization type. +agent_params.algorithm.distributed_coach_synchronization_type = DistributedCoachSynchronizationType.SYNC agent_params.input_filter = InputFilter() agent_params.exploration = AdditiveNoiseParameters() diff --git a/rl_coach/presets/Mujoco_PPO.py b/rl_coach/presets/Mujoco_PPO.py index 4eb2f72..d5deaa2 100644 --- a/rl_coach/presets/Mujoco_PPO.py +++ b/rl_coach/presets/Mujoco_PPO.py @@ -1,6 +1,6 @@ from rl_coach.agents.ppo_agent import PPOAgentParameters from rl_coach.architectures.layers import Dense -from rl_coach.base_parameters import VisualizationParameters, PresetValidationParameters +from rl_coach.base_parameters import VisualizationParameters, PresetValidationParameters, DistributedCoachSynchronizationType from rl_coach.core_types import TrainingSteps, EnvironmentEpisodes, EnvironmentSteps from rl_coach.environments.environment import SingleLevelSelection from rl_coach.environments.gym_environment import GymVectorEnvironment, mujoco_v2 @@ -33,6 +33,9 @@ agent_params.network_wrappers['critic'].middleware_parameters.scheme = [Dense(64 agent_params.input_filter = InputFilter() agent_params.input_filter.add_observation_filter('observation', 'normalize', ObservationNormalizationFilter()) +# Distributed Coach synchronization type. +agent_params.algorithm.distributed_coach_synchronization_type = DistributedCoachSynchronizationType.SYNC + ############### # Environment # ############### From 524f8436a298a243bd81b8860f85ab6145b4b26f Mon Sep 17 00:00:00 2001 From: Scott Leishman Date: Wed, 14 Nov 2018 07:40:22 -0800 Subject: [PATCH 110/162] create per environment Dockerfiles. (#70) * create per environment Dockerfiles. Adjust CI setup to better parallelize runs. Fix a couple of issues in golden and trace tests. Update a few of the docs. * bugfix in mmc agent. Also install kubectl for CI, update badge branch. * remove integration test parallelism. --- .circleci/config.yml | 279 ++++++++++++++++++++-- README.md | 1 + docker/Dockerfile | 36 ++- docker/Dockerfile.base | 28 +-- docker/Dockerfile.build | 26 -- docker/Dockerfile.doom_environment | 20 ++ docker/Dockerfile.gym_environment | 20 ++ docker/Dockerfile.mujoco_environment | 31 +++ docker/Makefile | 38 +-- docker/README.md | 27 +++ docker/docker_entrypoint.sh | 19 -- docs_raw/docs/usage.md | 4 +- requirements.txt | 2 +- rl_coach/agents/mmc_agent.py | 2 +- rl_coach/environments/doom_environment.py | 8 +- rl_coach/tests/README.md | 9 +- rl_coach/tests/test_eks.py | 1 + rl_coach/tests/test_golden.py | 20 +- rl_coach/tests/trace_tests.py | 8 +- setup.py | 8 +- 20 files changed, 448 insertions(+), 139 deletions(-) delete mode 100644 docker/Dockerfile.build create mode 100644 docker/Dockerfile.doom_environment create mode 100644 docker/Dockerfile.gym_environment create mode 100644 docker/Dockerfile.mujoco_environment create mode 100644 docker/README.md delete mode 100644 docker/docker_entrypoint.sh diff --git a/.circleci/config.yml b/.circleci/config.yml index fdac537..d966349 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -30,10 +30,12 @@ aliases: sudo curl -o /usr/local/bin/aws-iam-authenticator https://amazon-eks.s3-us-west-2.amazonaws.com/1.10.3/2018-07-26/bin/linux/amd64/aws-iam-authenticator sudo chmod a+x /usr/local/bin/aws-iam-authenticator aws eks update-kubeconfig --name coach-aws-cicd + sudo curl -o /usr/local/bin/kubectl https://storage.googleapis.com/kubernetes-release/release/$(curl -s https://storage.googleapis.com/kubernetes-release/release/stable.txt)/bin/linux/amd64/kubectl + sudo chmod a+x /usr/local/bin/kubectl version: 2 jobs: - build: + build_base: <<: *executor_prep steps: - checkout @@ -41,7 +43,7 @@ jobs: - *restore_cache - *aws_prep - run: - name: Build and push container + name: Build and push base and main container command: | REGISTRY=316971102342.dkr.ecr.us-west-2.amazonaws.com TAG=$(git describe --tags --always --dirty) @@ -53,10 +55,64 @@ jobs: docker tag ${REGISTRY}/coach-base:${TAG} coach-base:master - docker build -t ${REGISTRY}/coach:${TAG} -f docker/Dockerfile . + docker build --build-arg MUJOCO_KEY=${MUJOCO_KEY} -t ${REGISTRY}/coach:${TAG} -f docker/Dockerfile . docker push ${REGISTRY}/coach:${TAG} no_output_timeout: 30m + build_gym_env: + <<: *executor_prep + steps: + - checkout + - *remote_docker + - *restore_cache + - *aws_prep + - run: + name: Build and push gym environment container + command: | + REGISTRY=316971102342.dkr.ecr.us-west-2.amazonaws.com + TAG=$(git describe --tags --always --dirty) + docker pull ${REGISTRY}/coach-base:${MASTER_BRANCH} + docker tag ${REGISTRY}/coach-base:${MASTER_BRANCH} coach-base:master + docker build --cache-from ${REGISTRY}/coach-base:${MASTER_BRANCH} -t ${REGISTRY}/coach-gym_environment:${TAG} -f docker/Dockerfile.gym_environment . + docker push ${REGISTRY}/coach-gym_environment:${TAG} + no_output_timeout: 10m + + build_doom_env: + <<: *executor_prep + steps: + - checkout + - *remote_docker + - *restore_cache + - *aws_prep + - run: + name: Build and push vizdoom environment container + command: | + REGISTRY=316971102342.dkr.ecr.us-west-2.amazonaws.com + TAG=$(git describe --tags --always --dirty) + docker pull ${REGISTRY}/coach-base:${MASTER_BRANCH} + docker tag ${REGISTRY}/coach-base:${MASTER_BRANCH} coach-base:master + docker build --cache-from ${REGISTRY}/coach-base:${MASTER_BRANCH} -t ${REGISTRY}/coach-doom_environment:${TAG} -f docker/Dockerfile.doom_environment . + docker push ${REGISTRY}/coach-doom_environment:${TAG} + no_output_timeout: 10m + + build_mujoco_env: + <<: *executor_prep + steps: + - checkout + - *remote_docker + - *restore_cache + - *aws_prep + - run: + name: Build and push mujoco environment container + command: | + REGISTRY=316971102342.dkr.ecr.us-west-2.amazonaws.com + TAG=$(git describe --tags --always --dirty) + docker pull ${REGISTRY}/coach-base:${MASTER_BRANCH} + docker tag ${REGISTRY}/coach-base:${MASTER_BRANCH} coach-base:master + docker build --cache-from ${REGISTRY}/coach-base:${MASTER_BRANCH} --build-arg MUJOCO_KEY=${MUJOCO_KEY} -t ${REGISTRY}/coach-mujoco_environment:${TAG} -f docker/Dockerfile.mujoco_environment . + docker push ${REGISTRY}/coach-mujoco_environment:${TAG} + no_output_timeout: 10m + unit_tests: <<: *executor_prep steps: @@ -70,6 +126,14 @@ jobs: export AWS_ACCESS_KEY_ID=`echo ${AWS_ACCESS_KEY_ID} | base64 --decode` export AWS_SECRET_ACCESS_KEY=`echo ${AWS_SECRET_ACCESS_KEY} | base64 --decode` python3 rl_coach/tests/test_eks.py -c coach-test -bn ${CIRCLE_BUILD_NUM} -tn unit-test -tc 'make unit_tests_without_docker' -i 316971102342.dkr.ecr.us-west-2.amazonaws.com/coach:$(git describe --tags --always --dirty) -cpu 2048 -mem 4096 + - run: + name: cleanup + command: | + export AWS_ACCESS_KEY_ID=`echo ${AWS_ACCESS_KEY_ID} | base64 --decode` + export AWS_SECRET_ACCESS_KEY=`echo ${AWS_SECRET_ACCESS_KEY} | base64 --decode` + kubectl delete --all pods --namespace=unit-test-${CIRCLE_BUILD_NUM} || true + kubectl delete ns unit-test-${CIRCLE_BUILD_NUM} || true + when: always integration_tests: <<: *executor_prep @@ -84,8 +148,16 @@ jobs: export AWS_ACCESS_KEY_ID=`echo ${AWS_ACCESS_KEY_ID} | base64 --decode` export AWS_SECRET_ACCESS_KEY=`echo ${AWS_SECRET_ACCESS_KEY} | base64 --decode` python3 rl_coach/tests/test_eks.py -c coach-test -bn ${CIRCLE_BUILD_NUM} -tn integration-test -tc 'make integration_tests_without_docker' -i 316971102342.dkr.ecr.us-west-2.amazonaws.com/coach:$(git describe --tags --always --dirty) -cpu 2048 -mem 4096 + - run: + name: cleanup + command: | + export AWS_ACCESS_KEY_ID=`echo ${AWS_ACCESS_KEY_ID} | base64 --decode` + export AWS_SECRET_ACCESS_KEY=`echo ${AWS_SECRET_ACCESS_KEY} | base64 --decode` + kubectl delete --all pods --namespace=integration-test-${CIRCLE_BUILD_NUM} || true + kubectl delete ns integration-test-${CIRCLE_BUILD_NUM} || true + when: always - golden_tests: + golden_test_gym: <<: *executor_prep steps: - checkout @@ -93,13 +165,23 @@ jobs: - *restore_cache - *aws_prep - run: - name: run golden tests + name: run gym related golden tests command: | export AWS_ACCESS_KEY_ID=`echo ${AWS_ACCESS_KEY_ID} | base64 --decode` export AWS_SECRET_ACCESS_KEY=`echo ${AWS_SECRET_ACCESS_KEY} | base64 --decode` - python3 rl_coach/tests/test_eks.py -c coach-test -bn ${CIRCLE_BUILD_NUM} -tn golden-test -tc 'make golden_tests_without_docker' -i 316971102342.dkr.ecr.us-west-2.amazonaws.com/coach:$(git describe --tags --always --dirty) -cpu 2048 -mem 4096 + export PRESETS='CartPole_A3C,CartPole_Dueling_DDQN,CartPole_NStepQ,CartPole_DQN,CartPole_DFP,CartPole_PG,CartPole_NEC,CartPole_ClippedPPO,CartPole_PAL' + python3 rl_coach/tests/test_eks.py -c coach-test -bn ${CIRCLE_BUILD_NUM} -tn golden-test-gym -tc "export PRESETS=${PRESETS} && make golden_tests_without_docker" -i 316971102342.dkr.ecr.us-west-2.amazonaws.com/coach-gym_environment:$(git describe --tags --always --dirty) -cpu 2048 -mem 4096 + no_output_timeout: 30m + - run: + name: cleanup + command: | + export AWS_ACCESS_KEY_ID=`echo ${AWS_ACCESS_KEY_ID} | base64 --decode` + export AWS_SECRET_ACCESS_KEY=`echo ${AWS_SECRET_ACCESS_KEY} | base64 --decode` + kubectl delete --all pods --namespace=golden-test-gym-${CIRCLE_BUILD_NUM} || true + kubectl delete ns golden-test-gym-${CIRCLE_BUILD_NUM} || true + when: always - trace_tests: + golden_test_doom: <<: *executor_prep steps: - checkout @@ -107,30 +189,197 @@ jobs: - *restore_cache - *aws_prep - run: - name: run trace tests + name: run doom related golden tests command: | export AWS_ACCESS_KEY_ID=`echo ${AWS_ACCESS_KEY_ID} | base64 --decode` export AWS_SECRET_ACCESS_KEY=`echo ${AWS_SECRET_ACCESS_KEY} | base64 --decode` - python3 rl_coach/tests/test_eks.py -c coach-test -bn ${CIRCLE_BUILD_NUM} -tn trace-test -tc 'make trace_tests_without_docker' -i 316971102342.dkr.ecr.us-west-2.amazonaws.com/coach:$(git describe --tags --always --dirty) -cpu 2048 -mem 4096 + export PRESETS='Doom_Basic_DQN,Doom_Basic_A3C,Doom_Health_DFP' + python3 rl_coach/tests/test_eks.py -c coach-test -bn ${CIRCLE_BUILD_NUM} -tn golden-test-doom -tc "export PRESETS=${PRESETS} && make golden_tests_without_docker" -i 316971102342.dkr.ecr.us-west-2.amazonaws.com/coach-doom_environment:$(git describe --tags --always --dirty) -cpu 2048 -mem 4096 + no_output_timeout: 30m + - run: + name: cleanup + command: | + export AWS_ACCESS_KEY_ID=`echo ${AWS_ACCESS_KEY_ID} | base64 --decode` + export AWS_SECRET_ACCESS_KEY=`echo ${AWS_SECRET_ACCESS_KEY} | base64 --decode` + kubectl delete --all pods --namespace=golden-test-doom-${CIRCLE_BUILD_NUM} || true + kubectl delete ns golden-test-doom-${CIRCLE_BUILD_NUM} || true + when: always + + golden_test_mujoco: + <<: *executor_prep + steps: + - checkout + - *remote_docker + - *restore_cache + - *aws_prep + - run: + name: run mujoco related golden tests + command: | + export AWS_ACCESS_KEY_ID=`echo ${AWS_ACCESS_KEY_ID} | base64 --decode` + export AWS_SECRET_ACCESS_KEY=`echo ${AWS_SECRET_ACCESS_KEY} | base64 --decode` + export PRESETS='BitFlip_DQN_HER,BitFlip_DQN,Mujoco_A3C,Mujoco_A3C_LSTM,Mujoco_PPO,Mujoco_ClippedPPO,Mujoco_DDPG' + python3 rl_coach/tests/test_eks.py -c coach-test -bn ${CIRCLE_BUILD_NUM} -tn golden-test-mujoco -tc "export PRESETS=${PRESETS} && make golden_tests_without_docker" -i 316971102342.dkr.ecr.us-west-2.amazonaws.com/coach-mujoco_environment:$(git describe --tags --always --dirty) -cpu 2048 -mem 4096 + no_output_timeout: 30m + - run: + name: cleanup + command: | + export AWS_ACCESS_KEY_ID=`echo ${AWS_ACCESS_KEY_ID} | base64 --decode` + export AWS_SECRET_ACCESS_KEY=`echo ${AWS_SECRET_ACCESS_KEY} | base64 --decode` + kubectl delete --all pods --namespace=golden-test-mujoco-${CIRCLE_BUILD_NUM} || true + kubectl delete ns golden-test-mujoco-${CIRCLE_BUILD_NUM} || true + when: always + + trace_test_gym: + <<: *executor_prep + steps: + - checkout + - *remote_docker + - *restore_cache + - *aws_prep + - run: + name: run gym related trace tests + command: | + export AWS_ACCESS_KEY_ID=`echo ${AWS_ACCESS_KEY_ID} | base64 --decode` + export AWS_SECRET_ACCESS_KEY=`echo ${AWS_SECRET_ACCESS_KEY} | base64 --decode` + export PRESETS='CartPole_A3C,CartPole_Dueling_DDQN,CartPole_NStepQ,CartPole_DQN,CartPole_DFP,CartPole_PG,CartPole_NEC,CartPole_ClippedPPO,CartPole_PAL' + python3 rl_coach/tests/test_eks.py -c coach-test -bn ${CIRCLE_BUILD_NUM} -tn trace-test-gym -tc "export PRESETS=${PRESETS} && make trace_tests_without_docker" -i 316971102342.dkr.ecr.us-west-2.amazonaws.com/coach-gym_environment:$(git describe --tags --always --dirty) -cpu 2048 -mem 4096 + no_output_timeout: 30m + - run: + name: cleanup + command: | + export AWS_ACCESS_KEY_ID=`echo ${AWS_ACCESS_KEY_ID} | base64 --decode` + export AWS_SECRET_ACCESS_KEY=`echo ${AWS_SECRET_ACCESS_KEY} | base64 --decode` + kubectl delete --all pods --namespace=trace-test-gym-${CIRCLE_BUILD_NUM} || true + kubectl delete ns trace-test-gym-${CIRCLE_BUILD_NUM} || true + when: always + + trace_test_doom: + <<: *executor_prep + steps: + - checkout + - *remote_docker + - *restore_cache + - *aws_prep + - run: + name: run doom related trace tests + command: | + export AWS_ACCESS_KEY_ID=`echo ${AWS_ACCESS_KEY_ID} | base64 --decode` + export AWS_SECRET_ACCESS_KEY=`echo ${AWS_SECRET_ACCESS_KEY} | base64 --decode` + export PRESETS='Doom_Basic_DQN,Doom_Basic_A3C,Doom_Health_DFP' + python3 rl_coach/tests/test_eks.py -c coach-test -bn ${CIRCLE_BUILD_NUM} -tn trace-test-doom -tc "export PRESETS=${PRESETS} && make trace_tests_without_docker" -i 316971102342.dkr.ecr.us-west-2.amazonaws.com/coach-doom_environment:$(git describe --tags --always --dirty) -cpu 2048 -mem 4096 + no_output_timeout: 30m + - run: + name: cleanup + command: | + export AWS_ACCESS_KEY_ID=`echo ${AWS_ACCESS_KEY_ID} | base64 --decode` + export AWS_SECRET_ACCESS_KEY=`echo ${AWS_SECRET_ACCESS_KEY} | base64 --decode` + kubectl delete --all pods --namespace=trace-test-doom-${CIRCLE_BUILD_NUM} || true + kubectl delete ns trace-test-doom-${CIRCLE_BUILD_NUM} || true + when: always + + trace_test_mujoco: + <<: *executor_prep + steps: + - checkout + - *remote_docker + - *restore_cache + - *aws_prep + - run: + name: run mujoco related trace tests + command: | + export AWS_ACCESS_KEY_ID=`echo ${AWS_ACCESS_KEY_ID} | base64 --decode` + export AWS_SECRET_ACCESS_KEY=`echo ${AWS_SECRET_ACCESS_KEY} | base64 --decode` + export PRESETS='BitFlip_DQN_HER,BitFlip_DQN,Mujoco_A3C,Mujoco_A3C_LSTM,Mujoco_PPO,Mujoco_ClippedPPO,Mujoco_DDPG' + python3 rl_coach/tests/test_eks.py -c coach-test -bn ${CIRCLE_BUILD_NUM} -tn trace-test-mujoco -tc "export PRESETS=${PRESETS} && make trace_tests_without_docker" -i 316971102342.dkr.ecr.us-west-2.amazonaws.com/coach-mujoco_environment:$(git describe --tags --always --dirty) -cpu 2048 -mem 4096 + no_output_timeout: 30m + - run: + name: cleanup + command: | + export AWS_ACCESS_KEY_ID=`echo ${AWS_ACCESS_KEY_ID} | base64 --decode` + export AWS_SECRET_ACCESS_KEY=`echo ${AWS_SECRET_ACCESS_KEY} | base64 --decode` + kubectl delete --all pods --namespace=trace-test-mujoco-${CIRCLE_BUILD_NUM} || true + kubectl delete ns trace-test-mujoco-${CIRCLE_BUILD_NUM} || true + when: always + + container_deploy: + <<: *executor_prep + steps: + - checkout + - *remote_docker + - *restore_cache + - *aws_prep + - run: + name: Tag and push updated base and main container + command: | + REGISTRY=316971102342.dkr.ecr.us-west-2.amazonaws.com + TAG=$(git describe --tags --always --dirty) + + docker pull ${REGISTRY}/coach-base:${TAG} + docker tag ${REGISTRY}/coach-base:${TAG} coach-base:${MASTER_BRANCH} + docker push ${REGISTRY}/coach-base:${MASTER_BRANCH} + + docker pull ${REGISTRY}/coach:${TAG} + docker tag ${REGISTRY}/coach:${TAG} coach:${MASTER_BRANCH} + docker push ${REGISTRY}/coach:${MASTER_BRANCH} workflows: version: 2 build_and_test: jobs: - - build + - build_base - unit_tests: requires: - - build + - build_base - integration_tests: requires: - - build + - build_base - e2e_approval: type: approval requires: - - build - - golden_tests: + - build_base + - build_gym_env: requires: - e2e_approval - - trace_tests: + - build_doom_env: requires: - e2e_approval + - build_mujoco_env: + requires: + - e2e_approval + - gym_approval: + type: approval + requires: + - golden_test_gym + - doom_approval: + type: approval + requires: + - golden_test_doom + - mujoco_approval: + type: approval + requires: + - golden_test_mujoco + - golden_test_gym: + requires: + - build_gym_env + - golden_test_doom: + requires: + - build_doom_env + - golden_test_mujoco: + requires: + - build_mujoco_env + - trace_test_gym: + requires: + - gym_approval + - trace_test_doom: + requires: + - doom_approval + - trace_test_mujoco: + requires: + - mujoco_approval + - container_deploy: + requires: + - unit_tests + - integration_tests + filters: + branches: + only: 0.11.0-release diff --git a/README.md b/README.md index d03fc8c..7100caa 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,6 @@ # Coach +[![CircleCI](https://circleci.com/gh/IntelAI/coach-aws.svg?style=svg&circle-token=e2b3ca534b4956baff8b66459faf0f796117e803)](https://circleci.com/gh/IntelAI/coach-aws) [![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://github.com/NervanaSystems/coach/blob/master/LICENSE) [![Docs](https://readthedocs.org/projects/carla/badge/?version=latest)](https://nervanasystems.github.io/coach/) [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.1134898.svg)](https://doi.org/10.5281/zenodo.1134898) diff --git a/docker/Dockerfile b/docker/Dockerfile index 1237e64..420ac8f 100644 --- a/docker/Dockerfile +++ b/docker/Dockerfile @@ -1,16 +1,32 @@ -FROM coach-base:master +FROM coach-base:master as builder + +# prep some of the more common environments +# Gym (installed with coach) +# Mujoco +RUN mkdir -p ~/.mujoco \ + && wget https://www.roboti.us/download/mjpro150_linux.zip -O mujoco.zip \ + && unzip mujoco.zip -d ~/.mujoco \ + && rm mujoco.zip +ARG MUJOCO_KEY +ENV MUJOCO_KEY=$MUJOCO_KEY +ENV LD_LIBRARY_PATH /root/.mujoco/mjpro150/bin:$LD_LIBRARY_PATH +RUN echo $MUJOCO_KEY | base64 --decode > /root/.mujoco/mjkey.txt +RUN pip3 install mujoco_py +# Vizdoom +RUN pip3 install vizdoom RUN mkdir /root/src COPY setup.py /root/src/. COPY requirements.txt /root/src/. +RUN pip3 install -r /root/src/requirements.txt + +FROM coach-base:master +WORKDIR /root/src +COPY --from=builder /root/.mujoco /root/.mujoco +ENV LD_LIBRARY_PATH /root/.mujoco/mjpro150/bin:$LD_LIBRARY_PATH +COPY --from=builder /root/.cache /root/.cache +COPY setup.py /root/src/. +COPY requirements.txt /root/src/. COPY README.md /root/src/. -WORKDIR /root/src -RUN pip3 install -e .[all] - -# everything above here should be cached most of the time +RUN pip3 install mujoco_py vizdoom && pip3 install -e .[all] && rm -rf /root/.cache COPY . /root/src -WORKDIR /root/src -RUN pip3 install -e .[all] - -RUN chmod 777 /root/src/docker/docker_entrypoint.sh -ENTRYPOINT ["/root/src/docker/docker_entrypoint.sh"] diff --git a/docker/Dockerfile.base b/docker/Dockerfile.base index 8659096..75cc615 100644 --- a/docker/Dockerfile.base +++ b/docker/Dockerfile.base @@ -1,16 +1,4 @@ -FROM nvidia/cuda:9.0-cudnn7-devel-ubuntu16.04 - -# https://github.com/NVIDIA/nvidia-docker/issues/619 -RUN rm /etc/apt/sources.list.d/cuda.list -RUN apt-get update && \ - apt-get upgrade -y && \ - apt-get clean autoclean && \ - apt-get autoremove -y && apt-get update && \ - apt-get install -y python-pip && \ - apt-get clean autoclean && \ - apt-get autoremove -y -RUN pip install pip --upgrade -WORKDIR /root +FROM nvidia/cuda:9.0-cudnn7-runtime-ubuntu16.04 ################################ # Install apt-get Requirements # @@ -45,19 +33,7 @@ RUN apt-get update && \ # Install Pip Requirements # ############################ RUN pip3 install --upgrade pip -RUN pip3 install pytest -RUN pip3 install pytest-xdist - -# initial installation of coach, so that the docker build won't install everything from scratch -RUN pip3 install rl_coach>=0.10.0 && pip3 install gym[atari]==0.10.5 && \ - pip3 install mujoco_py==1.50.1.56 && pip3 install vizdoom==1.1.6 - -RUN mkdir -p ~/.mujoco \ - && wget https://www.roboti.us/download/mjpro150_linux.zip -O mujoco.zip \ - && unzip mujoco.zip -d ~/.mujoco \ - && rm mujoco.zip -# COPY ./mjkey.txt /root/.mujoco/ -ENV LD_LIBRARY_PATH /root/.mujoco/mjpro150/bin:$LD_LIBRARY_PATH +RUN pip3 install setuptools==39.1.0 && pip3 install pytest && pip3 install pytest-xdist RUN curl -o /usr/local/bin/patchelf https://s3-us-west-2.amazonaws.com/openai-sci-artifacts/manual-builds/patchelf_0.9_amd64.elf \ && chmod +x /usr/local/bin/patchelf diff --git a/docker/Dockerfile.build b/docker/Dockerfile.build deleted file mode 100644 index 176ea8f..0000000 --- a/docker/Dockerfile.build +++ /dev/null @@ -1,26 +0,0 @@ -FROM ubuntu:16.04 - -RUN apt-get update \ - && apt-get install -y \ - python3-pip cmake zlib1g-dev python3-tk python-opencv \ - libboost-all-dev \ - libblas-dev liblapack-dev libatlas-base-dev gfortran \ - libsdl-dev libsdl-image1.2-dev libsdl-mixer1.2-dev libsdl-ttf2.0-dev \ - libsmpeg-dev libportmidi-dev libavformat-dev libswscale-dev \ - dpkg-dev build-essential python3.5-dev libjpeg-dev libtiff-dev \ - libsdl1.2-dev libnotify-dev freeglut3 freeglut3-dev libsm-dev \ - libgtk2.0-dev libgtk-3-dev libwebkitgtk-dev libgtk-3-dev \ - libwebkitgtk-3.0-dev libgstreamer-plugins-base1.0-dev \ - libav-tools libsdl2-dev swig - -RUN pip3 install --upgrade pip - -COPY requirements.txt /coach/requirements.txt - -WORKDIR /coach - -RUN pip3 install -r requirements.txt - -COPY . /coach - -RUN pip3 install . diff --git a/docker/Dockerfile.doom_environment b/docker/Dockerfile.doom_environment new file mode 100644 index 0000000..4498e80 --- /dev/null +++ b/docker/Dockerfile.doom_environment @@ -0,0 +1,20 @@ +FROM coach-base:master as builder + +# prep vizdoom and any of its related requirements. +RUN pip3 install vizdoom + +# add coach source starting with files that could trigger +# re-build if dependencies change. +RUN mkdir /root/src +COPY setup.py /root/src/. +COPY requirements.txt /root/src/. +RUN pip3 install -r /root/src/requirements.txt + +FROM coach-base:master +WORKDIR /root/src +COPY --from=builder /root/.cache /root/.cache +COPY setup.py /root/src/. +COPY requirements.txt /root/src/. +COPY README.md /root/src/. +RUN pip3 install vizdoom && pip3 install -e .[all] && rm -rf /root/.cache +COPY . /root/src diff --git a/docker/Dockerfile.gym_environment b/docker/Dockerfile.gym_environment new file mode 100644 index 0000000..f667798 --- /dev/null +++ b/docker/Dockerfile.gym_environment @@ -0,0 +1,20 @@ +FROM coach-base:master as builder + +# prep gym and any of its related requirements. +RUN pip3 install gym[atari,box2d,classic_control]==0.10.5 + +# add coach source starting with files that could trigger +# re-build if dependencies change. +RUN mkdir /root/src +COPY setup.py /root/src/. +COPY requirements.txt /root/src/. +RUN pip3 install -r /root/src/requirements.txt + +FROM coach-base:master +WORKDIR /root/src +COPY --from=builder /root/.cache /root/.cache +COPY setup.py /root/src/. +COPY requirements.txt /root/src/. +COPY README.md /root/src/. +RUN pip3 install gym[atari,box2d,classic_control]==0.10.5 && pip3 install -e .[all] && rm -rf /root/.cache +COPY . /root/src diff --git a/docker/Dockerfile.mujoco_environment b/docker/Dockerfile.mujoco_environment new file mode 100644 index 0000000..1959283 --- /dev/null +++ b/docker/Dockerfile.mujoco_environment @@ -0,0 +1,31 @@ +FROM coach-base:master as builder + +# prep mujoco and any of its related requirements. +# Mujoco +RUN mkdir -p ~/.mujoco \ + && wget https://www.roboti.us/download/mjpro150_linux.zip -O mujoco.zip \ + && unzip -n mujoco.zip -d ~/.mujoco \ + && rm mujoco.zip +ARG MUJOCO_KEY +ENV MUJOCO_KEY=$MUJOCO_KEY +ENV LD_LIBRARY_PATH /root/.mujoco/mjpro150/bin:$LD_LIBRARY_PATH +RUN echo $MUJOCO_KEY | base64 --decode > /root/.mujoco/mjkey.txt +RUN pip3 install mujoco_py + +# add coach source starting with files that could trigger +# re-build if dependencies change. +RUN mkdir /root/src +COPY setup.py /root/src/. +COPY requirements.txt /root/src/. +RUN pip3 install -r /root/src/requirements.txt + +FROM coach-base:master +WORKDIR /root/src +COPY --from=builder /root/.mujoco /root/.mujoco +ENV LD_LIBRARY_PATH /root/.mujoco/mjpro150/bin:$LD_LIBRARY_PATH +COPY --from=builder /root/.cache /root/.cache +COPY setup.py /root/src/. +COPY requirements.txt /root/src/. +COPY README.md /root/src/. +RUN pip3 install mujoco_py && pip3 install -e .[all] && rm -rf /root/.cache +COPY . /root/src diff --git a/docker/Makefile b/docker/Makefile index c409f8a..d101b7a 100644 --- a/docker/Makefile +++ b/docker/Makefile @@ -1,8 +1,7 @@ -# REGISTRY=nervana-dockrepo01.fm.intel.com:5001/ -# REGISTRY=gcr.io/ -REGISTRY=docker.io/ -IMAGE=zdwiel/coach -# IMAGE=gcr.io/deep-greens/inference:v5 +# REGISTRY=gcr.io +REGISTRY=docker.io +ORGANIZATION=nervana +IMAGE=coach BUILD_ARGUMENTS= RUN_ARGUMENTS= @@ -15,15 +14,23 @@ ifdef https_proxy BUILD_ARGUMENTS+=--build-arg https_proxy=$(https_proxy) RUN_ARGUMENTS+=--env https_proxy=$(https_proxy) endif +ifdef MUJOCO_KEY + BUILD_ARGUMENTS+=--build-arg MUJOCO_KEY=$(MUJOCO_KEY) +endif RUN_ARGUMENTS+=--rm RUN_ARGUMENTS+=--net host RUN_ARGUMENTS+=-v /tmp/checkpoint:/checkpoint UNIT_TESTS=python3 -m pytest rl_coach/tests -m unit_test -INTEGRATION_TESTS=python3 -m pytest rl_coach/tests -m integration_test -n auto --tb=short -GOLDEN_TESTS=python3 -m pytest rl_coach/tests -m golden_test -n auto -TRACE_TESTS=python3 rl_coach/tests/trace_tests.py -prl +INTEGRATION_TESTS=python3 -m pytest rl_coach/tests -m integration_test --tb=short +ifdef PRESETS + PRESETS := -p $(PRESETS) +else + PRESETS := +endif +GOLDEN_TESTS=python3 rl_coach/tests/test_golden.py ${PRESETS} +TRACE_TESTS=python3 rl_coach/tests/trace_tests.py -prl ${PRESETS} CONTEXT = $(realpath ..) @@ -31,7 +38,10 @@ ifndef DOCKER DOCKER = docker endif -build: +build_base: + ${DOCKER} build -f=Dockerfile.base -t=${IMAGE}-base:master ${BUILD_ARGUMENTS} ${CONTEXT} + +build: build_base ${DOCKER} build -f=Dockerfile -t=${IMAGE} ${BUILD_ARGUMENTS} ${CONTEXT} mkdir -p /tmp/checkpoint rm -rf /tmp/checkpoint/* @@ -40,13 +50,13 @@ shell: build ${DOCKER} run ${RUN_ARGUMENTS} -it ${IMAGE} /bin/bash unit_tests: build - ${DOCKER} run ${RUN_ARGUMENTS} -it ${IMAGE} ${UNIT_TESTS} -n 8 + ${DOCKER} run ${RUN_ARGUMENTS} -it ${IMAGE} ${UNIT_TESTS} integration_tests: build ${DOCKER} run ${RUN_ARGUMENTS} -it ${IMAGE} ${INTEGRATION_TESTS} golden_tests: build - ${DOCKER} run ${RUN_ARGUMENTS} -it ${IMAGE} ${GOLDEN_TESTS} + ${DOCKER} run ${RUN_ARGUMENTS} -it ${IMAGE} ${GOLDEN_TESTS} ${PRESETS} trace_tests: build ${DOCKER} run ${RUN_ARGUMENTS} -it ${IMAGE} ${TRACE_TESTS} @@ -61,7 +71,7 @@ run_rollout_worker: build ${DOCKER} run ${RUN_ARGUMENTS} -it ${IMAGE} python3 rl_coach/rollout_worker.py --preset CartPole_DQN_distributed bootstrap_kubernetes: build push - kubectl run -i --tty --attach --image=${REGISTRY}${IMAGE} --restart=Never distributed-coach -- python3 rl_coach/orchestrators/start_training.py --preset CartPole_DQN_distributed --image ${IMAGE} -ns 10.63.249.182 -np / + kubectl run -i --tty --attach --image=${REGISTRY}/${IMAGE} --restart=Never distributed-coach -- python3 rl_coach/orchestrators/start_training.py --preset CartPole_DQN_distributed --image ${IMAGE} -ns 10.63.249.182 -np / stop_kubernetes: kubectl delete service --ignore-not-found redis-service @@ -75,8 +85,8 @@ kubernetes: stop_kubernetes python3 ${CONTEXT}/rl_coach/orchestrators/start_training.py --preset CartPole_DQN_distributed --image ${IMAGE} -ns 10.63.249.182 -np / push: build - ${DOCKER} tag ${IMAGE} ${REGISTRY}${IMAGE} - ${DOCKER} push ${REGISTRY}${IMAGE} + ${DOCKER} tag ${IMAGE} ${REGISTRY}/${ORGANIZATION}/${IMAGE} + ${DOCKER} push ${REGISTRY}/${ORGANIZATION}/${IMAGE} unit_tests_without_docker: cd .. && ${UNIT_TESTS} diff --git a/docker/README.md b/docker/README.md new file mode 100644 index 0000000..b9a8e8f --- /dev/null +++ b/docker/README.md @@ -0,0 +1,27 @@ +# Container Images + +In this directory we've put together several different Dockerfile's that can be used to build +containers that have coach and other environments/dependencies installed. How to build these +and what each contains is defined below: + +## default `Dockerfile` +* `make build` to create the image +* will create a basic Coach installation along with Gym (atari), Mujoco, and Vizdoom environments. +* useful for running unit/integration tests `make unit_tests` to run these in the container +* `make shell` will launch this container locally, and provide a bash shell prompt. +* includes GPU support (derives from `Dockerfile.base` which is a CUDA ubuntu 16.04 derived image) + +## `Dockerfile.mujoco_environment` +* `docker build --build-arg MUJOCO_KEY=${MUJOCO_KEY} -f docker/Dockerfile.mujoco_environment .` + from the parent dir to create the image +* contains mujoco environment and Coach. +* you need to supply your own license key (base64 encrypted) as an environment variable `MUJOCO_KEY` + to ensure you get the complete Mujoco environment + +## `Dockerfile.gym_environment` +* `docker build -f docker/Dockerfile.gym_environment .` from the parent dir to create the image +* contains OpenAI Gym environment (and all extras) and Coach. + +## `Dockerfile.doom_environment` +* `docker build -f docker/Dockerfile.doom_environment .` from the parent dir to create the image +* contains vizdoom environment and Coach. diff --git a/docker/docker_entrypoint.sh b/docker/docker_entrypoint.sh deleted file mode 100644 index feccda2..0000000 --- a/docker/docker_entrypoint.sh +++ /dev/null @@ -1,19 +0,0 @@ -#!/bin/sh -set -e - -# # download mjpro150 -# mkdir /root/.mujoco -# cd /root/.mujoco -# wget https://www.roboti.us/download/mjpro150_linux.zip -# unzip mjpro150_linux.zip - -# copy the mujoco license key into the container -# echo $MUJOCO_KEY | base64 --decode > /root/.mujoco/mjkey.txt -# export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/root/.mujoco/mjpro150/bin - -# git clone https://github.com/deepmind/dm_control.git -# pip3 install ./dm_control - -export VIZDOOM_ROOT=`pip show vizdoom 2>/dev/null | awk '/Location/{print $2}'`/vizdoom - -bash -c "$@" diff --git a/docs_raw/docs/usage.md b/docs_raw/docs/usage.md index 26d13b9..5edc860 100644 --- a/docs_raw/docs/usage.md +++ b/docs_raw/docs/usage.md @@ -12,7 +12,7 @@ This is the most common case. Just choose a preset using the `-p` flag and press ### Multi-threaded Algorithms -Multi-threaded algorithms are very common this days. +Multi-threaded algorithms are very common these days. They typically achieve the best results, and scale gracefully with the number of threads. In Coach, running such algorithms is done by selecting a suitable preset, and choosing the number of threads to run using the `-n` flag. @@ -130,4 +130,4 @@ The most up to date description can be found by using the `-h` flag. |`-et ENVIRONMENT_TYPE`, `--environment_type ENVIRONMENT_TYPE`|string|Choose an environment type class to override on top of the selected preset. If no preset is defined, a preset can be set from the command-line by combining settings which are set by using `--agent_type`, `--experiment_type`, `--environemnt_type`| |`-ept EXPLORATION_POLICY_TYPE`, `--exploration_policy_type EXPLORATION_POLICY_TYPE`|string|Choose an exploration policy type class to override on top of the selected preset.If no preset is defined, a preset can be set from the command-line by combining settings which are set by using `--agent_type`, `--experiment_type`, `--environemnt_type`| |`-lvl LEVEL`, `--level LEVEL` |string|Choose the level that will be played in the environment that was selected. This value will override the level parameter in the environment class.| -|`-cp CUSTOM_PARAMETER`, `--custom_parameter CUSTOM_PARAMETER`|string| Semicolon separated parameters used to override specific parameters on top of the selected preset (or on top of the command-line assembled one). Whenever a parameter value is a string, it should be inputted as `'\"string\"'`. For ex.: `"visualization.render=False;` `num_training_iterations=500;` `optimizer='rmsprop'"`| \ No newline at end of file +|`-cp CUSTOM_PARAMETER`, `--custom_parameter CUSTOM_PARAMETER`|string| Semicolon separated parameters used to override specific parameters on top of the selected preset (or on top of the command-line assembled one). Whenever a parameter value is a string, it should be inputted as `'\"string\"'`. For ex.: `"visualization.render=False;` `num_training_iterations=500;` `optimizer='rmsprop'"`| diff --git a/requirements.txt b/requirements.txt index 90c6785..2e1d515 100644 --- a/requirements.txt +++ b/requirements.txt @@ -8,7 +8,7 @@ PyOpenGL==3.1.0 scipy==0.19.0 scikit-image==0.13.0 box2d==2.3.2 -gym==0.10.5 +gym[atari]==0.10.5 bokeh==0.13.0 futures==3.1.1 wxPython==4.0.1 diff --git a/rl_coach/agents/mmc_agent.py b/rl_coach/agents/mmc_agent.py index 964d922..4e5fe0a 100644 --- a/rl_coach/agents/mmc_agent.py +++ b/rl_coach/agents/mmc_agent.py @@ -64,7 +64,7 @@ class MixedMonteCarloAgent(ValueOptimizationAgent): one_step_target = batch.rewards()[i] + \ (1.0 - batch.game_overs()[i]) * self.ap.algorithm.discount * \ q_st_plus_1[i][selected_actions[i]] - monte_carlo_target = total_returns()[i] + monte_carlo_target = total_returns[i] TD_targets[i, batch.actions()[i]] = (1 - self.mixing_rate) * one_step_target + \ self.mixing_rate * monte_carlo_target diff --git a/rl_coach/environments/doom_environment.py b/rl_coach/environments/doom_environment.py index d4269ba..74e55be 100644 --- a/rl_coach/environments/doom_environment.py +++ b/rl_coach/environments/doom_environment.py @@ -132,8 +132,12 @@ class DoomEnvironment(Environment): # load the emulator with the required level self.level = DoomLevel[level.upper()] local_scenarios_path = path.join(os.path.dirname(os.path.realpath(__file__)), 'doom') - self.scenarios_dir = local_scenarios_path if 'COACH_LOCAL' in level \ - else path.join(environ.get('VIZDOOM_ROOT'), 'scenarios') + if 'COACH_LOCAL' in level: + self.scenarios_dir = local_scenarios_path + elif 'VIZDOOM_ROOT' in environ: + self.scenarios_dir = path.join(environ.get('VIZDOOM_ROOT'), 'scenarios') + else: + self.scenarios_dir = path.join(os.path.dirname(os.path.realpath(vizdoom.__file__)), 'scenarios') self.game = vizdoom.DoomGame() self.game.load_config(path.join(self.scenarios_dir, self.level.value)) diff --git a/rl_coach/tests/README.md b/rl_coach/tests/README.md index 228829b..d07f35a 100644 --- a/rl_coach/tests/README.md +++ b/rl_coach/tests/README.md @@ -9,11 +9,12 @@ several parts, each testing the framework in different areas and strictness. * **Docker** - The docker image we supply checks Coach in terms of installation process, and verifies that all the components - are installed correctly. To build the Docke, use the command: + are installed correctly. To build the Docker image, use the command: ``` - docker build . -t coach - docker run -it coach /bin/bash + cd docker + make build_base && make build + make run ``` @@ -45,7 +46,7 @@ several parts, each testing the framework in different areas and strictness. The golden tests can be run using the following command: ``` - python3 rl_coach/tests/golden_tests.py + python3 rl_coach/tests/test_golden.py ``` * **Trace tests** - diff --git a/rl_coach/tests/test_eks.py b/rl_coach/tests/test_eks.py index f75ab04..1726713 100644 --- a/rl_coach/tests/test_eks.py +++ b/rl_coach/tests/test_eks.py @@ -37,6 +37,7 @@ class EKSHandler(): container = client.V1Container( name=self.test_name, image=self.image, + command=['/bin/bash', '-c'], args=[self.test_command], image_pull_policy='Always', working_dir=self.working_dir, diff --git a/rl_coach/tests/test_golden.py b/rl_coach/tests/test_golden.py index d2ce972..2bbcb82 100644 --- a/rl_coach/tests/test_golden.py +++ b/rl_coach/tests/test_golden.py @@ -94,14 +94,13 @@ def collect_presets(): yield preset_name -print(list(collect_presets())) @pytest.fixture(params=list(collect_presets())) def preset_name(request): return request.param @pytest.mark.golden_test -def test_preset_reward(preset_name, no_progress_bar=False, time_limit=60 * 60): +def test_preset_reward(preset_name, no_progress_bar=False, time_limit=60 * 60, verbose=False): preset_validation_params = validation_params(preset_name) win_size = 10 @@ -200,12 +199,12 @@ def test_preset_reward(preset_name, no_progress_bar=False, time_limit=60 * 60): else: if time.time() - start_time > time_limit: screen.error("Failed due to exceeding time limit", crash=False) - if args.verbose: + if verbose: screen.error("command exitcode: {}".format(p.returncode), crash=False) screen.error(open(log_file_name).read(), crash=False) elif csv_paths: screen.error("Failed due to insufficient reward", crash=False) - if args.verbose: + if verbose: screen.error("command exitcode: {}".format(p.returncode), crash=False) screen.error(open(log_file_name).read(), crash=False) screen.error("preset_validation_params.max_episodes_to_achieve_reward: {}".format( @@ -216,7 +215,7 @@ def test_preset_reward(preset_name, no_progress_bar=False, time_limit=60 * 60): screen.error("episode number: {}".format(csv['Episode #'].values[-1]), crash=False) else: screen.error("csv file never found", crash=False) - if args.verbose: + if verbose: screen.error("command exitcode: {}".format(p.returncode), crash=False) screen.error(open(log_file_name).read(), crash=False) @@ -227,12 +226,12 @@ def test_preset_reward(preset_name, no_progress_bar=False, time_limit=60 * 60): def main(): parser = argparse.ArgumentParser() - parser.add_argument('-p', '--preset', - help="(string) Name of a preset to run (as configured in presets.py)", + parser.add_argument('-p', '--preset', '--presets', + help="(string) Name of preset(s) to run (comma separated, and as configured in presets.py)", default=None, type=str) parser.add_argument('-ip', '--ignore_presets', - help="(string) Name of a preset(s) to ignore (comma separated, and as configured in presets.py)", + help="(string) Name of preset(s) to ignore (comma separated, and as configured in presets.py)", default=None, type=str) parser.add_argument('-v', '--verbose', @@ -251,7 +250,7 @@ def main(): args = parser.parse_args() if args.preset is not None: - presets_lists = [args.preset] + presets_lists = args.preset.split(',') else: presets_lists = all_presets() @@ -268,6 +267,7 @@ def main(): if args.stop_after_first_failure and fail_count > 0: break if preset_name not in presets_to_ignore: + print("Attempting to run Preset: %s" % preset_name) if not importable(preset_name): screen.error("Failed to load preset <{}>".format(preset_name), crash=False) fail_count += 1 @@ -278,7 +278,7 @@ def main(): continue test_count += 1 - test_passed = test_preset_reward(preset_name, args.no_progress_bar, args.time_limit) + test_passed = test_preset_reward(preset_name, args.no_progress_bar, args.time_limit, args.verbose) if not test_passed: fail_count += 1 diff --git a/rl_coach/tests/trace_tests.py b/rl_coach/tests/trace_tests.py index a307c5b..39095f7 100644 --- a/rl_coach/tests/trace_tests.py +++ b/rl_coach/tests/trace_tests.py @@ -168,12 +168,12 @@ def wait_and_check(args, processes, force=False): def main(): parser = argparse.ArgumentParser() - parser.add_argument('-p', '--preset', - help="(string) Name of a preset to run (as configured in presets.py)", + parser.add_argument('-p', '--preset', '--presets', + help="(string) Name of preset(s) to run (comma separated, as configured in presets.py)", default=None, type=str) parser.add_argument('-ip', '--ignore_presets', - help="(string) Name of a preset(s) to ignore (comma separated, and as configured in presets.py)", + help="(string) Name of preset(s) to ignore (comma separated, and as configured in presets.py)", default=None, type=str) parser.add_argument('-v', '--verbose', @@ -198,7 +198,7 @@ def main(): args.max_threads = 1 if args.preset is not None: - presets_lists = [args.preset] + presets_lists = args.preset.split(',') else: presets_lists = [f[:-3] for f in os.listdir(os.path.join('rl_coach', 'presets')) if f[-3:] == '.py' and not f == '__init__.py'] diff --git a/setup.py b/setup.py index 70790b2..9e6152a 100644 --- a/setup.py +++ b/setup.py @@ -48,6 +48,7 @@ with open(path.join(here, 'README.md'), encoding='utf-8') as f: long_description = f.read() install_requires = list() +extras = dict() with open(path.join(here, 'requirements.txt'), 'r') as f: for line in f: @@ -65,13 +66,10 @@ if not using_GPU: 'https://anaconda.org/intel/tensorflow/1.6.0/download/tensorflow-1.6.0-cp35-cp35m-linux_x86_64.whl'], shell=True) install_requires.append('tensorflow==1.6.0') + extras['mxnet'] = ['mxnet-cu90mkl>=1.3.0'] else: install_requires.append('tensorflow-gpu==1.9.0') - -# Framework-specific dependencies. -extras = { - 'mxnet': ['mxnet-cu90mkl>=1.3.0'] -} + extras['mxnet'] = ['mxnet-mkl>=1.3.0'] all_deps = [] for group_name in extras: From 6d40ad16508b1f330b16b6f00512d9e52c25fdef Mon Sep 17 00:00:00 2001 From: Itai Caspi <30383381+itaicaspi-intel@users.noreply.github.com> Date: Thu, 15 Nov 2018 15:00:13 +0200 Subject: [PATCH 111/162] update of api docstrings across coach and tutorials [WIP] (#91) * updating the documentation website * adding the built docs * update of api docstrings across coach and tutorials 0-2 * added some missing api documentation * New Sphinx based documentation --- .gitignore | 2 + docs/404.html | 244 - .../design_imgs => _images}/ac.png | Bin docs/{img => _images}/act.png | Bin docs/_images/algorithms.png | Bin 0 -> 51160 bytes docs/_images/attention_discretization.png | Bin 0 -> 16399 bytes docs/{img => _images}/bollinger_bands.png | Bin docs/_images/box_discretization.png | Bin 0 -> 17143 bytes docs/_images/box_masking.png | Bin 0 -> 12810 bytes .../design_imgs => _images}/bs_dqn.png | Bin docs/_images/cil.png | Bin 0 -> 27469 bytes .../compare_by_num_episodes.png | Bin docs/{img => _images}/compare_by_time.png | Bin .../design_imgs => _images}/ddpg.png | Bin docs/_images/design.png | Bin 0 -> 109083 bytes .../design_imgs => _images}/dfp.png | Bin docs/{img => 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Source code for rl_coach.agents.actor_critic_agent

+#
+# Copyright (c) 2017 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from typing import Union
+
+import numpy as np
+import scipy.signal
+
+from rl_coach.agents.policy_optimization_agent import PolicyOptimizationAgent, PolicyGradientRescaler
+from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
+from rl_coach.architectures.head_parameters import PolicyHeadParameters, VHeadParameters
+from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters
+from rl_coach.base_parameters import AlgorithmParameters, NetworkParameters, \
+    AgentParameters
+from rl_coach.exploration_policies.categorical import CategoricalParameters
+from rl_coach.exploration_policies.continuous_entropy import ContinuousEntropyParameters
+from rl_coach.logger import screen
+from rl_coach.memories.episodic.single_episode_buffer import SingleEpisodeBufferParameters
+from rl_coach.spaces import DiscreteActionSpace, BoxActionSpace
+from rl_coach.utils import last_sample
+
+
+
[docs]class ActorCriticAlgorithmParameters(AlgorithmParameters):
+    """
+    :param policy_gradient_rescaler: (PolicyGradientRescaler)
+        The value that will be used to rescale the policy gradient
+
+    :param apply_gradients_every_x_episodes: (int)
+        The number of episodes to wait before applying the accumulated gradients to the network.
+        The training iterations only accumulate gradients without actually applying them.
+
+    :param beta_entropy: (float)
+        The weight that will be given to the entropy regularization which is used in order to improve exploration.
+
+    :param num_steps_between_gradient_updates: (int)
+        Every num_steps_between_gradient_updates transitions will be considered as a single batch and use for
+        accumulating gradients. This is also the number of steps used for bootstrapping according to the n-step formulation.
+
+    :param gae_lambda: (float)
+        If the policy gradient rescaler was defined as PolicyGradientRescaler.GAE, the generalized advantage estimation
+        scheme will be used, in which case the lambda value controls the decay for the different n-step lengths.
+
+    :param estimate_state_value_using_gae: (bool)
+        If set to True, the state value targets for the V head will be estimated using the GAE scheme.
+    """
+    def __init__(self):
+        super().__init__()
+        self.policy_gradient_rescaler = PolicyGradientRescaler.A_VALUE
+        self.apply_gradients_every_x_episodes = 5
+        self.beta_entropy = 0
+        self.num_steps_between_gradient_updates = 5000  # this is called t_max in all the papers
+        self.gae_lambda = 0.96
+        self.estimate_state_value_using_gae = False

+
+
+class ActorCriticNetworkParameters(NetworkParameters):
+    def __init__(self):
+        super().__init__()
+        self.input_embedders_parameters = {'observation': InputEmbedderParameters()}
+        self.middleware_parameters = FCMiddlewareParameters()
+        self.heads_parameters = [VHeadParameters(loss_weight=0.5), PolicyHeadParameters(loss_weight=1.0)]
+        self.optimizer_type = 'Adam'
+        self.clip_gradients = 40.0
+        self.async_training = True
+
+
+class ActorCriticAgentParameters(AgentParameters):
+    def __init__(self):
+        super().__init__(algorithm=ActorCriticAlgorithmParameters(),
+                         exploration={DiscreteActionSpace: CategoricalParameters(),
+                                      BoxActionSpace: ContinuousEntropyParameters()},
+                         memory=SingleEpisodeBufferParameters(),
+                         networks={"main": ActorCriticNetworkParameters()})
+
+    @property
+    def path(self):
+        return 'rl_coach.agents.actor_critic_agent:ActorCriticAgent'
+
+
+# Actor Critic - https://arxiv.org/abs/1602.01783
+class ActorCriticAgent(PolicyOptimizationAgent):
+    def __init__(self, agent_parameters, parent: Union['LevelManager', 'CompositeAgent']=None):
+        super().__init__(agent_parameters, parent)
+        self.last_gradient_update_step_idx = 0
+        self.action_advantages = self.register_signal('Advantages')
+        self.state_values = self.register_signal('Values')
+        self.value_loss = self.register_signal('Value Loss')
+        self.policy_loss = self.register_signal('Policy Loss')
+
+    # Discounting function used to calculate discounted returns.
+    def discount(self, x, gamma):
+        return scipy.signal.lfilter([1], [1, -gamma], x[::-1], axis=0)[::-1]
+
+    def get_general_advantage_estimation_values(self, rewards, values):
+        # values contain n+1 elements (t ... t+n+1), rewards contain n elements (t ... t + n)
+        bootstrap_extended_rewards = np.array(rewards.tolist() + [values[-1]])
+
+        # Approximation based calculation of GAE (mathematically correct only when Tmax = inf,
+        # although in practice works even in much smaller Tmax values, e.g. 20)
+        deltas = rewards + self.ap.algorithm.discount * values[1:] - values[:-1]
+        gae = self.discount(deltas, self.ap.algorithm.discount * self.ap.algorithm.gae_lambda)
+
+        if self.ap.algorithm.estimate_state_value_using_gae:
+            discounted_returns = np.expand_dims(gae + values[:-1], -1)
+        else:
+            discounted_returns = np.expand_dims(np.array(self.discount(bootstrap_extended_rewards,
+                                                                       self.ap.algorithm.discount)), 1)[:-1]
+        return gae, discounted_returns
+
+    def learn_from_batch(self, batch):
+        # batch contains a list of episodes to learn from
+        network_keys = self.ap.network_wrappers['main'].input_embedders_parameters.keys()
+
+        # get the values for the current states
+
+        result = self.networks['main'].online_network.predict(batch.states(network_keys))
+        current_state_values = result[0]
+
+        self.state_values.add_sample(current_state_values)
+
+        # the targets for the state value estimator
+        num_transitions = batch.size
+        state_value_head_targets = np.zeros((num_transitions, 1))
+
+        # estimate the advantage function
+        action_advantages = np.zeros((num_transitions, 1))
+
+        if self.policy_gradient_rescaler == PolicyGradientRescaler.A_VALUE:
+            if batch.game_overs()[-1]:
+                R = 0
+            else:
+                R = self.networks['main'].online_network.predict(last_sample(batch.next_states(network_keys)))[0]
+
+            for i in reversed(range(num_transitions)):
+                R = batch.rewards()[i] + self.ap.algorithm.discount * R
+                state_value_head_targets[i] = R
+                action_advantages[i] = R - current_state_values[i]
+
+        elif self.policy_gradient_rescaler == PolicyGradientRescaler.GAE:
+            # get bootstraps
+            bootstrapped_value = self.networks['main'].online_network.predict(last_sample(batch.next_states(network_keys)))[0]
+            values = np.append(current_state_values, bootstrapped_value)
+            if batch.game_overs()[-1]:
+                values[-1] = 0
+
+            # get general discounted returns table
+            gae_values, state_value_head_targets = self.get_general_advantage_estimation_values(batch.rewards(), values)
+            action_advantages = np.vstack(gae_values)
+        else:
+            screen.warning("WARNING: The requested policy gradient rescaler is not available")
+
+        action_advantages = action_advantages.squeeze(axis=-1)
+        actions = batch.actions()
+        if not isinstance(self.spaces.action, DiscreteActionSpace) and len(actions.shape) < 2:
+            actions = np.expand_dims(actions, -1)
+
+        # train
+        result = self.networks['main'].online_network.accumulate_gradients({**batch.states(network_keys),
+                                                                            'output_1_0': actions},
+                                                                       [state_value_head_targets, action_advantages])
+
+        # logging
+        total_loss, losses, unclipped_grads = result[:3]
+        self.action_advantages.add_sample(action_advantages)
+        self.unclipped_grads.add_sample(unclipped_grads)
+        self.value_loss.add_sample(losses[0])
+        self.policy_loss.add_sample(losses[1])
+
+        return total_loss, losses, unclipped_grads
+
+    def get_prediction(self, states):
+        tf_input_state = self.prepare_batch_for_inference(states, "main")
+        return self.networks['main'].online_network.predict(tf_input_state)[1:]  # index 0 is the state value
+
+ +
+ +
+ + +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_modules/rl_coach/agents/agent.html b/docs/_modules/rl_coach/agents/agent.html new file mode 100644 index 0000000..59234c3 --- /dev/null +++ b/docs/_modules/rl_coach/agents/agent.html @@ -0,0 +1,1153 @@ + + + + + + + + + + + rl_coach.agents.agent — Reinforcement Learning Coach 0.11.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + +
+ +
+ + + + + + + + + + + + + + + + + +
+ + + + +
+
+
+
+ +

Source code for rl_coach.agents.agent

+#
+# Copyright (c) 2017 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import copy
+import random
+from collections import OrderedDict
+from typing import Dict, List, Union, Tuple
+
+import numpy as np
+from pandas import read_pickle
+from six.moves import range
+
+from rl_coach.agents.agent_interface import AgentInterface
+from rl_coach.architectures.network_wrapper import NetworkWrapper
+from rl_coach.base_parameters import AgentParameters, DistributedTaskParameters
+from rl_coach.core_types import RunPhase, PredictionType, EnvironmentEpisodes, ActionType, Batch, Episode, StateType
+from rl_coach.core_types import Transition, ActionInfo, TrainingSteps, EnvironmentSteps, EnvResponse
+from rl_coach.logger import screen, Logger, EpisodeLogger
+from rl_coach.memories.episodic.episodic_experience_replay import EpisodicExperienceReplay
+from rl_coach.spaces import SpacesDefinition, VectorObservationSpace, GoalsSpace, AttentionActionSpace
+from rl_coach.utils import Signal, force_list
+from rl_coach.utils import dynamic_import_and_instantiate_module_from_params
+from rl_coach.memories.backend.memory_impl import get_memory_backend
+
+
+
[docs]class Agent(AgentInterface):
+    def __init__(self, agent_parameters: AgentParameters, parent: Union['LevelManager', 'CompositeAgent']=None):
+        """
+        :param agent_parameters: A AgentParameters class instance with all the agent parameters
+        """
+        super().__init__()
+        self.ap = agent_parameters
+        self.task_id = self.ap.task_parameters.task_index
+        self.is_chief = self.task_id == 0
+        self.shared_memory = type(agent_parameters.task_parameters) == DistributedTaskParameters \
+                             and self.ap.memory.shared_memory
+        if self.shared_memory:
+            self.shared_memory_scratchpad = self.ap.task_parameters.shared_memory_scratchpad
+        self.name = agent_parameters.name
+        self.parent = parent
+        self.parent_level_manager = None
+        self.full_name_id = agent_parameters.full_name_id = self.name
+
+        if type(agent_parameters.task_parameters) == DistributedTaskParameters:
+            screen.log_title("Creating agent - name: {} task id: {} (may take up to 30 seconds due to "
+                             "tensorflow wake up time)".format(self.full_name_id, self.task_id))
+        else:
+            screen.log_title("Creating agent - name: {}".format(self.full_name_id))
+        self.imitation = False
+        self.agent_logger = Logger()
+        self.agent_episode_logger = EpisodeLogger()
+
+        # get the memory
+        # - distributed training + shared memory:
+        #   * is chief?  -> create the memory and add it to the scratchpad
+        #   * not chief? -> wait for the chief to create the memory and then fetch it
+        # - non distributed training / not shared memory:
+        #   * create memory
+        memory_name = self.ap.memory.path.split(':')[1]
+        self.memory_lookup_name = self.full_name_id + '.' + memory_name
+        if self.shared_memory and not self.is_chief:
+            self.memory = self.shared_memory_scratchpad.get(self.memory_lookup_name)
+        else:
+            # modules
+            self.memory = dynamic_import_and_instantiate_module_from_params(self.ap.memory)
+
+            if hasattr(self.ap.memory, 'memory_backend_params'):
+                self.memory_backend = get_memory_backend(self.ap.memory.memory_backend_params)
+
+                if self.ap.memory.memory_backend_params.run_type == 'trainer':
+                    self.memory_backend.subscribe(self)
+                else:
+                    self.memory.set_memory_backend(self.memory_backend)
+
+            if agent_parameters.memory.load_memory_from_file_path:
+                screen.log_title("Loading replay buffer from pickle. Pickle path: {}"
+                                 .format(agent_parameters.memory.load_memory_from_file_path))
+                self.memory.load(agent_parameters.memory.load_memory_from_file_path)
+
+            if self.shared_memory and self.is_chief:
+                self.shared_memory_scratchpad.add(self.memory_lookup_name, self.memory)
+
+        # set devices
+        if type(agent_parameters.task_parameters) == DistributedTaskParameters:
+            self.has_global = True
+            self.replicated_device = agent_parameters.task_parameters.device
+            self.worker_device = "/job:worker/task:{}".format(self.task_id)
+        else:
+            self.has_global = False
+            self.replicated_device = None
+            self.worker_device = ""
+        if agent_parameters.task_parameters.use_cpu:
+            self.worker_device += "/cpu:0"
+        else:
+            self.worker_device += "/device:GPU:0"
+
+        # filters
+        self.input_filter = self.ap.input_filter
+        self.output_filter = self.ap.output_filter
+        self.pre_network_filter = self.ap.pre_network_filter
+        device = self.replicated_device if self.replicated_device else self.worker_device
+        if hasattr(self.ap.memory, 'memory_backend_params') and self.ap.algorithm.distributed_coach_synchronization_type:
+            self.input_filter.set_device(device, memory_backend_params=self.ap.memory.memory_backend_params)
+            self.output_filter.set_device(device, memory_backend_params=self.ap.memory.memory_backend_params)
+            self.pre_network_filter.set_device(device, memory_backend_params=self.ap.memory.memory_backend_params)
+        else:
+            self.input_filter.set_device(device)
+            self.output_filter.set_device(device)
+            self.pre_network_filter.set_device(device)
+
+        # initialize all internal variables
+        self._phase = RunPhase.HEATUP
+        self.total_shaped_reward_in_current_episode = 0
+        self.total_reward_in_current_episode = 0
+        self.total_steps_counter = 0
+        self.running_reward = None
+        self.training_iteration = 0
+        self.last_target_network_update_step = 0
+        self.last_training_phase_step = 0
+        self.current_episode = self.ap.current_episode = 0
+        self.curr_state = {}
+        self.current_hrl_goal = None
+        self.current_episode_steps_counter = 0
+        self.episode_running_info = {}
+        self.last_episode_evaluation_ran = 0
+        self.running_observations = []
+        self.agent_logger.set_current_time(self.current_episode)
+        self.exploration_policy = None
+        self.networks = {}
+        self.last_action_info = None
+        self.running_observation_stats = None
+        self.running_reward_stats = None
+        self.accumulated_rewards_across_evaluation_episodes = 0
+        self.accumulated_shaped_rewards_across_evaluation_episodes = 0
+        self.num_successes_across_evaluation_episodes = 0
+        self.num_evaluation_episodes_completed = 0
+        self.current_episode_buffer = Episode(discount=self.ap.algorithm.discount, n_step=self.ap.algorithm.n_step)
+        # TODO: add agents observation rendering for debugging purposes (not the same as the environment rendering)
+
+        # environment parameters
+        self.spaces = None
+        self.in_action_space = self.ap.algorithm.in_action_space
+
+        # signals
+        self.episode_signals = []
+        self.step_signals = []
+        self.loss = self.register_signal('Loss')
+        self.curr_learning_rate = self.register_signal('Learning Rate')
+        self.unclipped_grads = self.register_signal('Grads (unclipped)')
+        self.reward = self.register_signal('Reward', dump_one_value_per_episode=False, dump_one_value_per_step=True)
+        self.shaped_reward = self.register_signal('Shaped Reward', dump_one_value_per_episode=False, dump_one_value_per_step=True)
+        self.discounted_return = self.register_signal('Discounted Return')
+        if isinstance(self.in_action_space, GoalsSpace):
+            self.distance_from_goal = self.register_signal('Distance From Goal', dump_one_value_per_step=True)
+        # use seed
+        if self.ap.task_parameters.seed is not None:
+            random.seed(self.ap.task_parameters.seed)
+            np.random.seed(self.ap.task_parameters.seed)
+        else:
+            # we need to seed the RNG since the different processes are initialized with the same parent seed
+            random.seed()
+            np.random.seed()
+
+    @property
+    def parent(self) -> 'LevelManager':
+        """
+        Get the parent class of the agent
+
+        :return: the current phase
+        """
+        return self._parent
+
+    @parent.setter
+    def parent(self, val) -> None:
+        """
+        Change the parent class of the agent.
+        Additionally, updates the full name of the agent
+
+        :param val: the new parent
+        :return: None
+        """
+        self._parent = val
+        if self._parent is not None:
+            if not hasattr(self._parent, 'name'):
+                raise ValueError("The parent of an agent must have a name")
+            self.full_name_id = self.ap.full_name_id = "{}/{}".format(self._parent.name, self.name)
+
+
[docs]    def setup_logger(self) -> None:
+        """
+        Setup the logger for the agent
+
+        :return: None
+        """
+        # dump documentation
+        logger_prefix = "{graph_name}.{level_name}.{agent_full_id}".\
+            format(graph_name=self.parent_level_manager.parent_graph_manager.name,
+                   level_name=self.parent_level_manager.name,
+                   agent_full_id='.'.join(self.full_name_id.split('/')))
+        self.agent_logger.set_logger_filenames(self.ap.task_parameters.experiment_path, logger_prefix=logger_prefix,
+                                               add_timestamp=True, task_id=self.task_id)
+        if self.ap.visualization.dump_in_episode_signals:
+            self.agent_episode_logger.set_logger_filenames(self.ap.task_parameters.experiment_path,
+                                                           logger_prefix=logger_prefix,
+                                                           add_timestamp=True, task_id=self.task_id)

+
+
[docs]    def set_session(self, sess) -> None:
+        """
+        Set the deep learning framework session for all the agents in the composite agent
+
+        :return: None
+        """
+        self.input_filter.set_session(sess)
+        self.output_filter.set_session(sess)
+        self.pre_network_filter.set_session(sess)
+        [network.set_session(sess) for network in self.networks.values()]

+
+
[docs]    def register_signal(self, signal_name: str, dump_one_value_per_episode: bool=True,
+                        dump_one_value_per_step: bool=False) -> Signal:
+        """
+        Register a signal such that its statistics will be dumped and be viewable through dashboard
+
+        :param signal_name: the name of the signal as it will appear in dashboard
+        :param dump_one_value_per_episode: should the signal value be written for each episode?
+        :param dump_one_value_per_step: should the signal value be written for each step?
+        :return: the created signal
+        """
+        signal = Signal(signal_name)
+        if dump_one_value_per_episode:
+            self.episode_signals.append(signal)
+        if dump_one_value_per_step:
+            self.step_signals.append(signal)
+        return signal

+
+
[docs]    def set_environment_parameters(self, spaces: SpacesDefinition):
+        """
+        Sets the parameters that are environment dependent. As a side effect, initializes all the components that are
+        dependent on those values, by calling init_environment_dependent_modules
+
+        :param spaces: the environment spaces definition
+        :return: None
+        """
+        self.spaces = copy.deepcopy(spaces)
+
+        if self.ap.algorithm.use_accumulated_reward_as_measurement:
+            if 'measurements' in self.spaces.state.sub_spaces:
+                self.spaces.state['measurements'].shape += 1
+                self.spaces.state['measurements'].measurements_names += ['accumulated_reward']
+            else:
+                self.spaces.state['measurements'] = VectorObservationSpace(1, measurements_names=['accumulated_reward'])
+
+        for observation_name in self.spaces.state.sub_spaces.keys():
+            self.spaces.state[observation_name] = \
+                self.pre_network_filter.get_filtered_observation_space(observation_name,
+                    self.input_filter.get_filtered_observation_space(observation_name,
+                                                                     self.spaces.state[observation_name]))
+
+        self.spaces.reward = self.pre_network_filter.get_filtered_reward_space(
+            self.input_filter.get_filtered_reward_space(self.spaces.reward))
+
+        self.spaces.action = self.output_filter.get_unfiltered_action_space(self.spaces.action)
+
+        if isinstance(self.in_action_space, GoalsSpace):
+            # TODO: what if the goal type is an embedding / embedding change?
+            self.spaces.goal = self.in_action_space
+            self.spaces.goal.set_target_space(self.spaces.state[self.spaces.goal.goal_name])
+
+        self.init_environment_dependent_modules()

+
+
[docs]    def create_networks(self) -> Dict[str, NetworkWrapper]:
+        """
+        Create all the networks of the agent.
+        The network creation will be done after setting the environment parameters for the agent, since they are needed
+        for creating the network.
+
+        :return: A list containing all the networks
+        """
+        networks = {}
+        for network_name in sorted(self.ap.network_wrappers.keys()):
+            networks[network_name] = NetworkWrapper(name=network_name,
+                                                    agent_parameters=self.ap,
+                                                    has_target=self.ap.network_wrappers[network_name].create_target_network,
+                                                    has_global=self.has_global,
+                                                    spaces=self.spaces,
+                                                    replicated_device=self.replicated_device,
+                                                    worker_device=self.worker_device)
+
+            if self.ap.visualization.print_networks_summary:
+                print(networks[network_name])
+
+        return networks

+
+
[docs]    def init_environment_dependent_modules(self) -> None:
+        """
+        Initialize any modules that depend on knowing information about the environment such as the action space or
+        the observation space
+
+        :return: None
+        """
+        # initialize exploration policy
+        if isinstance(self.ap.exploration, dict):
+            if self.spaces.action.__class__ in self.ap.exploration.keys():
+                self.ap.exploration = self.ap.exploration[self.spaces.action.__class__]
+            else:
+                raise ValueError("The exploration parameters were defined as a mapping between action space types and "
+                                 "exploration types, but the action space used by the environment ({}) was not part of "
+                                 "the exploration parameters dictionary keys ({})"
+                                 .format(self.spaces.action.__class__, list(self.ap.exploration.keys())))
+        self.ap.exploration.action_space = self.spaces.action
+        self.exploration_policy = dynamic_import_and_instantiate_module_from_params(self.ap.exploration)
+
+        # create all the networks of the agent
+        self.networks = self.create_networks()

+
+    @property
+    def phase(self) -> RunPhase:
+        """
+        The current running phase of the agent
+
+        :return: RunPhase
+        """
+        return self._phase
+
+    @phase.setter
+    def phase(self, val: RunPhase) -> None:
+        """
+        Change the phase of the run for the agent and all the sub components
+
+        :param val: the new run phase (TRAIN, TEST, etc.)
+        :return: None
+        """
+        self.reset_evaluation_state(val)
+        self._phase = val
+        self.exploration_policy.change_phase(val)
+
+
[docs]    def reset_evaluation_state(self, val: RunPhase) -> None:
+        """
+        Perform accumulators initialization when entering an evaluation phase, and signal dumping when exiting an
+        evaluation phase. Entering or exiting the evaluation phase is determined according to the new phase given
+        by val, and by the current phase set in self.phase.
+
+        :param val: The new phase to change to
+        :return: None
+        """
+        starting_evaluation = (val == RunPhase.TEST)
+        ending_evaluation = (self.phase == RunPhase.TEST)
+
+        if starting_evaluation:
+            self.accumulated_rewards_across_evaluation_episodes = 0
+            self.accumulated_shaped_rewards_across_evaluation_episodes = 0
+            self.num_successes_across_evaluation_episodes = 0
+            self.num_evaluation_episodes_completed = 0
+            if self.ap.is_a_highest_level_agent or self.ap.task_parameters.verbosity == "high":
+                screen.log_title("{}: Starting evaluation phase".format(self.name))
+
+        elif ending_evaluation:
+            # we write to the next episode, because it could be that the current episode was already written
+            # to disk and then we won't write it again
+            self.agent_logger.set_current_time(self.current_episode + 1)
+            self.agent_logger.create_signal_value(
+                'Evaluation Reward',
+                self.accumulated_rewards_across_evaluation_episodes / self.num_evaluation_episodes_completed)
+            self.agent_logger.create_signal_value(
+                'Shaped Evaluation Reward',
+                self.accumulated_shaped_rewards_across_evaluation_episodes / self.num_evaluation_episodes_completed)
+            success_rate = self.num_successes_across_evaluation_episodes / self.num_evaluation_episodes_completed
+            self.agent_logger.create_signal_value(
+                "Success Rate",
+                success_rate
+            )
+            if self.ap.is_a_highest_level_agent or self.ap.task_parameters.verbosity == "high":
+                screen.log_title("{}: Finished evaluation phase. Success rate = {}"
+                             .format(self.name, np.round(success_rate, 2)))

+
+
[docs]    def call_memory(self, func, args=()):
+        """
+        This function is a wrapper to allow having the same calls for shared or unshared memories.
+        It should be used instead of calling the memory directly in order to allow different algorithms to work
+        both with a shared and a local memory.
+
+        :param func: the name of the memory function to call
+        :param args: the arguments to supply to the function
+        :return: the return value of the function
+        """
+        if self.shared_memory:
+            result = self.shared_memory_scratchpad.internal_call(self.memory_lookup_name, func, args)
+        else:
+            if type(args) != tuple:
+                args = (args,)
+            result = getattr(self.memory, func)(*args)
+        return result

+
+
[docs]    def log_to_screen(self) -> None:
+        """
+        Write an episode summary line to the terminal
+
+        :return: None
+        """
+        # log to screen
+        log = OrderedDict()
+        log["Name"] = self.full_name_id
+        if self.task_id is not None:
+            log["Worker"] = self.task_id
+        log["Episode"] = self.current_episode
+        log["Total reward"] = np.round(self.total_reward_in_current_episode, 2)
+        log["Exploration"] = np.round(self.exploration_policy.get_control_param(), 2)
+        log["Steps"] = self.total_steps_counter
+        log["Training iteration"] = self.training_iteration
+        screen.log_dict(log, prefix=self.phase.value)

+
+
[docs]    def update_step_in_episode_log(self) -> None:
+        """
+        Updates the in-episode log file with all the signal values from the most recent step.
+
+        :return: None
+        """
+        # log all the signals to file
+        self.agent_episode_logger.set_current_time(self.current_episode_steps_counter)
+        self.agent_episode_logger.create_signal_value('Training Iter', self.training_iteration)
+        self.agent_episode_logger.create_signal_value('In Heatup', int(self._phase == RunPhase.HEATUP))
+        self.agent_episode_logger.create_signal_value('ER #Transitions', self.call_memory('num_transitions'))
+        self.agent_episode_logger.create_signal_value('ER #Episodes', self.call_memory('length'))
+        self.agent_episode_logger.create_signal_value('Total steps', self.total_steps_counter)
+        self.agent_episode_logger.create_signal_value("Epsilon", self.exploration_policy.get_control_param())
+        self.agent_episode_logger.create_signal_value("Shaped Accumulated Reward", self.total_shaped_reward_in_current_episode)
+        self.agent_episode_logger.create_signal_value('Update Target Network', 0, overwrite=False)
+        self.agent_episode_logger.update_wall_clock_time(self.current_episode_steps_counter)
+
+        for signal in self.step_signals:
+            self.agent_episode_logger.create_signal_value(signal.name, signal.get_last_value())
+
+        # dump
+        self.agent_episode_logger.dump_output_csv()

+
+
[docs]    def update_log(self) -> None:
+        """
+        Updates the episodic log file with all the signal values from the most recent episode.
+        Additional signals for logging can be set by the creating a new signal using self.register_signal,
+        and then updating it with some internal agent values.
+
+        :return: None
+        """
+        # log all the signals to file
+        self.agent_logger.set_current_time(self.current_episode)
+        self.agent_logger.create_signal_value('Training Iter', self.training_iteration)
+        self.agent_logger.create_signal_value('In Heatup', int(self._phase == RunPhase.HEATUP))
+        self.agent_logger.create_signal_value('ER #Transitions', self.call_memory('num_transitions'))
+        self.agent_logger.create_signal_value('ER #Episodes', self.call_memory('length'))
+        self.agent_logger.create_signal_value('Episode Length', self.current_episode_steps_counter)
+        self.agent_logger.create_signal_value('Total steps', self.total_steps_counter)
+        self.agent_logger.create_signal_value("Epsilon", np.mean(self.exploration_policy.get_control_param()))
+        self.agent_logger.create_signal_value("Shaped Training Reward", self.total_shaped_reward_in_current_episode
+                                   if self._phase == RunPhase.TRAIN else np.nan)
+        self.agent_logger.create_signal_value("Training Reward", self.total_reward_in_current_episode
+                                   if self._phase == RunPhase.TRAIN else np.nan)
+
+        self.agent_logger.create_signal_value('Update Target Network', 0, overwrite=False)
+        self.agent_logger.update_wall_clock_time(self.current_episode)
+
+        if self._phase != RunPhase.TEST:
+            self.agent_logger.create_signal_value('Evaluation Reward', np.nan, overwrite=False)
+            self.agent_logger.create_signal_value('Shaped Evaluation Reward', np.nan, overwrite=False)
+            self.agent_logger.create_signal_value('Success Rate', np.nan, overwrite=False)
+
+        for signal in self.episode_signals:
+            self.agent_logger.create_signal_value("{}/Mean".format(signal.name), signal.get_mean())
+            self.agent_logger.create_signal_value("{}/Stdev".format(signal.name), signal.get_stdev())
+            self.agent_logger.create_signal_value("{}/Max".format(signal.name), signal.get_max())
+            self.agent_logger.create_signal_value("{}/Min".format(signal.name), signal.get_min())
+
+        # dump
+        if self.current_episode % self.ap.visualization.dump_signals_to_csv_every_x_episodes == 0 \
+                and self.current_episode > 0:
+            self.agent_logger.dump_output_csv()

+
+
[docs]    def handle_episode_ended(self) -> None:
+        """
+        Make any changes needed when each episode is ended.
+        This includes incrementing counters, updating full episode dependent values, updating logs, etc.
+        This function is called right after each episode is ended.
+
+        :return: None
+        """
+        self.current_episode_buffer.is_complete = True
+        self.current_episode_buffer.update_transitions_rewards_and_bootstrap_data()
+
+        for transition in self.current_episode_buffer.transitions:
+            self.discounted_return.add_sample(transition.n_step_discounted_rewards)
+
+        if self.phase != RunPhase.TEST or self.ap.task_parameters.evaluate_only:
+            self.current_episode += 1
+
+        if self.phase != RunPhase.TEST:
+            if isinstance(self.memory, EpisodicExperienceReplay):
+                self.call_memory('store_episode', self.current_episode_buffer)
+            elif self.ap.algorithm.store_transitions_only_when_episodes_are_terminated:
+                for transition in self.current_episode_buffer.transitions:
+                    self.call_memory('store', transition)
+
+        if self.phase == RunPhase.TEST:
+            self.accumulated_rewards_across_evaluation_episodes += self.total_reward_in_current_episode
+            self.accumulated_shaped_rewards_across_evaluation_episodes += self.total_shaped_reward_in_current_episode
+            self.num_evaluation_episodes_completed += 1
+
+            if self.spaces.reward.reward_success_threshold and \
+                    self.total_reward_in_current_episode >= self.spaces.reward.reward_success_threshold:
+                self.num_successes_across_evaluation_episodes += 1
+
+        if self.ap.visualization.dump_csv:
+            self.update_log()
+
+        if self.ap.is_a_highest_level_agent or self.ap.task_parameters.verbosity == "high":
+            self.log_to_screen()

+
+
[docs]    def reset_internal_state(self) -> None:
+        """
+        Reset all the episodic parameters. This function is called right before each episode starts.
+
+        :return: None
+        """
+        for signal in self.episode_signals:
+            signal.reset()
+        for signal in self.step_signals:
+            signal.reset()
+        self.agent_episode_logger.set_episode_idx(self.current_episode)
+        self.total_shaped_reward_in_current_episode = 0
+        self.total_reward_in_current_episode = 0
+        self.curr_state = {}
+        self.current_episode_steps_counter = 0
+        self.episode_running_info = {}
+        self.current_episode_buffer = Episode(discount=self.ap.algorithm.discount, n_step=self.ap.algorithm.n_step)
+        if self.exploration_policy:
+            self.exploration_policy.reset()
+        self.input_filter.reset()
+        self.output_filter.reset()
+        self.pre_network_filter.reset()
+        if isinstance(self.memory, EpisodicExperienceReplay):
+            self.call_memory('verify_last_episode_is_closed')
+
+        for network in self.networks.values():
+            network.online_network.reset_internal_memory()

+
+
[docs]    def learn_from_batch(self, batch) -> Tuple[float, List, List]:
+        """
+        Given a batch of transitions, calculates their target values and updates the network.
+
+        :param batch: A list of transitions
+        :return: The total loss of the training, the loss per head and the unclipped gradients
+        """
+        return 0, [], []

+
+    def _should_update_online_weights_to_target(self):
+        """
+        Determine if online weights should be copied to the target.
+
+        :return: boolean: True if the online weights should be copied to the target.
+        """
+
+        # update the target network of every network that has a target network
+        step_method = self.ap.algorithm.num_steps_between_copying_online_weights_to_target
+        if step_method.__class__ == TrainingSteps:
+            should_update = (self.training_iteration - self.last_target_network_update_step) >= step_method.num_steps
+            if should_update:
+                self.last_target_network_update_step = self.training_iteration
+        elif step_method.__class__ == EnvironmentSteps:
+            should_update = (self.total_steps_counter - self.last_target_network_update_step) >= step_method.num_steps
+            if should_update:
+                self.last_target_network_update_step = self.total_steps_counter
+        else:
+            raise ValueError("The num_steps_between_copying_online_weights_to_target parameter should be either "
+                             "EnvironmentSteps or TrainingSteps. Instead it is {}".format(step_method.__class__))
+        return should_update
+
+    def _should_train(self, wait_for_full_episode=False) -> bool:
+        """
+        Determine if we should start a training phase according to the number of steps passed since the last training
+
+        :return:  boolean: True if we should start a training phase
+        """
+
+        should_update = self._should_train_helper(wait_for_full_episode=wait_for_full_episode)
+
+        step_method = self.ap.algorithm.num_consecutive_playing_steps
+
+        if should_update:
+            if step_method.__class__ == EnvironmentEpisodes:
+                self.last_training_phase_step = self.current_episode
+            if step_method.__class__ == EnvironmentSteps:
+                self.last_training_phase_step = self.total_steps_counter
+
+        return should_update
+
+    def _should_train_helper(self, wait_for_full_episode=False):
+
+        step_method = self.ap.algorithm.num_consecutive_playing_steps
+
+        if step_method.__class__ == EnvironmentEpisodes:
+            should_update = (self.current_episode - self.last_training_phase_step) >= step_method.num_steps
+            should_update = should_update and self.call_memory('length') > 0
+
+        elif step_method.__class__ == EnvironmentSteps:
+            should_update = (self.total_steps_counter - self.last_training_phase_step) >= step_method.num_steps
+            should_update = should_update and self.call_memory('num_transitions') > 0
+
+            if wait_for_full_episode:
+                should_update = should_update and self.current_episode_buffer.is_complete
+        else:
+            raise ValueError("The num_consecutive_playing_steps parameter should be either "
+                             "EnvironmentSteps or Episodes. Instead it is {}".format(step_method.__class__))
+
+        return should_update
+
+
[docs]    def train(self) -> float:
+        """
+        Check if a training phase should be done as configured by num_consecutive_playing_steps.
+        If it should, then do several training steps as configured by num_consecutive_training_steps.
+        A single training iteration: Sample a batch, train on it and update target networks.
+
+        :return: The total training loss during the training iterations.
+        """
+        loss = 0
+        if self._should_train():
+            for network in self.networks.values():
+                network.set_is_training(True)
+
+            for training_step in range(self.ap.algorithm.num_consecutive_training_steps):
+                # TODO: this should be network dependent
+                network_parameters = list(self.ap.network_wrappers.values())[0]
+
+                # update counters
+                self.training_iteration += 1
+
+                # sample a batch and train on it
+                batch = self.call_memory('sample', network_parameters.batch_size)
+                if self.pre_network_filter is not None:
+                    batch = self.pre_network_filter.filter(batch, update_internal_state=False, deep_copy=False)
+
+                # if the batch returned empty then there are not enough samples in the replay buffer -> skip
+                # training step
+                if len(batch) > 0:
+                    # train
+                    batch = Batch(batch)
+                    total_loss, losses, unclipped_grads = self.learn_from_batch(batch)
+                    loss += total_loss
+                    self.unclipped_grads.add_sample(unclipped_grads)
+
+                    # TODO: the learning rate decay should be done through the network instead of here
+                    # decay learning rate
+                    if network_parameters.learning_rate_decay_rate != 0:
+                        self.curr_learning_rate.add_sample(self.networks['main'].sess.run(
+                            self.networks['main'].online_network.current_learning_rate))
+                    else:
+                        self.curr_learning_rate.add_sample(network_parameters.learning_rate)
+
+                    if any([network.has_target for network in self.networks.values()]) \
+                            and self._should_update_online_weights_to_target():
+                        for network in self.networks.values():
+                            network.update_target_network(self.ap.algorithm.rate_for_copying_weights_to_target)
+
+                        self.agent_logger.create_signal_value('Update Target Network', 1)
+                    else:
+                        self.agent_logger.create_signal_value('Update Target Network', 0, overwrite=False)
+
+                    self.loss.add_sample(loss)
+
+                    if self.imitation:
+                        self.log_to_screen()
+
+            for network in self.networks.values():
+                network.set_is_training(False)
+
+            # run additional commands after the training is done
+            self.post_training_commands()
+
+        return loss

+
+
[docs]    def choose_action(self, curr_state):
+        """
+        choose an action to act with in the current episode being played. Different behavior might be exhibited when
+        training or testing.
+
+        :param curr_state: the current state to act upon.
+        :return: chosen action, some action value describing the action (q-value, probability, etc)
+        """
+        pass

+
+
[docs]    def prepare_batch_for_inference(self, states: Union[Dict[str, np.ndarray], List[Dict[str, np.ndarray]]],
+                                    network_name: str) -> Dict[str, np.array]:
+        """
+        Convert curr_state into input tensors tensorflow is expecting. i.e. if we have several inputs states, stack all
+        observations together, measurements together, etc.
+
+        :param states: A list of environment states, where each one is a dict mapping from an observation name to its
+                       corresponding observation
+        :param network_name: The agent network name to prepare the batch for. this is needed in order to extract only
+                             the observation relevant for the network from the states.
+        :return: A dictionary containing a list of values from all the given states for each of the observations
+        """
+        # convert to batch so we can run it through the network
+        states = force_list(states)
+        batches_dict = {}
+        for key in self.ap.network_wrappers[network_name].input_embedders_parameters.keys():
+            # there are cases (e.g. ddpg) where the state does not contain all the information needed for running
+            # through the network and this has to be added externally (e.g. ddpg where the action needs to be given in
+            # addition to the current_state, so that all the inputs of the network will be filled)
+            if key in states[0].keys():
+                batches_dict[key] = np.array([np.array(state[key]) for state in states])
+
+        return batches_dict

+
+
[docs]    def act(self) -> ActionInfo:
+        """
+        Given the agents current knowledge, decide on the next action to apply to the environment
+
+        :return: An ActionInfo object, which contains the action and any additional info from the action decision process
+        """
+        if self.phase == RunPhase.TRAIN and self.ap.algorithm.num_consecutive_playing_steps.num_steps == 0:
+            # This agent never plays  while training (e.g. behavioral cloning)
+            return None
+
+        # count steps (only when training or if we are in the evaluation worker)
+        if self.phase != RunPhase.TEST or self.ap.task_parameters.evaluate_only:
+            self.total_steps_counter += 1
+        self.current_episode_steps_counter += 1
+
+        # decide on the action
+        if self.phase == RunPhase.HEATUP and not self.ap.algorithm.heatup_using_network_decisions:
+            # random action
+            self.last_action_info = self.spaces.action.sample_with_info()
+        else:
+            # informed action
+            if self.pre_network_filter is not None:
+                # before choosing an action, first use the pre_network_filter to filter out the current state
+                curr_state = self.run_pre_network_filter_for_inference(self.curr_state)
+
+            else:
+                curr_state = self.curr_state
+            self.last_action_info = self.choose_action(curr_state)
+
+        filtered_action_info = self.output_filter.filter(self.last_action_info)
+
+        return filtered_action_info

+
+
[docs]    def run_pre_network_filter_for_inference(self, state: StateType) -> StateType:
+        """
+        Run filters which where defined for being applied right before using the state for inference.
+
+        :param state: The state to run the filters on
+        :return: The filtered state
+        """
+        dummy_env_response = EnvResponse(next_state=state, reward=0, game_over=False)
+        return self.pre_network_filter.filter(dummy_env_response)[0].next_state

+
+
[docs]    def get_state_embedding(self, state: dict) -> np.ndarray:
+        """
+        Given a state, get the corresponding state embedding  from the main network
+
+        :param state: a state dict
+        :return: a numpy embedding vector
+        """
+        # TODO: this won't work anymore
+        # TODO: instead of the state embedding (which contains the goal) we should use the observation embedding
+        embedding = self.networks['main'].online_network.predict(
+            self.prepare_batch_for_inference(state, "main"),
+            outputs=self.networks['main'].online_network.state_embedding)
+        return embedding

+
+
[docs]    def update_transition_before_adding_to_replay_buffer(self, transition: Transition) -> Transition:
+        """
+        Allows agents to update the transition just before adding it to the replay buffer.
+        Can be useful for agents that want to tweak the reward, termination signal, etc.
+
+        :param transition: the transition to update
+        :return: the updated transition
+        """
+        return transition

+
+
[docs]    def observe(self, env_response: EnvResponse) -> bool:
+        """
+        Given a response from the environment, distill the observation from it and store it for later use.
+        The response should be a dictionary containing the performed action, the new observation and measurements,
+        the reward, a game over flag and any additional information necessary.
+
+        :param env_response: result of call from environment.step(action)
+        :return: a boolean value which determines if the agent has decided to terminate the episode after seeing the
+                 given observation
+        """
+
+        # filter the env_response
+        filtered_env_response = self.input_filter.filter(env_response)[0]
+
+        # inject agent collected statistics, if required
+        if self.ap.algorithm.use_accumulated_reward_as_measurement:
+            if 'measurements' in filtered_env_response.next_state:
+                filtered_env_response.next_state['measurements'] = np.append(filtered_env_response.next_state['measurements'],
+                                                                             self.total_shaped_reward_in_current_episode)
+            else:
+                filtered_env_response.next_state['measurements'] = np.array([self.total_shaped_reward_in_current_episode])
+
+        # if we are in the first step in the episode, then we don't have a a next state and a reward and thus no
+        # transition yet, and therefore we don't need to store anything in the memory.
+        # also we did not reach the goal yet.
+        if self.current_episode_steps_counter == 0:
+            # initialize the current state
+            self.curr_state = filtered_env_response.next_state
+            return env_response.game_over
+        else:
+            transition = Transition(state=copy.copy(self.curr_state), action=self.last_action_info.action,
+                                    reward=filtered_env_response.reward, next_state=filtered_env_response.next_state,
+                                    game_over=filtered_env_response.game_over, info=filtered_env_response.info)
+
+            # now that we have formed a basic transition - the next state progresses to be the current state
+            self.curr_state = filtered_env_response.next_state
+
+            # make agent specific changes to the transition if needed
+            transition = self.update_transition_before_adding_to_replay_buffer(transition)
+
+            # merge the intrinsic reward in
+            if self.ap.algorithm.scale_external_reward_by_intrinsic_reward_value:
+                transition.reward = transition.reward * (1 + self.last_action_info.action_intrinsic_reward)
+            else:
+                transition.reward = transition.reward + self.last_action_info.action_intrinsic_reward
+
+            # sum up the total shaped reward
+            self.total_shaped_reward_in_current_episode += transition.reward
+            self.total_reward_in_current_episode += env_response.reward
+            self.shaped_reward.add_sample(transition.reward)
+            self.reward.add_sample(env_response.reward)
+
+            # add action info to transition
+            if type(self.parent).__name__ == 'CompositeAgent':
+                transition.add_info(self.parent.last_action_info.__dict__)
+            else:
+                transition.add_info(self.last_action_info.__dict__)
+
+            # create and store the transition
+            if self.phase in [RunPhase.TRAIN, RunPhase.HEATUP]:
+                # for episodic memories we keep the transitions in a local buffer until the episode is ended.
+                # for regular memories we insert the transitions directly to the memory
+                self.current_episode_buffer.insert(transition)
+                if not isinstance(self.memory, EpisodicExperienceReplay) \
+                        and not self.ap.algorithm.store_transitions_only_when_episodes_are_terminated:
+                    self.call_memory('store', transition)
+
+            if self.ap.visualization.dump_in_episode_signals:
+                self.update_step_in_episode_log()
+
+            return transition.game_over

+
+
[docs]    def post_training_commands(self) -> None:
+        """
+        A function which allows adding any functionality that is required to run right after the training phase ends.
+
+        :return: None
+        """
+        pass

+
+
[docs]    def get_predictions(self, states: List[Dict[str, np.ndarray]], prediction_type: PredictionType):
+        """
+        Get a prediction from the agent with regard to the requested prediction_type.
+        If the agent cannot predict this type of prediction_type, or if there is more than possible way to do so,
+        raise a ValueException.
+
+        :param states: The states to get a prediction for
+        :param prediction_type: The type of prediction to get for the states. For example, the state-value prediction.
+        :return: the predicted values
+        """
+
+        predictions = self.networks['main'].online_network.predict_with_prediction_type(
+            # states=self.dict_state_to_batches_dict(states, 'main'), prediction_type=prediction_type)
+            states=states, prediction_type=prediction_type)
+
+        if len(predictions.keys()) != 1:
+            raise ValueError("The network has more than one component {} matching the requested prediction_type {}. ".
+                             format(list(predictions.keys()), prediction_type))
+        return list(predictions.values())[0]

+
+
[docs]    def set_incoming_directive(self, action: ActionType) -> None:
+        """
+        Allows setting a directive for the agent to follow. This is useful in hierarchy structures, where the agent
+        has another master agent that is controlling it. In such cases, the master agent can define the goals for the
+        slave agent, define it's observation, possible actions, etc. The directive type is defined by the agent
+        in-action-space.
+
+        :param action: The action that should be set as the directive
+        :return:
+        """
+        if isinstance(self.in_action_space, GoalsSpace):
+            self.current_hrl_goal = action
+        elif isinstance(self.in_action_space, AttentionActionSpace):
+            self.input_filter.observation_filters['attention'].crop_low = action[0]
+            self.input_filter.observation_filters['attention'].crop_high = action[1]
+            self.output_filter.action_filters['masking'].set_masking(action[0], action[1])

+
+
[docs]    def save_checkpoint(self, checkpoint_id: int) -> None:
+        """
+        Allows agents to store additional information when saving checkpoints.
+
+        :param checkpoint_id: the id of the checkpoint
+        :return: None
+        """
+        pass

+
+
[docs]    def sync(self) -> None:
+        """
+        Sync the global network parameters to local networks
+
+        :return: None
+        """
+        for network in self.networks.values():
+            network.sync()

+
+    def get_success_rate(self) -> float:
+        return self.num_successes_across_evaluation_episodes / self.num_evaluation_episodes_completed

+
+ +
+ +
+ + +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_modules/rl_coach/agents/bc_agent.html b/docs/_modules/rl_coach/agents/bc_agent.html new file mode 100644 index 0000000..7b6529d --- /dev/null +++ b/docs/_modules/rl_coach/agents/bc_agent.html @@ -0,0 +1,308 @@ + + + + + + + + + + + rl_coach.agents.bc_agent — Reinforcement Learning Coach 0.11.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + +
+ +
+ + + + + + + + + + + + + + + + + +
+ +
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    • + +
  • rl_coach.agents.bc_agent
    • + + +
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    • + +
+ + +
+
+
+
+ +

Source code for rl_coach.agents.bc_agent

+#
+# Copyright (c) 2017 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from typing import Union
+
+import numpy as np
+
+from rl_coach.agents.imitation_agent import ImitationAgent
+from rl_coach.architectures.head_parameters import PolicyHeadParameters
+from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters
+from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
+from rl_coach.base_parameters import AgentParameters, AlgorithmParameters, NetworkParameters, \
+    MiddlewareScheme
+from rl_coach.exploration_policies.e_greedy import EGreedyParameters
+from rl_coach.memories.episodic.episodic_experience_replay import EpisodicExperienceReplayParameters
+from rl_coach.memories.non_episodic.experience_replay import ExperienceReplayParameters
+
+
+
[docs]class BCAlgorithmParameters(AlgorithmParameters):
+    def __init__(self):
+        super().__init__()

+
+
+class BCNetworkParameters(NetworkParameters):
+    def __init__(self):
+        super().__init__()
+        self.input_embedders_parameters = {'observation': InputEmbedderParameters()}
+        self.middleware_parameters = FCMiddlewareParameters(scheme=MiddlewareScheme.Medium)
+        self.heads_parameters = [PolicyHeadParameters()]
+        self.optimizer_type = 'Adam'
+        self.batch_size = 32
+        self.replace_mse_with_huber_loss = False
+        self.create_target_network = False
+
+
+class BCAgentParameters(AgentParameters):
+    def __init__(self):
+        super().__init__(algorithm=BCAlgorithmParameters(),
+                         exploration=EGreedyParameters(),
+                         memory=ExperienceReplayParameters(),
+                         networks={"main": BCNetworkParameters()})
+
+    @property
+    def path(self):
+        return 'rl_coach.agents.bc_agent:BCAgent'
+
+
+# Behavioral Cloning Agent
+class BCAgent(ImitationAgent):
+    def __init__(self, agent_parameters, parent: Union['LevelManager', 'CompositeAgent']=None):
+        super().__init__(agent_parameters, parent)
+
+    def learn_from_batch(self, batch):
+        network_keys = self.ap.network_wrappers['main'].input_embedders_parameters.keys()
+
+        # When using a policy head, the targets refer to the advantages that we are normally feeding the head with.
+        # In this case, we need the policy head to just predict probabilities, so while we usually train the network
+        # with log(Pi)*Advantages, in this specific case we will train it to log(Pi), which after the softmax will
+        # predict Pi (=probabilities)
+        targets = np.ones(batch.actions().shape[0])
+
+        result = self.networks['main'].train_and_sync_networks({**batch.states(network_keys),
+                                                                'output_0_0': batch.actions()},
+                                                               targets)
+        total_loss, losses, unclipped_grads = result[:3]
+
+        return total_loss, losses, unclipped_grads
+
+
+ +
+ +
+ + +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_modules/rl_coach/agents/categorical_dqn_agent.html b/docs/_modules/rl_coach/agents/categorical_dqn_agent.html new file mode 100644 index 0000000..12e04e0 --- /dev/null +++ b/docs/_modules/rl_coach/agents/categorical_dqn_agent.html @@ -0,0 +1,382 @@ + + + + + + + + + + + rl_coach.agents.categorical_dqn_agent — Reinforcement Learning Coach 0.11.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + +
+ +
+ + + + + + + + + + + + + + + + + +
+ +
    + +
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    • + +
  • rl_coach.agents.categorical_dqn_agent
    • + + +
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    • + +
+ + +
+
+
+
+ +

Source code for rl_coach.agents.categorical_dqn_agent

+#
+# Copyright (c) 2017 Intel Corporation 
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from typing import Union
+
+import numpy as np
+from rl_coach.agents.dqn_agent import DQNNetworkParameters, DQNAlgorithmParameters, DQNAgentParameters
+from rl_coach.agents.value_optimization_agent import ValueOptimizationAgent
+from rl_coach.architectures.head_parameters import CategoricalQHeadParameters
+from rl_coach.core_types import StateType
+from rl_coach.exploration_policies.e_greedy import EGreedyParameters
+from rl_coach.memories.non_episodic.prioritized_experience_replay import PrioritizedExperienceReplay
+from rl_coach.schedules import LinearSchedule
+
+
+class CategoricalDQNNetworkParameters(DQNNetworkParameters):
+    def __init__(self):
+        super().__init__()
+        self.heads_parameters = [CategoricalQHeadParameters()]
+
+
+
[docs]class CategoricalDQNAlgorithmParameters(DQNAlgorithmParameters):
+    """
+    :param v_min: (float)
+        The minimal value that will be represented in the network output for predicting the Q value.
+        Corresponds to :math:`v_{min}` in the paper.
+
+    :param v_max: (float)
+        The maximum value that will be represented in the network output for predicting the Q value.
+        Corresponds to :math:`v_{max}` in the paper.
+
+    :param atoms: (int)
+        The number of atoms that will be used to discretize the range between v_min and v_max.
+        For the C51 algorithm described in the paper, the number of atoms is 51.
+    """
+    def __init__(self):
+        super().__init__()
+        self.v_min = -10.0
+        self.v_max = 10.0
+        self.atoms = 51

+
+
+class CategoricalDQNExplorationParameters(EGreedyParameters):
+    def __init__(self):
+        super().__init__()
+        self.epsilon_schedule = LinearSchedule(1, 0.01, 1000000)
+        self.evaluation_epsilon = 0.001
+
+
+class CategoricalDQNAgentParameters(DQNAgentParameters):
+    def __init__(self):
+        super().__init__()
+        self.algorithm = CategoricalDQNAlgorithmParameters()
+        self.exploration = CategoricalDQNExplorationParameters()
+        self.network_wrappers = {"main": CategoricalDQNNetworkParameters()}
+
+    @property
+    def path(self):
+        return 'rl_coach.agents.categorical_dqn_agent:CategoricalDQNAgent'
+
+
+# Categorical Deep Q Network - https://arxiv.org/pdf/1707.06887.pdf
+class CategoricalDQNAgent(ValueOptimizationAgent):
+    def __init__(self, agent_parameters, parent: Union['LevelManager', 'CompositeAgent']=None):
+        super().__init__(agent_parameters, parent)
+        self.z_values = np.linspace(self.ap.algorithm.v_min, self.ap.algorithm.v_max, self.ap.algorithm.atoms)
+
+    def distribution_prediction_to_q_values(self, prediction):
+        return np.dot(prediction, self.z_values)
+
+    # prediction's format is (batch,actions,atoms)
+    def get_all_q_values_for_states(self, states: StateType):
+        if self.exploration_policy.requires_action_values():
+            prediction = self.get_prediction(states)
+            q_values = self.distribution_prediction_to_q_values(prediction)
+        else:
+            q_values = None
+        return q_values
+
+    def learn_from_batch(self, batch):
+        network_keys = self.ap.network_wrappers['main'].input_embedders_parameters.keys()
+
+        # for the action we actually took, the error is calculated by the atoms distribution
+        # for all other actions, the error is 0
+        distributional_q_st_plus_1, TD_targets = self.networks['main'].parallel_prediction([
+            (self.networks['main'].target_network, batch.next_states(network_keys)),
+            (self.networks['main'].online_network, batch.states(network_keys))
+        ])
+
+        # select the optimal actions for the next state
+        target_actions = np.argmax(self.distribution_prediction_to_q_values(distributional_q_st_plus_1), axis=1)
+        m = np.zeros((self.ap.network_wrappers['main'].batch_size, self.z_values.size))
+
+        batches = np.arange(self.ap.network_wrappers['main'].batch_size)
+
+        # an alternative to the for loop. 3.7x perf improvement vs. the same code done with for looping.
+        # only 10% speedup overall - leaving commented out as the code is not as clear.
+
+        # tzj_ = np.fmax(np.fmin(batch.rewards() + (1.0 - batch.game_overs()) * self.ap.algorithm.discount *
+        #                        np.transpose(np.repeat(self.z_values[np.newaxis, :], batch.size, axis=0), (1, 0)),
+        #                     self.z_values[-1]),
+        #             self.z_values[0])
+        #
+        # bj_ = (tzj_ - self.z_values[0]) / (self.z_values[1] - self.z_values[0])
+        # u_ = (np.ceil(bj_)).astype(int)
+        # l_ = (np.floor(bj_)).astype(int)
+        # m_ = np.zeros((self.ap.network_wrappers['main'].batch_size, self.z_values.size))
+        # np.add.at(m_, [batches, l_],
+        #           np.transpose(distributional_q_st_plus_1[batches, target_actions], (1, 0)) * (u_ - bj_))
+        # np.add.at(m_, [batches, u_],
+        #           np.transpose(distributional_q_st_plus_1[batches, target_actions], (1, 0)) * (bj_ - l_))
+
+        for j in range(self.z_values.size):
+            tzj = np.fmax(np.fmin(batch.rewards() +
+                                  (1.0 - batch.game_overs()) * self.ap.algorithm.discount * self.z_values[j],
+                                  self.z_values[-1]),
+                          self.z_values[0])
+            bj = (tzj - self.z_values[0])/(self.z_values[1] - self.z_values[0])
+            u = (np.ceil(bj)).astype(int)
+            l = (np.floor(bj)).astype(int)
+            m[batches, l] += (distributional_q_st_plus_1[batches, target_actions, j] * (u - bj))
+            m[batches, u] += (distributional_q_st_plus_1[batches, target_actions, j] * (bj - l))
+
+        # total_loss = cross entropy between actual result above and predicted result for the given action
+        # only update the action that we have actually done in this transition
+        TD_targets[batches, batch.actions()] = m
+
+        # update errors in prioritized replay buffer
+        importance_weights = batch.info('weight') if isinstance(self.memory, PrioritizedExperienceReplay) else None
+
+        result = self.networks['main'].train_and_sync_networks(batch.states(network_keys), TD_targets,
+                                                               importance_weights=importance_weights)
+
+        total_loss, losses, unclipped_grads = result[:3]
+
+        # TODO: fix this spaghetti code
+        if isinstance(self.memory, PrioritizedExperienceReplay):
+            errors = losses[0][np.arange(batch.size), batch.actions()]
+            self.call_memory('update_priorities', (batch.info('idx'), errors))
+
+        return total_loss, losses, unclipped_grads
+
+
+ +
+ +
+ + +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_modules/rl_coach/agents/cil_agent.html b/docs/_modules/rl_coach/agents/cil_agent.html new file mode 100644 index 0000000..3fc44b0 --- /dev/null +++ b/docs/_modules/rl_coach/agents/cil_agent.html @@ -0,0 +1,314 @@ + + + + + + + + + + + rl_coach.agents.cil_agent — Reinforcement Learning Coach 0.11.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + +
+ +
+ + + + + + + + + + + + + + + + + +
+ +
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+ +

Source code for rl_coach.agents.cil_agent

+#
+# Copyright (c) 2017 Intel Corporation 
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from typing import Union
+
+from rl_coach.agents.imitation_agent import ImitationAgent
+from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
+from rl_coach.architectures.head_parameters import RegressionHeadParameters
+from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters
+from rl_coach.base_parameters import AgentParameters, MiddlewareScheme, NetworkParameters, AlgorithmParameters
+from rl_coach.exploration_policies.e_greedy import EGreedyParameters
+from rl_coach.memories.non_episodic.balanced_experience_replay import BalancedExperienceReplayParameters
+
+
+
[docs]class CILAlgorithmParameters(AlgorithmParameters):
+    """
+    :param state_key_with_the_class_index: (str)
+        The key of the state dictionary which corresponds to the value that will be used to control the class index.
+    """
+    def __init__(self):
+        super().__init__()
+        self.state_key_with_the_class_index = 'high_level_command'

+
+
+class CILNetworkParameters(NetworkParameters):
+    def __init__(self):
+        super().__init__()
+        self.input_embedders_parameters = {'observation': InputEmbedderParameters()}
+        self.middleware_parameters = FCMiddlewareParameters(scheme=MiddlewareScheme.Medium)
+        self.heads_parameters = [RegressionHeadParameters()]
+        self.optimizer_type = 'Adam'
+        self.batch_size = 32
+        self.replace_mse_with_huber_loss = False
+        self.create_target_network = False
+
+
+class CILAgentParameters(AgentParameters):
+    def __init__(self):
+        super().__init__(algorithm=CILAlgorithmParameters(),
+                         exploration=EGreedyParameters(),
+                         memory=BalancedExperienceReplayParameters(),
+                         networks={"main": CILNetworkParameters()})
+
+    @property
+    def path(self):
+        return 'rl_coach.agents.cil_agent:CILAgent'
+
+
+# Conditional Imitation Learning Agent: https://arxiv.org/abs/1710.02410
+class CILAgent(ImitationAgent):
+    def __init__(self, agent_parameters, parent: Union['LevelManager', 'CompositeAgent']=None):
+        super().__init__(agent_parameters, parent)
+        self.current_high_level_control = 0
+
+    def choose_action(self, curr_state):
+        self.current_high_level_control = curr_state[self.ap.algorithm.state_key_with_the_class_index]
+        return super().choose_action(curr_state)
+
+    def extract_action_values(self, prediction):
+        return prediction[self.current_high_level_control].squeeze()
+
+    def learn_from_batch(self, batch):
+        network_keys = self.ap.network_wrappers['main'].input_embedders_parameters.keys()
+
+        target_values = self.networks['main'].online_network.predict({**batch.states(network_keys)})
+
+        branch_to_update = batch.states([self.ap.algorithm.state_key_with_the_class_index])[self.ap.algorithm.state_key_with_the_class_index]
+        for idx, branch in enumerate(branch_to_update):
+            target_values[branch][idx] = batch.actions()[idx]
+
+        result = self.networks['main'].train_and_sync_networks({**batch.states(network_keys)}, target_values)
+        total_loss, losses, unclipped_grads = result[:3]
+
+        return total_loss, losses, unclipped_grads
+
+ +
+ +
+ + +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_modules/rl_coach/agents/clipped_ppo_agent.html b/docs/_modules/rl_coach/agents/clipped_ppo_agent.html new file mode 100644 index 0000000..c24e2ef --- /dev/null +++ b/docs/_modules/rl_coach/agents/clipped_ppo_agent.html @@ -0,0 +1,563 @@ + + + + + + + + + + + rl_coach.agents.clipped_ppo_agent — Reinforcement Learning Coach 0.11.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + +
+ +
+ + + + + + + + + + + + + + + + + +
+ +
    + +
  • Docs »
    • + +
  • Module code »
    • + +
  • rl_coach.agents.clipped_ppo_agent
    • + + +
  • + +
    • + +
+ + +
+
+
+
+ +

Source code for rl_coach.agents.clipped_ppo_agent

+#
+# Copyright (c) 2017 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import copy
+from collections import OrderedDict
+from random import shuffle
+from typing import Union
+
+import numpy as np
+
+from rl_coach.agents.actor_critic_agent import ActorCriticAgent
+from rl_coach.agents.policy_optimization_agent import PolicyGradientRescaler
+from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
+from rl_coach.architectures.head_parameters import PPOHeadParameters, VHeadParameters
+from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters
+from rl_coach.base_parameters import AlgorithmParameters, NetworkParameters, \
+    AgentParameters
+from rl_coach.core_types import EnvironmentSteps, Batch, EnvResponse, StateType
+from rl_coach.exploration_policies.additive_noise import AdditiveNoiseParameters
+from rl_coach.exploration_policies.categorical import CategoricalParameters
+from rl_coach.logger import screen
+from rl_coach.memories.episodic.episodic_experience_replay import EpisodicExperienceReplayParameters
+from rl_coach.schedules import ConstantSchedule
+from rl_coach.spaces import DiscreteActionSpace, BoxActionSpace
+
+
+class ClippedPPONetworkParameters(NetworkParameters):
+    def __init__(self):
+        super().__init__()
+        self.input_embedders_parameters = {'observation': InputEmbedderParameters(activation_function='tanh')}
+        self.middleware_parameters = FCMiddlewareParameters(activation_function='tanh')
+        self.heads_parameters = [VHeadParameters(), PPOHeadParameters()]
+        self.batch_size = 64
+        self.optimizer_type = 'Adam'
+        self.clip_gradients = None
+        self.use_separate_networks_per_head = True
+        self.async_training = False
+        self.l2_regularization = 0
+
+        # The target network is used in order to freeze the old policy, while making updates to the new one
+        # in train_network()
+        self.create_target_network = True
+        self.shared_optimizer = True
+        self.scale_down_gradients_by_number_of_workers_for_sync_training = True
+
+
+
[docs]class ClippedPPOAlgorithmParameters(AlgorithmParameters):
+    """
+    :param policy_gradient_rescaler: (PolicyGradientRescaler)
+        This represents how the critic will be used to update the actor. The critic value function is typically used
+        to rescale the gradients calculated by the actor. There are several ways for doing this, such as using the
+        advantage of the action, or the generalized advantage estimation (GAE) value.
+
+    :param gae_lambda: (float)
+        The :math:`\lambda` value is used within the GAE function in order to weight different bootstrap length
+        estimations. Typical values are in the range 0.9-1, and define an exponential decay over the different
+        n-step estimations.
+
+    :param clip_likelihood_ratio_using_epsilon: (float)
+        If not None, the likelihood ratio between the current and new policy in the PPO loss function will be
+        clipped to the range [1-clip_likelihood_ratio_using_epsilon, 1+clip_likelihood_ratio_using_epsilon].
+        This is typically used in the Clipped PPO version of PPO, and should be set to None in regular PPO
+        implementations.
+
+    :param value_targets_mix_fraction: (float)
+        The targets for the value network are an exponential weighted moving average which uses this mix fraction to
+        define how much of the new targets will be taken into account when calculating the loss.
+        This value should be set to the range (0,1], where 1 means that only the new targets will be taken into account.
+
+    :param estimate_state_value_using_gae: (bool)
+        If set to True, the state value will be estimated using the GAE technique.
+
+    :param use_kl_regularization: (bool)
+        If set to True, the loss function will be regularized using the KL diveregence between the current and new
+        policy, to bound the change of the policy during the network update.
+
+    :param beta_entropy: (float)
+        An entropy regulaization term can be added to the loss function in order to control exploration. This term
+        is weighted using the :math:`\beta` value defined by beta_entropy.
+
+    :param optimization_epochs: (int)
+        For each training phase, the collected dataset will be used for multiple epochs, which are defined by the
+        optimization_epochs value.
+
+    :param optimization_epochs: (Schedule)
+        Can be used to define a schedule over the clipping of the likelihood ratio.
+
+    """
+    def __init__(self):
+        super().__init__()
+        self.num_episodes_in_experience_replay = 1000000
+        self.policy_gradient_rescaler = PolicyGradientRescaler.GAE
+        self.gae_lambda = 0.95
+        self.use_kl_regularization = False
+        self.clip_likelihood_ratio_using_epsilon = 0.2
+        self.estimate_state_value_using_gae = True
+        self.beta_entropy = 0.01  # should be 0 for mujoco
+        self.num_consecutive_playing_steps = EnvironmentSteps(2048)
+        self.optimization_epochs = 10
+        self.normalization_stats = None
+        self.clipping_decay_schedule = ConstantSchedule(1)

+
+
+class ClippedPPOAgentParameters(AgentParameters):
+    def __init__(self):
+        super().__init__(algorithm=ClippedPPOAlgorithmParameters(),
+                         exploration={DiscreteActionSpace: CategoricalParameters(),
+                                      BoxActionSpace: AdditiveNoiseParameters()},
+                         memory=EpisodicExperienceReplayParameters(),
+                         networks={"main": ClippedPPONetworkParameters()})
+
+    @property
+    def path(self):
+        return 'rl_coach.agents.clipped_ppo_agent:ClippedPPOAgent'
+
+
+# Clipped Proximal Policy Optimization - https://arxiv.org/abs/1707.06347
+class ClippedPPOAgent(ActorCriticAgent):
+    def __init__(self, agent_parameters, parent: Union['LevelManager', 'CompositeAgent']=None):
+        super().__init__(agent_parameters, parent)
+        # signals definition
+        self.value_loss = self.register_signal('Value Loss')
+        self.policy_loss = self.register_signal('Policy Loss')
+        self.total_kl_divergence_during_training_process = 0.0
+        self.unclipped_grads = self.register_signal('Grads (unclipped)')
+        self.value_targets = self.register_signal('Value Targets')
+        self.kl_divergence = self.register_signal('KL Divergence')
+        self.likelihood_ratio = self.register_signal('Likelihood Ratio')
+        self.clipped_likelihood_ratio = self.register_signal('Clipped Likelihood Ratio')
+
+    def set_session(self, sess):
+        super().set_session(sess)
+        if self.ap.algorithm.normalization_stats is not None:
+            self.ap.algorithm.normalization_stats.set_session(sess)
+
+    def fill_advantages(self, batch):
+        network_keys = self.ap.network_wrappers['main'].input_embedders_parameters.keys()
+
+        current_state_values = self.networks['main'].online_network.predict(batch.states(network_keys))[0]
+        current_state_values = current_state_values.squeeze()
+        self.state_values.add_sample(current_state_values)
+
+        # calculate advantages
+        advantages = []
+        value_targets = []
+        total_returns = batch.n_step_discounted_rewards()
+
+        if self.policy_gradient_rescaler == PolicyGradientRescaler.A_VALUE:
+            advantages = total_returns - current_state_values
+        elif self.policy_gradient_rescaler == PolicyGradientRescaler.GAE:
+            # get bootstraps
+            episode_start_idx = 0
+            advantages = np.array([])
+            value_targets = np.array([])
+            for idx, game_over in enumerate(batch.game_overs()):
+                if game_over:
+                    # get advantages for the rollout
+                    value_bootstrapping = np.zeros((1,))
+                    rollout_state_values = np.append(current_state_values[episode_start_idx:idx+1], value_bootstrapping)
+
+                    rollout_advantages, gae_based_value_targets = \
+                        self.get_general_advantage_estimation_values(batch.rewards()[episode_start_idx:idx+1],
+                                                                     rollout_state_values)
+                    episode_start_idx = idx + 1
+                    advantages = np.append(advantages, rollout_advantages)
+                    value_targets = np.append(value_targets, gae_based_value_targets)
+        else:
+            screen.warning("WARNING: The requested policy gradient rescaler is not available")
+
+        # standardize
+        advantages = (advantages - np.mean(advantages)) / np.std(advantages)
+
+        for transition, advantage, value_target in zip(batch.transitions, advantages, value_targets):
+            transition.info['advantage'] = advantage
+            transition.info['gae_based_value_target'] = value_target
+
+        self.action_advantages.add_sample(advantages)
+
+    def train_network(self, batch, epochs):
+        batch_results = []
+        for j in range(epochs):
+            batch.shuffle()
+            batch_results = {
+                'total_loss': [],
+                'losses': [],
+                'unclipped_grads': [],
+                'kl_divergence': [],
+                'entropy': []
+            }
+
+            fetches = [self.networks['main'].online_network.output_heads[1].kl_divergence,
+                       self.networks['main'].online_network.output_heads[1].entropy,
+                       self.networks['main'].online_network.output_heads[1].likelihood_ratio,
+                       self.networks['main'].online_network.output_heads[1].clipped_likelihood_ratio]
+
+            for i in range(int(batch.size / self.ap.network_wrappers['main'].batch_size)):
+                start = i * self.ap.network_wrappers['main'].batch_size
+                end = (i + 1) * self.ap.network_wrappers['main'].batch_size
+
+                network_keys = self.ap.network_wrappers['main'].input_embedders_parameters.keys()
+                actions = batch.actions()[start:end]
+                gae_based_value_targets = batch.info('gae_based_value_target')[start:end]
+                if not isinstance(self.spaces.action, DiscreteActionSpace) and len(actions.shape) == 1:
+                    actions = np.expand_dims(actions, -1)
+
+                # get old policy probabilities and distribution
+
+                # TODO-perf - the target network ("old_policy") is not changing. this can be calculated once for all epochs.
+                # the shuffling being done, should only be performed on the indices.
+                result = self.networks['main'].target_network.predict({k: v[start:end] for k, v in batch.states(network_keys).items()})
+                old_policy_distribution = result[1:]
+
+                total_returns = batch.n_step_discounted_rewards(expand_dims=True)
+
+                # calculate gradients and apply on both the local policy network and on the global policy network
+                if self.ap.algorithm.estimate_state_value_using_gae:
+                    value_targets = np.expand_dims(gae_based_value_targets, -1)
+                else:
+                    value_targets = total_returns[start:end]
+
+                inputs = copy.copy({k: v[start:end] for k, v in batch.states(network_keys).items()})
+                inputs['output_1_0'] = actions
+
+                # The old_policy_distribution needs to be represented as a list, because in the event of
+                # discrete controls, it has just a mean. otherwise, it has both a mean and standard deviation
+                for input_index, input in enumerate(old_policy_distribution):
+                    inputs['output_1_{}'.format(input_index + 1)] = input
+
+                # update the clipping decay schedule value
+                inputs['output_1_{}'.format(len(old_policy_distribution)+1)] = \
+                    self.ap.algorithm.clipping_decay_schedule.current_value
+
+                total_loss, losses, unclipped_grads, fetch_result = \
+                    self.networks['main'].train_and_sync_networks(
+                        inputs, [value_targets, batch.info('advantage')[start:end]], additional_fetches=fetches
+                    )
+
+                batch_results['total_loss'].append(total_loss)
+                batch_results['losses'].append(losses)
+                batch_results['unclipped_grads'].append(unclipped_grads)
+                batch_results['kl_divergence'].append(fetch_result[0])
+                batch_results['entropy'].append(fetch_result[1])
+
+                self.unclipped_grads.add_sample(unclipped_grads)
+                self.value_targets.add_sample(value_targets)
+                self.likelihood_ratio.add_sample(fetch_result[2])
+                self.clipped_likelihood_ratio.add_sample(fetch_result[3])
+
+            for key in batch_results.keys():
+                batch_results[key] = np.mean(batch_results[key], 0)
+
+            self.value_loss.add_sample(batch_results['losses'][0])
+            self.policy_loss.add_sample(batch_results['losses'][1])
+            self.loss.add_sample(batch_results['total_loss'])
+
+            if self.ap.network_wrappers['main'].learning_rate_decay_rate != 0:
+                curr_learning_rate = self.networks['main'].online_network.get_variable_value(
+                    self.networks['main'].online_network.adaptive_learning_rate_scheme)
+                self.curr_learning_rate.add_sample(curr_learning_rate)
+            else:
+                curr_learning_rate = self.ap.network_wrappers['main'].learning_rate
+
+            # log training parameters
+            screen.log_dict(
+                OrderedDict([
+                    ("Surrogate loss", batch_results['losses'][1]),
+                    ("KL divergence", batch_results['kl_divergence']),
+                    ("Entropy", batch_results['entropy']),
+                    ("training epoch", j),
+                    ("learning_rate", curr_learning_rate)
+                ]),
+                prefix="Policy training"
+            )
+
+        self.total_kl_divergence_during_training_process = batch_results['kl_divergence']
+        self.entropy.add_sample(batch_results['entropy'])
+        self.kl_divergence.add_sample(batch_results['kl_divergence'])
+        return batch_results['losses']
+
+    def post_training_commands(self):
+        # clean memory
+        self.call_memory('clean')
+
+    def _should_train_helper(self, wait_for_full_episode=True):
+        return super()._should_train_helper(True)
+
+    def train(self):
+        if self._should_train(wait_for_full_episode=True):
+            for network in self.networks.values():
+                network.set_is_training(True)
+
+            dataset = self.memory.transitions
+            dataset = self.pre_network_filter.filter(dataset, deep_copy=False)
+            batch = Batch(dataset)
+
+            for training_step in range(self.ap.algorithm.num_consecutive_training_steps):
+                self.networks['main'].sync()
+                self.fill_advantages(batch)
+
+                # take only the requested number of steps
+                if isinstance(self.ap.algorithm.num_consecutive_playing_steps, EnvironmentSteps):
+                    dataset = dataset[:self.ap.algorithm.num_consecutive_playing_steps.num_steps]
+                shuffle(dataset)
+                batch = Batch(dataset)
+
+                self.train_network(batch, self.ap.algorithm.optimization_epochs)
+
+            for network in self.networks.values():
+                network.set_is_training(False)
+
+            self.post_training_commands()
+            self.training_iteration += 1
+            # should be done in order to update the data that has been accumulated * while not playing *
+            self.update_log()
+            return None
+
+    def run_pre_network_filter_for_inference(self, state: StateType):
+        dummy_env_response = EnvResponse(next_state=state, reward=0, game_over=False)
+        return self.pre_network_filter.filter(dummy_env_response, update_internal_state=False)[0].next_state
+
+    def choose_action(self, curr_state):
+        self.ap.algorithm.clipping_decay_schedule.step()
+        return super().choose_action(curr_state)
+
+ +
+ +
+ + +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_modules/rl_coach/agents/ddpg_agent.html b/docs/_modules/rl_coach/agents/ddpg_agent.html new file mode 100644 index 0000000..14c7c30 --- /dev/null +++ b/docs/_modules/rl_coach/agents/ddpg_agent.html @@ -0,0 +1,443 @@ + + + + + + + + + + + rl_coach.agents.ddpg_agent — Reinforcement Learning Coach 0.11.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + +
+ +
+ + + + + + + + + + + + + + + + + +
+ +
    + +
  • Docs »
    • + +
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    • + +
  • rl_coach.agents.ddpg_agent
    • + + +
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+ + +
+
+
+
+ +

Source code for rl_coach.agents.ddpg_agent

+#
+# Copyright (c) 2017 Intel Corporation 
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import copy
+from typing import Union
+from collections import OrderedDict
+
+import numpy as np
+
+from rl_coach.agents.actor_critic_agent import ActorCriticAgent
+from rl_coach.agents.agent import Agent
+from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
+from rl_coach.architectures.head_parameters import DDPGActorHeadParameters, VHeadParameters
+from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters
+from rl_coach.base_parameters import NetworkParameters, AlgorithmParameters, \
+    AgentParameters, EmbedderScheme
+from rl_coach.core_types import ActionInfo, EnvironmentSteps
+from rl_coach.exploration_policies.ou_process import OUProcessParameters
+from rl_coach.memories.episodic.episodic_experience_replay import EpisodicExperienceReplayParameters
+from rl_coach.spaces import BoxActionSpace, GoalsSpace
+
+
+class DDPGCriticNetworkParameters(NetworkParameters):
+    def __init__(self):
+        super().__init__()
+        self.input_embedders_parameters = {'observation': InputEmbedderParameters(batchnorm=True),
+                                            'action': InputEmbedderParameters(scheme=EmbedderScheme.Shallow)}
+        self.middleware_parameters = FCMiddlewareParameters()
+        self.heads_parameters = [VHeadParameters()]
+        self.optimizer_type = 'Adam'
+        self.batch_size = 64
+        self.async_training = False
+        self.learning_rate = 0.001
+        self.create_target_network = True
+        self.shared_optimizer = True
+        self.scale_down_gradients_by_number_of_workers_for_sync_training = False
+
+
+class DDPGActorNetworkParameters(NetworkParameters):
+    def __init__(self):
+        super().__init__()
+        self.input_embedders_parameters = {'observation': InputEmbedderParameters(batchnorm=True)}
+        self.middleware_parameters = FCMiddlewareParameters(batchnorm=True)
+        self.heads_parameters = [DDPGActorHeadParameters()]
+        self.optimizer_type = 'Adam'
+        self.batch_size = 64
+        self.async_training = False
+        self.learning_rate = 0.0001
+        self.create_target_network = True
+        self.shared_optimizer = True
+        self.scale_down_gradients_by_number_of_workers_for_sync_training = False
+
+
+
[docs]class DDPGAlgorithmParameters(AlgorithmParameters):
+    """
+    :param num_steps_between_copying_online_weights_to_target: (StepMethod)
+        The number of steps between copying the online network weights to the target network weights.
+
+    :param rate_for_copying_weights_to_target: (float)
+        When copying the online network weights to the target network weights, a soft update will be used, which
+        weight the new online network weights by rate_for_copying_weights_to_target
+
+    :param num_consecutive_playing_steps: (StepMethod)
+        The number of consecutive steps to act between every two training iterations
+
+    :param use_target_network_for_evaluation: (bool)
+        If set to True, the target network will be used for predicting the actions when choosing actions to act.
+        Since the target network weights change more slowly, the predicted actions will be more consistent.
+
+    :param action_penalty: (float)
+        The amount by which to penalize the network on high action feature (pre-activation) values.
+        This can prevent the actions features from saturating the TanH activation function, and therefore prevent the
+        gradients from becoming very low.
+
+    :param clip_critic_targets: (Tuple[float, float] or None)
+        The range to clip the critic target to in order to prevent overestimation of the action values.
+
+    :param use_non_zero_discount_for_terminal_states: (bool)
+        If set to True, the discount factor will be used for terminal states to bootstrap the next predicted state
+        values. If set to False, the terminal states reward will be taken as the target return for the network.
+    """
+    def __init__(self):
+        super().__init__()
+        self.num_steps_between_copying_online_weights_to_target = EnvironmentSteps(1)
+        self.rate_for_copying_weights_to_target = 0.001
+        self.num_consecutive_playing_steps = EnvironmentSteps(1)
+        self.use_target_network_for_evaluation = False
+        self.action_penalty = 0
+        self.clip_critic_targets = None  # expected to be a tuple of the form (min_clip_value, max_clip_value) or None
+        self.use_non_zero_discount_for_terminal_states = False

+
+
+class DDPGAgentParameters(AgentParameters):
+    def __init__(self):
+        super().__init__(algorithm=DDPGAlgorithmParameters(),
+                         exploration=OUProcessParameters(),
+                         memory=EpisodicExperienceReplayParameters(),
+                         networks=OrderedDict([("actor", DDPGActorNetworkParameters()),
+                                               ("critic", DDPGCriticNetworkParameters())]))
+
+    @property
+    def path(self):
+        return 'rl_coach.agents.ddpg_agent:DDPGAgent'
+
+
+# Deep Deterministic Policy Gradients Network - https://arxiv.org/pdf/1509.02971.pdf
+class DDPGAgent(ActorCriticAgent):
+    def __init__(self, agent_parameters, parent: Union['LevelManager', 'CompositeAgent']=None):
+        super().__init__(agent_parameters, parent)
+
+        self.q_values = self.register_signal("Q")
+        self.TD_targets_signal = self.register_signal("TD targets")
+        self.action_signal = self.register_signal("actions")
+
+    def learn_from_batch(self, batch):
+        actor = self.networks['actor']
+        critic = self.networks['critic']
+
+        actor_keys = self.ap.network_wrappers['actor'].input_embedders_parameters.keys()
+        critic_keys = self.ap.network_wrappers['critic'].input_embedders_parameters.keys()
+
+        # TD error = r + discount*max(q_st_plus_1) - q_st
+        next_actions, actions_mean = actor.parallel_prediction([
+            (actor.target_network, batch.next_states(actor_keys)),
+            (actor.online_network, batch.states(actor_keys))
+        ])
+
+        critic_inputs = copy.copy(batch.next_states(critic_keys))
+        critic_inputs['action'] = next_actions
+        q_st_plus_1 = critic.target_network.predict(critic_inputs)
+
+        # calculate the bootstrapped TD targets while discounting terminal states according to
+        # use_non_zero_discount_for_terminal_states
+        if self.ap.algorithm.use_non_zero_discount_for_terminal_states:
+            TD_targets = batch.rewards(expand_dims=True) + self.ap.algorithm.discount * q_st_plus_1
+        else:
+            TD_targets = batch.rewards(expand_dims=True) + \
+                         (1.0 - batch.game_overs(expand_dims=True)) * self.ap.algorithm.discount * q_st_plus_1
+
+        # clip the TD targets to prevent overestimation errors
+        if self.ap.algorithm.clip_critic_targets:
+            TD_targets = np.clip(TD_targets, *self.ap.algorithm.clip_critic_targets)
+
+        self.TD_targets_signal.add_sample(TD_targets)
+
+        # get the gradients of the critic output with respect to the action
+        critic_inputs = copy.copy(batch.states(critic_keys))
+        critic_inputs['action'] = actions_mean
+        action_gradients = critic.online_network.predict(critic_inputs,
+                                                         outputs=critic.online_network.gradients_wrt_inputs[0]['action'])
+
+        # train the critic
+        critic_inputs = copy.copy(batch.states(critic_keys))
+        critic_inputs['action'] = batch.actions(len(batch.actions().shape) == 1)
+        result = critic.train_and_sync_networks(critic_inputs, TD_targets)
+        total_loss, losses, unclipped_grads = result[:3]
+
+        # apply the gradients from the critic to the actor
+        initial_feed_dict = {actor.online_network.gradients_weights_ph[0]: -action_gradients}
+        gradients = actor.online_network.predict(batch.states(actor_keys),
+                                                 outputs=actor.online_network.weighted_gradients[0],
+                                                 initial_feed_dict=initial_feed_dict)
+
+        if actor.has_global:
+            actor.apply_gradients_to_global_network(gradients)
+            actor.update_online_network()
+        else:
+            actor.apply_gradients_to_online_network(gradients)
+
+        return total_loss, losses, unclipped_grads
+
+    def train(self):
+        return Agent.train(self)
+
+    def choose_action(self, curr_state):
+        if not (isinstance(self.spaces.action, BoxActionSpace) or isinstance(self.spaces.action, GoalsSpace)):
+            raise ValueError("DDPG works only for continuous control problems")
+        # convert to batch so we can run it through the network
+        tf_input_state = self.prepare_batch_for_inference(curr_state, 'actor')
+        if self.ap.algorithm.use_target_network_for_evaluation:
+            actor_network = self.networks['actor'].target_network
+        else:
+            actor_network = self.networks['actor'].online_network
+
+        action_values = actor_network.predict(tf_input_state).squeeze()
+
+        action = self.exploration_policy.get_action(action_values)
+
+        self.action_signal.add_sample(action)
+
+        # get q value
+        tf_input_state = self.prepare_batch_for_inference(curr_state, 'critic')
+        action_batch = np.expand_dims(action, 0)
+        if type(action) != np.ndarray:
+            action_batch = np.array([[action]])
+        tf_input_state['action'] = action_batch
+        q_value = self.networks['critic'].online_network.predict(tf_input_state)[0]
+        self.q_values.add_sample(q_value)
+
+        action_info = ActionInfo(action=action,
+                                 action_value=q_value)
+
+        return action_info
+
+ +
+ +
+ + +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_modules/rl_coach/agents/dfp_agent.html b/docs/_modules/rl_coach/agents/dfp_agent.html new file mode 100644 index 0000000..2734312 --- /dev/null +++ b/docs/_modules/rl_coach/agents/dfp_agent.html @@ -0,0 +1,475 @@ + + + + + + + + + + + rl_coach.agents.dfp_agent — Reinforcement Learning Coach 0.11.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + +
+ +
+ + + + + + + + + + + + + + + + + +
+ +
    + +
  • Docs »
    • + +
  • Module code »
    • + +
  • rl_coach.agents.dfp_agent
    • + + +
  • + +
    • + +
+ + +
+
+
+
+ +

Source code for rl_coach.agents.dfp_agent

+#
+# Copyright (c) 2017 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import copy
+from enum import Enum
+from typing import Union
+
+import numpy as np
+
+from rl_coach.agents.agent import Agent
+from rl_coach.architectures.head_parameters import MeasurementsPredictionHeadParameters
+from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
+from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters
+from rl_coach.architectures.tensorflow_components.layers import Conv2d, Dense
+from rl_coach.base_parameters import AlgorithmParameters, AgentParameters, NetworkParameters, \
+     MiddlewareScheme
+from rl_coach.core_types import ActionInfo, EnvironmentSteps, RunPhase
+from rl_coach.exploration_policies.e_greedy import EGreedyParameters
+from rl_coach.memories.episodic.episodic_experience_replay import EpisodicExperienceReplayParameters
+from rl_coach.memories.memory import MemoryGranularity
+from rl_coach.spaces import SpacesDefinition, VectorObservationSpace
+
+
+class HandlingTargetsAfterEpisodeEnd(Enum):
+    LastStep = 0
+    NAN = 1
+
+
+class DFPNetworkParameters(NetworkParameters):
+    def __init__(self):
+        super().__init__()
+        self.input_embedders_parameters = {'observation': InputEmbedderParameters(activation_function='leaky_relu'),
+                                            'measurements': InputEmbedderParameters(activation_function='leaky_relu'),
+                                            'goal': InputEmbedderParameters(activation_function='leaky_relu')}
+
+        self.input_embedders_parameters['observation'].scheme = [
+            Conv2d(32, 8, 4),
+            Conv2d(64, 4, 2),
+            Conv2d(64, 3, 1),
+            Dense(512),
+        ]
+
+        self.input_embedders_parameters['measurements'].scheme = [
+            Dense(128),
+            Dense(128),
+            Dense(128),
+        ]
+
+        self.input_embedders_parameters['goal'].scheme = [
+            Dense(128),
+            Dense(128),
+            Dense(128),
+        ]
+
+        self.middleware_parameters = FCMiddlewareParameters(activation_function='leaky_relu',
+                                                            scheme=MiddlewareScheme.Empty)
+        self.heads_parameters = [MeasurementsPredictionHeadParameters(activation_function='leaky_relu')]
+        self.async_training = False
+        self.batch_size = 64
+        self.adam_optimizer_beta1 = 0.95
+
+
+class DFPMemoryParameters(EpisodicExperienceReplayParameters):
+    def __init__(self):
+        self.max_size = (MemoryGranularity.Transitions, 20000)
+        self.shared_memory = True
+        super().__init__()
+
+
+
[docs]class DFPAlgorithmParameters(AlgorithmParameters):
+    """
+    :param num_predicted_steps_ahead: (int)
+        Number of future steps to predict measurements for. The future steps won't be sequential, but rather jump
+        in multiples of 2. For example, if num_predicted_steps_ahead = 3, then the steps will be: t+1, t+2, t+4
+
+    :param goal_vector: (List[float])
+        The goal vector will weight each of the measurements to form an optimization goal. The vector should have
+        the same length as the number of measurements, and it will be vector multiplied by the measurements.
+        Positive values correspond to trying to maximize the particular measurement, and negative values
+        correspond to trying to minimize the particular measurement.
+
+    :param future_measurements_weights: (List[float])
+        The future_measurements_weights weight the contribution of each of the predicted timesteps to the optimization
+        goal. For example, if there are 6 steps predicted ahead, and a future_measurements_weights vector with 3 values,
+        then only the 3 last timesteps will be taken into account, according to the weights in the
+        future_measurements_weights vector.
+
+    :param use_accumulated_reward_as_measurement: (bool)
+        If set to True, the accumulated reward from the beginning of the episode will be added as a measurement to
+        the measurements vector in the state. This van be useful in environments where the given measurements don't
+        include enough information for the particular goal the agent should achieve.
+
+    :param handling_targets_after_episode_end: (HandlingTargetsAfterEpisodeEnd)
+        Dictates how to handle measurements that are outside the episode length.
+
+    :param scale_measurements_targets: (Dict[str, float])
+        Allows rescaling the values of each of the measurements available. This van be useful when the measurements
+        have a different scale and you want to normalize them to the same scale.
+    """
+    def __init__(self):
+        super().__init__()
+        self.num_predicted_steps_ahead = 6
+        self.goal_vector = [1.0, 1.0]
+        self.future_measurements_weights = [0.5, 0.5, 1.0]
+        self.use_accumulated_reward_as_measurement = False
+        self.handling_targets_after_episode_end = HandlingTargetsAfterEpisodeEnd.NAN
+        self.scale_measurements_targets = {}
+        self.num_consecutive_playing_steps = EnvironmentSteps(8)

+
+
+class DFPAgentParameters(AgentParameters):
+    def __init__(self):
+        super().__init__(algorithm=DFPAlgorithmParameters(),
+                         exploration=EGreedyParameters(),
+                         memory=DFPMemoryParameters(),
+                         networks={"main": DFPNetworkParameters()})
+
+    @property
+    def path(self):
+        return 'rl_coach.agents.dfp_agent:DFPAgent'
+
+
+# Direct Future Prediction Agent - http://vladlen.info/papers/learning-to-act.pdf
+class DFPAgent(Agent):
+    def __init__(self, agent_parameters, parent: Union['LevelManager', 'CompositeAgent']=None):
+        super().__init__(agent_parameters, parent)
+        self.current_goal = self.ap.algorithm.goal_vector
+        self.target_measurements_scale_factors = None
+
+    def learn_from_batch(self, batch):
+        network_keys = self.ap.network_wrappers['main'].input_embedders_parameters.keys()
+
+        network_inputs = batch.states(network_keys)
+        network_inputs['goal'] = np.repeat(np.expand_dims(self.current_goal, 0),
+                                           self.ap.network_wrappers['main'].batch_size, axis=0)
+
+        # get the current outputs of the network
+        targets = self.networks['main'].online_network.predict(network_inputs)
+
+        # change the targets for the taken actions
+        for i in range(self.ap.network_wrappers['main'].batch_size):
+            targets[i, batch.actions()[i]] = batch[i].info['future_measurements'].flatten()
+
+        result = self.networks['main'].train_and_sync_networks(network_inputs, targets)
+        total_loss, losses, unclipped_grads = result[:3]
+
+        return total_loss, losses, unclipped_grads
+
+    def choose_action(self, curr_state):
+        if self.exploration_policy.requires_action_values():
+            # predict the future measurements
+            tf_input_state = self.prepare_batch_for_inference(curr_state, 'main')
+            tf_input_state['goal'] = np.expand_dims(self.current_goal, 0)
+            measurements_future_prediction = self.networks['main'].online_network.predict(tf_input_state)[0]
+            action_values = np.zeros(len(self.spaces.action.actions))
+            num_steps_used_for_objective = len(self.ap.algorithm.future_measurements_weights)
+
+            # calculate the score of each action by multiplying it's future measurements with the goal vector
+            for action_idx in range(len(self.spaces.action.actions)):
+                action_measurements = measurements_future_prediction[action_idx]
+                action_measurements = np.reshape(action_measurements,
+                                                 (self.ap.algorithm.num_predicted_steps_ahead,
+                                                  self.spaces.state['measurements'].shape[0]))
+                future_steps_values = np.dot(action_measurements, self.current_goal)
+                action_values[action_idx] = np.dot(future_steps_values[-num_steps_used_for_objective:],
+                                                   self.ap.algorithm.future_measurements_weights)
+        else:
+            action_values = None
+
+        # choose action according to the exploration policy and the current phase (evaluating or training the agent)
+        action = self.exploration_policy.get_action(action_values)
+
+        if action_values is not None:
+            action_values = action_values.squeeze()
+            action_info = ActionInfo(action=action, action_value=action_values[action])
+        else:
+            action_info = ActionInfo(action=action)
+
+        return action_info
+
+    def set_environment_parameters(self, spaces: SpacesDefinition):
+        self.spaces = copy.deepcopy(spaces)
+        self.spaces.goal = VectorObservationSpace(shape=self.spaces.state['measurements'].shape,
+                                                  measurements_names=
+                                                  self.spaces.state['measurements'].measurements_names)
+
+        # if the user has filled some scale values, check that he got the names right
+        if set(self.spaces.state['measurements'].measurements_names).intersection(
+                self.ap.algorithm.scale_measurements_targets.keys()) !=\
+                set(self.ap.algorithm.scale_measurements_targets.keys()):
+            raise ValueError("Some of the keys in parameter scale_measurements_targets ({})  are not defined in "
+                             "the measurements space {}".format(self.ap.algorithm.scale_measurements_targets.keys(),
+                                                                self.spaces.state['measurements'].measurements_names))
+
+        super().set_environment_parameters(self.spaces)
+
+        # the below is done after calling the base class method, as it might add accumulated reward as a measurement
+
+        # fill out the missing measurements scale factors
+        for measurement_name in self.spaces.state['measurements'].measurements_names:
+            if measurement_name not in self.ap.algorithm.scale_measurements_targets:
+                self.ap.algorithm.scale_measurements_targets[measurement_name] = 1
+
+        self.target_measurements_scale_factors = \
+            np.array([self.ap.algorithm.scale_measurements_targets[measurement_name] for measurement_name in
+                      self.spaces.state['measurements'].measurements_names])
+
+    def handle_episode_ended(self):
+        last_episode = self.current_episode_buffer
+        if self.phase in [RunPhase.TRAIN, RunPhase.HEATUP] and last_episode:
+            self._update_measurements_targets(last_episode,
+                                              self.ap.algorithm.num_predicted_steps_ahead)
+        super().handle_episode_ended()
+
+    def _update_measurements_targets(self, episode, num_steps):
+        if 'measurements' not in episode.transitions[0].state or episode.transitions[0].state['measurements'] == []:
+            raise ValueError("Measurements are not present in the transitions of the last episode played. ")
+        measurements_size = self.spaces.state['measurements'].shape[0]
+        for transition_idx, transition in enumerate(episode.transitions):
+            transition.info['future_measurements'] = np.zeros((num_steps, measurements_size))
+            for step in range(num_steps):
+                offset_idx = transition_idx + 2 ** step
+
+                if offset_idx >= episode.length():
+                    if self.ap.algorithm.handling_targets_after_episode_end == HandlingTargetsAfterEpisodeEnd.NAN:
+                        # the special MSE loss will ignore those entries so that the gradient will be 0 for these
+                        transition.info['future_measurements'][step] = np.nan
+                        continue
+
+                    elif self.ap.algorithm.handling_targets_after_episode_end == HandlingTargetsAfterEpisodeEnd.LastStep:
+                        offset_idx = - 1
+
+                transition.info['future_measurements'][step] = \
+                    self.target_measurements_scale_factors * \
+                    (episode.transitions[offset_idx].state['measurements'] - transition.state['measurements'])
+
+ +
+ +
+ + +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_modules/rl_coach/agents/dqn_agent.html b/docs/_modules/rl_coach/agents/dqn_agent.html new file mode 100644 index 0000000..c60551f --- /dev/null +++ b/docs/_modules/rl_coach/agents/dqn_agent.html @@ -0,0 +1,326 @@ + + + + + + + + + + + rl_coach.agents.dqn_agent — Reinforcement Learning Coach 0.11.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + +
+ +
+ + + + + + + + + + + + + + + + + +
+ +
    + +
  • Docs »
    • + +
  • Module code »
    • + +
  • rl_coach.agents.dqn_agent
    • + + +
  • + +
    • + +
+ + +
+
+
+
+ +

Source code for rl_coach.agents.dqn_agent

+#
+# Copyright (c) 2017 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from typing import Union
+
+import numpy as np
+
+from rl_coach.agents.value_optimization_agent import ValueOptimizationAgent
+from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
+from rl_coach.architectures.head_parameters import QHeadParameters
+from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters
+from rl_coach.base_parameters import AlgorithmParameters, NetworkParameters, AgentParameters, \
+    MiddlewareScheme
+from rl_coach.core_types import EnvironmentSteps
+from rl_coach.exploration_policies.e_greedy import EGreedyParameters
+from rl_coach.memories.non_episodic.experience_replay import ExperienceReplayParameters
+from rl_coach.schedules import LinearSchedule
+
+
+
[docs]class DQNAlgorithmParameters(AlgorithmParameters):
+    def __init__(self):
+        super().__init__()
+        self.num_steps_between_copying_online_weights_to_target = EnvironmentSteps(10000)
+        self.num_consecutive_playing_steps = EnvironmentSteps(4)
+        self.discount = 0.99

+
+
+class DQNNetworkParameters(NetworkParameters):
+    def __init__(self):
+        super().__init__()
+        self.input_embedders_parameters = {'observation': InputEmbedderParameters()}
+        self.middleware_parameters = FCMiddlewareParameters(scheme=MiddlewareScheme.Medium)
+        self.heads_parameters = [QHeadParameters()]
+        self.optimizer_type = 'Adam'
+        self.batch_size = 32
+        self.replace_mse_with_huber_loss = True
+        self.create_target_network = True
+
+
+class DQNAgentParameters(AgentParameters):
+    def __init__(self):
+        super().__init__(algorithm=DQNAlgorithmParameters(),
+                         exploration=EGreedyParameters(),
+                         memory=ExperienceReplayParameters(),
+                         networks={"main": DQNNetworkParameters()})
+        self.exploration.epsilon_schedule = LinearSchedule(1, 0.1, 1000000)
+        self.exploration.evaluation_epsilon = 0.05
+
+    @property
+    def path(self):
+        return 'rl_coach.agents.dqn_agent:DQNAgent'
+
+
+# Deep Q Network - https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf
+
[docs]class DQNAgent(ValueOptimizationAgent):
+    def __init__(self, agent_parameters, parent: Union['LevelManager', 'CompositeAgent']=None):
+        super().__init__(agent_parameters, parent)
+
+
[docs]    def learn_from_batch(self, batch):
+        network_keys = self.ap.network_wrappers['main'].input_embedders_parameters.keys()
+
+        # for the action we actually took, the error is:
+        # TD error = r + discount*max(q_st_plus_1) - q_st
+        # # for all other actions, the error is 0
+        q_st_plus_1, TD_targets = self.networks['main'].parallel_prediction([
+            (self.networks['main'].target_network, batch.next_states(network_keys)),
+            (self.networks['main'].online_network, batch.states(network_keys))
+        ])
+
+        #  only update the action that we have actually done in this transition
+        TD_errors = []
+        for i in range(self.ap.network_wrappers['main'].batch_size):
+            new_target = batch.rewards()[i] +\
+                         (1.0 - batch.game_overs()[i]) * self.ap.algorithm.discount * np.max(q_st_plus_1[i], 0)
+            TD_errors.append(np.abs(new_target - TD_targets[i, batch.actions()[i]]))
+            TD_targets[i, batch.actions()[i]] = new_target
+
+        # update errors in prioritized replay buffer
+        importance_weights = self.update_transition_priorities_and_get_weights(TD_errors, batch)
+
+        result = self.networks['main'].train_and_sync_networks(batch.states(network_keys), TD_targets,
+                                                               importance_weights=importance_weights)
+
+        total_loss, losses, unclipped_grads = result[:3]
+
+        return total_loss, losses, unclipped_grads

+
+ +
+ +
+ + +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_modules/rl_coach/agents/mmc_agent.html b/docs/_modules/rl_coach/agents/mmc_agent.html new file mode 100644 index 0000000..d27a727 --- /dev/null +++ b/docs/_modules/rl_coach/agents/mmc_agent.html @@ -0,0 +1,306 @@ + + + + + + + + + + + rl_coach.agents.mmc_agent — Reinforcement Learning Coach 0.11.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Source code for rl_coach.agents.mmc_agent

+#
+# Copyright (c) 2017 Intel Corporation 
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from typing import Union
+
+import numpy as np
+
+from rl_coach.agents.dqn_agent import DQNAgentParameters, DQNAlgorithmParameters
+from rl_coach.agents.value_optimization_agent import ValueOptimizationAgent
+from rl_coach.memories.episodic.episodic_experience_replay import EpisodicExperienceReplayParameters
+
+
+
[docs]class MixedMonteCarloAlgorithmParameters(DQNAlgorithmParameters):
+    """
+    :param monte_carlo_mixing_rate: (float)
+        The mixing rate is used for setting the amount of monte carlo estimate (full return) that will be mixes into
+        the single-step bootstrapped targets.
+    """
+    def __init__(self):
+        super().__init__()
+        self.monte_carlo_mixing_rate = 0.1

+
+
+class MixedMonteCarloAgentParameters(DQNAgentParameters):
+    def __init__(self):
+        super().__init__()
+        self.algorithm = MixedMonteCarloAlgorithmParameters()
+        self.memory = EpisodicExperienceReplayParameters()
+
+    @property
+    def path(self):
+        return 'rl_coach.agents.mmc_agent:MixedMonteCarloAgent'
+
+
+class MixedMonteCarloAgent(ValueOptimizationAgent):
+    def __init__(self, agent_parameters, parent: Union['LevelManager', 'CompositeAgent']=None):
+        super().__init__(agent_parameters, parent)
+        self.mixing_rate = agent_parameters.algorithm.monte_carlo_mixing_rate
+
+    def learn_from_batch(self, batch):
+        network_keys = self.ap.network_wrappers['main'].input_embedders_parameters.keys()
+
+        # for the 1-step, we use the double-dqn target. hence actions are taken greedily according to the online network
+        selected_actions = np.argmax(self.networks['main'].online_network.predict(batch.next_states(network_keys)), 1)
+
+        # TD_targets are initialized with the current prediction so that we will
+        #  only update the action that we have actually done in this transition
+        q_st_plus_1, TD_targets = self.networks['main'].parallel_prediction([
+            (self.networks['main'].target_network, batch.next_states(network_keys)),
+            (self.networks['main'].online_network, batch.states(network_keys))
+        ])
+
+        total_returns = batch.n_step_discounted_rewards()
+
+        for i in range(self.ap.network_wrappers['main'].batch_size):
+            one_step_target = batch.rewards()[i] + \
+                              (1.0 - batch.game_overs()[i]) * self.ap.algorithm.discount * \
+                              q_st_plus_1[i][selected_actions[i]]
+            monte_carlo_target = total_returns[i]
+            TD_targets[i, batch.actions()[i]] = (1 - self.mixing_rate) * one_step_target + \
+                                                self.mixing_rate * monte_carlo_target
+
+        result = self.networks['main'].train_and_sync_networks(batch.states(network_keys), TD_targets)
+        total_loss, losses, unclipped_grads = result[:3]
+
+        return total_loss, losses, unclipped_grads
+
+ +
+ +
+ + +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_modules/rl_coach/agents/n_step_q_agent.html b/docs/_modules/rl_coach/agents/n_step_q_agent.html new file mode 100644 index 0000000..bb1f371 --- /dev/null +++ b/docs/_modules/rl_coach/agents/n_step_q_agent.html @@ -0,0 +1,373 @@ + + + + + + + + + + + rl_coach.agents.n_step_q_agent — Reinforcement Learning Coach 0.11.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
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+ +

Source code for rl_coach.agents.n_step_q_agent

+#
+# Copyright (c) 2017 Intel Corporation 
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from typing import Union
+
+import numpy as np
+
+from rl_coach.agents.policy_optimization_agent import PolicyOptimizationAgent
+from rl_coach.agents.value_optimization_agent import ValueOptimizationAgent
+from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
+from rl_coach.architectures.head_parameters import QHeadParameters
+from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters
+from rl_coach.base_parameters import AlgorithmParameters, AgentParameters, NetworkParameters
+
+from rl_coach.core_types import EnvironmentSteps
+from rl_coach.exploration_policies.e_greedy import EGreedyParameters
+from rl_coach.memories.episodic.single_episode_buffer import SingleEpisodeBufferParameters
+from rl_coach.utils import last_sample
+
+
+class NStepQNetworkParameters(NetworkParameters):
+    def __init__(self):
+        super().__init__()
+        self.input_embedders_parameters = {'observation': InputEmbedderParameters()}
+        self.middleware_parameters = FCMiddlewareParameters()
+        self.heads_parameters = [QHeadParameters()]
+        self.optimizer_type = 'Adam'
+        self.async_training = True
+        self.shared_optimizer = True
+        self.create_target_network = True
+
+
+
[docs]class NStepQAlgorithmParameters(AlgorithmParameters):
+    """
+    :param num_steps_between_copying_online_weights_to_target: (StepMethod)
+        The number of steps between copying the online network weights to the target network weights.
+
+    :param apply_gradients_every_x_episodes: (int)
+        The number of episodes between applying the accumulated gradients to the network. After every
+        num_steps_between_gradient_updates steps, the agent will calculate the gradients for the collected data,
+        it will then accumulate it in internal accumulators, and will only apply them to the network once in every
+        apply_gradients_every_x_episodes episodes.
+
+    :param num_steps_between_gradient_updates: (int)
+        The number of steps between calculating gradients for the collected data. In the A3C paper, this parameter is
+        called t_max. Since this algorithm is on-policy, only the steps collected between each two gradient calculations
+        are used in the batch.
+
+    :param targets_horizon: (str)
+        Should be either 'N-Step' or '1-Step', and defines the length for which to bootstrap the network values over.
+        Essentially, 1-Step follows the regular 1 step bootstrapping Q learning update. For more information,
+        please refer to the original paper (https://arxiv.org/abs/1602.01783)
+    """
+    def __init__(self):
+        super().__init__()
+        self.num_steps_between_copying_online_weights_to_target = EnvironmentSteps(10000)
+        self.apply_gradients_every_x_episodes = 1
+        self.num_steps_between_gradient_updates = 5  # this is called t_max in all the papers
+        self.targets_horizon = 'N-Step'

+
+
+class NStepQAgentParameters(AgentParameters):
+    def __init__(self):
+        super().__init__(algorithm=NStepQAlgorithmParameters(),
+                         exploration=EGreedyParameters(),
+                         memory=SingleEpisodeBufferParameters(),
+                         networks={"main": NStepQNetworkParameters()})
+
+    @property
+    def path(self):
+        return 'rl_coach.agents.n_step_q_agent:NStepQAgent'
+
+
+# N Step Q Learning Agent - https://arxiv.org/abs/1602.01783
+class NStepQAgent(ValueOptimizationAgent, PolicyOptimizationAgent):
+    def __init__(self, agent_parameters, parent: Union['LevelManager', 'CompositeAgent']=None):
+        super().__init__(agent_parameters, parent)
+        self.last_gradient_update_step_idx = 0
+        self.q_values = self.register_signal('Q Values')
+        self.value_loss = self.register_signal('Value Loss')
+
+    def learn_from_batch(self, batch):
+        # batch contains a list of episodes to learn from
+        network_keys = self.ap.network_wrappers['main'].input_embedders_parameters.keys()
+
+        # get the values for the current states
+        state_value_head_targets = self.networks['main'].online_network.predict(batch.states(network_keys))
+
+        # the targets for the state value estimator
+        if self.ap.algorithm.targets_horizon == '1-Step':
+            # 1-Step Q learning
+            q_st_plus_1 = self.networks['main'].target_network.predict(batch.next_states(network_keys))
+
+            for i in reversed(range(batch.size)):
+                state_value_head_targets[i][batch.actions()[i]] = \
+                    batch.rewards()[i] \
+                    + (1.0 - batch.game_overs()[i]) * self.ap.algorithm.discount * np.max(q_st_plus_1[i], 0)
+
+        elif self.ap.algorithm.targets_horizon == 'N-Step':
+            # N-Step Q learning
+            if batch.game_overs()[-1]:
+                R = 0
+            else:
+                R = np.max(self.networks['main'].target_network.predict(last_sample(batch.next_states(network_keys))))
+
+            for i in reversed(range(batch.size)):
+                R = batch.rewards()[i] + self.ap.algorithm.discount * R
+                state_value_head_targets[i][batch.actions()[i]] = R
+
+        else:
+            assert True, 'The available values for targets_horizon are: 1-Step, N-Step'
+
+        # train
+        result = self.networks['main'].online_network.accumulate_gradients(batch.states(network_keys), [state_value_head_targets])
+
+        # logging
+        total_loss, losses, unclipped_grads = result[:3]
+        self.value_loss.add_sample(losses[0])
+
+        return total_loss, losses, unclipped_grads
+
+    def train(self):
+        # update the target network of every network that has a target network
+        if any([network.has_target for network in self.networks.values()]) \
+                and self._should_update_online_weights_to_target():
+            for network in self.networks.values():
+                network.update_target_network(self.ap.algorithm.rate_for_copying_weights_to_target)
+
+            self.agent_logger.create_signal_value('Update Target Network', 1)
+        else:
+            self.agent_logger.create_signal_value('Update Target Network', 0, overwrite=False)
+
+        return PolicyOptimizationAgent.train(self)
+
+ +
+ +
+ + +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_modules/rl_coach/agents/naf_agent.html b/docs/_modules/rl_coach/agents/naf_agent.html new file mode 100644 index 0000000..72d71b3 --- /dev/null +++ b/docs/_modules/rl_coach/agents/naf_agent.html @@ -0,0 +1,354 @@ + + + + + + + + + + + rl_coach.agents.naf_agent — Reinforcement Learning Coach 0.11.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + +
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+ +

Source code for rl_coach.agents.naf_agent

+#
+# Copyright (c) 2017 Intel Corporation 
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from typing import Union
+
+import numpy as np
+
+from rl_coach.agents.value_optimization_agent import ValueOptimizationAgent
+from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
+from rl_coach.architectures.head_parameters import NAFHeadParameters
+from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters
+from rl_coach.base_parameters import AlgorithmParameters, AgentParameters, \
+    NetworkParameters
+
+from rl_coach.core_types import ActionInfo, EnvironmentSteps
+from rl_coach.exploration_policies.ou_process import OUProcessParameters
+from rl_coach.memories.episodic.episodic_experience_replay import EpisodicExperienceReplayParameters
+from rl_coach.spaces import BoxActionSpace
+
+
+class NAFNetworkParameters(NetworkParameters):
+    def __init__(self):
+        super().__init__()
+        self.input_embedders_parameters = {'observation': InputEmbedderParameters()}
+        self.middleware_parameters = FCMiddlewareParameters()
+        self.heads_parameters = [NAFHeadParameters()]
+        self.optimizer_type = 'Adam'
+        self.learning_rate = 0.001
+        self.async_training = True
+        self.create_target_network = True
+
+
+
[docs]class NAFAlgorithmParameters(AlgorithmParameters):
+    def __init__(self):
+        super().__init__()
+        self.num_consecutive_training_steps = 5
+        self.num_steps_between_copying_online_weights_to_target = EnvironmentSteps(1)
+        self.rate_for_copying_weights_to_target = 0.001

+
+
+class NAFAgentParameters(AgentParameters):
+    def __init__(self):
+        super().__init__(algorithm=NAFAlgorithmParameters(),
+                         exploration=OUProcessParameters(),
+                         memory=EpisodicExperienceReplayParameters(),
+                         networks={"main": NAFNetworkParameters()})
+
+    @property
+    def path(self):
+        return 'rl_coach.agents.naf_agent:NAFAgent'
+
+
+# Normalized Advantage Functions - https://arxiv.org/pdf/1603.00748.pdf
+class NAFAgent(ValueOptimizationAgent):
+    def __init__(self, agent_parameters, parent: Union['LevelManager', 'CompositeAgent']=None):
+        super().__init__(agent_parameters, parent)
+        self.l_values = self.register_signal("L")
+        self.a_values = self.register_signal("Advantage")
+        self.mu_values = self.register_signal("Action")
+        self.v_values = self.register_signal("V")
+        self.TD_targets = self.register_signal("TD targets")
+
+    def learn_from_batch(self, batch):
+        network_keys = self.ap.network_wrappers['main'].input_embedders_parameters.keys()
+
+        # TD error = r + discount*v_st_plus_1 - q_st
+        v_st_plus_1 = self.networks['main'].target_network.predict(
+            batch.next_states(network_keys),
+            self.networks['main'].target_network.output_heads[0].V,
+            squeeze_output=False,
+        )
+        TD_targets = np.expand_dims(batch.rewards(), -1) + \
+                     (1.0 - np.expand_dims(batch.game_overs(), -1)) * self.ap.algorithm.discount * v_st_plus_1
+
+        self.TD_targets.add_sample(TD_targets)
+
+        result = self.networks['main'].train_and_sync_networks({**batch.states(network_keys),
+                                                                'output_0_0': batch.actions(len(batch.actions().shape) == 1)
+                                                                }, TD_targets)
+        total_loss, losses, unclipped_grads = result[:3]
+
+        return total_loss, losses, unclipped_grads
+
+    def choose_action(self, curr_state):
+        if type(self.spaces.action) != BoxActionSpace:
+            raise ValueError('NAF works only for continuous control problems')
+
+        # convert to batch so we can run it through the network
+        tf_input_state = self.prepare_batch_for_inference(curr_state, 'main')
+        naf_head = self.networks['main'].online_network.output_heads[0]
+        action_values = self.networks['main'].online_network.predict(tf_input_state, outputs=naf_head.mu,
+                                                                     squeeze_output=False)
+
+        # get the actual action to use
+        action = self.exploration_policy.get_action(action_values)
+
+        # get the internal values for logging
+        outputs = [naf_head.mu, naf_head.Q, naf_head.L, naf_head.A, naf_head.V]
+        result = self.networks['main'].online_network.predict(
+            {**tf_input_state, 'output_0_0': action_values},
+            outputs=outputs
+        )
+        mu, Q, L, A, V = result
+
+        # store the q values statistics for logging
+        self.q_values.add_sample(Q)
+        self.l_values.add_sample(L)
+        self.a_values.add_sample(A)
+        self.mu_values.add_sample(mu)
+        self.v_values.add_sample(V)
+
+        action_info = ActionInfo(action=action, action_value=Q)
+        
+        return action_info
+
+ +
+ +
+ + +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_modules/rl_coach/agents/nec_agent.html b/docs/_modules/rl_coach/agents/nec_agent.html new file mode 100644 index 0000000..8b63939 --- /dev/null +++ b/docs/_modules/rl_coach/agents/nec_agent.html @@ -0,0 +1,435 @@ + + + + + + + + + + + rl_coach.agents.nec_agent — Reinforcement Learning Coach 0.11.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + +
+ +
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+ +

Source code for rl_coach.agents.nec_agent

+#
+# Copyright (c) 2017 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import os
+import pickle
+from typing import Union
+
+import numpy as np
+
+from rl_coach.agents.value_optimization_agent import ValueOptimizationAgent
+from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
+from rl_coach.architectures.head_parameters import DNDQHeadParameters
+from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters
+from rl_coach.base_parameters import AlgorithmParameters, NetworkParameters, AgentParameters
+
+from rl_coach.core_types import RunPhase, EnvironmentSteps, Episode, StateType
+from rl_coach.exploration_policies.e_greedy import EGreedyParameters
+from rl_coach.logger import screen
+from rl_coach.memories.episodic.episodic_experience_replay import EpisodicExperienceReplayParameters, MemoryGranularity
+from rl_coach.schedules import ConstantSchedule
+
+
+class NECNetworkParameters(NetworkParameters):
+    def __init__(self):
+        super().__init__()
+        self.input_embedders_parameters = {'observation': InputEmbedderParameters()}
+        self.middleware_parameters = FCMiddlewareParameters()
+        self.heads_parameters = [DNDQHeadParameters()]
+        self.optimizer_type = 'Adam'
+
+
+
[docs]class NECAlgorithmParameters(AlgorithmParameters):
+    """
+    :param dnd_size: (int)
+        Defines the number of transitions that will be stored in each one of the DNDs. Note that the total number
+        of transitions that will be stored is dnd_size x num_actions.
+
+    :param l2_norm_added_delta: (float)
+        A small value that will be added when calculating the weight of each of the DND entries. This follows the
+        :math:`\delta` patameter defined in the paper.
+
+    :param new_value_shift_coefficient: (float)
+        In the case where a ew embedding that was added to the DND was already present, the value that will be stored
+        in the DND is a mix between the existing value and the new value. The mix rate is defined by
+        new_value_shift_coefficient.
+
+    :param number_of_knn: (int)
+        The number of neighbors that will be retrieved for each DND query.
+
+    :param DND_key_error_threshold: (float)
+        When the DND is queried for a specific embedding, this threshold will be used to determine if the embedding
+        exists in the DND, since exact matches of embeddings are very rare.
+
+    :param propagate_updates_to_DND: (bool)
+        If set to True, when the gradients of the network will be calculated, the gradients will also be
+        backpropagated through the keys of the DND. The keys will then be updated as well, as if they were regular
+        network weights.
+
+    :param n_step: (int)
+        The bootstrap length that will be used when calculating the state values to store in the DND.
+
+    :param bootstrap_total_return_from_old_policy: (bool)
+        If set to True, the bootstrap that will be used to calculate each state-action value, is the network value
+        when the state was first seen, and not the latest, most up-to-date network value.
+    """
+    def __init__(self):
+        super().__init__()
+        self.dnd_size = 500000
+        self.l2_norm_added_delta = 0.001
+        self.new_value_shift_coefficient = 0.1
+        self.number_of_knn = 50
+        self.DND_key_error_threshold = 0
+        self.num_consecutive_playing_steps = EnvironmentSteps(4)
+        self.propagate_updates_to_DND = False
+        self.n_step = 100
+        self.bootstrap_total_return_from_old_policy = True

+
+
+class NECMemoryParameters(EpisodicExperienceReplayParameters):
+    def __init__(self):
+        super().__init__()
+        self.max_size = (MemoryGranularity.Transitions, 100000)
+
+
+class NECAgentParameters(AgentParameters):
+    def __init__(self):
+        super().__init__(algorithm=NECAlgorithmParameters(),
+                         exploration=EGreedyParameters(),
+                         memory=NECMemoryParameters(),
+                         networks={"main": NECNetworkParameters()})
+        self.exploration.epsilon_schedule = ConstantSchedule(0.1)
+        self.exploration.evaluation_epsilon = 0.01
+
+    @property
+    def path(self):
+        return 'rl_coach.agents.nec_agent:NECAgent'
+
+
+# Neural Episodic Control - https://arxiv.org/pdf/1703.01988.pdf
+class NECAgent(ValueOptimizationAgent):
+    def __init__(self, agent_parameters, parent: Union['LevelManager', 'CompositeAgent']=None):
+        super().__init__(agent_parameters, parent)
+        self.current_episode_state_embeddings = []
+        self.training_started = False
+        self.current_episode_buffer = \
+            Episode(discount=self.ap.algorithm.discount,
+                    n_step=self.ap.algorithm.n_step,
+                    bootstrap_total_return_from_old_policy=self.ap.algorithm.bootstrap_total_return_from_old_policy)
+
+    def learn_from_batch(self, batch):
+        if not self.networks['main'].online_network.output_heads[0].DND.has_enough_entries(self.ap.algorithm.number_of_knn):
+            return 0, [], 0
+        else:
+            if not self.training_started:
+                self.training_started = True
+                screen.log_title("Finished collecting initial entries in DND. Starting to train network...")
+
+        network_keys = self.ap.network_wrappers['main'].input_embedders_parameters.keys()
+
+        TD_targets = self.networks['main'].online_network.predict(batch.states(network_keys))
+        bootstrapped_return_from_old_policy = batch.n_step_discounted_rewards()
+        #  only update the action that we have actually done in this transition
+        for i in range(self.ap.network_wrappers['main'].batch_size):
+            TD_targets[i, batch.actions()[i]] = bootstrapped_return_from_old_policy[i]
+
+        # set the gradients to fetch for the DND update
+        fetches = []
+        head = self.networks['main'].online_network.output_heads[0]
+        if self.ap.algorithm.propagate_updates_to_DND:
+            fetches = [head.dnd_embeddings_grad, head.dnd_values_grad, head.dnd_indices]
+
+        # train the neural network
+        result = self.networks['main'].train_and_sync_networks(batch.states(network_keys), TD_targets, fetches)
+
+        total_loss, losses, unclipped_grads = result[:3]
+
+        # update the DND keys and values using the extracted gradients
+        if self.ap.algorithm.propagate_updates_to_DND:
+            embedding_gradients = np.swapaxes(result[-1][0], 0, 1)
+            value_gradients = np.swapaxes(result[-1][1], 0, 1)
+            indices = np.swapaxes(result[-1][2], 0, 1)
+            head.DND.update_keys_and_values(batch.actions(), embedding_gradients, value_gradients, indices)
+
+        return total_loss, losses, unclipped_grads
+
+    def act(self):
+        if self.phase == RunPhase.HEATUP:
+            # get embedding in heatup (otherwise we get it through get_prediction)
+            embedding = self.networks['main'].online_network.predict(
+                self.prepare_batch_for_inference(self.curr_state, 'main'),
+                outputs=self.networks['main'].online_network.state_embedding)
+            self.current_episode_state_embeddings.append(embedding)
+
+        return super().act()
+
+    def get_all_q_values_for_states(self, states: StateType):
+        # we need to store the state embeddings regardless if the action is random or not
+        return self.get_prediction(states)
+
+    def get_prediction(self, states):
+        # get the actions q values and the state embedding
+        embedding, actions_q_values = self.networks['main'].online_network.predict(
+            self.prepare_batch_for_inference(states, 'main'),
+            outputs=[self.networks['main'].online_network.state_embedding,
+                     self.networks['main'].online_network.output_heads[0].output]
+        )
+        if self.phase != RunPhase.TEST:
+            # store the state embedding for inserting it to the DND later
+            self.current_episode_state_embeddings.append(embedding.squeeze())
+        actions_q_values = actions_q_values[0][0]
+        return actions_q_values
+
+    def reset_internal_state(self):
+        super().reset_internal_state()
+        self.current_episode_state_embeddings = []
+        self.current_episode_buffer = \
+            Episode(discount=self.ap.algorithm.discount,
+                    n_step=self.ap.algorithm.n_step,
+                    bootstrap_total_return_from_old_policy=self.ap.algorithm.bootstrap_total_return_from_old_policy)
+
+    def handle_episode_ended(self):
+        super().handle_episode_ended()
+
+        # get the last full episode that we have collected
+        episode = self.call_memory('get_last_complete_episode')
+        if episode is not None and self.phase != RunPhase.TEST:
+            assert len(self.current_episode_state_embeddings) == episode.length()
+            discounted_rewards = episode.get_transitions_attribute('n_step_discounted_rewards')
+            actions = episode.get_transitions_attribute('action')
+            self.networks['main'].online_network.output_heads[0].DND.add(self.current_episode_state_embeddings,
+                                                                         actions, discounted_rewards)
+
+    def save_checkpoint(self, checkpoint_id):
+        with open(os.path.join(self.ap.task_parameters.checkpoint_save_dir, str(checkpoint_id) + '.dnd'), 'wb') as f:
+            pickle.dump(self.networks['main'].online_network.output_heads[0].DND, f, pickle.HIGHEST_PROTOCOL)
+
+ +
+ +
+ + +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_modules/rl_coach/agents/pal_agent.html b/docs/_modules/rl_coach/agents/pal_agent.html new file mode 100644 index 0000000..0344d4c --- /dev/null +++ b/docs/_modules/rl_coach/agents/pal_agent.html @@ -0,0 +1,334 @@ + + + + + + + + + + + rl_coach.agents.pal_agent — Reinforcement Learning Coach 0.11.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + +
+ +
+ + + + + + + + + + + + + + + + + +
+ +
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  • rl_coach.agents.pal_agent
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+ + +
+
+
+
+ +

Source code for rl_coach.agents.pal_agent

+#
+# Copyright (c) 2017 Intel Corporation 
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from typing import Union
+
+import numpy as np
+
+from rl_coach.agents.dqn_agent import DQNAgentParameters, DQNAlgorithmParameters
+from rl_coach.agents.value_optimization_agent import ValueOptimizationAgent
+from rl_coach.memories.episodic.episodic_experience_replay import EpisodicExperienceReplayParameters
+
+
+
[docs]class PALAlgorithmParameters(DQNAlgorithmParameters):
+    """
+    :param pal_alpha: (float)
+        A factor that weights the amount by which the advantage learning update will be taken into account.
+
+    :param persistent_advantage_learning: (bool)
+        If set to True, the persistent mode of advantage learning will be used, which encourages the agent to take
+        the same actions one after the other instead of changing actions.
+
+    :param monte_carlo_mixing_rate: (float)
+        The amount of monte carlo values to mix into the targets of the network. The monte carlo values are just the
+        total discounted returns, and they can help reduce the time it takes for the network to update to the newly
+        seen values, since it is not based on bootstrapping the current network values.
+    """
+    def __init__(self):
+        super().__init__()
+        self.pal_alpha = 0.9
+        self.persistent_advantage_learning = False
+        self.monte_carlo_mixing_rate = 0.1

+
+
+class PALAgentParameters(DQNAgentParameters):
+    def __init__(self):
+        super().__init__()
+        self.algorithm = PALAlgorithmParameters()
+        self.memory = EpisodicExperienceReplayParameters()
+
+    @property
+    def path(self):
+        return 'rl_coach.agents.pal_agent:PALAgent'
+
+
+# Persistent Advantage Learning - https://arxiv.org/pdf/1512.04860.pdf
+class PALAgent(ValueOptimizationAgent):
+    def __init__(self, agent_parameters, parent: Union['LevelManager', 'CompositeAgent']=None):
+        super().__init__(agent_parameters, parent)
+        self.alpha = agent_parameters.algorithm.pal_alpha
+        self.persistent = agent_parameters.algorithm.persistent_advantage_learning
+        self.monte_carlo_mixing_rate = agent_parameters.algorithm.monte_carlo_mixing_rate
+
+    def learn_from_batch(self, batch):
+        network_keys = self.ap.network_wrappers['main'].input_embedders_parameters.keys()
+
+        # next state values
+        q_st_plus_1_target, q_st_plus_1_online = self.networks['main'].parallel_prediction([
+            (self.networks['main'].target_network, batch.next_states(network_keys)),
+            (self.networks['main'].online_network, batch.next_states(network_keys))
+        ])
+        selected_actions = np.argmax(q_st_plus_1_online, 1)
+        v_st_plus_1_target = np.max(q_st_plus_1_target, 1)
+
+        # current state values
+        q_st_target, q_st_online = self.networks['main'].parallel_prediction([
+            (self.networks['main'].target_network, batch.states(network_keys)),
+            (self.networks['main'].online_network, batch.states(network_keys))
+        ])
+        v_st_target = np.max(q_st_target, 1)
+
+        # calculate TD error
+        TD_targets = np.copy(q_st_online)
+        total_returns = batch.n_step_discounted_rewards()
+        for i in range(self.ap.network_wrappers['main'].batch_size):
+            TD_targets[i, batch.actions()[i]] = batch.rewards()[i] + \
+                                        (1.0 - batch.game_overs()[i]) * self.ap.algorithm.discount * \
+                                                     q_st_plus_1_target[i][selected_actions[i]]
+            advantage_learning_update = v_st_target[i] - q_st_target[i, batch.actions()[i]]
+            next_advantage_learning_update = v_st_plus_1_target[i] - q_st_plus_1_target[i, selected_actions[i]]
+            # Persistent Advantage Learning or Regular Advantage Learning
+            if self.persistent:
+                TD_targets[i, batch.actions()[i]] -= self.alpha * min(advantage_learning_update, next_advantage_learning_update)
+            else:
+                TD_targets[i, batch.actions()[i]] -= self.alpha * advantage_learning_update
+
+            # mixing monte carlo updates
+            monte_carlo_target = total_returns[i]
+            TD_targets[i, batch.actions()[i]] = (1 - self.monte_carlo_mixing_rate) * TD_targets[i, batch.actions()[i]] \
+                                        + self.monte_carlo_mixing_rate * monte_carlo_target
+
+        result = self.networks['main'].train_and_sync_networks(batch.states(network_keys), TD_targets)
+        total_loss, losses, unclipped_grads = result[:3]
+
+        return total_loss, losses, unclipped_grads
+
+ +
+ +
+ + +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_modules/rl_coach/agents/policy_gradients_agent.html b/docs/_modules/rl_coach/agents/policy_gradients_agent.html new file mode 100644 index 0000000..adf3150 --- /dev/null +++ b/docs/_modules/rl_coach/agents/policy_gradients_agent.html @@ -0,0 +1,356 @@ + + + + + + + + + + + rl_coach.agents.policy_gradients_agent — Reinforcement Learning Coach 0.11.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + +
+ +
+ + + + + + + + + + + + + + + + + +
+ +
    + +
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  • rl_coach.agents.policy_gradients_agent
    • + + +
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+ + +
+
+
+
+ +

Source code for rl_coach.agents.policy_gradients_agent

+#
+# Copyright (c) 2017 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from typing import Union
+
+import numpy as np
+
+from rl_coach.agents.policy_optimization_agent import PolicyOptimizationAgent, PolicyGradientRescaler
+from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
+from rl_coach.architectures.head_parameters import PolicyHeadParameters
+from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters
+from rl_coach.base_parameters import NetworkParameters, AlgorithmParameters, \
+    AgentParameters
+
+from rl_coach.exploration_policies.additive_noise import AdditiveNoiseParameters
+from rl_coach.exploration_policies.categorical import CategoricalParameters
+from rl_coach.logger import screen
+from rl_coach.memories.episodic.single_episode_buffer import SingleEpisodeBufferParameters
+from rl_coach.spaces import DiscreteActionSpace, BoxActionSpace
+
+
+class PolicyGradientNetworkParameters(NetworkParameters):
+    def __init__(self):
+        super().__init__()
+        self.input_embedders_parameters = {'observation': InputEmbedderParameters()}
+        self.middleware_parameters = FCMiddlewareParameters()
+        self.heads_parameters = [PolicyHeadParameters()]
+        self.async_training = True
+
+
+
[docs]class PolicyGradientAlgorithmParameters(AlgorithmParameters):
+    """
+    :param policy_gradient_rescaler: (PolicyGradientRescaler)
+        The rescaler type to use for the policy gradient loss. For policy gradients, we calculate log probability of
+        the action and then multiply it by the policy gradient rescaler. The most basic rescaler is the discounter
+        return, but there are other rescalers that are intended for reducing the variance of the updates.
+
+    :param apply_gradients_every_x_episodes: (int)
+        The number of episodes between applying the accumulated gradients to the network. After every
+        num_steps_between_gradient_updates steps, the agent will calculate the gradients for the collected data,
+        it will then accumulate it in internal accumulators, and will only apply them to the network once in every
+        apply_gradients_every_x_episodes episodes.
+
+    :param beta_entropy: (float)
+        A factor which defines the amount of entropy regularization to apply to the network. The entropy of the actions
+        will be added to the loss and scaled by the given beta factor.
+
+    :param num_steps_between_gradient_updates: (int)
+        The number of steps between calculating gradients for the collected data. In the A3C paper, this parameter is
+        called t_max. Since this algorithm is on-policy, only the steps collected between each two gradient calculations
+        are used in the batch.
+    """
+    def __init__(self):
+        super().__init__()
+        self.policy_gradient_rescaler = PolicyGradientRescaler.FUTURE_RETURN_NORMALIZED_BY_TIMESTEP
+        self.apply_gradients_every_x_episodes = 5
+        self.beta_entropy = 0
+        self.num_steps_between_gradient_updates = 20000  # this is called t_max in all the papers

+
+
+class PolicyGradientsAgentParameters(AgentParameters):
+    def __init__(self):
+        super().__init__(algorithm=PolicyGradientAlgorithmParameters(),
+                         exploration={DiscreteActionSpace: CategoricalParameters(),
+                                      BoxActionSpace: AdditiveNoiseParameters()},
+                         memory=SingleEpisodeBufferParameters(),
+                         networks={"main": PolicyGradientNetworkParameters()})
+
+    @property
+    def path(self):
+        return 'rl_coach.agents.policy_gradients_agent:PolicyGradientsAgent'
+
+
+class PolicyGradientsAgent(PolicyOptimizationAgent):
+    def __init__(self, agent_parameters, parent: Union['LevelManager', 'CompositeAgent']=None):
+        super().__init__(agent_parameters, parent)
+        self.returns_mean = self.register_signal('Returns Mean')
+        self.returns_variance = self.register_signal('Returns Variance')
+        self.last_gradient_update_step_idx = 0
+
+    def learn_from_batch(self, batch):
+        # batch contains a list of episodes to learn from
+        network_keys = self.ap.network_wrappers['main'].input_embedders_parameters.keys()
+
+        total_returns = batch.n_step_discounted_rewards()
+        for i in reversed(range(batch.size)):
+            if self.policy_gradient_rescaler == PolicyGradientRescaler.TOTAL_RETURN:
+                total_returns[i] = total_returns[0]
+            elif self.policy_gradient_rescaler == PolicyGradientRescaler.FUTURE_RETURN:
+                # just take the total return as it is
+                pass
+            elif self.policy_gradient_rescaler == PolicyGradientRescaler.FUTURE_RETURN_NORMALIZED_BY_EPISODE:
+                # we can get a single transition episode while playing Doom Basic, causing the std to be 0
+                if self.std_discounted_return != 0:
+                    total_returns[i] = (total_returns[i] - self.mean_discounted_return) / self.std_discounted_return
+                else:
+                    total_returns[i] = 0
+            elif self.policy_gradient_rescaler == PolicyGradientRescaler.FUTURE_RETURN_NORMALIZED_BY_TIMESTEP:
+                total_returns[i] -= self.mean_return_over_multiple_episodes[i]
+            else:
+                screen.warning("WARNING: The requested policy gradient rescaler is not available")
+
+        targets = total_returns
+        actions = batch.actions()
+        if type(self.spaces.action) != DiscreteActionSpace and len(actions.shape) < 2:
+            actions = np.expand_dims(actions, -1)
+
+        self.returns_mean.add_sample(np.mean(total_returns))
+        self.returns_variance.add_sample(np.std(total_returns))
+
+        result = self.networks['main'].online_network.accumulate_gradients(
+            {**batch.states(network_keys), 'output_0_0': actions}, targets
+        )
+        total_loss, losses, unclipped_grads = result[:3]
+
+        return total_loss, losses, unclipped_grads
+
+ +
+ +
+ + +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_modules/rl_coach/agents/ppo_agent.html b/docs/_modules/rl_coach/agents/ppo_agent.html new file mode 100644 index 0000000..619ec6d --- /dev/null +++ b/docs/_modules/rl_coach/agents/ppo_agent.html @@ -0,0 +1,620 @@ + + + + + + + + + + + rl_coach.agents.ppo_agent — Reinforcement Learning Coach 0.11.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + +
+ +
+ + + + + + + + + + + + + + + + + +
+ +
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+ +

Source code for rl_coach.agents.ppo_agent

+#
+# Copyright (c) 2017 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import copy
+from collections import OrderedDict
+from typing import Union
+
+import numpy as np
+
+from rl_coach.agents.actor_critic_agent import ActorCriticAgent
+from rl_coach.agents.policy_optimization_agent import PolicyGradientRescaler
+from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
+from rl_coach.architectures.head_parameters import PPOHeadParameters, VHeadParameters
+from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters
+from rl_coach.base_parameters import AlgorithmParameters, NetworkParameters, \
+    AgentParameters, DistributedTaskParameters
+
+from rl_coach.core_types import EnvironmentSteps, Batch
+from rl_coach.exploration_policies.additive_noise import AdditiveNoiseParameters
+from rl_coach.exploration_policies.categorical import CategoricalParameters
+from rl_coach.logger import screen
+from rl_coach.memories.episodic.episodic_experience_replay import EpisodicExperienceReplayParameters
+from rl_coach.spaces import DiscreteActionSpace, BoxActionSpace
+from rl_coach.utils import force_list
+
+
+class PPOCriticNetworkParameters(NetworkParameters):
+    def __init__(self):
+        super().__init__()
+        self.input_embedders_parameters = {'observation': InputEmbedderParameters(activation_function='tanh')}
+        self.middleware_parameters = FCMiddlewareParameters(activation_function='tanh')
+        self.heads_parameters = [VHeadParameters()]
+        self.async_training = True
+        self.l2_regularization = 0
+        self.create_target_network = True
+        self.batch_size = 128
+
+
+class PPOActorNetworkParameters(NetworkParameters):
+    def __init__(self):
+        super().__init__()
+        self.input_embedders_parameters = {'observation': InputEmbedderParameters(activation_function='tanh')}
+        self.middleware_parameters = FCMiddlewareParameters(activation_function='tanh')
+        self.heads_parameters = [PPOHeadParameters()]
+        self.optimizer_type = 'Adam'
+        self.async_training = True
+        self.l2_regularization = 0
+        self.create_target_network = True
+        self.batch_size = 128
+
+
+
[docs]class PPOAlgorithmParameters(AlgorithmParameters):
+    """
+    :param policy_gradient_rescaler: (PolicyGradientRescaler)
+        This represents how the critic will be used to update the actor. The critic value function is typically used
+        to rescale the gradients calculated by the actor. There are several ways for doing this, such as using the
+        advantage of the action, or the generalized advantage estimation (GAE) value.
+
+    :param gae_lambda: (float)
+        The :math:`\lambda` value is used within the GAE function in order to weight different bootstrap length
+        estimations. Typical values are in the range 0.9-1, and define an exponential decay over the different
+        n-step estimations.
+
+    :param target_kl_divergence: (float)
+        The target kl divergence between the current policy distribution and the new policy. PPO uses a heuristic to
+        bring the KL divergence to this value, by adding a penalty if the kl divergence is higher.
+
+    :param initial_kl_coefficient: (float)
+        The initial weight that will be given to the KL divergence between the current and the new policy in the
+        regularization factor.
+
+    :param high_kl_penalty_coefficient: (float)
+        The penalty that will be given for KL divergence values which are highes than what was defined as the target.
+
+    :param clip_likelihood_ratio_using_epsilon: (float)
+        If not None, the likelihood ratio between the current and new policy in the PPO loss function will be
+        clipped to the range [1-clip_likelihood_ratio_using_epsilon, 1+clip_likelihood_ratio_using_epsilon].
+        This is typically used in the Clipped PPO version of PPO, and should be set to None in regular PPO
+        implementations.
+
+    :param value_targets_mix_fraction: (float)
+        The targets for the value network are an exponential weighted moving average which uses this mix fraction to
+        define how much of the new targets will be taken into account when calculating the loss.
+        This value should be set to the range (0,1], where 1 means that only the new targets will be taken into account.
+
+    :param estimate_state_value_using_gae: (bool)
+        If set to True, the state value will be estimated using the GAE technique.
+
+    :param use_kl_regularization: (bool)
+        If set to True, the loss function will be regularized using the KL diveregence between the current and new
+        policy, to bound the change of the policy during the network update.
+
+    :param beta_entropy: (float)
+        An entropy regulaization term can be added to the loss function in order to control exploration. This term
+        is weighted using the :math:`\beta` value defined by beta_entropy.
+
+    """
+    def __init__(self):
+        super().__init__()
+        self.policy_gradient_rescaler = PolicyGradientRescaler.GAE
+        self.gae_lambda = 0.96
+        self.target_kl_divergence = 0.01
+        self.initial_kl_coefficient = 1.0
+        self.high_kl_penalty_coefficient = 1000
+        self.clip_likelihood_ratio_using_epsilon = None
+        self.value_targets_mix_fraction = 0.1
+        self.estimate_state_value_using_gae = True
+        self.use_kl_regularization = True
+        self.beta_entropy = 0.01
+        self.num_consecutive_playing_steps = EnvironmentSteps(5000)

+
+
+class PPOAgentParameters(AgentParameters):
+    def __init__(self):
+        super().__init__(algorithm=PPOAlgorithmParameters(),
+                         exploration={DiscreteActionSpace: CategoricalParameters(),
+                                      BoxActionSpace: AdditiveNoiseParameters()},
+                         memory=EpisodicExperienceReplayParameters(),
+                         networks={"critic": PPOCriticNetworkParameters(), "actor": PPOActorNetworkParameters()})
+
+    @property
+    def path(self):
+        return 'rl_coach.agents.ppo_agent:PPOAgent'
+
+
+# Proximal Policy Optimization - https://arxiv.org/pdf/1707.06347.pdf
+class PPOAgent(ActorCriticAgent):
+    def __init__(self, agent_parameters, parent: Union['LevelManager', 'CompositeAgent']=None):
+        super().__init__(agent_parameters, parent)
+
+        # signals definition
+        self.value_loss = self.register_signal('Value Loss')
+        self.policy_loss = self.register_signal('Policy Loss')
+        self.kl_divergence = self.register_signal('KL Divergence')
+        self.total_kl_divergence_during_training_process = 0.0
+        self.unclipped_grads = self.register_signal('Grads (unclipped)')
+
+    def fill_advantages(self, batch):
+        batch = Batch(batch)
+        network_keys = self.ap.network_wrappers['critic'].input_embedders_parameters.keys()
+
+        # * Found not to have any impact *
+        # current_states_with_timestep = self.concat_state_and_timestep(batch)
+
+        current_state_values = self.networks['critic'].online_network.predict(batch.states(network_keys)).squeeze()
+        total_returns = batch.n_step_discounted_rewards()
+        # calculate advantages
+        advantages = []
+        if self.policy_gradient_rescaler == PolicyGradientRescaler.A_VALUE:
+            advantages = total_returns - current_state_values
+        elif self.policy_gradient_rescaler == PolicyGradientRescaler.GAE:
+            # get bootstraps
+            episode_start_idx = 0
+            advantages = np.array([])
+            # current_state_values[batch.game_overs()] = 0
+            for idx, game_over in enumerate(batch.game_overs()):
+                if game_over:
+                    # get advantages for the rollout
+                    value_bootstrapping = np.zeros((1,))
+                    rollout_state_values = np.append(current_state_values[episode_start_idx:idx+1], value_bootstrapping)
+
+                    rollout_advantages, _ = \
+                        self.get_general_advantage_estimation_values(batch.rewards()[episode_start_idx:idx+1],
+                                                                     rollout_state_values)
+                    episode_start_idx = idx + 1
+                    advantages = np.append(advantages, rollout_advantages)
+        else:
+            screen.warning("WARNING: The requested policy gradient rescaler is not available")
+
+        # standardize
+        advantages = (advantages - np.mean(advantages)) / np.std(advantages)
+
+        # TODO: this will be problematic with a shared memory
+        for transition, advantage in zip(self.memory.transitions, advantages):
+            transition.info['advantage'] = advantage
+
+        self.action_advantages.add_sample(advantages)
+
+    def train_value_network(self, dataset, epochs):
+        loss = []
+        batch = Batch(dataset)
+        network_keys = self.ap.network_wrappers['critic'].input_embedders_parameters.keys()
+
+        # * Found not to have any impact *
+        # add a timestep to the observation
+        # current_states_with_timestep = self.concat_state_and_timestep(dataset)
+
+        mix_fraction = self.ap.algorithm.value_targets_mix_fraction
+        total_returns = batch.n_step_discounted_rewards(True)
+        for j in range(epochs):
+            curr_batch_size = batch.size
+            if self.networks['critic'].online_network.optimizer_type != 'LBFGS':
+                curr_batch_size = self.ap.network_wrappers['critic'].batch_size
+            for i in range(batch.size // curr_batch_size):
+                # split to batches for first order optimization techniques
+                current_states_batch = {
+                    k: v[i * curr_batch_size:(i + 1) * curr_batch_size]
+                    for k, v in batch.states(network_keys).items()
+                }
+                total_return_batch = total_returns[i * curr_batch_size:(i + 1) * curr_batch_size]
+                old_policy_values = force_list(self.networks['critic'].target_network.predict(
+                    current_states_batch).squeeze())
+                if self.networks['critic'].online_network.optimizer_type != 'LBFGS':
+                    targets = total_return_batch
+                else:
+                    current_values = self.networks['critic'].online_network.predict(current_states_batch)
+                    targets = current_values * (1 - mix_fraction) + total_return_batch * mix_fraction
+
+                inputs = copy.copy(current_states_batch)
+                for input_index, input in enumerate(old_policy_values):
+                    name = 'output_0_{}'.format(input_index)
+                    if name in self.networks['critic'].online_network.inputs:
+                        inputs[name] = input
+
+                value_loss = self.networks['critic'].online_network.accumulate_gradients(inputs, targets)
+
+                self.networks['critic'].apply_gradients_to_online_network()
+                if isinstance(self.ap.task_parameters, DistributedTaskParameters):
+                    self.networks['critic'].apply_gradients_to_global_network()
+                self.networks['critic'].online_network.reset_accumulated_gradients()
+
+                loss.append([value_loss[0]])
+        loss = np.mean(loss, 0)
+        return loss
+
+    def concat_state_and_timestep(self, dataset):
+        current_states_with_timestep = [np.append(transition.state['observation'], transition.info['timestep'])
+                                        for transition in dataset]
+        current_states_with_timestep = np.expand_dims(current_states_with_timestep, -1)
+        return current_states_with_timestep
+
+    def train_policy_network(self, dataset, epochs):
+        loss = []
+        for j in range(epochs):
+            loss = {
+                'total_loss': [],
+                'policy_losses': [],
+                'unclipped_grads': [],
+                'fetch_result': []
+            }
+            #shuffle(dataset)
+            for i in range(len(dataset) // self.ap.network_wrappers['actor'].batch_size):
+                batch = Batch(dataset[i * self.ap.network_wrappers['actor'].batch_size:
+                                      (i + 1) * self.ap.network_wrappers['actor'].batch_size])
+
+                network_keys = self.ap.network_wrappers['actor'].input_embedders_parameters.keys()
+
+                advantages = batch.info('advantage')
+                actions = batch.actions()
+                if not isinstance(self.spaces.action, DiscreteActionSpace) and len(actions.shape) == 1:
+                    actions = np.expand_dims(actions, -1)
+
+                # get old policy probabilities and distribution
+                old_policy = force_list(self.networks['actor'].target_network.predict(batch.states(network_keys)))
+
+                # calculate gradients and apply on both the local policy network and on the global policy network
+                fetches = [self.networks['actor'].online_network.output_heads[0].kl_divergence,
+                           self.networks['actor'].online_network.output_heads[0].entropy]
+
+                inputs = copy.copy(batch.states(network_keys))
+                inputs['output_0_0'] = actions
+
+                # old_policy_distribution needs to be represented as a list, because in the event of discrete controls,
+                # it has just a mean. otherwise, it has both a mean and standard deviation
+                for input_index, input in enumerate(old_policy):
+                    inputs['output_0_{}'.format(input_index + 1)] = input
+
+                total_loss, policy_losses, unclipped_grads, fetch_result =\
+                    self.networks['actor'].online_network.accumulate_gradients(
+                        inputs, [advantages], additional_fetches=fetches)
+
+                self.networks['actor'].apply_gradients_to_online_network()
+                if isinstance(self.ap.task_parameters, DistributedTaskParameters):
+                    self.networks['actor'].apply_gradients_to_global_network()
+
+                self.networks['actor'].online_network.reset_accumulated_gradients()
+
+                loss['total_loss'].append(total_loss)
+                loss['policy_losses'].append(policy_losses)
+                loss['unclipped_grads'].append(unclipped_grads)
+                loss['fetch_result'].append(fetch_result)
+
+                self.unclipped_grads.add_sample(unclipped_grads)
+
+            for key in loss.keys():
+                loss[key] = np.mean(loss[key], 0)
+
+            if self.ap.network_wrappers['critic'].learning_rate_decay_rate != 0:
+                curr_learning_rate = self.networks['critic'].online_network.get_variable_value(self.ap.learning_rate)
+                self.curr_learning_rate.add_sample(curr_learning_rate)
+            else:
+                curr_learning_rate = self.ap.network_wrappers['critic'].learning_rate
+
+            # log training parameters
+            screen.log_dict(
+                OrderedDict([
+                    ("Surrogate loss", loss['policy_losses'][0]),
+                    ("KL divergence", loss['fetch_result'][0]),
+                    ("Entropy", loss['fetch_result'][1]),
+                    ("training epoch", j),
+                    ("learning_rate", curr_learning_rate)
+                ]),
+                prefix="Policy training"
+            )
+
+        self.total_kl_divergence_during_training_process = loss['fetch_result'][0]
+        self.entropy.add_sample(loss['fetch_result'][1])
+        self.kl_divergence.add_sample(loss['fetch_result'][0])
+        return loss['total_loss']
+
+    def update_kl_coefficient(self):
+        # John Schulman takes the mean kl divergence only over the last epoch which is strange but we will follow
+        # his implementation for now because we know it works well
+        screen.log_title("KL = {}".format(self.total_kl_divergence_during_training_process))
+
+        # update kl coefficient
+        kl_target = self.ap.algorithm.target_kl_divergence
+        kl_coefficient = self.networks['actor'].online_network.get_variable_value(
+            self.networks['actor'].online_network.output_heads[0].kl_coefficient)
+        new_kl_coefficient = kl_coefficient
+        if self.total_kl_divergence_during_training_process > 1.3 * kl_target:
+            # kl too high => increase regularization
+            new_kl_coefficient *= 1.5
+        elif self.total_kl_divergence_during_training_process < 0.7 * kl_target:
+            # kl too low => decrease regularization
+            new_kl_coefficient /= 1.5
+
+        # update the kl coefficient variable
+        if kl_coefficient != new_kl_coefficient:
+            self.networks['actor'].online_network.set_variable_value(
+                self.networks['actor'].online_network.output_heads[0].assign_kl_coefficient,
+                new_kl_coefficient,
+                self.networks['actor'].online_network.output_heads[0].kl_coefficient_ph)
+
+        screen.log_title("KL penalty coefficient change = {} -> {}".format(kl_coefficient, new_kl_coefficient))
+
+    def post_training_commands(self):
+        if self.ap.algorithm.use_kl_regularization:
+            self.update_kl_coefficient()
+
+        # clean memory
+        self.call_memory('clean')
+
+    def _should_train_helper(self, wait_for_full_episode=True):
+        return super()._should_train_helper(True)
+
+    def train(self):
+        loss = 0
+        if self._should_train(wait_for_full_episode=True):
+            for network in self.networks.values():
+                network.set_is_training(True)
+
+            for training_step in range(self.ap.algorithm.num_consecutive_training_steps):
+                self.networks['actor'].sync()
+                self.networks['critic'].sync()
+
+                dataset = self.memory.transitions
+
+                self.fill_advantages(dataset)
+
+                # take only the requested number of steps
+                dataset = dataset[:self.ap.algorithm.num_consecutive_playing_steps.num_steps]
+
+                value_loss = self.train_value_network(dataset, 1)
+                policy_loss = self.train_policy_network(dataset, 10)
+
+                self.value_loss.add_sample(value_loss)
+                self.policy_loss.add_sample(policy_loss)
+
+            for network in self.networks.values():
+                network.set_is_training(False)
+
+            self.post_training_commands()
+            self.training_iteration += 1
+            self.update_log()  # should be done in order to update the data that has been accumulated * while not playing *
+            return np.append(value_loss, policy_loss)
+
+    def get_prediction(self, states):
+        tf_input_state = self.prepare_batch_for_inference(states, "actor")
+        return self.networks['actor'].online_network.predict(tf_input_state)
+
+ +
+ +
+ + +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_modules/rl_coach/agents/qr_dqn_agent.html b/docs/_modules/rl_coach/agents/qr_dqn_agent.html new file mode 100644 index 0000000..e3bb992 --- /dev/null +++ b/docs/_modules/rl_coach/agents/qr_dqn_agent.html @@ -0,0 +1,347 @@ + + + + + + + + + + + rl_coach.agents.qr_dqn_agent — Reinforcement Learning Coach 0.11.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + +
+ +
+ + + + + + + + + + + + + + + + + +
+ +
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+ +

Source code for rl_coach.agents.qr_dqn_agent

+#
+# Copyright (c) 2017 Intel Corporation 
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from typing import Union
+
+import numpy as np
+
+from rl_coach.agents.dqn_agent import DQNAgentParameters, DQNNetworkParameters, DQNAlgorithmParameters
+from rl_coach.agents.value_optimization_agent import ValueOptimizationAgent
+from rl_coach.architectures.head_parameters import QuantileRegressionQHeadParameters
+from rl_coach.core_types import StateType
+from rl_coach.schedules import LinearSchedule
+
+
+class QuantileRegressionDQNNetworkParameters(DQNNetworkParameters):
+    def __init__(self):
+        super().__init__()
+        self.heads_parameters = [QuantileRegressionQHeadParameters()]
+        self.learning_rate = 0.00005
+        self.optimizer_epsilon = 0.01 / 32
+
+
+
[docs]class QuantileRegressionDQNAlgorithmParameters(DQNAlgorithmParameters):
+    """
+    :param atoms: (int)
+        the number of atoms to predict for each action
+
+    :param huber_loss_interval: (float)
+        One of the huber loss parameters, and is referred to as :math:`\kapa` in the paper.
+        It describes the interval [-k, k] in which the huber loss acts as a MSE loss.
+    """
+    def __init__(self):
+        super().__init__()
+        self.atoms = 200
+        self.huber_loss_interval = 1  # called k in the paper

+
+
+class QuantileRegressionDQNAgentParameters(DQNAgentParameters):
+    def __init__(self):
+        super().__init__()
+        self.algorithm = QuantileRegressionDQNAlgorithmParameters()
+        self.network_wrappers = {"main": QuantileRegressionDQNNetworkParameters()}
+        self.exploration.epsilon_schedule = LinearSchedule(1, 0.01, 1000000)
+        self.exploration.evaluation_epsilon = 0.001
+
+    @property
+    def path(self):
+        return 'rl_coach.agents.qr_dqn_agent:QuantileRegressionDQNAgent'
+
+
+# Quantile Regression Deep Q Network - https://arxiv.org/pdf/1710.10044v1.pdf
+class QuantileRegressionDQNAgent(ValueOptimizationAgent):
+    def __init__(self, agent_parameters, parent: Union['LevelManager', 'CompositeAgent']=None):
+        super().__init__(agent_parameters, parent)
+        self.quantile_probabilities = np.ones(self.ap.algorithm.atoms) / float(self.ap.algorithm.atoms)
+
+    def get_q_values(self, quantile_values):
+        return np.dot(quantile_values, self.quantile_probabilities)
+
+    # prediction's format is (batch,actions,atoms)
+    def get_all_q_values_for_states(self, states: StateType):
+        if self.exploration_policy.requires_action_values():
+            quantile_values = self.get_prediction(states)
+            actions_q_values = self.get_q_values(quantile_values)
+        else:
+            actions_q_values = None
+        return actions_q_values
+
+    def learn_from_batch(self, batch):
+        network_keys = self.ap.network_wrappers['main'].input_embedders_parameters.keys()
+
+        # get the quantiles of the next states and current states
+        next_state_quantiles, current_quantiles = self.networks['main'].parallel_prediction([
+            (self.networks['main'].target_network, batch.next_states(network_keys)),
+            (self.networks['main'].online_network, batch.states(network_keys))
+        ])
+
+        # get the optimal actions to take for the next states
+        target_actions = np.argmax(self.get_q_values(next_state_quantiles), axis=1)
+
+        # calculate the Bellman update
+        batch_idx = list(range(self.ap.network_wrappers['main'].batch_size))
+
+        TD_targets = batch.rewards(True) + (1.0 - batch.game_overs(True)) * self.ap.algorithm.discount \
+                               * next_state_quantiles[batch_idx, target_actions]
+
+        # get the locations of the selected actions within the batch for indexing purposes
+        actions_locations = [[b, a] for b, a in zip(batch_idx, batch.actions())]
+
+        # calculate the cumulative quantile probabilities and reorder them to fit the sorted quantiles order
+        cumulative_probabilities = np.array(range(self.ap.algorithm.atoms + 1)) / float(self.ap.algorithm.atoms) # tau_i
+        quantile_midpoints = 0.5*(cumulative_probabilities[1:] + cumulative_probabilities[:-1])  # tau^hat_i
+        quantile_midpoints = np.tile(quantile_midpoints, (self.ap.network_wrappers['main'].batch_size, 1))
+        sorted_quantiles = np.argsort(current_quantiles[batch_idx, batch.actions()])
+        for idx in range(self.ap.network_wrappers['main'].batch_size):
+            quantile_midpoints[idx, :] = quantile_midpoints[idx, sorted_quantiles[idx]]
+
+        # train
+        result = self.networks['main'].train_and_sync_networks({
+            **batch.states(network_keys),
+            'output_0_0': actions_locations,
+            'output_0_1': quantile_midpoints,
+        }, TD_targets)
+        total_loss, losses, unclipped_grads = result[:3]
+
+        return total_loss, losses, unclipped_grads
+
+
+ +
+ +
+ + +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_modules/rl_coach/agents/rainbow_dqn_agent.html b/docs/_modules/rl_coach/agents/rainbow_dqn_agent.html new file mode 100644 index 0000000..f71cbf9 --- /dev/null +++ b/docs/_modules/rl_coach/agents/rainbow_dqn_agent.html @@ -0,0 +1,359 @@ + + + + + + + + + + + rl_coach.agents.rainbow_dqn_agent — Reinforcement Learning Coach 0.11.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + +
+ +
+ + + + + + + + + + + + + + + + + +
+ +
    + +
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    • + +
  • rl_coach.agents.rainbow_dqn_agent
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+ + +
+
+
+
+ +

Source code for rl_coach.agents.rainbow_dqn_agent

+#
+# Copyright (c) 2017 Intel Corporation 
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from typing import Union
+
+import numpy as np
+
+from rl_coach.agents.categorical_dqn_agent import CategoricalDQNAlgorithmParameters, \
+    CategoricalDQNAgent, CategoricalDQNAgentParameters
+from rl_coach.agents.dqn_agent import DQNNetworkParameters
+from rl_coach.architectures.head_parameters import RainbowQHeadParameters
+from rl_coach.architectures.middleware_parameters import FCMiddlewareParameters
+from rl_coach.base_parameters import MiddlewareScheme
+from rl_coach.exploration_policies.parameter_noise import ParameterNoiseParameters
+from rl_coach.memories.non_episodic.prioritized_experience_replay import PrioritizedExperienceReplayParameters, \
+    PrioritizedExperienceReplay
+
+
+class RainbowDQNNetworkParameters(DQNNetworkParameters):
+    def __init__(self):
+        super().__init__()
+        self.heads_parameters = [RainbowQHeadParameters()]
+        self.middleware_parameters = FCMiddlewareParameters(scheme=MiddlewareScheme.Empty)
+
+
+
[docs]class RainbowDQNAlgorithmParameters(CategoricalDQNAlgorithmParameters):
+    """
+    :param n_step: (int)
+        The number of steps to bootstrap the network over. The first N-1 steps actual rewards will be accumulated
+        using an exponentially growing discount factor, and the Nth step will be bootstrapped from the network
+        prediction.
+
+    :param store_transitions_only_when_episodes_are_terminated: (bool)
+        If set to True, the transitions will be stored in an Episode object until the episode ends, and just then
+        written to the memory. This is useful since we want to calculate the N-step discounted rewards before saving the
+        transitions into the memory, and to do so we need the entire episode first.
+    """
+    def __init__(self):
+        super().__init__()
+        self.n_step = 3
+
+        # needed for n-step updates to work. i.e. waiting for a full episode to be closed before storing each transition
+        self.store_transitions_only_when_episodes_are_terminated = True

+
+
+class RainbowDQNAgentParameters(CategoricalDQNAgentParameters):
+    def __init__(self):
+        super().__init__()
+        self.algorithm = RainbowDQNAlgorithmParameters()
+        self.exploration = ParameterNoiseParameters(self)
+        self.memory = PrioritizedExperienceReplayParameters()
+        self.network_wrappers = {"main": RainbowDQNNetworkParameters()}
+
+    @property
+    def path(self):
+        return 'rl_coach.agents.rainbow_dqn_agent:RainbowDQNAgent'
+
+
+# Rainbow Deep Q Network - https://arxiv.org/abs/1710.02298
+# Agent implementation is composed of:
+# 1. NoisyNets
+# 2. C51
+# 3. Prioritized ER
+# 4. DDQN
+# 5. Dueling DQN
+# 6. N-step returns
+
+class RainbowDQNAgent(CategoricalDQNAgent):
+    def __init__(self, agent_parameters, parent: Union['LevelManager', 'CompositeAgent']=None):
+        super().__init__(agent_parameters, parent)
+
+    def learn_from_batch(self, batch):
+        network_keys = self.ap.network_wrappers['main'].input_embedders_parameters.keys()
+
+        ddqn_selected_actions = np.argmax(self.distribution_prediction_to_q_values(
+            self.networks['main'].online_network.predict(batch.next_states(network_keys))), axis=1)
+
+        # for the action we actually took, the error is calculated by the atoms distribution
+        # for all other actions, the error is 0
+        distributional_q_st_plus_n, TD_targets = self.networks['main'].parallel_prediction([
+            (self.networks['main'].target_network, batch.next_states(network_keys)),
+            (self.networks['main'].online_network, batch.states(network_keys))
+        ])
+
+        # only update the action that we have actually done in this transition (using the Double-DQN selected actions)
+        target_actions = ddqn_selected_actions
+        m = np.zeros((self.ap.network_wrappers['main'].batch_size, self.z_values.size))
+
+        batches = np.arange(self.ap.network_wrappers['main'].batch_size)
+        for j in range(self.z_values.size):
+            # we use batch.info('should_bootstrap_next_state') instead of (1 - batch.game_overs()) since with n-step,
+            # we will not bootstrap for the last n-step transitions in the episode
+            tzj = np.fmax(np.fmin(batch.n_step_discounted_rewards() + batch.info('should_bootstrap_next_state') *
+                                  (self.ap.algorithm.discount ** self.ap.algorithm.n_step) * self.z_values[j],
+                                  self.z_values[-1]), self.z_values[0])
+            bj = (tzj - self.z_values[0])/(self.z_values[1] - self.z_values[0])
+            u = (np.ceil(bj)).astype(int)
+            l = (np.floor(bj)).astype(int)
+            m[batches, l] += (distributional_q_st_plus_n[batches, target_actions, j] * (u - bj))
+            m[batches, u] += (distributional_q_st_plus_n[batches, target_actions, j] * (bj - l))
+
+        # total_loss = cross entropy between actual result above and predicted result for the given action
+        TD_targets[batches, batch.actions()] = m
+
+        # update errors in prioritized replay buffer
+        importance_weights = batch.info('weight') if isinstance(self.memory, PrioritizedExperienceReplay) else None
+
+        result = self.networks['main'].train_and_sync_networks(batch.states(network_keys), TD_targets,
+                                                               importance_weights=importance_weights)
+
+        total_loss, losses, unclipped_grads = result[:3]
+
+        # TODO: fix this spaghetti code
+        if isinstance(self.memory, PrioritizedExperienceReplay):
+            errors = losses[0][np.arange(batch.size), batch.actions()]
+            self.call_memory('update_priorities', (batch.info('idx'), errors))
+
+        return total_loss, losses, unclipped_grads
+
+
+ +
+ +
+ + +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_modules/rl_coach/agents/value_optimization_agent.html b/docs/_modules/rl_coach/agents/value_optimization_agent.html new file mode 100644 index 0000000..88a4267 --- /dev/null +++ b/docs/_modules/rl_coach/agents/value_optimization_agent.html @@ -0,0 +1,325 @@ + + + + + + + + + + + rl_coach.agents.value_optimization_agent — Reinforcement Learning Coach 0.11.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + +
+ +
+ + + + + + + + + + + + + + + + + +
+ +
    + +
  • Docs »
    • + +
  • Module code »
    • + +
  • rl_coach.agents.value_optimization_agent
    • + + +
  • + +
    • + +
+ + +
+
+
+
+ +

Source code for rl_coach.agents.value_optimization_agent

+#
+# Copyright (c) 2017 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from typing import Union
+
+import numpy as np
+
+from rl_coach.agents.agent import Agent
+from rl_coach.core_types import ActionInfo, StateType
+from rl_coach.memories.non_episodic.prioritized_experience_replay import PrioritizedExperienceReplay
+from rl_coach.spaces import DiscreteActionSpace
+
+
+## This is an abstract agent - there is no learn_from_batch method ##
+
+
+class ValueOptimizationAgent(Agent):
+    def __init__(self, agent_parameters, parent: Union['LevelManager', 'CompositeAgent']=None):
+        super().__init__(agent_parameters, parent)
+        self.q_values = self.register_signal("Q")
+        self.q_value_for_action = {}
+
+    def init_environment_dependent_modules(self):
+        super().init_environment_dependent_modules()
+        if isinstance(self.spaces.action, DiscreteActionSpace):
+            for i in range(len(self.spaces.action.actions)):
+                self.q_value_for_action[i] = self.register_signal("Q for action {}".format(i),
+                                                                  dump_one_value_per_episode=False,
+                                                                  dump_one_value_per_step=True)
+
+    # Algorithms for which q_values are calculated from predictions will override this function
+    def get_all_q_values_for_states(self, states: StateType):
+        if self.exploration_policy.requires_action_values():
+            actions_q_values = self.get_prediction(states)
+        else:
+            actions_q_values = None
+        return actions_q_values
+
+    def get_prediction(self, states):
+        return self.networks['main'].online_network.predict(self.prepare_batch_for_inference(states, 'main'))
+
+    def update_transition_priorities_and_get_weights(self, TD_errors, batch):
+        # update errors in prioritized replay buffer
+        importance_weights = None
+        if isinstance(self.memory, PrioritizedExperienceReplay):
+            self.call_memory('update_priorities', (batch.info('idx'), TD_errors))
+            importance_weights = batch.info('weight')
+        return importance_weights
+
+    def _validate_action(self, policy, action):
+        if np.array(action).shape != ():
+            raise ValueError((
+                'The exploration_policy {} returned a vector of actions '
+                'instead of a single action. ValueOptimizationAgents '
+                'require exploration policies which return a single action.'
+            ).format(policy.__class__.__name__))
+
+    def choose_action(self, curr_state):
+        actions_q_values = self.get_all_q_values_for_states(curr_state)
+
+        # choose action according to the exploration policy and the current phase (evaluating or training the agent)
+        action = self.exploration_policy.get_action(actions_q_values)
+        self._validate_action(self.exploration_policy, action)
+
+        if actions_q_values is not None:
+            # this is for bootstrapped dqn
+            if type(actions_q_values) == list and len(actions_q_values) > 0:
+                actions_q_values = self.exploration_policy.last_action_values
+            actions_q_values = actions_q_values.squeeze()
+
+            # store the q values statistics for logging
+            self.q_values.add_sample(actions_q_values)
+            for i, q_value in enumerate(actions_q_values):
+                self.q_value_for_action[i].add_sample(q_value)
+
+            action_info = ActionInfo(action=action,
+                                     action_value=actions_q_values[action],
+                                     max_action_value=np.max(actions_q_values))
+        else:
+            action_info = ActionInfo(action=action)
+
+        return action_info
+
+    def learn_from_batch(self, batch):
+        raise NotImplementedError("ValueOptimizationAgent is an abstract agent. Not to be used directly.")
+
+ +
+ +
+ + +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_modules/rl_coach/architectures/architecture.html b/docs/_modules/rl_coach/architectures/architecture.html new file mode 100644 index 0000000..940f357 --- /dev/null +++ b/docs/_modules/rl_coach/architectures/architecture.html @@ -0,0 +1,442 @@ + + + + + + + + + + + rl_coach.architectures.architecture — Reinforcement Learning Coach 0.11.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + +
+ +
+ + + + + + + + + + + + + + + + + +
+ +
    + +
  • Docs »
    • + +
  • Module code »
    • + +
  • rl_coach.architectures.architecture
    • + + +
  • + +
    • + +
+ + +
+
+
+
+ +

Source code for rl_coach.architectures.architecture

+#
+# Copyright (c) 2017 Intel Corporation 
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from typing import Any, Dict, List, Tuple
+
+import numpy as np
+
+from rl_coach.base_parameters import AgentParameters
+from rl_coach.spaces import SpacesDefinition
+
+
+
[docs]class Architecture(object):
+    def __init__(self, agent_parameters: AgentParameters, spaces: SpacesDefinition, name: str= ""):
+        """
+        Creates a neural network 'architecture', that can be trained and used for inference.
+
+        :param agent_parameters: the agent parameters
+        :param spaces: the spaces (observation, action, etc.) definition of the agent
+        :param name: the name of the network
+        """
+        self.spaces = spaces
+        self.name = name
+        self.network_wrapper_name = self.name.split('/')[0]  # e.g. 'main/online' --> 'main'
+        self.full_name = "{}/{}".format(agent_parameters.full_name_id, name)
+        self.network_parameters = agent_parameters.network_wrappers[self.network_wrapper_name]
+        self.batch_size = self.network_parameters.batch_size
+        self.learning_rate = self.network_parameters.learning_rate
+        self.optimizer = None
+        self.ap = agent_parameters
+
+
[docs]    def predict(self,
+                inputs: Dict[str, np.ndarray],
+                outputs: List[Any] = None,
+                squeeze_output: bool = True,
+                initial_feed_dict: Dict[Any, np.ndarray] = None) -> Tuple[np.ndarray, ...]:
+        """
+        Given input observations, use the model to make predictions (e.g. action or value).
+
+        :param inputs: current state (i.e. observations, measurements, goals, etc.)
+            (e.g. `{'observation': numpy.ndarray}` of shape (batch_size, observation_space_size))
+        :param outputs: list of outputs to return. Return all outputs if unspecified. Type of the list elements
+            depends on the framework backend.
+        :param squeeze_output: call squeeze_list on output before returning if True
+        :param initial_feed_dict: a dictionary of extra inputs for forward pass.
+        :return: predictions of action or value of shape (batch_size, action_space_size) for action predictions)
+        """
+        raise NotImplementedError

+
+
[docs]    @staticmethod
+    def parallel_predict(sess: Any,
+                         network_input_tuples: List[Tuple['Architecture', Dict[str, np.ndarray]]]) -> \
+            Tuple[np.ndarray, ...]:
+        """
+        :param sess: active session to use for prediction
+        :param network_input_tuples: tuple of network and corresponding input
+        :return: list or tuple of outputs from all networks
+        """
+        raise NotImplementedError

+
+
[docs]    def train_on_batch(self,
+                       inputs: Dict[str, np.ndarray],
+                       targets: List[np.ndarray],
+                       scaler: float=1.,
+                       additional_fetches: list=None,
+                       importance_weights: np.ndarray=None) -> Tuple[float, List[float], float, list]:
+        """
+        Given a batch of inputs (e.g. states) and targets (e.g. discounted rewards), takes a training step: i.e. runs a
+        forward pass and backward pass of the network, accumulates the gradients and applies an optimization step to
+        update the weights.
+        Calls `accumulate_gradients` followed by `apply_and_reset_gradients`.
+        Note: Currently an unused method.
+
+        :param inputs: typically the environment states (but can also contain other data necessary for loss).
+            (e.g. `{'observation': numpy.ndarray}` with `observation` of shape (batch_size, observation_space_size) or
+            (batch_size, observation_space_size, stack_size) or
+            `{'observation': numpy.ndarray, 'output_0_0': numpy.ndarray}` with `output_0_0` of shape (batch_size,))
+        :param targets: target values of shape (batch_size, ). For example discounted rewards for value network
+            for calculating the value-network loss would be a target. Length of list and order of arrays in
+            the list matches that of network losses which are defined by network parameters
+        :param scaler: value to scale gradients by before optimizing network weights
+        :param additional_fetches: list of additional values to fetch and return. The type of each list
+            element is framework dependent.
+        :param importance_weights: ndarray of shape (batch_size,) to multiply with batch loss.
+        :return: tuple of total_loss, losses, norm_unclipped_grads, fetched_tensors
+            total_loss (float): sum of all head losses
+            losses (list of float): list of all losses. The order is list of target losses followed by list
+                of regularization losses. The specifics of losses is dependant on the network parameters
+                (number of heads, etc.)
+            norm_unclippsed_grads (float): global norm of all gradients before any gradient clipping is applied
+            fetched_tensors: all values for additional_fetches
+        """
+        raise NotImplementedError

+
+
[docs]    def get_weights(self) -> List[np.ndarray]:
+        """
+        Gets model weights as a list of ndarrays. It is used for synchronizing weight between two identical networks.
+
+        :return: list weights as ndarray
+        """
+        raise NotImplementedError

+
+
[docs]    def set_weights(self, weights: List[np.ndarray], rate: float=1.0) -> None:
+        """
+        Sets model weights for provided layer parameters.
+
+        :param weights: list of model weights in the same order as received in get_weights
+        :param rate: controls the mixture of given weight values versus old weight values.
+            i.e. new_weight = rate * given_weight + (1 - rate) * old_weight
+        :return: None
+        """
+        raise NotImplementedError

+
+
[docs]    def reset_accumulated_gradients(self) -> None:
+        """
+        Sets gradient of all parameters to 0.
+
+        Once gradients are reset, they must be accessible by `accumulated_gradients` property of this class,
+        which must return a list of numpy ndarrays. Child class must ensure that `accumulated_gradients` is set.
+        """
+        raise NotImplementedError

+
+
[docs]    def accumulate_gradients(self,
+                             inputs: Dict[str, np.ndarray],
+                             targets: List[np.ndarray],
+                             additional_fetches: list=None,
+                             importance_weights: np.ndarray=None,
+                             no_accumulation: bool=False) -> Tuple[float, List[float], float, list]:
+        """
+        Given a batch of inputs (i.e. states) and targets (e.g. discounted rewards), computes and accumulates the
+        gradients for model parameters. Will run forward and backward pass to compute gradients, clip the gradient
+        values if required and then accumulate gradients from all learners. It does not update the model weights,
+        that's performed in `apply_and_reset_gradients` method.
+
+        Once gradients are accumulated, they are accessed by `accumulated_gradients` property of this class.å
+
+        :param inputs: typically the environment states (but can also contain other data for loss)
+            (e.g. `{'observation': numpy.ndarray}` with `observation` of shape (batch_size, observation_space_size) or
+             (batch_size, observation_space_size, stack_size) or
+            `{'observation': numpy.ndarray, 'output_0_0': numpy.ndarray}` with `output_0_0` of shape (batch_size,))
+        :param targets: targets for calculating loss. For example discounted rewards for value network
+            for calculating the value-network loss would be a target. Length of list and order of arrays in
+            the list matches that of network losses which are defined by network parameters
+        :param additional_fetches: list of additional values to fetch and return. The type of each list
+            element is framework dependent.
+        :param importance_weights: ndarray of shape (batch_size,) to multiply with batch loss.
+        :param no_accumulation: if True, set gradient values to the new gradients, otherwise sum with previously
+            calculated gradients
+        :return: tuple of total_loss, losses, norm_unclipped_grads, fetched_tensors
+            total_loss (float): sum of all head losses
+            losses (list of float): list of all losses. The order is list of target losses followed by list of
+                regularization losses. The specifics of losses is dependant on the network parameters
+                (number of heads, etc.)
+            norm_unclippsed_grads (float): global norm of all gradients before any gradient clipping is applied
+            fetched_tensors: all values for additional_fetches
+        """
+        raise NotImplementedError

+
+
[docs]    def apply_and_reset_gradients(self, gradients: List[np.ndarray], scaler: float=1.) -> None:
+        """
+        Applies the given gradients to the network weights and resets the gradient accumulations.
+        Has the same impact as calling `apply_gradients`, then `reset_accumulated_gradients`.
+
+        :param gradients: gradients for the parameter weights, taken from `accumulated_gradients` property
+            of an identical network (either self or another identical network)
+        :param scaler: A scaling factor that allows rescaling the gradients before applying them
+        """
+        raise NotImplementedError

+
+
[docs]    def apply_gradients(self, gradients: List[np.ndarray], scaler: float=1.) -> None:
+        """
+        Applies the given gradients to the network weights.
+        Will be performed sync or async depending on `network_parameters.async_training`
+
+        :param gradients: gradients for the parameter weights, taken from `accumulated_gradients` property
+            of an identical network (either self or another identical network)
+        :param scaler: A scaling factor that allows rescaling the gradients before applying them
+        """
+        raise NotImplementedError

+
+
[docs]    def get_variable_value(self, variable: Any) -> np.ndarray:
+        """
+        Gets value of a specified variable. Type of variable is dependant on the framework.
+        Example of a variable is head.kl_coefficient, which could be a symbol for evaluation
+        or could be a string representing the value.
+
+        :param variable: variable of interest
+        :return: value of the specified variable
+        """
+        raise NotImplementedError

+
+
[docs]    def set_variable_value(self, assign_op: Any, value: np.ndarray, placeholder: Any):
+        """
+        Updates the value of a specified variable. Type of assign_op is dependant on the framework
+        and is a unique identifier for assigning value to a variable. For example an agent may use
+        head.assign_kl_coefficient. There is a one to one mapping between assign_op and placeholder
+        (in the example above, placeholder would be head.kl_coefficient_ph).
+
+        :param assign_op: a parameter representing the operation for assigning value to a specific variable
+        :param value: value of the specified variable used for update
+        :param placeholder: a placeholder for binding the value to assign_op.
+        """
+        raise NotImplementedError

+
+ +
+ +
+ + +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_modules/rl_coach/architectures/network_wrapper.html b/docs/_modules/rl_coach/architectures/network_wrapper.html new file mode 100644 index 0000000..24ec906 --- /dev/null +++ b/docs/_modules/rl_coach/architectures/network_wrapper.html @@ -0,0 +1,480 @@ + + + + + + + + + + + rl_coach.architectures.network_wrapper — Reinforcement Learning Coach 0.11.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + +
+ +
+ + + + + + + + + + + + + + + + + +
+ +
    + +
  • Docs »
    • + +
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    • + +
  • rl_coach.architectures.network_wrapper
    • + + +
  • + +
    • + +
+ + +
+
+
+
+ +

Source code for rl_coach.architectures.network_wrapper

+#
+# Copyright (c) 2017 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from typing import List, Tuple
+
+from rl_coach.base_parameters import Frameworks, AgentParameters
+from rl_coach.logger import failed_imports
+from rl_coach.spaces import SpacesDefinition
+try:
+    import tensorflow as tf
+    from rl_coach.architectures.tensorflow_components.general_network import GeneralTensorFlowNetwork
+except ImportError:
+    failed_imports.append("tensorflow")
+
+try:
+    import mxnet as mx
+    from rl_coach.architectures.mxnet_components.general_network import GeneralMxnetNetwork
+except ImportError:
+    failed_imports.append("mxnet")
+
+
+
[docs]class NetworkWrapper(object):
+    """
+    The network wrapper contains multiple copies of the same network, each one with a different set of weights which is
+    updating in a different time scale. The network wrapper will always contain an online network.
+    It will contain an additional slow updating target network if it was requested by the user,
+    and it will contain a global network shared between different workers, if Coach is run in a single-node
+    multi-process distributed mode. The network wrapper contains functionality for managing these networks and syncing
+    between them.
+    """
+    def __init__(self, agent_parameters: AgentParameters, has_target: bool, has_global: bool, name: str,
+                 spaces: SpacesDefinition, replicated_device=None, worker_device=None):
+        self.ap = agent_parameters
+        self.network_parameters = self.ap.network_wrappers[name]
+        self.has_target = has_target
+        self.has_global = has_global
+        self.name = name
+        self.sess = None
+
+        if self.network_parameters.framework == Frameworks.tensorflow:
+            if "tensorflow" not in failed_imports:
+                general_network = GeneralTensorFlowNetwork
+            else:
+                raise Exception('Install tensorflow before using it as framework')
+        elif self.network_parameters.framework == Frameworks.mxnet:
+            if "mxnet" not in failed_imports:
+                general_network = GeneralMxnetNetwork
+            else:
+                raise Exception('Install mxnet before using it as framework')
+        else:
+            raise Exception("{} Framework is not supported"
+                            .format(Frameworks().to_string(self.network_parameters.framework)))
+
+        with tf.variable_scope("{}/{}".format(self.ap.full_name_id, name)):
+
+            # Global network - the main network shared between threads
+            self.global_network = None
+            if self.has_global:
+                # we assign the parameters of this network on the parameters server
+                with tf.device(replicated_device):
+                    self.global_network = general_network(agent_parameters=agent_parameters,
+                                                          name='{}/global'.format(name),
+                                                          global_network=None,
+                                                          network_is_local=False,
+                                                          spaces=spaces,
+                                                          network_is_trainable=True)
+
+            # Online network - local copy of the main network used for playing
+            self.online_network = None
+            with tf.device(worker_device):
+                self.online_network = general_network(agent_parameters=agent_parameters,
+                                                      name='{}/online'.format(name),
+                                                      global_network=self.global_network,
+                                                      network_is_local=True,
+                                                      spaces=spaces,
+                                                      network_is_trainable=True)
+
+            # Target network - a local, slow updating network used for stabilizing the learning
+            self.target_network = None
+            if self.has_target:
+                with tf.device(worker_device):
+                    self.target_network = general_network(agent_parameters=agent_parameters,
+                                                          name='{}/target'.format(name),
+                                                          global_network=self.global_network,
+                                                          network_is_local=True,
+                                                          spaces=spaces,
+                                                          network_is_trainable=False)
+
+
[docs]    def sync(self):
+        """
+        Initializes the weights of the networks to match each other
+
+        :return:
+        """
+        self.update_online_network()
+        self.update_target_network()

+
+
[docs]    def update_target_network(self, rate=1.0):
+        """
+        Copy weights: online network >>> target network
+
+        :param rate: the rate of copying the weights - 1 for copying exactly
+        """
+        if self.target_network:
+            self.target_network.set_weights(self.online_network.get_weights(), rate)

+
+
[docs]    def update_online_network(self, rate=1.0):
+        """
+        Copy weights: global network >>> online network
+
+        :param rate: the rate of copying the weights - 1 for copying exactly
+        """
+        if self.global_network:
+            self.online_network.set_weights(self.global_network.get_weights(), rate)

+
+
[docs]    def apply_gradients_to_global_network(self, gradients=None):
+        """
+        Apply gradients from the online network on the global network
+
+        :param gradients: optional gradients that will be used instead of teh accumulated gradients
+        :return:
+        """
+        if gradients is None:
+            gradients = self.online_network.accumulated_gradients
+        if self.network_parameters.shared_optimizer:
+            self.global_network.apply_gradients(gradients)
+        else:
+            self.online_network.apply_gradients(gradients)

+
+
[docs]    def apply_gradients_to_online_network(self, gradients=None):
+        """
+        Apply gradients from the online network on itself
+
+        :return:
+        """
+        if gradients is None:
+            gradients = self.online_network.accumulated_gradients
+        self.online_network.apply_gradients(gradients)

+
+
[docs]    def train_and_sync_networks(self, inputs, targets, additional_fetches=[], importance_weights=None):
+        """
+        A generic training function that enables multi-threading training using a global network if necessary.
+
+        :param inputs: The inputs for the network.
+        :param targets: The targets corresponding to the given inputs
+        :param additional_fetches: Any additional tensor the user wants to fetch
+        :param importance_weights: A coefficient for each sample in the batch, which will be used to rescale the loss
+                                   error of this sample. If it is not given, the samples losses won't be scaled
+        :return: The loss of the training iteration
+        """
+        result = self.online_network.accumulate_gradients(inputs, targets, additional_fetches=additional_fetches,
+                                                          importance_weights=importance_weights, no_accumulation=True)
+        self.apply_gradients_and_sync_networks(reset_gradients=False)
+        return result

+
+
[docs]    def apply_gradients_and_sync_networks(self, reset_gradients=True):
+        """
+        Applies the gradients accumulated in the online network to the global network or to itself and syncs the
+        networks if necessary
+
+        :param reset_gradients: If set to True, the accumulated gradients wont be reset to 0 after applying them to
+                                the network. this is useful when the accumulated gradients are overwritten instead
+                                if accumulated by the accumulate_gradients function. this allows reducing time
+                                complexity for this function by around 10%
+        """
+        if self.global_network:
+            self.apply_gradients_to_global_network()
+            if reset_gradients:
+                self.online_network.reset_accumulated_gradients()
+            self.update_online_network()
+        else:
+            if reset_gradients:
+                self.online_network.apply_and_reset_gradients(self.online_network.accumulated_gradients)
+            else:
+                self.online_network.apply_gradients(self.online_network.accumulated_gradients)

+
+
[docs]    def parallel_prediction(self, network_input_tuples: List[Tuple]):
+        """
+        Run several network prediction in parallel. Currently this only supports running each of the network once.
+
+        :param network_input_tuples: a list of tuples where the first element is the network (online_network,
+                                     target_network or global_network) and the second element is the inputs
+        :return: the outputs of all the networks in the same order as the inputs were given
+        """
+        return type(self.online_network).parallel_predict(self.sess, network_input_tuples)

+
+
[docs]    def get_local_variables(self):
+        """
+        Get all the variables that are local to the thread
+
+        :return: a list of all the variables that are local to the thread
+        """
+        local_variables = [v for v in tf.local_variables() if self.online_network.name in v.name]
+        if self.has_target:
+            local_variables += [v for v in tf.local_variables() if self.target_network.name in v.name]
+        return local_variables

+
+
[docs]    def get_global_variables(self):
+        """
+        Get all the variables that are shared between threads
+
+        :return: a list of all the variables that are shared between threads
+        """
+        global_variables = [v for v in tf.global_variables() if self.global_network.name in v.name]
+        return global_variables

+
+
[docs]    def set_is_training(self, state: bool):
+        """
+        Set the phase of the network between training and testing
+
+        :param state: The current state (True = Training, False = Testing)
+        :return: None
+        """
+        self.online_network.set_is_training(state)
+        if self.has_target:
+            self.target_network.set_is_training(state)

+
+    def set_session(self, sess):
+        self.sess = sess
+        self.online_network.set_session(sess)
+        if self.global_network:
+            self.global_network.set_session(sess)
+        if self.target_network:
+            self.target_network.set_session(sess)
+
+    def __str__(self):
+        sub_networks = []
+        if self.global_network:
+            sub_networks.append("global network")
+        if self.online_network:
+            sub_networks.append("online network")
+        if self.target_network:
+            sub_networks.append("target network")
+
+        result = []
+        result.append("Network: {}, Copies: {} ({})".format(self.name, len(sub_networks), ' | '.join(sub_networks)))
+        result.append("-"*len(result[-1]))
+        result.append(str(self.online_network))
+        result.append("")
+        return '\n'.join(result)

+
+ +
+ +
+ + +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_modules/rl_coach/base_parameters.html b/docs/_modules/rl_coach/base_parameters.html new file mode 100644 index 0000000..adbd3d3 --- /dev/null +++ b/docs/_modules/rl_coach/base_parameters.html @@ -0,0 +1,801 @@ + + + + + + + + + + + rl_coach.base_parameters — Reinforcement Learning Coach 0.11.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + +
+ +
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+ +
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    • + +
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+ +

Source code for rl_coach.base_parameters

+#
+# Copyright (c) 2017 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+import inspect
+import json
+import os
+import sys
+import types
+from collections import OrderedDict
+from enum import Enum
+from typing import Dict, List, Union
+
+from rl_coach.core_types import TrainingSteps, EnvironmentSteps, GradientClippingMethod, RunPhase, \
+    SelectedPhaseOnlyDumpFilter, MaxDumpFilter
+from rl_coach.filters.filter import NoInputFilter
+
+
+class Frameworks(Enum):
+    tensorflow = "TensorFlow"
+    mxnet = "MXNet"
+
+
+class EmbedderScheme(Enum):
+    Empty = "Empty"
+    Shallow = "Shallow"
+    Medium = "Medium"
+    Deep = "Deep"
+
+
+class MiddlewareScheme(Enum):
+    Empty = "Empty"
+    Shallow = "Shallow"
+    Medium = "Medium"
+    Deep = "Deep"
+
+
+class EmbeddingMergerType(Enum):
+    Concat = 0
+    Sum = 1
+    #ConcatDepthWise = 2
+    #Multiply = 3
+
+
+# DistributedCoachSynchronizationType provides the synchronization type for distributed Coach.
+# The default value is None, which means the algorithm or preset cannot be used with distributed Coach.
+class DistributedCoachSynchronizationType(Enum):
+    # In SYNC mode, the trainer waits for all the experiences to be gathered from distributed rollout workers before
+    # training a new policy and the rollout workers wait for a new policy before gathering experiences.
+    SYNC = "sync"
+
+    # In ASYNC mode, the trainer doesn't wait for any set of experiences to be gathered from distributed rollout workers
+    # and the rollout workers continously gather experiences loading new policies, whenever they become available.
+    ASYNC = "async"
+
+
+def iterable_to_items(obj):
+    if isinstance(obj, dict) or isinstance(obj, OrderedDict) or isinstance(obj, types.MappingProxyType):
+        items = obj.items()
+    elif isinstance(obj, list):
+        items = enumerate(obj)
+    else:
+        raise ValueError("The given object is not a dict or a list")
+    return items
+
+
+def unfold_dict_or_list(obj: Union[Dict, List, OrderedDict]):
+    """
+    Recursively unfolds all the parameters in dictionaries and lists
+    :param obj: a dictionary or list to unfold
+    :return: the unfolded parameters dictionary
+    """
+    parameters = OrderedDict()
+    items = iterable_to_items(obj)
+    for k, v in items:
+        if isinstance(v, dict) or isinstance(v, list) or isinstance(v, OrderedDict):
+            if 'tensorflow.' not in str(v.__class__):
+                parameters[k] = unfold_dict_or_list(v)
+        elif 'tensorflow.' in str(v.__class__):
+            parameters[k] = v
+        elif hasattr(v, '__dict__'):
+            sub_params = v.__dict__
+            if '__objclass__' not in sub_params.keys():
+                try:
+                    parameters[k] = unfold_dict_or_list(sub_params)
+                except RecursionError:
+                    parameters[k] = sub_params
+                parameters[k]['__class__'] = v.__class__.__name__
+            else:
+                # unfolding this type of object will result in infinite recursion
+                parameters[k] = sub_params
+        else:
+            parameters[k] = v
+    if not isinstance(obj, OrderedDict) and not isinstance(obj, list):
+        parameters = OrderedDict(sorted(parameters.items()))
+    return parameters
+
+
+class Parameters(object):
+    def __setattr__(self, key, value):
+        caller_name = sys._getframe(1).f_code.co_name
+
+        if caller_name != '__init__' and not hasattr(self, key):
+            raise TypeError("Parameter '{}' does not exist in {}. Parameters are only to be defined in a constructor of"
+                            " a class inheriting from Parameters. In order to explicitly register a new parameter "
+                            "outside of a constructor use register_var().".
+                            format(key, self.__class__))
+        object.__setattr__(self, key, value)
+
+    @property
+    def path(self):
+        if hasattr(self, 'parameterized_class_name'):
+            module_path = os.path.relpath(inspect.getfile(self.__class__), os.getcwd())[:-3] + '.py'
+
+            return ':'.join([module_path, self.parameterized_class_name])
+        else:
+            raise ValueError("The parameters class does not have an attached class it parameterizes. "
+                             "The self.parameterized_class_name should be set to the parameterized class.")
+
+    def register_var(self, key, value):
+        if hasattr(self, key):
+            raise TypeError("Cannot register an already existing parameter '{}'. ".format(key))
+        object.__setattr__(self, key, value)
+
+    def __str__(self):
+        result = "\"{}\" {}\n".format(self.__class__.__name__,
+                                   json.dumps(unfold_dict_or_list(self.__dict__), indent=4, default=repr))
+        return result
+
+
+class AlgorithmParameters(Parameters):
+    def __init__(self):
+        # Architecture parameters
+        self.use_accumulated_reward_as_measurement = False
+
+        # Agent parameters
+        self.num_consecutive_playing_steps = EnvironmentSteps(1)
+        self.num_consecutive_training_steps = 1  # TODO: update this to TrainingSteps
+
+        self.heatup_using_network_decisions = False
+        self.discount = 0.99
+        self.apply_gradients_every_x_episodes = 5
+        self.num_steps_between_copying_online_weights_to_target = TrainingSteps(0)
+        self.rate_for_copying_weights_to_target = 1.0
+        self.load_memory_from_file_path = None
+        self.store_transitions_only_when_episodes_are_terminated = False
+
+        # HRL / HER related params
+        self.in_action_space = None
+
+        # distributed agents params
+        self.share_statistics_between_workers = True
+
+        # intrinsic reward
+        self.scale_external_reward_by_intrinsic_reward_value = False
+
+        # n-step returns
+        self.n_step = -1  # calculate the total return (no bootstrap, by default)
+
+        # Distributed Coach params
+        self.distributed_coach_synchronization_type = None
+
+
+
[docs]class PresetValidationParameters(Parameters):
+    def __init__(self,
+                 test=False,
+                 min_reward_threshold=0,
+                 max_episodes_to_achieve_reward=1,
+                 num_workers=1,
+                 reward_test_level=None,
+                 test_using_a_trace_test=True,
+                 trace_test_levels=None,
+                 trace_max_env_steps=5000):
+        """
+        :param test:
+            A flag which specifies if the preset should be tested as part of the validation process.
+        :param min_reward_threshold:
+            The minimum reward that the agent should pass after max_episodes_to_achieve_reward episodes when the
+            preset is run.
+        :param max_episodes_to_achieve_reward:
+            The maximum number of episodes that the agent should train using the preset in order to achieve the
+            reward specified by min_reward_threshold.
+        :param num_workers:
+            The number of workers that should be used when running this preset in the test suite for validation.
+        :param reward_test_level:
+            The environment level or levels, given by a list of strings, that should be tested as part of the
+            reward tests suite.
+        :param test_using_a_trace_test:
+            A flag that specifies if the preset should be run as part of the trace tests suite.
+        :param trace_test_levels:
+            The environment level or levels, given by a list of strings, that should be tested as part of the
+            trace tests suite.
+        :param trace_max_env_steps:
+            An integer representing the maximum number of environment steps to run when running this preset as part
+            of the trace tests suite.
+        """
+        super().__init__()
+
+        # setting a seed will only work for non-parallel algorithms. Parallel algorithms add uncontrollable noise in
+        # the form of different workers starting at different times, and getting different assignments of CPU
+        # time from the OS.
+
+        # Testing parameters
+        self.test = test
+        self.min_reward_threshold = min_reward_threshold
+        self.max_episodes_to_achieve_reward = max_episodes_to_achieve_reward
+        self.num_workers = num_workers
+        self.reward_test_level = reward_test_level
+        self.test_using_a_trace_test = test_using_a_trace_test
+        self.trace_test_levels = trace_test_levels
+        self.trace_max_env_steps = trace_max_env_steps

+
+
+
[docs]class NetworkParameters(Parameters):
+    def __init__(self,
+                 force_cpu=False,
+                 async_training=False,
+                 shared_optimizer=True,
+                 scale_down_gradients_by_number_of_workers_for_sync_training=True,
+                 clip_gradients=None,
+                 gradients_clipping_method=GradientClippingMethod.ClipByGlobalNorm,
+                 l2_regularization=0,
+                 learning_rate=0.00025,
+                 learning_rate_decay_rate=0,
+                 learning_rate_decay_steps=0,
+                 input_embedders_parameters={},
+                 embedding_merger_type=EmbeddingMergerType.Concat,
+                 middleware_parameters=None,
+                 heads_parameters=[],
+                 use_separate_networks_per_head=False,
+                 optimizer_type='Adam',
+                 optimizer_epsilon=0.0001,
+                 adam_optimizer_beta1=0.9,
+                 adam_optimizer_beta2=0.99,
+                 rms_prop_optimizer_decay=0.9,
+                 batch_size=32,
+                 replace_mse_with_huber_loss=False,
+                 create_target_network=False,
+                 tensorflow_support=True):
+        """
+        :param force_cpu:
+            Force the neural networks to run on the CPU even if a GPU is available
+        :param async_training:
+            If set to True, asynchronous training will be used, meaning that each workers will progress in its own
+            speed, while not waiting for the rest of the workers to calculate their gradients.
+        :param shared_optimizer:
+            If set to True, a central optimizer which will be shared with all the workers will be used for applying
+            gradients to the network. Otherwise, each worker will have its own optimizer with its own internal
+            parameters that will only be affected by the gradients calculated by that worker
+        :param scale_down_gradients_by_number_of_workers_for_sync_training:
+            If set to True, in synchronous training, the gradients of each worker will be scaled down by the
+            number of workers. This essentially means that the gradients applied to the network are the average
+            of the gradients over all the workers.
+        :param clip_gradients:
+            A value that will be used for clipping the gradients of the network. If set to None, no gradient clipping
+            will be applied. Otherwise, the gradients will be clipped according to the gradients_clipping_method.
+        :param gradients_clipping_method:
+            A gradient clipping method, defined by a GradientClippingMethod enum, and that will be used to clip the
+            gradients of the network. This will only be used if the clip_gradients value is defined as a value other
+            than None.
+        :param l2_regularization:
+            A L2 regularization weight that will be applied to the network weights while calculating the loss function
+        :param learning_rate:
+            The learning rate for the network
+        :param learning_rate_decay_rate:
+            If this value is larger than 0, an exponential decay will be applied to the network learning rate.
+            The rate of the decay is defined by this parameter, and the number of training steps the decay will be
+            applied is defined by learning_rate_decay_steps. Notice that both parameters should be defined in order
+            for this to work correctly.
+        :param learning_rate_decay_steps:
+            If the learning_rate_decay_rate of the network is larger than 0, an exponential decay will be applied to
+            the network learning rate. The number of steps the decay will be applied is defined by this parameter.
+            Notice that both this parameter, as well as learning_rate_decay_rate should be defined in order for the
+            learning rate decay to work correctly.
+        :param input_embedders_parameters:
+            A dictionary mapping between input names and input embedders (InputEmbedderParameters) to use for the
+            network. Each of the keys is an input name as returned from the environment in the state.
+            For example, if the environment returns a state containing 'observation' and 'measurements', then
+            the keys for the input embedders dictionary can be either 'observation' to use the observation as input,
+            'measurements' to use the measurements as input, or both.
+            The embedder type will be automatically selected according to the input type. Vector inputs will
+            produce a fully connected embedder, and image inputs will produce a convolutional embedder.
+        :param embedding_merger_type:
+            The type of embedding merging to use, given by one of the EmbeddingMergerType enum values.
+            This will be used to merge the outputs of all the input embedders into a single embbeding.
+        :param middleware_parameters:
+            The parameters of the middleware to use, given by a MiddlewareParameters object.
+            Each network will have only a single middleware embedder which will take the merged embeddings from the
+            input embedders and pass them through more neural network layers.
+        :param heads_parameters:
+            A list of heads for the network given by their corresponding HeadParameters.
+            Each network can have one or multiple network heads, where each one will take the output of the middleware
+            and make some additional computation on top of it. Additionally, each head calculates a weighted loss value,
+            and the loss values from all the heads will be summed later on.
+        :param use_separate_networks_per_head:
+            A flag that allows using different copies of the input embedders and middleware for each one of the heads.
+            Regularly, the heads will have a shared input, but in the case where use_separate_networks_per_head is set
+            to True, each one of the heads will get a different input.
+        :param optimizer_type:
+            A string specifying the optimizer type to use for updating the network. The available optimizers are
+            Adam, RMSProp and LBFGS.
+        :param optimizer_epsilon:
+            An internal optimizer parameter used for Adam and RMSProp.
+        :param adam_optimizer_beta1:
+            An beta1 internal optimizer parameter used for Adam. It will be used only if Adam was selected as the
+            optimizer for the network.
+        :param adam_optimizer_beta2:
+            An beta2 internal optimizer parameter used for Adam. It will be used only if Adam was selected as the
+            optimizer for the network.
+        :param rms_prop_optimizer_decay:
+            The decay value for the RMSProp optimizer, which will be used only in case the RMSProp optimizer was
+            selected for this network.
+        :param batch_size:
+            The batch size to use when updating the network.
+        :param replace_mse_with_huber_loss:
+        :param create_target_network:
+            If this flag is set to True, an additional copy of the network will be created and initialized with the
+            same weights as the online network. It can then be queried, and its weights can be synced from the
+            online network at will.
+        :param tensorflow_support:
+            A flag which specifies if the network is supported by the TensorFlow framework.
+        """
+        super().__init__()
+        self.framework = Frameworks.tensorflow
+        self.sess = None
+
+        # hardware parameters
+        self.force_cpu = force_cpu
+
+        # distributed training options
+        self.async_training = async_training
+        self.shared_optimizer = shared_optimizer
+        self.scale_down_gradients_by_number_of_workers_for_sync_training = scale_down_gradients_by_number_of_workers_for_sync_training
+
+        # regularization
+        self.clip_gradients = clip_gradients
+        self.gradients_clipping_method = gradients_clipping_method
+        self.l2_regularization = l2_regularization
+
+        # learning rate
+        self.learning_rate = learning_rate
+        self.learning_rate_decay_rate = learning_rate_decay_rate
+        self.learning_rate_decay_steps = learning_rate_decay_steps
+
+        # structure
+        self.input_embedders_parameters = input_embedders_parameters
+        self.embedding_merger_type = embedding_merger_type
+        self.middleware_parameters = middleware_parameters
+        self.heads_parameters = heads_parameters
+        self.use_separate_networks_per_head = use_separate_networks_per_head
+        self.optimizer_type = optimizer_type
+        self.optimizer_epsilon = optimizer_epsilon
+        self.adam_optimizer_beta1 = adam_optimizer_beta1
+        self.adam_optimizer_beta2 = adam_optimizer_beta2
+        self.rms_prop_optimizer_decay = rms_prop_optimizer_decay
+        self.batch_size = batch_size
+        self.replace_mse_with_huber_loss = replace_mse_with_huber_loss
+        self.create_target_network = create_target_network
+
+        # Framework support
+        self.tensorflow_support = tensorflow_support

+
+
+class NetworkComponentParameters(Parameters):
+    def __init__(self, dense_layer):
+        self.dense_layer = dense_layer
+
+
+
[docs]class VisualizationParameters(Parameters):
+    def __init__(self,
+                 print_networks_summary=False,
+                 dump_csv=True,
+                 dump_signals_to_csv_every_x_episodes=5,
+                 dump_gifs=False,
+                 dump_mp4=False,
+                 video_dump_methods=None,
+                 dump_in_episode_signals=False,
+                 dump_parameters_documentation=True,
+                 render=False,
+                 native_rendering=False,
+                 max_fps_for_human_control=10,
+                 tensorboard=False,
+                 add_rendered_image_to_env_response=False):
+        """
+        :param print_networks_summary:
+            If set to True, a summary of all the networks structure will be printed at the beginning of the experiment
+        :param dump_csv:
+            If set to True, the logger will dump logs to a csv file once in every dump_signals_to_csv_every_x_episodes
+            episodes. The logs can be later used to visualize the training process using Coach Dashboard.
+        :param dump_signals_to_csv_every_x_episodes:
+            Defines the number of episodes between writing new data to the csv log files. Lower values can affect
+            performance, as writing to disk may take time, and it is done synchronously.
+        :param dump_gifs:
+            If set to True, GIF videos of the environment will be stored into the experiment directory according to
+            the filters defined in video_dump_methods.
+        :param dump_mp4:
+            If set to True, MP4 videos of the environment will be stored into the experiment directory according to
+            the filters defined in video_dump_methods.
+        :param dump_in_episode_signals:
+            If set to True, csv files will be dumped for each episode for inspecting different metrics within the
+            episode. This means that for each step in each episode, different metrics such as the reward, the
+            future return, etc. will be saved. Setting this to True may affect performance severely, and therefore
+            this should be used only for debugging purposes.
+        :param dump_parameters_documentation:
+            If set to True, a json file containing all the agent parameters will be saved in the experiment directory.
+            This may be very useful for inspecting the values defined for each parameters and making sure that all
+            the parameters are defined as expected.
+        :param render:
+            If set to True, the environment render function will be called for each step, rendering the image of the
+            environment. This may affect the performance of training, and is highly dependent on the environment.
+            By default, Coach uses PyGame to render the environment image instead of the environment specific rendered.
+            To change this, use the native_rendering flag.
+        :param native_rendering:
+            If set to True, the environment native renderer will be used for rendering the environment image.
+            In some cases this can be slower than rendering using PyGame through Coach, but in other cases the
+            environment opens its native renderer by default, so rendering with PyGame is an unnecessary overhead.
+        :param max_fps_for_human_control:
+            The maximum number of frames per second used while playing the environment as a human. This only has
+            effect while using the --play flag for Coach.
+        :param tensorboard:
+            If set to True, TensorBoard summaries will be stored in the experiment directory. This can later be
+            loaded in TensorBoard in order to visualize the training process.
+        :param video_dump_methods:
+            A list of dump methods that will be used as filters for deciding when to save videos.
+            The filters in the list will be checked one after the other until the first dump method that returns
+            false for should_dump() in the environment class. This list will only be used if dump_mp4 or dump_gif are
+            set to True.
+        :param add_rendered_image_to_env_response:
+            Some environments have a different observation compared to the one displayed while rendering.
+            For some cases it can be useful to pass the rendered image to the agent for visualization purposes.
+            If this flag is set to True, the rendered image will be added to the environment EnvResponse object,
+            which will be passed to the agent and allow using those images.
+        """
+        super().__init__()
+        if video_dump_methods is None:
+            video_dump_methods = [SelectedPhaseOnlyDumpFilter(RunPhase.TEST), MaxDumpFilter()]
+        self.print_networks_summary = print_networks_summary
+        self.dump_csv = dump_csv
+        self.dump_gifs = dump_gifs
+        self.dump_mp4 = dump_mp4
+        self.dump_signals_to_csv_every_x_episodes = dump_signals_to_csv_every_x_episodes
+        self.dump_in_episode_signals = dump_in_episode_signals
+        self.dump_parameters_documentation = dump_parameters_documentation
+        self.render = render
+        self.native_rendering = native_rendering
+        self.max_fps_for_human_control = max_fps_for_human_control
+        self.tensorboard = tensorboard
+        self.video_dump_filters = video_dump_methods
+        self.add_rendered_image_to_env_response = add_rendered_image_to_env_response

+
+
+
[docs]class AgentParameters(Parameters):
+    def __init__(self, algorithm: AlgorithmParameters, exploration: 'ExplorationParameters', memory: 'MemoryParameters',
+                 networks: Dict[str, NetworkParameters], visualization: VisualizationParameters=VisualizationParameters()):
+        """
+        :param algorithm:
+            A class inheriting AlgorithmParameters.
+            The parameters used for the specific algorithm used by the agent.
+            These parameters can be later referenced in the agent implementation through self.ap.algorithm.
+        :param exploration:
+            Either a class inheriting ExplorationParameters or a dictionary mapping between action
+            space types and their corresponding ExplorationParameters. If a dictionary was used,
+            when the agent will be instantiated, the correct exploration policy parameters will be used
+            according to the real type of the environment action space.
+            These parameters will be used to instantiate the exporation policy.
+        :param memory:
+            A class inheriting MemoryParameters. It defines all the parameters used by the memory module.
+        :param networks:
+            A dictionary mapping between network names and their corresponding network parmeters, defined
+            as a class inheriting NetworkParameters. Each element will be used in order to instantiate
+            a NetworkWrapper class, and all the network wrappers will be stored in the agent under
+            self.network_wrappers. self.network_wrappers is a dict mapping between the network name that
+            was given in the networks dict, and the instantiated network wrapper.
+        :param visualization:
+            A class inheriting VisualizationParameters and defining various parameters that can be
+            used for visualization purposes, such as printing to the screen, rendering, and saving videos.
+        """
+        super().__init__()
+        self.visualization = visualization
+        self.algorithm = algorithm
+        self.exploration = exploration
+        self.memory = memory
+        self.network_wrappers = networks
+        self.input_filter = None
+        self.output_filter = None
+        self.pre_network_filter = NoInputFilter()
+        self.full_name_id = None  # TODO: do we really want to hold this parameter here?
+        self.name = None
+        self.is_a_highest_level_agent = True
+        self.is_a_lowest_level_agent = True
+        self.task_parameters = None
+
+    @property
+    def path(self):
+        return 'rl_coach.agents.agent:Agent'

+
+
+
[docs]class TaskParameters(Parameters):
+    def __init__(self, framework_type: Frameworks=Frameworks.tensorflow, evaluate_only: bool=False, use_cpu: bool=False,
+                 experiment_path='/tmp', seed=None, checkpoint_save_secs=None, checkpoint_restore_dir=None,
+                 checkpoint_save_dir=None, export_onnx_graph: bool=False):
+        """
+        :param framework_type: deep learning framework type. currently only tensorflow is supported
+        :param evaluate_only: the task will be used only for evaluating the model
+        :param use_cpu: use the cpu for this task
+        :param experiment_path: the path to the directory which will store all the experiment outputs
+        :param seed: a seed to use for the random numbers generator
+        :param checkpoint_save_secs: the number of seconds between each checkpoint saving
+        :param checkpoint_restore_dir: the directory to restore the checkpoints from
+        :param checkpoint_save_dir: the directory to store the checkpoints in
+        :param export_onnx_graph: If set to True, this will export an onnx graph each time a checkpoint is saved
+        """
+        self.framework_type = framework_type
+        self.task_index = 0  # TODO: not really needed
+        self.evaluate_only = evaluate_only
+        self.use_cpu = use_cpu
+        self.experiment_path = experiment_path
+        self.checkpoint_save_secs = checkpoint_save_secs
+        self.checkpoint_restore_dir = checkpoint_restore_dir
+        self.checkpoint_save_dir = checkpoint_save_dir
+        self.seed = seed
+        self.export_onnx_graph = export_onnx_graph

+
+
+
[docs]class DistributedTaskParameters(TaskParameters):
+    def __init__(self, framework_type: Frameworks, parameters_server_hosts: str, worker_hosts: str, job_type: str,
+                 task_index: int, evaluate_only: bool=False, num_tasks: int=None,
+                 num_training_tasks: int=None, use_cpu: bool=False, experiment_path=None, dnd=None,
+                 shared_memory_scratchpad=None, seed=None, checkpoint_save_secs=None, checkpoint_restore_dir=None,
+                 checkpoint_save_dir=None, export_onnx_graph: bool=False):
+        """
+        :param framework_type: deep learning framework type. currently only tensorflow is supported
+        :param evaluate_only: the task will be used only for evaluating the model
+        :param parameters_server_hosts: comma-separated list of hostname:port pairs to which the parameter servers are
+                                        assigned
+        :param worker_hosts: comma-separated list of hostname:port pairs to which the workers are assigned
+        :param job_type: the job type - either ps (short for parameters server) or worker
+        :param task_index: the index of the process
+        :param num_tasks: the number of total tasks that are running (not including the parameters server)
+        :param num_training_tasks: the number of tasks that are training (not including the parameters server)
+        :param use_cpu: use the cpu for this task
+        :param experiment_path: the path to the directory which will store all the experiment outputs
+        :param dnd: an external DND to use for NEC. This is a workaround needed for a shared DND not using the scratchpad.
+        :param seed: a seed to use for the random numbers generator
+        :param checkpoint_save_secs: the number of seconds between each checkpoint saving
+        :param checkpoint_restore_dir: the directory to restore the checkpoints from
+        :param checkpoint_save_dir: the directory to store the checkpoints in
+        :param export_onnx_graph: If set to True, this will export an onnx graph each time a checkpoint is saved
+        """
+        super().__init__(framework_type=framework_type, evaluate_only=evaluate_only, use_cpu=use_cpu,
+                         experiment_path=experiment_path, seed=seed, checkpoint_save_secs=checkpoint_save_secs,
+                         checkpoint_restore_dir=checkpoint_restore_dir, checkpoint_save_dir=checkpoint_save_dir,
+                         export_onnx_graph=export_onnx_graph)
+        self.parameters_server_hosts = parameters_server_hosts
+        self.worker_hosts = worker_hosts
+        self.job_type = job_type
+        self.task_index = task_index
+        self.num_tasks = num_tasks
+        self.num_training_tasks = num_training_tasks
+        self.device = None  # the replicated device which will be used for the global parameters
+        self.worker_target = None
+        self.dnd = dnd
+        self.shared_memory_scratchpad = shared_memory_scratchpad

+
+ +
+ +
+ + +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_modules/rl_coach/core_types.html b/docs/_modules/rl_coach/core_types.html new file mode 100644 index 0000000..a783c7d --- /dev/null +++ b/docs/_modules/rl_coach/core_types.html @@ -0,0 +1,1092 @@ + + + + + + + + + + + rl_coach.core_types — Reinforcement Learning Coach 0.11.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + +
+ +
+ + + + + + + + + + + + + + + + + +
+ + + + +
+
+
+
+ +

Source code for rl_coach.core_types

+#
+# Copyright (c) 2017 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+
+import copy
+from enum import Enum
+from random import shuffle
+from typing import List, Union, Dict, Any, Type
+
+import numpy as np
+
+from rl_coach.utils import force_list
+
+ActionType = Union[int, float, np.ndarray, List]
+GoalType = Union[None, np.ndarray]
+ObservationType = np.ndarray
+RewardType = Union[int, float, np.ndarray]
+StateType = Dict[str, np.ndarray]
+
+
+class GoalTypes(Enum):
+    Embedding = 1
+    EmbeddingChange = 2
+    Observation = 3
+    Measurements = 4
+
+
+# step methods
+
+class StepMethod(object):
+    def __init__(self, num_steps: int):
+        self._num_steps = self.num_steps = num_steps
+
+    @property
+    def num_steps(self) -> int:
+        return self._num_steps
+
+    @num_steps.setter
+    def num_steps(self, val: int) -> None:
+        self._num_steps = val
+
+
+class Frames(StepMethod):
+    def __init__(self, num_steps):
+        super().__init__(num_steps)
+
+
+class EnvironmentSteps(StepMethod):
+    def __init__(self, num_steps):
+        super().__init__(num_steps)
+
+
+class EnvironmentEpisodes(StepMethod):
+    def __init__(self, num_steps):
+        super().__init__(num_steps)
+
+
+class TrainingSteps(StepMethod):
+    def __init__(self, num_steps):
+        super().__init__(num_steps)
+
+
+class Time(StepMethod):
+    def __init__(self, num_steps):
+        super().__init__(num_steps)
+
+
+class PredictionType(object):
+    pass
+
+
+class VStateValue(PredictionType):
+    pass
+
+
+class QActionStateValue(PredictionType):
+    pass
+
+
+class ActionProbabilities(PredictionType):
+    pass
+
+
+class Embedding(PredictionType):
+    pass
+
+
+class InputEmbedding(Embedding):
+    pass
+
+
+class MiddlewareEmbedding(Embedding):
+    pass
+
+
+class InputImageEmbedding(InputEmbedding):
+    pass
+
+
+class InputVectorEmbedding(InputEmbedding):
+    pass
+
+
+class Middleware_FC_Embedding(MiddlewareEmbedding):
+    pass
+
+
+class Middleware_LSTM_Embedding(MiddlewareEmbedding):
+    pass
+
+
+class Measurements(PredictionType):
+    pass
+
+
+PlayingStepsType = Union[EnvironmentSteps, EnvironmentEpisodes, Frames]
+
+
+# run phases
+class RunPhase(Enum):
+    HEATUP = "Heatup"
+    TRAIN = "Training"
+    TEST = "Testing"
+    UNDEFINED = "Undefined"
+
+
+# transitions
+
+
[docs]class Transition(object):
+    def __init__(self, state: Dict[str, np.ndarray]=None, action: ActionType=None, reward: RewardType=None,
+                 next_state: Dict[str, np.ndarray]=None, game_over: bool=None, info: Dict=None):
+        """
+        A transition is a tuple containing the information of a single step of interaction
+        between the agent and the environment. The most basic version should contain the following values:
+        (current state, action, reward, next state, game over)
+        For imitation learning algorithms, if the reward, next state or game over is not known,
+        it is sufficient to store the current state and action taken by the expert.
+
+        :param state: The current state. Assumed to be a dictionary where the observation
+                      is located at state['observation']
+        :param action: The current action that was taken
+        :param reward: The reward received from the environment
+        :param next_state: The next state of the environment after applying the action.
+                           The next state should be similar to the state in its structure.
+        :param game_over: A boolean which should be True if the episode terminated after
+                          the execution of the action.
+        :param info: A dictionary containing any additional information to be stored in the transition
+        """
+
+        self._state = self.state = state
+        self._action = self.action = action
+        self._reward = self.reward = reward
+        self._n_step_discounted_rewards = self.n_step_discounted_rewards = None
+        if not next_state:
+            next_state = state
+        self._next_state = self._next_state = next_state
+        self._game_over = self.game_over = game_over
+        if info is None:
+            self.info = {}
+        else:
+            self.info = info
+
+    def __repr__(self):
+        return str(self.__dict__)
+
+    @property
+    def state(self):
+        if self._state is None:
+            raise Exception("The state was not filled by any of the modules between the environment and the agent")
+        return self._state
+
+    @state.setter
+    def state(self, val):
+        self._state = val
+
+    @property
+    def action(self):
+        if self._action is None:
+            raise Exception("The action was not filled by any of the modules between the environment and the agent")
+        return self._action
+
+    @action.setter
+    def action(self, val):
+        self._action = val
+
+    @property
+    def reward(self):
+
+        if self._reward is None:
+            raise Exception("The reward was not filled by any of the modules between the environment and the agent")
+        return self._reward
+
+    @reward.setter
+    def reward(self, val):
+        self._reward = val
+
+    @property
+    def n_step_discounted_rewards(self):
+        if self._n_step_discounted_rewards is None:
+            raise Exception("The n_step_discounted_rewards were not filled by any of the modules between the "
+                            "environment and the agent.  Make sure that you are using an episodic experience replay.")
+        return self._n_step_discounted_rewards
+
+    @n_step_discounted_rewards.setter
+    def n_step_discounted_rewards(self, val):
+        self._n_step_discounted_rewards = val
+
+    @property
+    def game_over(self):
+        if self._game_over is None:
+            raise Exception("The done flag was not filled by any of the modules between the environment and the agent")
+        return self._game_over
+
+    @game_over.setter
+    def game_over(self, val):
+        self._game_over = val
+
+    @property
+    def next_state(self):
+        if self._next_state is None:
+            raise Exception("The next state was not filled by any of the modules between the environment and the agent")
+        return self._next_state
+
+    @next_state.setter
+    def next_state(self, val):
+        self._next_state = val
+
+    def add_info(self, new_info: Dict[str, Any]) -> None:
+        if not new_info.keys().isdisjoint(self.info.keys()):
+            raise ValueError("The new info dictionary can not be appended to the existing info dictionary since there "
+                             "are overlapping keys between the two. old keys: {}, new keys: {}"
+                             .format(self.info.keys(), new_info.keys()))
+        self.info.update(new_info)
+
+    def __copy__(self):
+        new_transition = type(self)()
+        new_transition.__dict__.update(self.__dict__)
+        new_transition.state = copy.copy(new_transition.state)
+        new_transition.next_state = copy.copy(new_transition.next_state)
+        new_transition.info = copy.copy(new_transition.info)
+        return new_transition

+
+
+
[docs]class EnvResponse(object):
+    def __init__(self, next_state: Dict[str, ObservationType], reward: RewardType, game_over: bool, info: Dict=None,
+                 goal: ObservationType=None):
+        """
+        An env response is a collection containing the information returning from the environment after a single action
+        has been performed on it.
+
+        :param next_state: The new state that the environment has transitioned into. Assumed to be a dictionary where the
+                          observation is located at state['observation']
+        :param reward: The reward received from the environment
+        :param game_over: A boolean which should be True if the episode terminated after
+                          the execution of the action.
+        :param info: any additional info from the environment
+        :param goal: a goal defined by the environment
+        """
+        self._next_state = self.next_state = next_state
+        self._reward = self.reward = reward
+        self._game_over = self.game_over = game_over
+        self._goal = self.goal = goal
+        if info is None:
+            self.info = {}
+        else:
+            self.info = info
+
+    def __repr__(self):
+        return str(self.__dict__)
+
+    @property
+    def next_state(self):
+        return self._next_state
+
+    @next_state.setter
+    def next_state(self, val):
+        self._next_state = val
+
+    @property
+    def reward(self):
+        return self._reward
+
+    @reward.setter
+    def reward(self, val):
+        self._reward = val
+
+    @property
+    def game_over(self):
+        return self._game_over
+
+    @game_over.setter
+    def game_over(self, val):
+        self._game_over = val
+
+    @property
+    def goal(self):
+        return self._goal
+
+    @goal.setter
+    def goal(self, val):
+        self._goal = val
+
+    def add_info(self, info: Dict[str, Any]) -> None:
+        if info.keys().isdisjoint(self.info.keys()):
+            raise ValueError("The new info dictionary can not be appended to the existing info dictionary since there"
+                             "are overlapping keys between the two")
+        self.info.update(info)

+
+
+
[docs]class ActionInfo(object):
+    """
+    Action info is a class that holds an action and various additional information details about it
+    """
+
+    def __init__(self, action: ActionType, action_probability: float=0,
+                 action_value: float=0., state_value: float=0., max_action_value: float=None,
+                 action_intrinsic_reward: float=0):
+        """
+        :param action: the action
+        :param action_probability: the probability that the action was given when selecting it
+        :param action_value: the state-action value (Q value) of the action
+        :param state_value: the state value (V value) of the state where the action was taken
+        :param max_action_value: in case this is an action that was selected randomly, this is the value of the action
+                                 that received the maximum value. if no value is given, the action is assumed to be the
+                                 action with the maximum value
+        :param action_intrinsic_reward: can contain any intrinsic reward that the agent wants to add to this action
+                                        selection
+        """
+        self.action = action
+        self.action_probability = action_probability
+        self.action_value = action_value
+        self.state_value = state_value
+        if not max_action_value:
+            self.max_action_value = action_value
+        else:
+            self.max_action_value = max_action_value
+        self.action_intrinsic_reward = action_intrinsic_reward

+
+
+
[docs]class Batch(object):
+    """
+    A wrapper around a list of transitions that helps extracting batches of parameters from it.
+    For example, one can extract a list of states corresponding to the list of transitions.
+    The class uses lazy evaluation in order to return each of the available parameters.
+    """
+    def __init__(self, transitions: List[Transition]):
+        """
+        :param transitions: a list of transitions to extract the batch from
+        """
+        self.transitions = transitions
+        self._states = {}
+        self._actions = None
+        self._rewards = None
+        self._n_step_discounted_rewards = None
+        self._game_overs = None
+        self._next_states = {}
+        self._goals = None
+        self._info = {}
+
+
[docs]    def slice(self, start, end) -> None:
+        """
+        Keep a slice from the batch and discard the rest of the batch
+
+        :param start: the start index in the slice
+        :param end: the end index in the slice
+        :return: None
+        """
+
+        self.transitions = self.transitions[start:end]
+        for k, v in self._states.items():
+            self._states[k] = v[start:end]
+        if self._actions is not None:
+            self._actions = self._actions[start:end]
+        if self._rewards is not None:
+            self._rewards = self._rewards[start:end]
+        if self._n_step_discounted_rewards is not None:
+            self._n_step_discounted_rewards = self._n_step_discounted_rewards[start:end]
+        if self._game_overs is not None:
+            self._game_overs = self._game_overs[start:end]
+        for k, v in self._next_states.items():
+            self._next_states[k] = v[start:end]
+        if self._goals is not None:
+            self._goals = self._goals[start:end]
+        for k, v in self._info.items():
+            self._info[k] = v[start:end]

+
+
[docs]    def shuffle(self) -> None:
+        """
+        Shuffle all the transitions in the batch
+
+        :return: None
+        """
+        batch_order = list(range(self.size))
+        shuffle(batch_order)
+        self.transitions = [self.transitions[i] for i in batch_order]
+        self._states = {}
+        self._actions = None
+        self._rewards = None
+        self._n_step_discounted_rewards = None
+        self._game_overs = None
+        self._next_states = {}
+        self._goals = None
+        self._info = {}

+
+        # This seems to be slower
+        # for k, v in self._states.items():
+        #     self._states[k] = [v[i] for i in batch_order]
+        # if self._actions is not None:
+        #     self._actions = [self._actions[i] for i in batch_order]
+        # if self._rewards is not None:
+        #     self._rewards = [self._rewards[i] for i in batch_order]
+        # if self._total_returns is not None:
+        #     self._total_returns = [self._total_returns[i] for i in batch_order]
+        # if self._game_overs is not None:
+        #     self._game_overs = [self._game_overs[i] for i in batch_order]
+        # for k, v in self._next_states.items():
+        #     self._next_states[k] = [v[i] for i in batch_order]
+        # if self._goals is not None:
+        #     self._goals = [self._goals[i] for i in batch_order]
+        # for k, v in self._info.items():
+        #     self._info[k] = [v[i] for i in batch_order]
+
+
[docs]    def states(self, fetches: List[str], expand_dims=False) -> Dict[str, np.ndarray]:
+        """
+        follow the keys in fetches to extract the corresponding items from the states in the batch
+        if these keys were not already extracted before. return only the values corresponding to those keys
+
+        :param fetches: the keys of the state dictionary to extract
+        :param expand_dims: add an extra dimension to each of the value batches
+        :return: a dictionary containing a batch of values correponding to each of the given fetches keys
+        """
+        current_states = {}
+        # there are cases (e.g. ddpg) where the state does not contain all the information needed for running
+        # through the network and this has to be added externally (e.g. ddpg where the action needs to be given in
+        # addition to the current_state, so that all the inputs of the network will be filled)
+        for key in set(fetches).intersection(self.transitions[0].state.keys()):
+            if key not in self._states.keys():
+                self._states[key] = np.array([np.array(transition.state[key]) for transition in self.transitions])
+            if expand_dims:
+                current_states[key] = np.expand_dims(self._states[key], -1)
+            else:
+                current_states[key] = self._states[key]
+        return current_states

+
+
[docs]    def actions(self, expand_dims=False) -> np.ndarray:
+        """
+        if the actions were not converted to a batch before, extract them to a batch and then return the batch
+
+        :param expand_dims: add an extra dimension to the actions batch
+        :return: a numpy array containing all the actions of the batch
+        """
+        if self._actions is None:
+            self._actions = np.array([transition.action for transition in self.transitions])
+        if expand_dims:
+            return np.expand_dims(self._actions, -1)
+        return self._actions

+
+
[docs]    def rewards(self, expand_dims=False) -> np.ndarray:
+        """
+        if the rewards were not converted to a batch before, extract them to a batch and then return the batch
+
+        :param expand_dims: add an extra dimension to the rewards batch
+        :return: a numpy array containing all the rewards of the batch
+        """
+        if self._rewards is None:
+            self._rewards = np.array([transition.reward for transition in self.transitions])
+        if expand_dims:
+            return np.expand_dims(self._rewards, -1)
+        return self._rewards

+
+
[docs]    def n_step_discounted_rewards(self, expand_dims=False) -> np.ndarray:
+        """
+        if the n_step_discounted_rewards were not converted to a batch before, extract them to a batch and then return
+         the batch
+        if the n step discounted rewards were not filled, this will raise an exception
+        :param expand_dims: add an extra dimension to the total_returns batch
+        :return: a numpy array containing all the total return values of the batch
+        """
+        if self._n_step_discounted_rewards is None:
+            self._n_step_discounted_rewards = np.array([transition.n_step_discounted_rewards for transition in
+                                                        self.transitions])
+        if expand_dims:
+            return np.expand_dims(self._n_step_discounted_rewards, -1)
+        return self._n_step_discounted_rewards

+
+
[docs]    def game_overs(self, expand_dims=False) -> np.ndarray:
+        """
+        if the game_overs were not converted to a batch before, extract them to a batch and then return the batch
+
+        :param expand_dims: add an extra dimension to the game_overs batch
+        :return: a numpy array containing all the game over flags of the batch
+        """
+        if self._game_overs is None:
+            self._game_overs = np.array([transition.game_over for transition in self.transitions])
+        if expand_dims:
+            return np.expand_dims(self._game_overs, -1)
+        return self._game_overs

+
+
[docs]    def next_states(self, fetches: List[str], expand_dims=False) -> Dict[str, np.ndarray]:
+        """
+        follow the keys in fetches to extract the corresponding items from the next states in the batch
+        if these keys were not already extracted before. return only the values corresponding to those keys
+
+        :param fetches: the keys of the state dictionary to extract
+        :param expand_dims: add an extra dimension to each of the value batches
+        :return: a dictionary containing a batch of values correponding to each of the given fetches keys
+        """
+        next_states = {}
+        # there are cases (e.g. ddpg) where the state does not contain all the information needed for running
+        # through the network and this has to be added externally (e.g. ddpg where the action needs to be given in
+        # addition to the current_state, so that all the inputs of the network will be filled)
+        for key in set(fetches).intersection(self.transitions[0].next_state.keys()):
+            if key not in self._next_states.keys():
+                self._next_states[key] = np.array(
+                    [np.array(transition.next_state[key]) for transition in self.transitions])
+            if expand_dims:
+                next_states[key] = np.expand_dims(self._next_states[key], -1)
+            else:
+                next_states[key] = self._next_states[key]
+        return next_states

+
+
[docs]    def goals(self, expand_dims=False) -> np.ndarray:
+        """
+        if the goals were not converted to a batch before, extract them to a batch and then return the batch
+        if the goal was not filled, this will raise an exception
+
+        :param expand_dims: add an extra dimension to the goals batch
+        :return: a numpy array containing all the goals of the batch
+        """
+        if self._goals is None:
+            self._goals = np.array([transition.goal for transition in self.transitions])
+        if expand_dims:
+            return np.expand_dims(self._goals, -1)
+        return self._goals

+
+
[docs]    def info_as_list(self, key) -> list:
+        """
+        get the info and store it internally as a list, if wasn't stored before. return it as a list
+        :param expand_dims: add an extra dimension to the info batch
+        :return: a list containing all the info values of the batch corresponding to the given key
+        """
+        if key not in self._info.keys():
+            self._info[key] = [transition.info[key] for transition in self.transitions]
+        return self._info[key]

+
+
[docs]    def info(self, key, expand_dims=False) -> np.ndarray:
+        """
+        if the given info dictionary key was not converted to a batch before, extract it to a batch and then return the
+        batch. if the key is not part of the keys in the info dictionary, this will raise an exception
+
+        :param expand_dims: add an extra dimension to the info batch
+        :return: a numpy array containing all the info values of the batch corresponding to the given key
+        """
+        info_list = self.info_as_list(key)
+
+        if expand_dims:
+            return np.expand_dims(info_list, -1)
+        return np.array(info_list)

+
+    @property
+    def size(self) -> int:
+        """
+        :return: the size of the batch
+        """
+        return len(self.transitions)
+
+    def __getitem__(self, key):
+        """
+        get an item from the transitions list
+
+        :param key: index of the transition in the batch
+        :return: the transition corresponding to the given index
+        """
+        return self.transitions[key]
+
+    def __setitem__(self, key, item):
+        """
+        set an item in the transition list
+
+        :param key: index of the transition in the batch
+        :param item: the transition to place in the given index
+        :return: None
+        """
+        self.transitions[key] = item

+
+
+class TotalStepsCounter(object):
+    """
+    A wrapper around a dictionary counting different StepMethods steps done.
+    """
+
+    def __init__(self):
+        self.counters = {
+            EnvironmentEpisodes: 0,
+            EnvironmentSteps: 0,
+            TrainingSteps: 0
+        }
+
+    def __getitem__(self, key: Type[StepMethod]) -> int:
+        """
+        get counter value
+
+        :param key: counter type
+        :return: the counter value
+        """
+        return self.counters[key]
+
+    def __setitem__(self, key: StepMethod, item: int) -> None:
+        """
+        set an item in the transition list
+
+        :param key: counter type
+        :param item: an integer representing the new counter value
+        :return: None
+        """
+        self.counters[key] = item
+
+    def __add__(self, other: Type[StepMethod]) -> Type[StepMethod]:
+        return other.__class__(self.counters[other.__class__] + other.num_steps)
+
+    def __lt__(self, other: Type[StepMethod]):
+        return self.counters[other.__class__] < other.num_steps
+
+
+class GradientClippingMethod(Enum):
+    ClipByGlobalNorm = 0
+    ClipByNorm = 1
+    ClipByValue = 2
+
+
+
[docs]class Episode(object):
+    """
+    An Episode represents a set of sequential transitions, that end with a terminal state.
+    """
+    def __init__(self, discount: float=0.99, bootstrap_total_return_from_old_policy: bool=False, n_step: int=-1):
+        """
+        :param discount: the discount factor to use when calculating total returns
+        :param bootstrap_total_return_from_old_policy: should the total return be bootstrapped from the values in the
+                                                       memory
+        :param n_step: the number of future steps to sum the reward over before bootstrapping
+        """
+        self.transitions = []
+        self._length = 0
+        self.discount = discount
+        self.bootstrap_total_return_from_old_policy = bootstrap_total_return_from_old_policy
+        self.n_step = n_step
+        self.is_complete = False
+
+
[docs]    def insert(self, transition: Transition) -> None:
+        """
+        Insert a new transition to the episode. If the game_over flag in the transition is set to True,
+        the episode will be marked as complete.
+
+        :param transition: The new transition to insert to the episode
+        :return: None
+        """
+        self.transitions.append(transition)
+        self._length += 1
+        if transition.game_over:
+            self.is_complete = True

+
+
[docs]    def is_empty(self) -> bool:
+        """
+        Check if the episode is empty
+
+        :return: A boolean value determining if the episode is empty or not
+        """
+        return self.length() == 0

+
+
[docs]    def length(self) -> int:
+        """
+        Return the length of the episode, which is the number of transitions it holds.
+
+        :return: The number of transitions in the episode
+        """
+        return self._length

+
+    def __len__(self):
+        return self.length()
+
+
[docs]    def get_transition(self, transition_idx: int) -> Transition:
+        """
+        Get a specific transition by its index.
+
+        :param transition_idx: The index of the transition to get
+        :return: The transition which is stored in the given index
+        """
+        return self.transitions[transition_idx]

+
+
[docs]    def get_last_transition(self) -> Transition:
+        """
+        Get the last transition in the episode, or None if there are no transition available
+
+        :return: The last transition in the episode
+        """
+        return self.get_transition(-1) if self.length() > 0 else None

+
+
[docs]    def get_first_transition(self) -> Transition:
+        """
+        Get the first transition in the episode, or None if there are no transitions available
+
+        :return: The first transition in the episode
+        """
+        return self.get_transition(0) if self.length() > 0 else None

+
+
[docs]    def update_discounted_rewards(self):
+        """
+        Update the discounted returns for all the transitions in the episode.
+        The returns will be calculated according to the rewards of each transition, together with the number of steps
+        to bootstrap from and the discount factor, as defined by n_step and discount respectively when initializing
+        the episode.
+
+        :return: None
+        """
+        if self.n_step == -1 or self.n_step > self.length():
+            curr_n_step = self.length()
+        else:
+            curr_n_step = self.n_step
+
+        rewards = np.array([t.reward for t in self.transitions])
+        rewards = rewards.astype('float')
+        discounted_rewards = rewards.copy()
+        current_discount = self.discount
+        for i in range(1, curr_n_step):
+            discounted_rewards += current_discount * np.pad(rewards[i:], (0, i), 'constant', constant_values=0)
+            current_discount *= self.discount
+
+        # calculate the bootstrapped returns
+        if self.bootstrap_total_return_from_old_policy:
+            bootstraps = np.array([np.squeeze(t.info['max_action_value']) for t in self.transitions[curr_n_step:]])
+            bootstrapped_return = discounted_rewards + current_discount * np.pad(bootstraps, (0, curr_n_step),
+                                                                                 'constant', constant_values=0)
+            discounted_rewards = bootstrapped_return
+
+        for transition_idx in range(self.length()):
+            self.transitions[transition_idx].n_step_discounted_rewards = discounted_rewards[transition_idx]

+
+    def update_transitions_rewards_and_bootstrap_data(self):
+        if not isinstance(self.n_step, int) or (self.n_step < 1 and self.n_step != -1):
+            raise ValueError("n-step should be an integer with value >= 1, or set to -1 for always setting to episode"
+                             " length.")
+        elif self.n_step > 1:
+            curr_n_step = self.n_step if self.n_step < self.length() else self.length()
+
+            for idx, transition in enumerate(self.transitions):
+                next_n_step_transition_idx = (idx + curr_n_step)
+                if next_n_step_transition_idx < len(self.transitions):
+                    # next state will now point to the n-step next state
+                    transition.next_state = self.transitions[next_n_step_transition_idx].state
+                    transition.info['should_bootstrap_next_state'] = True
+                else:
+                    transition.next_state = self.transitions[-1].next_state
+                    transition.info['should_bootstrap_next_state'] = False
+
+        self.update_discounted_rewards()
+
+
+
+
[docs]    def get_transitions_attribute(self, attribute_name: str) -> List[Any]:
+        """
+        Get the values for some transition attribute from all the transitions in the episode.
+        For example, this allows getting the rewards for all the transitions as a list by calling
+        get_transitions_attribute('reward')
+
+        :param attribute_name: The name of the attribute to extract from all the transitions
+        :return: A list of values from all the transitions according to the attribute given in attribute_name
+        """
+        if len(self.transitions) > 0 and hasattr(self.transitions[0], attribute_name):
+            return [getattr(t, attribute_name) for t in self.transitions]
+        elif len(self.transitions) == 0:
+            return []
+        else:
+            raise ValueError("The transitions have no such attribute name")

+
+    def __getitem__(self, sliced):
+        return self.transitions[sliced]

+
+
+"""
+Video Dumping Methods
+"""
+
+
+class VideoDumpFilter(object):
+    """
+    Method used to decide when to dump videos
+    """
+    def should_dump(self, episode_terminated=False, **kwargs):
+        raise NotImplementedError("")
+
+
+class AlwaysDumpFilter(VideoDumpFilter):
+    """
+    Dump video for every episode
+    """
+    def __init__(self):
+        super().__init__()
+
+    def should_dump(self, episode_terminated=False, **kwargs):
+        return True
+
+
+class MaxDumpFilter(VideoDumpFilter):
+    """
+    Dump video every time a new max total reward has been achieved
+    """
+    def __init__(self):
+        super().__init__()
+        self.max_reward_achieved = -np.inf
+
+    def should_dump(self, episode_terminated=False, **kwargs):
+        # if the episode has not finished yet we want to be prepared for dumping a video
+        if not episode_terminated:
+            return True
+        if kwargs['total_reward_in_current_episode'] > self.max_reward_achieved:
+            self.max_reward_achieved = kwargs['total_reward_in_current_episode']
+            return True
+        else:
+            return False
+
+
+class EveryNEpisodesDumpFilter(object):
+    """
+    Dump videos once in every N episodes
+    """
+    def __init__(self, num_episodes_between_dumps: int):
+        super().__init__()
+        self.num_episodes_between_dumps = num_episodes_between_dumps
+        self.last_dumped_episode = 0
+        if num_episodes_between_dumps < 1:
+            raise ValueError("the number of episodes between dumps should be a positive number")
+
+    def should_dump(self, episode_terminated=False, **kwargs):
+        if kwargs['episode_idx'] >= self.last_dumped_episode + self.num_episodes_between_dumps - 1:
+            self.last_dumped_episode = kwargs['episode_idx']
+            return True
+        else:
+            return False
+
+
+class SelectedPhaseOnlyDumpFilter(object):
+    """
+    Dump videos when the phase of the environment matches a predefined phase
+    """
+    def __init__(self, run_phases: Union[RunPhase, List[RunPhase]]):
+        self.run_phases = force_list(run_phases)
+
+    def should_dump(self, episode_terminated=False, **kwargs):
+        if kwargs['_phase'] in self.run_phases:
+            return True
+        else:
+            return False
+
+ +
+ +
+ + +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_modules/rl_coach/environments/carla_environment.html b/docs/_modules/rl_coach/environments/carla_environment.html new file mode 100644 index 0000000..ff99f1c --- /dev/null +++ b/docs/_modules/rl_coach/environments/carla_environment.html @@ -0,0 +1,695 @@ + + + + + + + + + + + rl_coach.environments.carla_environment — Reinforcement Learning Coach 0.11.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + +
+ +
+ + + + + + + + + + + + + + + + + +
+ +
    + +
  • Docs »
    • + +
  • Module code »
    • + +
  • rl_coach.environments.carla_environment
    • + + +
  • + +
    • + +
+ + +
+
+
+
+ +

Source code for rl_coach.environments.carla_environment

+#
+# Copyright (c) 2017 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import random
+import sys
+from os import path, environ
+
+from rl_coach.logger import screen
+from rl_coach.filters.action.partial_discrete_action_space_map import PartialDiscreteActionSpaceMap
+from rl_coach.filters.observation.observation_rgb_to_y_filter import ObservationRGBToYFilter
+from rl_coach.filters.observation.observation_to_uint8_filter import ObservationToUInt8Filter
+
+try:
+    if 'CARLA_ROOT' in environ:
+        sys.path.append(path.join(environ.get('CARLA_ROOT'), 'PythonClient'))
+    else:
+        screen.error("CARLA_ROOT was not defined. Please set it to point to the CARLA root directory and try again.")
+    from carla.client import CarlaClient
+    from carla.settings import CarlaSettings
+    from carla.tcp import TCPConnectionError
+    from carla.sensor import Camera
+    from carla.client import VehicleControl
+    from carla.planner.planner import Planner
+    from carla.driving_benchmark.experiment_suites.experiment_suite import ExperimentSuite
+except ImportError:
+    from rl_coach.logger import failed_imports
+    failed_imports.append("CARLA")
+
+import logging
+import subprocess
+from rl_coach.environments.environment import Environment, EnvironmentParameters, LevelSelection
+from rl_coach.spaces import BoxActionSpace, ImageObservationSpace, StateSpace, \
+    VectorObservationSpace
+from rl_coach.utils import get_open_port, force_list
+from enum import Enum
+import os
+import signal
+from typing import List, Union
+from rl_coach.base_parameters import VisualizationParameters
+from rl_coach.filters.filter import InputFilter, NoOutputFilter
+from rl_coach.filters.observation.observation_rescale_to_size_filter import ObservationRescaleToSizeFilter
+from rl_coach.filters.observation.observation_stacking_filter import ObservationStackingFilter
+import numpy as np
+
+
+# enum of the available levels and their path
+class CarlaLevel(Enum):
+    TOWN1 = {"map_name": "Town01", "map_path": "/Game/Maps/Town01"}
+    TOWN2 = {"map_name": "Town02", "map_path": "/Game/Maps/Town02"}
+
+key_map = {
+    'BRAKE': (274,),  # down arrow
+    'GAS': (273,),  # up arrow
+    'TURN_LEFT': (276,),  # left arrow
+    'TURN_RIGHT': (275,),  # right arrow
+    'GAS_AND_TURN_LEFT': (273, 276),
+    'GAS_AND_TURN_RIGHT': (273, 275),
+    'BRAKE_AND_TURN_LEFT': (274, 276),
+    'BRAKE_AND_TURN_RIGHT': (274, 275),
+}
+
+CarlaInputFilter = InputFilter(is_a_reference_filter=True)
+CarlaInputFilter.add_observation_filter('forward_camera', 'rescaling',
+                                        ObservationRescaleToSizeFilter(ImageObservationSpace(np.array([128, 180, 3]),
+                                                                                             high=255)))
+CarlaInputFilter.add_observation_filter('forward_camera', 'to_grayscale', ObservationRGBToYFilter())
+CarlaInputFilter.add_observation_filter('forward_camera', 'to_uint8', ObservationToUInt8Filter(0, 255))
+CarlaInputFilter.add_observation_filter('forward_camera', 'stacking', ObservationStackingFilter(4))
+
+CarlaOutputFilter = NoOutputFilter()
+
+
+class CameraTypes(Enum):
+    FRONT = "forward_camera"
+    LEFT = "left_camera"
+    RIGHT = "right_camera"
+    SEGMENTATION = "segmentation"
+    DEPTH = "depth"
+    LIDAR = "lidar"
+
+
+class CarlaEnvironmentParameters(EnvironmentParameters):
+    class Quality(Enum):
+        LOW = "Low"
+        EPIC = "Epic"
+
+    def __init__(self, level="town1"):
+        super().__init__(level=level)
+        self.frame_skip = 3  # the frame skip affects the fps of the server directly. fps = 30 / frameskip
+        self.server_height = 512
+        self.server_width = 720
+        self.camera_height = 128
+        self.camera_width = 180
+        self.experiment_suite = None  # an optional CARLA experiment suite to use
+        self.config = None
+        self.level = level
+        self.quality = self.Quality.LOW
+        self.cameras = [CameraTypes.FRONT]
+        self.weather_id = [1]
+        self.verbose = True
+        self.episode_max_time = 100000  # miliseconds for each episode
+        self.allow_braking = False
+        self.separate_actions_for_throttle_and_brake = False
+        self.num_speedup_steps = 30
+        self.max_speed = 35.0  # km/h
+        self.default_input_filter = CarlaInputFilter
+        self.default_output_filter = CarlaOutputFilter
+
+    @property
+    def path(self):
+        return 'rl_coach.environments.carla_environment:CarlaEnvironment'
+
+
+
[docs]class CarlaEnvironment(Environment):
+    def __init__(self, level: LevelSelection,
+                 seed: int, frame_skip: int, human_control: bool, custom_reward_threshold: Union[int, float],
+                 visualization_parameters: VisualizationParameters,
+                 server_height: int, server_width: int, camera_height: int, camera_width: int,
+                 verbose: bool, experiment_suite: ExperimentSuite, config: str, episode_max_time: int,
+                 allow_braking: bool, quality: CarlaEnvironmentParameters.Quality,
+                 cameras: List[CameraTypes], weather_id: List[int], experiment_path: str,
+                 separate_actions_for_throttle_and_brake: bool,
+                 num_speedup_steps: int, max_speed: float, target_success_rate: float = 1.0, **kwargs):
+        super().__init__(level, seed, frame_skip, human_control, custom_reward_threshold, visualization_parameters, target_success_rate)
+
+        # server configuration
+        self.server_height = server_height
+        self.server_width = server_width
+        self.port = get_open_port()
+        self.host = 'localhost'
+        self.map_name = CarlaLevel[level.upper()].value['map_name']
+        self.map_path = CarlaLevel[level.upper()].value['map_path']
+        self.experiment_path = experiment_path
+
+        # client configuration
+        self.verbose = verbose
+        self.quality = quality
+        self.cameras = cameras
+        self.weather_id = weather_id
+        self.episode_max_time = episode_max_time
+        self.allow_braking = allow_braking
+        self.separate_actions_for_throttle_and_brake = separate_actions_for_throttle_and_brake
+        self.camera_width = camera_width
+        self.camera_height = camera_height
+
+        # setup server settings
+        self.experiment_suite = experiment_suite
+        self.config = config
+        if self.config:
+            # load settings from file
+            with open(self.config, 'r') as fp:
+                self.settings = fp.read()
+        else:
+            # hard coded settings
+            self.settings = CarlaSettings()
+            self.settings.set(
+                SynchronousMode=True,
+                SendNonPlayerAgentsInfo=False,
+                NumberOfVehicles=15,
+                NumberOfPedestrians=30,
+                WeatherId=random.choice(force_list(self.weather_id)),
+                QualityLevel=self.quality.value,
+                SeedVehicles=seed,
+                SeedPedestrians=seed)
+            if seed is None:
+                self.settings.randomize_seeds()
+
+            self.settings = self._add_cameras(self.settings, self.cameras, self.camera_width, self.camera_height)
+
+        # open the server
+        self.server = self._open_server()
+
+        logging.disable(40)
+
+        # open the client
+        self.game = CarlaClient(self.host, self.port, timeout=99999999)
+        self.game.connect()
+        if self.experiment_suite:
+            self.current_experiment_idx = 0
+            self.current_experiment = self.experiment_suite.get_experiments()[self.current_experiment_idx]
+            self.scene = self.game.load_settings(self.current_experiment.conditions)
+        else:
+            self.scene = self.game.load_settings(self.settings)
+
+        # get available start positions
+        self.positions = self.scene.player_start_spots
+        self.num_positions = len(self.positions)
+        self.current_start_position_idx = 0
+        self.current_pose = 0
+
+        # state space
+        self.state_space = StateSpace({
+            "measurements": VectorObservationSpace(4, measurements_names=["forward_speed", "x", "y", "z"])
+        })
+        for camera in self.scene.sensors:
+            self.state_space[camera.name] = ImageObservationSpace(
+                shape=np.array([self.camera_height, self.camera_width, 3]),
+                high=255)
+
+        # action space
+        if self.separate_actions_for_throttle_and_brake:
+            self.action_space = BoxActionSpace(shape=3, low=np.array([-1, 0, 0]), high=np.array([1, 1, 1]),
+                                               descriptions=["steer", "gas", "brake"])
+        else:
+            self.action_space = BoxActionSpace(shape=2, low=np.array([-1, -1]), high=np.array([1, 1]),
+                                               descriptions=["steer", "gas_and_brake"])
+
+        # human control
+        if self.human_control:
+            # convert continuous action space to discrete
+            self.steering_strength = 0.5
+            self.gas_strength = 1.0
+            self.brake_strength = 0.5
+            # TODO: reverse order of actions
+            self.action_space = PartialDiscreteActionSpaceMap(
+                target_actions=[[0., 0.],
+                                [0., -self.steering_strength],
+                                [0., self.steering_strength],
+                                [self.gas_strength, 0.],
+                                [-self.brake_strength, 0],
+                                [self.gas_strength, -self.steering_strength],
+                                [self.gas_strength, self.steering_strength],
+                                [self.brake_strength, -self.steering_strength],
+                                [self.brake_strength, self.steering_strength]],
+                descriptions=['NO-OP', 'TURN_LEFT', 'TURN_RIGHT', 'GAS', 'BRAKE',
+                              'GAS_AND_TURN_LEFT', 'GAS_AND_TURN_RIGHT',
+                              'BRAKE_AND_TURN_LEFT', 'BRAKE_AND_TURN_RIGHT']
+            )
+
+            # map keyboard keys to actions
+            for idx, action in enumerate(self.action_space.descriptions):
+                for key in key_map.keys():
+                    if action == key:
+                        self.key_to_action[key_map[key]] = idx
+
+        self.num_speedup_steps = num_speedup_steps
+        self.max_speed = max_speed
+
+        # measurements
+        self.autopilot = None
+        self.planner = Planner(self.map_name)
+
+        # env initialization
+        self.reset_internal_state(True)
+
+        # render
+        if self.is_rendered:
+            image = self.get_rendered_image()
+            self.renderer.create_screen(image.shape[1], image.shape[0])
+
+        self.target_success_rate = target_success_rate
+
+    def _add_cameras(self, settings, cameras, camera_width, camera_height):
+        # add a front facing camera
+        if CameraTypes.FRONT in cameras:
+            camera = Camera(CameraTypes.FRONT.value)
+            camera.set(FOV=100)
+            camera.set_image_size(camera_width, camera_height)
+            camera.set_position(2.0, 0, 1.4)
+            camera.set_rotation(-15.0, 0, 0)
+            settings.add_sensor(camera)
+
+        # add a left facing camera
+        if CameraTypes.LEFT in cameras:
+            camera = Camera(CameraTypes.LEFT.value)
+            camera.set(FOV=100)
+            camera.set_image_size(camera_width, camera_height)
+            camera.set_position(2.0, 0, 1.4)
+            camera.set_rotation(-15.0, -30, 0)
+            settings.add_sensor(camera)
+
+        # add a right facing camera
+        if CameraTypes.RIGHT in cameras:
+            camera = Camera(CameraTypes.RIGHT.value)
+            camera.set(FOV=100)
+            camera.set_image_size(camera_width, camera_height)
+            camera.set_position(2.0, 0, 1.4)
+            camera.set_rotation(-15.0, 30, 0)
+            settings.add_sensor(camera)
+
+        # add a front facing depth camera
+        if CameraTypes.DEPTH in cameras:
+            camera = Camera(CameraTypes.DEPTH.value)
+            camera.set_image_size(camera_width, camera_height)
+            camera.set_position(0.2, 0, 1.3)
+            camera.set_rotation(8, 30, 0)
+            camera.PostProcessing = 'Depth'
+            settings.add_sensor(camera)
+
+        # add a front facing semantic segmentation camera
+        if CameraTypes.SEGMENTATION in cameras:
+            camera = Camera(CameraTypes.SEGMENTATION.value)
+            camera.set_image_size(camera_width, camera_height)
+            camera.set_position(0.2, 0, 1.3)
+            camera.set_rotation(8, 30, 0)
+            camera.PostProcessing = 'SemanticSegmentation'
+            settings.add_sensor(camera)
+
+        return settings
+
+    def _get_directions(self, current_point, end_point):
+        """
+        Class that should return the directions to reach a certain goal
+        """
+
+        directions = self.planner.get_next_command(
+            (current_point.location.x,
+             current_point.location.y, 0.22),
+            (current_point.orientation.x,
+             current_point.orientation.y,
+             current_point.orientation.z),
+            (end_point.location.x, end_point.location.y, 0.22),
+            (end_point.orientation.x, end_point.orientation.y, end_point.orientation.z))
+        return directions
+
+    def _open_server(self):
+        log_path = path.join(self.experiment_path if self.experiment_path is not None else '.', 'logs',
+                             "CARLA_LOG_{}.txt".format(self.port))
+        if not os.path.exists(os.path.dirname(log_path)):
+            os.makedirs(os.path.dirname(log_path))
+        with open(log_path, "wb") as out:
+            cmd = [path.join(environ.get('CARLA_ROOT'), 'CarlaUE4.sh'), self.map_path,
+                   "-benchmark", "-carla-server", "-fps={}".format(30 / self.frame_skip),
+                   "-world-port={}".format(self.port),
+                   "-windowed -ResX={} -ResY={}".format(self.server_width, self.server_height),
+                   "-carla-no-hud"]
+
+            if self.config:
+                cmd.append("-carla-settings={}".format(self.config))
+            p = subprocess.Popen(cmd, stdout=out, stderr=out)
+
+        return p
+
+    def _close_server(self):
+        os.killpg(os.getpgid(self.server.pid), signal.SIGKILL)
+
+    def _update_state(self):
+        # get measurements and observations
+        measurements = []
+        while type(measurements) == list:
+            measurements, sensor_data = self.game.read_data()
+        self.state = {}
+
+        for camera in self.scene.sensors:
+            self.state[camera.name] = sensor_data[camera.name].data
+
+        self.location = [measurements.player_measurements.transform.location.x,
+                         measurements.player_measurements.transform.location.y,
+                         measurements.player_measurements.transform.location.z]
+
+        self.distance_from_goal = np.linalg.norm(np.array(self.location[:2]) -
+                                                 [self.current_goal.location.x, self.current_goal.location.y])
+
+        is_collision = measurements.player_measurements.collision_vehicles != 0 \
+                       or measurements.player_measurements.collision_pedestrians != 0 \
+                       or measurements.player_measurements.collision_other != 0
+
+        speed_reward = measurements.player_measurements.forward_speed - 1
+        if speed_reward > 30.:
+            speed_reward = 30.
+        self.reward = speed_reward \
+                      - (measurements.player_measurements.intersection_otherlane * 5) \
+                      - (measurements.player_measurements.intersection_offroad * 5) \
+                      - is_collision * 100 \
+                      - np.abs(self.control.steer) * 10
+
+        # update measurements
+        self.measurements = [measurements.player_measurements.forward_speed] + self.location
+        self.autopilot = measurements.player_measurements.autopilot_control
+
+        # The directions to reach the goal (0 Follow lane, 1 Left, 2 Right, 3 Straight)
+        directions = int(self._get_directions(measurements.player_measurements.transform, self.current_goal) - 2)
+        self.state['high_level_command'] = directions
+
+        if (measurements.game_timestamp >= self.episode_max_time) or is_collision:
+            self.done = True
+
+        self.state['measurements'] = np.array(self.measurements)
+
+    def _take_action(self, action):
+        self.control = VehicleControl()
+
+        if self.separate_actions_for_throttle_and_brake:
+            self.control.steer = np.clip(action[0], -1, 1)
+            self.control.throttle = np.clip(action[1], 0, 1)
+            self.control.brake = np.clip(action[2], 0, 1)
+        else:
+            # transform the 2 value action (steer, throttle - brake) into a 3 value action (steer, throttle, brake)
+            self.control.steer = np.clip(action[0], -1, 1)
+            self.control.throttle = np.clip(action[1], 0, 1)
+            self.control.brake = np.abs(np.clip(action[1], -1, 0))
+
+        # prevent braking
+        if not self.allow_braking or self.control.brake < 0.1 or self.control.throttle > self.control.brake:
+            self.control.brake = 0
+
+        # prevent over speeding
+        if hasattr(self, 'measurements') and self.measurements[0] * 3.6 > self.max_speed and self.control.brake == 0:
+            self.control.throttle = 0.0
+
+        self.control.hand_brake = False
+        self.control.reverse = False
+
+        self.game.send_control(self.control)
+
+    def _load_experiment(self, experiment_idx):
+        self.current_experiment = self.experiment_suite.get_experiments()[experiment_idx]
+        self.scene = self.game.load_settings(self.current_experiment.conditions)
+        self.positions = self.scene.player_start_spots
+        self.num_positions = len(self.positions)
+        self.current_start_position_idx = 0
+        self.current_pose = 0
+
+    def _restart_environment_episode(self, force_environment_reset=False):
+        # select start and end positions
+        if self.experiment_suite:
+            # if an expeirent suite is available, follow its given poses
+            if self.current_pose >= len(self.current_experiment.poses):
+                # load a new experiment
+                self.current_experiment_idx = (self.current_experiment_idx + 1) % len(self.experiment_suite.get_experiments())
+                self._load_experiment(self.current_experiment_idx)
+
+            self.current_start_position_idx = self.current_experiment.poses[self.current_pose][0]
+            self.current_goal = self.positions[self.current_experiment.poses[self.current_pose][1]]
+            self.current_pose += 1
+        else:
+            # go over all the possible positions in a cyclic manner
+            self.current_start_position_idx = (self.current_start_position_idx + 1) % self.num_positions
+
+            # choose a random goal destination
+            self.current_goal = random.choice(self.positions)
+
+        try:
+            self.game.start_episode(self.current_start_position_idx)
+        except:
+            self.game.connect()
+            self.game.start_episode(self.current_start_position_idx)
+
+        # start the game with some initial speed
+        for i in range(self.num_speedup_steps):
+            self.control = VehicleControl(throttle=1.0, brake=0, steer=0, hand_brake=False, reverse=False)
+            self.game.send_control(VehicleControl())
+
+    def get_rendered_image(self) -> np.ndarray:
+        """
+        Return a numpy array containing the image that will be rendered to the screen.
+        This can be different from the observation. For example, mujoco's observation is a measurements vector.
+        :return: numpy array containing the image that will be rendered to the screen
+        """
+        image = [self.state[camera.name] for camera in self.scene.sensors]
+        image = np.vstack(image)
+        return image
+
+    def get_target_success_rate(self) -> float:
+        return self.target_success_rate

+
+ +
+ +
+ + +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_modules/rl_coach/environments/control_suite_environment.html b/docs/_modules/rl_coach/environments/control_suite_environment.html new file mode 100644 index 0000000..a43cb1d --- /dev/null +++ b/docs/_modules/rl_coach/environments/control_suite_environment.html @@ -0,0 +1,426 @@ + + + + + + + + + + + rl_coach.environments.control_suite_environment — Reinforcement Learning Coach 0.11.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + +
+ +
+ + + + + + + + + + + + + + + + + +
+ +
    + +
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    • + + +
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    • + +
+ + +
+
+
+
+ +

Source code for rl_coach.environments.control_suite_environment

+#
+# Copyright (c) 2017 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+
+
+import random
+from enum import Enum
+from typing import Union
+
+import numpy as np
+
+try:
+    from dm_control import suite
+    from dm_control.suite.wrappers import pixels
+except ImportError:
+    from rl_coach.logger import failed_imports
+    failed_imports.append("DeepMind Control Suite")
+
+from rl_coach.base_parameters import VisualizationParameters
+from rl_coach.environments.environment import Environment, EnvironmentParameters, LevelSelection
+from rl_coach.filters.filter import NoInputFilter, NoOutputFilter
+from rl_coach.spaces import BoxActionSpace, ImageObservationSpace, VectorObservationSpace, StateSpace
+
+
+class ObservationType(Enum):
+    Measurements = 1
+    Image = 2
+    Image_and_Measurements = 3
+
+
+# Parameters
+class ControlSuiteEnvironmentParameters(EnvironmentParameters):
+    def __init__(self, level=None):
+        super().__init__(level=level)
+        self.observation_type = ObservationType.Measurements
+        self.default_input_filter = ControlSuiteInputFilter
+        self.default_output_filter = ControlSuiteOutputFilter
+
+    @property
+    def path(self):
+        return 'rl_coach.environments.control_suite_environment:ControlSuiteEnvironment'
+
+
+"""
+ControlSuite Environment Components
+"""
+ControlSuiteInputFilter = NoInputFilter()
+ControlSuiteOutputFilter = NoOutputFilter()
+
+control_suite_envs = {':'.join(env): ':'.join(env) for env in suite.BENCHMARKING}
+
+
+# Environment
+
[docs]class ControlSuiteEnvironment(Environment):
+    def __init__(self, level: LevelSelection, frame_skip: int, visualization_parameters: VisualizationParameters,
+                 target_success_rate: float=1.0, seed: Union[None, int]=None, human_control: bool=False,
+                 observation_type: ObservationType=ObservationType.Measurements,
+                 custom_reward_threshold: Union[int, float]=None, **kwargs):
+        """
+        :param level: (str)
+            A string representing the control suite level to run. This can also be a LevelSelection object.
+            For example, cartpole:swingup.
+
+        :param frame_skip: (int)
+            The number of frames to skip between any two actions given by the agent. The action will be repeated
+            for all the skipped frames.
+
+        :param visualization_parameters: (VisualizationParameters)
+            The parameters used for visualizing the environment, such as the render flag, storing videos etc.
+
+        :param target_success_rate: (float)
+            Stop experiment if given target success rate was achieved.
+
+        :param seed: (int)
+            A seed to use for the random number generator when running the environment.
+
+        :param human_control: (bool)
+            A flag that allows controlling the environment using the keyboard keys.
+
+        :param observation_type: (ObservationType)
+            An enum which defines which observation to use. The current options are to use:
+            * Measurements only - a vector of joint torques and similar measurements
+            * Image only - an image of the environment as seen by a camera attached to the simulator
+            * Measurements & Image - both type of observations will be returned in the state using the keys
+            'measurements' and 'pixels' respectively.
+
+        :param custom_reward_threshold: (float)
+            Allows defining a custom reward that will be used to decide when the agent succeeded in passing the environment.
+
+        """
+        super().__init__(level, seed, frame_skip, human_control, custom_reward_threshold, visualization_parameters, target_success_rate)
+
+        self.observation_type = observation_type
+
+        # load and initialize environment
+        domain_name, task_name = self.env_id.split(":")
+        self.env = suite.load(domain_name=domain_name, task_name=task_name, task_kwargs={'random': seed})
+
+        if observation_type != ObservationType.Measurements:
+            self.env = pixels.Wrapper(self.env, pixels_only=observation_type == ObservationType.Image)
+
+        # seed
+        if self.seed is not None:
+            np.random.seed(self.seed)
+            random.seed(self.seed)
+
+        self.state_space = StateSpace({})
+
+        # image observations
+        if observation_type != ObservationType.Measurements:
+            self.state_space['pixels'] = ImageObservationSpace(shape=self.env.observation_spec()['pixels'].shape,
+                                                               high=255)
+
+        # measurements observations
+        if observation_type != ObservationType.Image:
+            measurements_space_size = 0
+            measurements_names = []
+            for observation_space_name, observation_space in self.env.observation_spec().items():
+                if len(observation_space.shape) == 0:
+                    measurements_space_size += 1
+                    measurements_names.append(observation_space_name)
+                elif len(observation_space.shape) == 1:
+                    measurements_space_size += observation_space.shape[0]
+                    measurements_names.extend(["{}_{}".format(observation_space_name, i) for i in
+                                               range(observation_space.shape[0])])
+            self.state_space['measurements'] = VectorObservationSpace(shape=measurements_space_size,
+                                                                      measurements_names=measurements_names)
+
+        # actions
+        self.action_space = BoxActionSpace(
+            shape=self.env.action_spec().shape[0],
+            low=self.env.action_spec().minimum,
+            high=self.env.action_spec().maximum
+        )
+
+        # initialize the state by getting a new state from the environment
+        self.reset_internal_state(True)
+
+        # render
+        if self.is_rendered:
+            image = self.get_rendered_image()
+            scale = 1
+            if self.human_control:
+                scale = 2
+            if not self.native_rendering:
+                self.renderer.create_screen(image.shape[1]*scale, image.shape[0]*scale)
+
+        self.target_success_rate = target_success_rate
+
+    def _update_state(self):
+        self.state = {}
+
+        if self.observation_type != ObservationType.Measurements:
+            self.pixels = self.last_result.observation['pixels']
+            self.state['pixels'] = self.pixels
+
+        if self.observation_type != ObservationType.Image:
+            self.measurements = np.array([])
+            for sub_observation in self.last_result.observation.values():
+                if isinstance(sub_observation, np.ndarray) and len(sub_observation.shape) == 1:
+                    self.measurements = np.concatenate((self.measurements, sub_observation))
+                else:
+                    self.measurements = np.concatenate((self.measurements, np.array([sub_observation])))
+            self.state['measurements'] = self.measurements
+
+        self.reward = self.last_result.reward if self.last_result.reward is not None else 0
+
+        self.done = self.last_result.last()
+
+    def _take_action(self, action):
+        if type(self.action_space) == BoxActionSpace:
+            action = self.action_space.clip_action_to_space(action)
+
+        self.last_result = self.env.step(action)
+
+    def _restart_environment_episode(self, force_environment_reset=False):
+        self.last_result = self.env.reset()
+
+    def _render(self):
+        pass
+
+    def get_rendered_image(self):
+        return self.env.physics.render(camera_id=0)
+
+    def get_target_success_rate(self) -> float:
+        return self.target_success_rate

+
+ +
+ +
+ + +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_modules/rl_coach/environments/doom_environment.html b/docs/_modules/rl_coach/environments/doom_environment.html new file mode 100644 index 0000000..a71d746 --- /dev/null +++ b/docs/_modules/rl_coach/environments/doom_environment.html @@ -0,0 +1,495 @@ + + + + + + + + + + + rl_coach.environments.doom_environment — Reinforcement Learning Coach 0.11.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + +
+ +
+ + + + + + + + + + + + + + + + + +
+ +
    + +
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+ + +
+
+
+
+ +

Source code for rl_coach.environments.doom_environment

+#
+# Copyright (c) 2017 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+try:
+    import vizdoom
+except ImportError:
+    from rl_coach.logger import failed_imports
+    failed_imports.append("ViZDoom")
+
+import os
+from enum import Enum
+from os import path, environ
+from typing import Union, List
+
+import numpy as np
+
+from rl_coach.base_parameters import VisualizationParameters
+from rl_coach.environments.environment import Environment, EnvironmentParameters, LevelSelection
+from rl_coach.filters.action.full_discrete_action_space_map import FullDiscreteActionSpaceMap
+from rl_coach.filters.filter import InputFilter, OutputFilter
+from rl_coach.filters.observation.observation_rescale_to_size_filter import ObservationRescaleToSizeFilter
+from rl_coach.filters.observation.observation_rgb_to_y_filter import ObservationRGBToYFilter
+from rl_coach.filters.observation.observation_stacking_filter import ObservationStackingFilter
+from rl_coach.filters.observation.observation_to_uint8_filter import ObservationToUInt8Filter
+from rl_coach.spaces import MultiSelectActionSpace, ImageObservationSpace, \
+    VectorObservationSpace, StateSpace
+
+
+# enum of the available levels and their path
+class DoomLevel(Enum):
+    BASIC = "basic.cfg"
+    DEFEND = "defend_the_center.cfg"
+    DEATHMATCH = "deathmatch.cfg"
+    MY_WAY_HOME = "my_way_home.cfg"
+    TAKE_COVER = "take_cover.cfg"
+    HEALTH_GATHERING = "health_gathering.cfg"
+    HEALTH_GATHERING_SUPREME_COACH_LOCAL = "D2_navigation.cfg"  # from https://github.com/IntelVCL/DirectFuturePrediction/tree/master/maps
+    DEFEND_THE_LINE = "defend_the_line.cfg"
+    DEADLY_CORRIDOR = "deadly_corridor.cfg"
+    BATTLE_COACH_LOCAL = "D3_battle.cfg"  # from https://github.com/IntelVCL/DirectFuturePrediction/tree/master/maps
+
+key_map = {
+    'NO-OP': 96,  # `
+    'ATTACK': 13,  # enter
+    'CROUCH': 306,  # ctrl
+    'DROP_SELECTED_ITEM': ord("t"),
+    'DROP_SELECTED_WEAPON': ord("t"),
+    'JUMP': 32,  # spacebar
+    'LAND': ord("l"),
+    'LOOK_DOWN': 274,  # down arrow
+    'LOOK_UP': 273,  # up arrow
+    'MOVE_BACKWARD': ord("s"),
+    'MOVE_DOWN': ord("s"),
+    'MOVE_FORWARD': ord("w"),
+    'MOVE_LEFT': 276,
+    'MOVE_RIGHT': 275,
+    'MOVE_UP': ord("w"),
+    'RELOAD': ord("r"),
+    'SELECT_NEXT_WEAPON': ord("q"),
+    'SELECT_PREV_WEAPON': ord("e"),
+    'SELECT_WEAPON0': ord("0"),
+    'SELECT_WEAPON1': ord("1"),
+    'SELECT_WEAPON2': ord("2"),
+    'SELECT_WEAPON3': ord("3"),
+    'SELECT_WEAPON4': ord("4"),
+    'SELECT_WEAPON5': ord("5"),
+    'SELECT_WEAPON6': ord("6"),
+    'SELECT_WEAPON7': ord("7"),
+    'SELECT_WEAPON8': ord("8"),
+    'SELECT_WEAPON9': ord("9"),
+    'SPEED': 304,  # shift
+    'STRAFE': 9,  # tab
+    'TURN180': ord("u"),
+    'TURN_LEFT': ord("a"),  # left arrow
+    'TURN_RIGHT': ord("d"),  # right arrow
+    'USE': ord("f"),
+}
+
+
+DoomInputFilter = InputFilter(is_a_reference_filter=True)
+DoomInputFilter.add_observation_filter('observation', 'rescaling',
+                                       ObservationRescaleToSizeFilter(ImageObservationSpace(np.array([60, 76, 3]),
+                                                                                            high=255)))
+DoomInputFilter.add_observation_filter('observation', 'to_grayscale', ObservationRGBToYFilter())
+DoomInputFilter.add_observation_filter('observation', 'to_uint8', ObservationToUInt8Filter(0, 255))
+DoomInputFilter.add_observation_filter('observation', 'stacking', ObservationStackingFilter(3))
+
+
+DoomOutputFilter = OutputFilter(is_a_reference_filter=True)
+DoomOutputFilter.add_action_filter('to_discrete', FullDiscreteActionSpaceMap())
+
+
+class DoomEnvironmentParameters(EnvironmentParameters):
+    def __init__(self, level=None):
+        super().__init__(level=level)
+        self.default_input_filter = DoomInputFilter
+        self.default_output_filter = DoomOutputFilter
+        self.cameras = [DoomEnvironment.CameraTypes.OBSERVATION]
+
+    @property
+    def path(self):
+        return 'rl_coach.environments.doom_environment:DoomEnvironment'
+
+
+
[docs]class DoomEnvironment(Environment):
+    class CameraTypes(Enum):
+        OBSERVATION = ("observation", "screen_buffer")
+        DEPTH = ("depth", "depth_buffer")
+        LABELS = ("labels", "labels_buffer")
+        MAP = ("map", "automap_buffer")
+
+    def __init__(self, level: LevelSelection, seed: int, frame_skip: int, human_control: bool,
+                 custom_reward_threshold: Union[int, float], visualization_parameters: VisualizationParameters,
+                 cameras: List[CameraTypes], target_success_rate: float=1.0, **kwargs):
+        """
+        :param level: (str)
+            A string representing the doom level to run. This can also be a LevelSelection object.
+            This should be one of the levels defined in the DoomLevel enum. For example, HEALTH_GATHERING.
+
+        :param seed: (int)
+            A seed to use for the random number generator when running the environment.
+
+        :param frame_skip: (int)
+            The number of frames to skip between any two actions given by the agent. The action will be repeated
+            for all the skipped frames.
+
+        :param human_control: (bool)
+            A flag that allows controlling the environment using the keyboard keys.
+
+        :param custom_reward_threshold: (float)
+            Allows defining a custom reward that will be used to decide when the agent succeeded in passing the environment.
+
+        :param visualization_parameters: (VisualizationParameters)
+            The parameters used for visualizing the environment, such as the render flag, storing videos etc.
+
+        :param cameras: (List[CameraTypes])
+            A list of camera types to use as observation in the state returned from the environment.
+            Each camera should be an enum from CameraTypes, and there are several options like an RGB observation,
+            a depth map, a segmentation map, and a top down map of the enviornment.
+
+		:param target_success_rate: (float)
+			Stop experiment if given target success rate was achieved.
+
+        """
+        super().__init__(level, seed, frame_skip, human_control, custom_reward_threshold, visualization_parameters, target_success_rate)
+
+        self.cameras = cameras
+
+        # load the emulator with the required level
+        self.level = DoomLevel[level.upper()]
+        local_scenarios_path = path.join(os.path.dirname(os.path.realpath(__file__)), 'doom')
+        if 'COACH_LOCAL' in level:
+            self.scenarios_dir = local_scenarios_path
+        elif 'VIZDOOM_ROOT' in environ:
+            self.scenarios_dir = path.join(environ.get('VIZDOOM_ROOT'), 'scenarios')
+        else:
+            self.scenarios_dir = path.join(os.path.dirname(os.path.realpath(vizdoom.__file__)), 'scenarios')
+
+        self.game = vizdoom.DoomGame()
+        self.game.load_config(path.join(self.scenarios_dir, self.level.value))
+        self.game.set_window_visible(False)
+        self.game.add_game_args("+vid_forcesurface 1")
+
+        self.wait_for_explicit_human_action = True
+        if self.human_control:
+            self.game.set_screen_resolution(vizdoom.ScreenResolution.RES_640X480)
+        elif self.is_rendered:
+            self.game.set_screen_resolution(vizdoom.ScreenResolution.RES_320X240)
+        else:
+            # lower resolution since we actually take only 76x60 and we don't need to render
+            self.game.set_screen_resolution(vizdoom.ScreenResolution.RES_160X120)
+
+        self.game.set_render_hud(False)
+        self.game.set_render_crosshair(False)
+        self.game.set_render_decals(False)
+        self.game.set_render_particles(False)
+        for camera in self.cameras:
+            if hasattr(self.game, 'set_{}_enabled'.format(camera.value[1])):
+                getattr(self.game, 'set_{}_enabled'.format(camera.value[1]))(True)
+        self.game.init()
+
+        # actions
+        actions_description = ['NO-OP']
+        actions_description += [str(action).split(".")[1] for action in self.game.get_available_buttons()]
+        actions_description = actions_description[::-1]
+        self.action_space = MultiSelectActionSpace(self.game.get_available_buttons_size(),
+                                                   max_simultaneous_selected_actions=1,
+                                                   descriptions=actions_description,
+                                                   allow_no_action_to_be_selected=True)
+
+        # human control
+        if self.human_control:
+            # TODO: add this to the action space
+            # map keyboard keys to actions
+            for idx, action in enumerate(self.action_space.descriptions):
+                if action in key_map.keys():
+                    self.key_to_action[(key_map[action],)] = idx
+
+        # states
+        self.state_space = StateSpace({
+            "measurements": VectorObservationSpace(self.game.get_state().game_variables.shape[0],
+                                                   measurements_names=[str(m) for m in
+                                                                       self.game.get_available_game_variables()])
+        })
+        for camera in self.cameras:
+            self.state_space[camera.value[0]] = ImageObservationSpace(
+                shape=np.array([self.game.get_screen_height(), self.game.get_screen_width(), 3]),
+                high=255)
+
+        # seed
+        if seed is not None:
+            self.game.set_seed(seed)
+        self.reset_internal_state()
+
+        # render
+        if self.is_rendered:
+            image = self.get_rendered_image()
+            self.renderer.create_screen(image.shape[1], image.shape[0])
+
+        self.target_success_rate = target_success_rate
+
+    def _update_state(self):
+        # extract all data from the current state
+        state = self.game.get_state()
+        if state is not None and state.screen_buffer is not None:
+            self.measurements = state.game_variables
+            self.state = {'measurements': self.measurements}
+            for camera in self.cameras:
+                observation = getattr(state, camera.value[1])
+                if len(observation.shape) == 3:
+                    self.state[camera.value[0]] = np.transpose(observation, (1, 2, 0))
+                elif len(observation.shape) == 2:
+                    self.state[camera.value[0]] = np.repeat(np.expand_dims(observation, -1), 3, axis=-1)
+
+        self.reward = self.game.get_last_reward()
+        self.done = self.game.is_episode_finished()
+
+    def _take_action(self, action):
+        self.game.make_action(list(action), self.frame_skip)
+
+    def _restart_environment_episode(self, force_environment_reset=False):
+        self.game.new_episode()
+
+    def get_rendered_image(self) -> np.ndarray:
+        """
+        Return a numpy array containing the image that will be rendered to the screen.
+        This can be different from the observation. For example, mujoco's observation is a measurements vector.
+        :return: numpy array containing the image that will be rendered to the screen
+        """
+        image = [self.state[camera.value[0]] for camera in self.cameras]
+        image = np.vstack(image)
+        return image
+
+    def get_target_success_rate(self) -> float:
+        return self.target_success_rate

+
+ +
+ +
+ + +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_modules/rl_coach/environments/environment.html b/docs/_modules/rl_coach/environments/environment.html new file mode 100644 index 0000000..3f30f9f --- /dev/null +++ b/docs/_modules/rl_coach/environments/environment.html @@ -0,0 +1,721 @@ + + + + + + + + + + + rl_coach.environments.environment — Reinforcement Learning Coach 0.11.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + +
+ +
+ + + + + + + + + + + + + + + + + +
+ +
    + +
  • Docs »
    • + +
  • Module code »
    • + +
  • rl_coach.environments.environment
    • + + +
  • + +
    • + +
+ + +
+
+
+
+ +

Source code for rl_coach.environments.environment

+#
+# Copyright (c) 2017 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import operator
+import time
+from collections import OrderedDict
+from typing import Union, List, Tuple, Dict
+
+import numpy as np
+
+from rl_coach import logger
+from rl_coach.base_parameters import Parameters
+from rl_coach.base_parameters import VisualizationParameters
+from rl_coach.core_types import GoalType, ActionType, EnvResponse, RunPhase
+from rl_coach.environments.environment_interface import EnvironmentInterface
+from rl_coach.logger import screen
+from rl_coach.renderer import Renderer
+from rl_coach.spaces import ActionSpace, ObservationSpace, DiscreteActionSpace, RewardSpace, StateSpace
+from rl_coach.utils import squeeze_list, force_list
+
+
+class LevelSelection(object):
+    def __init__(self, level: str):
+        self.selected_level = level
+
+    def select(self, level: str):
+        self.selected_level = level
+
+    def __str__(self):
+        if self.selected_level is None:
+            logger.screen.error("No level has been selected. Please select a level using the -lvl command line flag, "
+                                "or change the level in the preset.", crash=True)
+        return self.selected_level
+
+
+class SingleLevelSelection(LevelSelection):
+    def __init__(self, levels: Union[str, List[str], Dict[str, str]]):
+        super().__init__(None)
+        self.levels = levels
+        if isinstance(levels, list):
+            self.levels = {level: level for level in levels}
+        if isinstance(levels, str):
+            self.levels = {levels: levels}
+
+    def __str__(self):
+        if self.selected_level is None:
+            logger.screen.error("No level has been selected. Please select a level using the -lvl command line flag, "
+                                "or change the level in the preset. \nThe available levels are: \n{}"
+                                .format(', '.join(sorted(self.levels.keys()))), crash=True)
+        if self.selected_level not in self.levels.keys():
+            logger.screen.error("The selected level ({}) is not part of the available levels ({})"
+                                .format(self.selected_level, ', '.join(self.levels.keys())), crash=True)
+        return self.levels[self.selected_level]
+
+
+# class SingleLevelPerPhase(LevelSelection):
+#     def __init__(self, levels: Dict[RunPhase, str]):
+#         super().__init__(None)
+#         self.levels = levels
+#
+#     def __str__(self):
+#         super().__str__()
+#         if self.selected_level not in self.levels.keys():
+#             logger.screen.error("The selected level ({}) is not part of the available levels ({})"
+#                                 .format(self.selected_level, self.levels.keys()), crash=True)
+#         return self.levels[self.selected_level]
+
+
+class CustomWrapper(object):
+    def __init__(self, environment):
+        super().__init__()
+        self.environment = environment
+
+    def __getattr__(self, attr):
+        if attr in self.__dict__:
+            return self.__dict__[attr]
+        else:
+            return getattr(self.environment, attr, False)
+
+
+class EnvironmentParameters(Parameters):
+    def __init__(self, level=None):
+        super().__init__()
+        self.level = level
+        self.frame_skip = 4
+        self.seed = None
+        self.human_control = False
+        self.custom_reward_threshold = None
+        self.default_input_filter = None
+        self.default_output_filter = None
+        self.experiment_path = None
+
+        # Set target reward and target_success if present
+        self.target_success_rate = 1.0
+
+    @property
+    def path(self):
+        return 'rl_coach.environments.environment:Environment'
+
+
+
[docs]class Environment(EnvironmentInterface):
+    def __init__(self, level: LevelSelection, seed: int, frame_skip: int, human_control: bool,
+                 custom_reward_threshold: Union[int, float], visualization_parameters: VisualizationParameters,
+                 target_success_rate: float=1.0, **kwargs):
+        """
+        :param level: The environment level. Each environment can have multiple levels
+        :param seed: a seed for the random number generator of the environment
+        :param frame_skip: number of frames to skip (while repeating the same action) between each two agent directives
+        :param human_control: human should control the environment
+        :param visualization_parameters: a blob of parameters used for visualization of the environment
+        :param **kwargs: as the class is instantiated by EnvironmentParameters, this is used to support having
+                         additional arguments which will be ignored by this class, but might be used by others
+        """
+        super().__init__()
+
+        # env initialization
+
+        self.game = []
+
+        self.state = {}
+        self.observation = None
+        self.goal = None
+        self.reward = 0
+        self.done = False
+        self.info = {}
+        self._last_env_response = None
+        self.last_action = 0
+        self.episode_idx = 0
+        self.total_steps_counter = 0
+        self.current_episode_steps_counter = 0
+        self.last_episode_time = time.time()
+        self.key_to_action = {}
+        self.last_episode_images = []
+
+        # rewards
+        self.total_reward_in_current_episode = 0
+        self.max_reward_achieved = -np.inf
+        self.reward_success_threshold = custom_reward_threshold
+
+        # spaces
+        self.state_space = self._state_space = None
+        self.goal_space = self._goal_space = None
+        self.action_space = self._action_space = None
+        self.reward_space = RewardSpace(1, reward_success_threshold=self.reward_success_threshold)  # TODO: add a getter and setter
+
+        self.env_id = str(level)
+        self.seed = seed
+        self.frame_skip = frame_skip
+
+        # human interaction and visualization
+        self.human_control = human_control
+        self.wait_for_explicit_human_action = False
+        self.is_rendered = visualization_parameters.render or self.human_control
+        self.native_rendering = visualization_parameters.native_rendering and not self.human_control
+        self.visualization_parameters = visualization_parameters
+        if not self.native_rendering:
+            self.renderer = Renderer()
+
+        # Set target reward and target_success if present
+        self.target_success_rate = target_success_rate
+
+    @property
+    def action_space(self) -> Union[List[ActionSpace], ActionSpace]:
+        """
+        Get the action space of the environment
+
+        :return: the action space
+        """
+        return self._action_space
+
+    @action_space.setter
+    def action_space(self, val: Union[List[ActionSpace], ActionSpace]):
+        """
+        Set the action space of the environment
+
+        :return: None
+        """
+        self._action_space = val
+
+    @property
+    def state_space(self) -> Union[List[StateSpace], StateSpace]:
+        """
+        Get the state space of the environment
+
+        :return: the observation space
+        """
+        return self._state_space
+
+    @state_space.setter
+    def state_space(self, val: Union[List[StateSpace], StateSpace]):
+        """
+        Set the state space of the environment
+
+        :return: None
+        """
+        self._state_space = val
+
+    @property
+    def goal_space(self) -> Union[List[ObservationSpace], ObservationSpace]:
+        """
+        Get the state space of the environment
+
+        :return: the observation space
+        """
+        return self._goal_space
+
+    @goal_space.setter
+    def goal_space(self, val: Union[List[ObservationSpace], ObservationSpace]):
+        """
+        Set the goal space of the environment
+
+        :return: None
+        """
+        self._goal_space = val
+
+
[docs]    def get_action_from_user(self) -> ActionType:
+        """
+        Get an action from the user keyboard
+
+        :return: action index
+        """
+        if self.wait_for_explicit_human_action:
+            while len(self.renderer.pressed_keys) == 0:
+                self.renderer.get_events()
+
+        if self.key_to_action == {}:
+            # the keys are the numbers on the keyboard corresponding to the action index
+            if len(self.renderer.pressed_keys) > 0:
+                action_idx = self.renderer.pressed_keys[0] - ord("1")
+                if 0 <= action_idx < self.action_space.shape[0]:
+                    return action_idx
+        else:
+            # the keys are mapped through the environment to more intuitive keyboard keys
+            # key = tuple(self.renderer.pressed_keys)
+            # for key in self.renderer.pressed_keys:
+            for env_keys in self.key_to_action.keys():
+                if set(env_keys) == set(self.renderer.pressed_keys):
+                    return self.action_space.actions[self.key_to_action[env_keys]]
+
+        # return the default action 0 so that the environment will continue running
+        return self.action_space.default_action

+
+    @property
+    def last_env_response(self) -> Union[List[EnvResponse], EnvResponse]:
+        """
+        Get the last environment response
+
+        :return: a dictionary that contains the state, reward, etc.
+        """
+        return squeeze_list(self._last_env_response)
+
+    @last_env_response.setter
+    def last_env_response(self, val: Union[List[EnvResponse], EnvResponse]):
+        """
+        Set the last environment response
+
+        :param val: the last environment response
+        """
+        self._last_env_response = force_list(val)
+
+
[docs]    def step(self, action: ActionType) -> EnvResponse:
+        """
+        Make a single step in the environment using the given action
+
+        :param action: an action to use for stepping the environment. Should follow the definition of the action space.
+        :return: the environment response as returned in get_last_env_response
+        """
+        action = self.action_space.clip_action_to_space(action)
+        if self.action_space and not self.action_space.val_matches_space_definition(action):
+            raise ValueError("The given action does not match the action space definition. "
+                             "Action = {}, action space definition = {}".format(action, self.action_space))
+
+        # store the last agent action done and allow passing None actions to repeat the previously done action
+        if action is None:
+            action = self.last_action
+        self.last_action = action
+        if self.visualization_parameters.add_rendered_image_to_env_response:
+            current_rendered_image = self.get_rendered_image()
+
+        self.current_episode_steps_counter += 1
+        if self.phase != RunPhase.UNDEFINED:
+            self.total_steps_counter += 1
+
+        # act
+        self._take_action(action)
+
+        # observe
+        self._update_state()
+
+        if self.is_rendered:
+            self.render()
+
+        self.total_reward_in_current_episode += self.reward
+
+        if self.visualization_parameters.add_rendered_image_to_env_response:
+            self.info['image'] = current_rendered_image
+
+        self.last_env_response = \
+            EnvResponse(
+                reward=self.reward,
+                next_state=self.state,
+                goal=self.goal,
+                game_over=self.done,
+                info=self.info
+            )
+
+        # store observations for video / gif dumping
+        if self.should_dump_video_of_the_current_episode(episode_terminated=False) and \
+            (self.visualization_parameters.dump_mp4 or self.visualization_parameters.dump_gifs):
+            self.last_episode_images.append(self.get_rendered_image())
+
+        return self.last_env_response

+
+
[docs]    def render(self) -> None:
+        """
+        Call the environment function for rendering to the screen
+
+        :return: None
+        """
+        if self.native_rendering:
+            self._render()
+        else:
+            self.renderer.render_image(self.get_rendered_image())

+
+
[docs]    def handle_episode_ended(self) -> None:
+        """
+        End an episode
+
+        :return: None
+        """
+        self.dump_video_of_last_episode_if_needed()

+
+
[docs]    def reset_internal_state(self, force_environment_reset=False) -> EnvResponse:
+        """
+        Reset the environment and all the variable of the wrapper
+
+        :param force_environment_reset: forces environment reset even when the game did not end
+        :return: A dictionary containing the observation, reward, done flag, action and measurements
+        """
+
+        self._restart_environment_episode(force_environment_reset)
+        self.last_episode_time = time.time()
+
+        if self.current_episode_steps_counter > 0 and self.phase != RunPhase.UNDEFINED:
+            self.episode_idx += 1
+
+        self.done = False
+        self.total_reward_in_current_episode = self.reward = 0.0
+        self.last_action = 0
+        self.current_episode_steps_counter = 0
+        self.last_episode_images = []
+        self._update_state()
+
+        # render before the preprocessing of the observation, so that the image will be in its original quality
+        if self.is_rendered:
+            self.render()
+
+        self.last_env_response = \
+            EnvResponse(
+                reward=self.reward,
+                next_state=self.state,
+                goal=self.goal,
+                game_over=self.done,
+                info=self.info
+            )
+
+        return self.last_env_response

+
+
[docs]    def get_random_action(self) -> ActionType:
+        """
+        Returns an action picked uniformly from the available actions
+
+        :return: a numpy array with a random action
+        """
+        return self.action_space.sample()

+
+
[docs]    def get_available_keys(self) -> List[Tuple[str, ActionType]]:
+        """
+        Return a list of tuples mapping between action names and the keyboard key that triggers them
+
+        :return: a list of tuples mapping between action names and the keyboard key that triggers them
+        """
+        available_keys = []
+        if self.key_to_action != {}:
+            for key, idx in sorted(self.key_to_action.items(), key=operator.itemgetter(1)):
+                if key != ():
+                    key_names = [self.renderer.get_key_names([k])[0] for k in key]
+                    available_keys.append((self.action_space.descriptions[idx], ' + '.join(key_names)))
+        elif type(self.action_space) == DiscreteActionSpace:
+            for action in range(self.action_space.shape):
+                available_keys.append(("Action {}".format(action + 1), action + 1))
+        return available_keys

+
+
[docs]    def get_goal(self) -> GoalType:
+        """
+        Get the current goal that the agents needs to achieve in the environment
+
+        :return: The goal
+        """
+        return self.goal

+
+
[docs]    def set_goal(self, goal: GoalType) -> None:
+        """
+        Set the current goal that the agent needs to achieve in the environment
+
+        :param goal: the goal that needs to be achieved
+        :return: None
+        """
+        self.goal = goal

+
+    def should_dump_video_of_the_current_episode(self, episode_terminated=False):
+        if self.visualization_parameters.video_dump_filters:
+            for video_dump_filter in force_list(self.visualization_parameters.video_dump_filters):
+                if not video_dump_filter.should_dump(episode_terminated, **self.__dict__):
+                    return False
+            return True
+        return True
+
+    def dump_video_of_last_episode_if_needed(self):
+        if self.last_episode_images != [] and self.should_dump_video_of_the_current_episode(episode_terminated=True):
+            self.dump_video_of_last_episode()
+
+    def dump_video_of_last_episode(self):
+        frame_skipping = max(1, int(5 / self.frame_skip))
+        file_name = 'episode-{}_score-{}'.format(self.episode_idx, self.total_reward_in_current_episode)
+        fps = 10
+        if self.visualization_parameters.dump_gifs:
+            logger.create_gif(self.last_episode_images[::frame_skipping], name=file_name, fps=fps)
+        if self.visualization_parameters.dump_mp4:
+            logger.create_mp4(self.last_episode_images[::frame_skipping], name=file_name, fps=fps)
+
+    # The following functions define the interaction with the environment.
+    # Any new environment that inherits the Environment class should use these signatures.
+    # Some of these functions are optional - please read their description for more details.
+
+    def _take_action(self, action_idx: ActionType) -> None:
+        """
+        An environment dependent function that sends an action to the simulator.
+
+        :param action_idx: the action to perform on the environment
+        :return: None
+        """
+        raise NotImplementedError("")
+
+    def _update_state(self) -> None:
+        """
+        Updates the state from the environment.
+        Should update self.observation, self.reward, self.done, self.measurements and self.info
+
+        :return: None
+        """
+        raise NotImplementedError("")
+
+    def _restart_environment_episode(self, force_environment_reset=False) -> None:
+        """
+        Restarts the simulator episode
+
+        :param force_environment_reset: Force the environment to reset even if the episode is not done yet.
+        :return: None
+        """
+        raise NotImplementedError("")
+
+    def _render(self) -> None:
+        """
+        Renders the environment using the native simulator renderer
+
+        :return: None
+        """
+        pass
+
+
[docs]    def get_rendered_image(self) -> np.ndarray:
+        """
+        Return a numpy array containing the image that will be rendered to the screen.
+        This can be different from the observation. For example, mujoco's observation is a measurements vector.
+
+        :return: numpy array containing the image that will be rendered to the screen
+        """
+        return np.transpose(self.state['observation'], [1, 2, 0])

+
+    def get_target_success_rate(self) -> float:
+        return self.target_success_rate

+
+ +
+ +
+ + +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_modules/rl_coach/environments/gym_environment.html b/docs/_modules/rl_coach/environments/gym_environment.html new file mode 100644 index 0000000..eea80ec --- /dev/null +++ b/docs/_modules/rl_coach/environments/gym_environment.html @@ -0,0 +1,703 @@ + + + + + + + + + + + rl_coach.environments.gym_environment — Reinforcement Learning Coach 0.11.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + +
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+ +
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    • + +
  • rl_coach.environments.gym_environment
    • + + +
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+ +

Source code for rl_coach.environments.gym_environment

+#
+# Copyright (c) 2017 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import gym
+import numpy as np
+import scipy.ndimage
+
+from rl_coach.graph_managers.graph_manager import ScheduleParameters
+from rl_coach.utils import lower_under_to_upper, short_dynamic_import
+
+try:
+    import roboschool
+    from OpenGL import GL
+except ImportError:
+    from rl_coach.logger import failed_imports
+    failed_imports.append("RoboSchool")
+
+try:
+    from rl_coach.gym_extensions.continuous import mujoco
+except:
+    from rl_coach.logger import failed_imports
+    failed_imports.append("GymExtensions")
+
+try:
+    import pybullet_envs
+except ImportError:
+    from rl_coach.logger import failed_imports
+    failed_imports.append("PyBullet")
+
+from typing import Dict, Any, Union
+from rl_coach.core_types import RunPhase, EnvironmentSteps
+from rl_coach.environments.environment import Environment, EnvironmentParameters, LevelSelection
+from rl_coach.spaces import DiscreteActionSpace, BoxActionSpace, ImageObservationSpace, VectorObservationSpace, \
+    StateSpace, RewardSpace
+from rl_coach.filters.filter import NoInputFilter, NoOutputFilter
+from rl_coach.filters.reward.reward_clipping_filter import RewardClippingFilter
+from rl_coach.filters.observation.observation_rescale_to_size_filter import ObservationRescaleToSizeFilter
+from rl_coach.filters.observation.observation_stacking_filter import ObservationStackingFilter
+from rl_coach.filters.observation.observation_rgb_to_y_filter import ObservationRGBToYFilter
+from rl_coach.filters.observation.observation_to_uint8_filter import ObservationToUInt8Filter
+from rl_coach.filters.filter import InputFilter
+import random
+from rl_coach.base_parameters import VisualizationParameters
+from rl_coach.logger import screen
+
+
+# Parameters
+class GymEnvironmentParameters(EnvironmentParameters):
+    def __init__(self, level=None):
+        super().__init__(level=level)
+        self.random_initialization_steps = 0
+        self.max_over_num_frames = 1
+        self.additional_simulator_parameters = {}
+
+    @property
+    def path(self):
+        return 'rl_coach.environments.gym_environment:GymEnvironment'
+
+
+# Generic parameters for vector environments such as mujoco, roboschool, bullet, etc.
+class GymVectorEnvironment(GymEnvironmentParameters):
+    def __init__(self, level=None):
+        super().__init__(level=level)
+        self.frame_skip = 1
+        self.default_input_filter = NoInputFilter()
+        self.default_output_filter = NoOutputFilter()
+
+
+# Roboschool
+gym_roboschool_envs = ['inverted_pendulum', 'inverted_pendulum_swingup', 'inverted_double_pendulum', 'reacher',
+                       'hopper', 'walker2d', 'half_cheetah', 'ant', 'humanoid', 'humanoid_flagrun',
+                       'humanoid_flagrun_harder', 'pong']
+roboschool_v0 = {e: "{}".format(lower_under_to_upper(e) + '-v0') for e in gym_roboschool_envs}
+
+# Mujoco
+gym_mujoco_envs = ['inverted_pendulum', 'inverted_double_pendulum', 'reacher', 'hopper', 'walker2d', 'half_cheetah',
+                   'ant', 'swimmer', 'humanoid', 'humanoid_standup', 'pusher', 'thrower', 'striker']
+
+mujoco_v2 = {e: "{}".format(lower_under_to_upper(e) + '-v2') for e in gym_mujoco_envs}
+mujoco_v2['walker2d'] = 'Walker2d-v2'
+
+# Fetch
+gym_fetch_envs = ['reach', 'slide', 'push', 'pick_and_place']
+fetch_v1 = {e: "{}".format('Fetch' + lower_under_to_upper(e) + '-v1') for e in gym_fetch_envs}
+
+
+"""
+Atari Environment Components
+"""
+
+AtariInputFilter = InputFilter(is_a_reference_filter=True)
+AtariInputFilter.add_reward_filter('clipping', RewardClippingFilter(-1.0, 1.0))
+AtariInputFilter.add_observation_filter('observation', 'rescaling',
+                                        ObservationRescaleToSizeFilter(ImageObservationSpace(np.array([84, 84, 3]),
+                                                                                             high=255)))
+AtariInputFilter.add_observation_filter('observation', 'to_grayscale', ObservationRGBToYFilter())
+AtariInputFilter.add_observation_filter('observation', 'to_uint8', ObservationToUInt8Filter(0, 255))
+AtariInputFilter.add_observation_filter('observation', 'stacking', ObservationStackingFilter(4))
+AtariOutputFilter = NoOutputFilter()
+
+
+class Atari(GymEnvironmentParameters):
+    def __init__(self, level=None):
+        super().__init__(level=level)
+        self.frame_skip = 4
+        self.max_over_num_frames = 2
+        self.random_initialization_steps = 30
+        self.default_input_filter = AtariInputFilter
+        self.default_output_filter = AtariOutputFilter
+
+
+gym_atari_envs = ['air_raid', 'alien', 'amidar', 'assault', 'asterix', 'asteroids', 'atlantis',
+                  'bank_heist', 'battle_zone', 'beam_rider', 'berzerk', 'bowling', 'boxing', 'breakout', 'carnival',
+                  'centipede', 'chopper_command', 'crazy_climber', 'demon_attack', 'double_dunk',
+                  'elevator_action', 'enduro', 'fishing_derby', 'freeway', 'frostbite', 'gopher', 'gravitar',
+                  'hero', 'ice_hockey', 'jamesbond', 'journey_escape', 'kangaroo', 'krull', 'kung_fu_master',
+                  'montezuma_revenge', 'ms_pacman', 'name_this_game', 'phoenix', 'pitfall', 'pong', 'pooyan',
+                  'private_eye', 'qbert', 'riverraid', 'road_runner', 'robotank', 'seaquest', 'skiing',
+                  'solaris', 'space_invaders', 'star_gunner', 'tennis', 'time_pilot', 'tutankham', 'up_n_down',
+                  'venture', 'video_pinball', 'wizard_of_wor', 'yars_revenge', 'zaxxon']
+atari_deterministic_v4 = {e: "{}".format(lower_under_to_upper(e) + 'Deterministic-v4') for e in gym_atari_envs}
+atari_no_frameskip_v4 = {e: "{}".format(lower_under_to_upper(e) + 'NoFrameskip-v4') for e in gym_atari_envs}
+
+
+# default atari schedule used in the DeepMind papers
+atari_schedule = ScheduleParameters()
+atari_schedule.improve_steps = EnvironmentSteps(50000000)
+atari_schedule.steps_between_evaluation_periods = EnvironmentSteps(250000)
+atari_schedule.evaluation_steps = EnvironmentSteps(135000)
+atari_schedule.heatup_steps = EnvironmentSteps(1)
+
+
+class MaxOverFramesAndFrameskipEnvWrapper(gym.Wrapper):
+    def __init__(self, env, frameskip=4, max_over_num_frames=2):
+        super().__init__(env)
+        self.max_over_num_frames = max_over_num_frames
+        self.observations_stack = []
+        self.frameskip = frameskip
+        self.first_frame_to_max_over = self.frameskip - self.max_over_num_frames
+
+    def reset(self):
+        return self.env.reset()
+
+    def step(self, action):
+        total_reward = 0.0
+        done = None
+        info = None
+        self.observations_stack = []
+        for i in range(self.frameskip):
+            observation, reward, done, info = self.env.step(action)
+            if i >= self.first_frame_to_max_over:
+                self.observations_stack.append(observation)
+            total_reward += reward
+            if done:
+                # deal with last state in episode
+                if not self.observations_stack:
+                    self.observations_stack.append(observation)
+                break
+
+        max_over_frames_observation = np.max(self.observations_stack, axis=0)
+
+        return max_over_frames_observation, total_reward, done, info
+
+
+# Environment
+
[docs]class GymEnvironment(Environment):
+    def __init__(self, level: LevelSelection, frame_skip: int, visualization_parameters: VisualizationParameters,
+                 target_success_rate: float=1.0, additional_simulator_parameters: Dict[str, Any] = {}, seed: Union[None, int]=None,
+                 human_control: bool=False, custom_reward_threshold: Union[int, float]=None,
+                 random_initialization_steps: int=1, max_over_num_frames: int=1, **kwargs):
+        """
+        :param level: (str)
+            A string representing the gym level to run. This can also be a LevelSelection object.
+            For example, BreakoutDeterministic-v0
+
+        :param frame_skip: (int)
+            The number of frames to skip between any two actions given by the agent. The action will be repeated
+            for all the skipped frames.
+
+        :param visualization_parameters: (VisualizationParameters)
+            The parameters used for visualizing the environment, such as the render flag, storing videos etc.
+
+        :param additional_simulator_parameters: (Dict[str, Any])
+            Any additional parameters that the user can pass to the Gym environment. These parameters should be
+            accepted by the __init__ function of the implemented Gym environment.
+
+        :param seed: (int)
+            A seed to use for the random number generator when running the environment.
+
+        :param human_control: (bool)
+            A flag that allows controlling the environment using the keyboard keys.
+
+        :param custom_reward_threshold: (float)
+            Allows defining a custom reward that will be used to decide when the agent succeeded in passing the environment.
+            If not set, this value will be taken from the Gym environment definition.
+
+        :param random_initialization_steps: (int)
+            The number of random steps that will be taken in the environment after each reset.
+            This is a feature presented in the DQN paper, which improves the variability of the episodes the agent sees.
+
+        :param max_over_num_frames: (int)
+            This value will be used for merging multiple frames into a single frame by taking the maximum value for each
+            of the pixels in the frame. This is particularly used in Atari games, where the frames flicker, and objects
+            can be seen in one frame but disappear in the next.
+        """
+        super().__init__(level, seed, frame_skip, human_control, custom_reward_threshold,
+                         visualization_parameters, target_success_rate)
+
+        self.random_initialization_steps = random_initialization_steps
+        self.max_over_num_frames = max_over_num_frames
+        self.additional_simulator_parameters = additional_simulator_parameters
+
+        # hide warnings
+        gym.logger.set_level(40)
+
+        """
+        load and initialize environment
+        environment ids can be defined in 3 ways:
+        1. Native gym environments like BreakoutDeterministic-v0 for example
+        2. Custom gym environments written and installed as python packages.
+           This environments should have a python module with a class inheriting gym.Env, implementing the
+           relevant functions (_reset, _step, _render) and defining the observation and action space
+           For example: my_environment_package:MyEnvironmentClass will run an environment defined in the
+           MyEnvironmentClass class
+        3. Custom gym environments written as an independent module which is not installed.
+           This environments should have a python module with a class inheriting gym.Env, implementing the
+           relevant functions (_reset, _step, _render) and defining the observation and action space.
+           For example: path_to_my_environment.sub_directory.my_module:MyEnvironmentClass will run an
+           environment defined in the MyEnvironmentClass class which is located in the module in the relative path
+           path_to_my_environment.sub_directory.my_module
+        """
+        if ':' in self.env_id:
+            # custom environments
+            if '/' in self.env_id or '.' in self.env_id:
+                # environment in a an absolute path module written as a unix path or in a relative path module
+                # written as a python import path
+                env_class = short_dynamic_import(self.env_id)
+            else:
+                # environment in a python package
+                env_class = gym.envs.registration.load(self.env_id)
+
+            # instantiate the environment
+            try:
+                self.env = env_class(**self.additional_simulator_parameters)
+            except:
+                screen.error("Failed to instantiate Gym environment class %s with arguments %s" %
+                             (env_class, self.additional_simulator_parameters), crash=False)
+                raise
+        else:
+            self.env = gym.make(self.env_id)
+
+        # for classic control we want to use the native renderer because otherwise we will get 2 renderer windows
+        environment_to_always_use_with_native_rendering = ['classic_control', 'mujoco', 'robotics']
+        self.native_rendering = self.native_rendering or \
+                                any([env in str(self.env.unwrapped.__class__)
+                                     for env in environment_to_always_use_with_native_rendering])
+        if self.native_rendering:
+            if hasattr(self, 'renderer'):
+                self.renderer.close()
+
+        # seed
+        if self.seed is not None:
+            self.env.seed(self.seed)
+            np.random.seed(self.seed)
+            random.seed(self.seed)
+
+        # frame skip and max between consecutive frames
+        self.is_robotics_env = 'robotics' in str(self.env.unwrapped.__class__)
+        self.is_mujoco_env = 'mujoco' in str(self.env.unwrapped.__class__)
+        self.is_atari_env = 'Atari' in str(self.env.unwrapped.__class__)
+        self.timelimit_env_wrapper = self.env
+        if self.is_atari_env:
+            self.env.unwrapped.frameskip = 1  # this accesses the atari env that is wrapped with a timelimit wrapper env
+            if self.env_id == "SpaceInvadersDeterministic-v4" and self.frame_skip == 4:
+                screen.warning("Warning: The frame-skip for Space Invaders was automatically updated from 4 to 3. "
+                               "This is following the DQN paper where it was noticed that a frame-skip of 3 makes the "
+                               "laser rays disappear. To force frame-skip of 4, please use SpaceInvadersNoFrameskip-v4.")
+                self.frame_skip = 3
+            self.env = MaxOverFramesAndFrameskipEnvWrapper(self.env,
+                                                           frameskip=self.frame_skip,
+                                                           max_over_num_frames=self.max_over_num_frames)
+        else:
+            self.env.unwrapped.frameskip = self.frame_skip
+
+        self.state_space = StateSpace({})
+
+        # observations
+        if not isinstance(self.env.observation_space, gym.spaces.dict_space.Dict):
+            state_space = {'observation': self.env.observation_space}
+        else:
+            state_space = self.env.observation_space.spaces
+
+        for observation_space_name, observation_space in state_space.items():
+            if len(observation_space.shape) == 3:
+                # we assume gym has image observations (with arbitrary number of channels) where their values are
+                # within 0-255, and where the channel dimension is the last dimension
+                self.state_space[observation_space_name] = ImageObservationSpace(
+                    shape=np.array(observation_space.shape),
+                    high=255,
+                    channels_axis=-1
+                )
+            else:
+                self.state_space[observation_space_name] = VectorObservationSpace(
+                    shape=observation_space.shape[0],
+                    low=observation_space.low,
+                    high=observation_space.high
+                )
+        if 'desired_goal' in state_space.keys():
+            self.goal_space = self.state_space['desired_goal']
+
+        # actions
+        if type(self.env.action_space) == gym.spaces.box.Box:
+            self.action_space = BoxActionSpace(
+                shape=self.env.action_space.shape,
+                low=self.env.action_space.low,
+                high=self.env.action_space.high
+            )
+        elif type(self.env.action_space) == gym.spaces.discrete.Discrete:
+            actions_description = []
+            if hasattr(self.env.unwrapped, 'get_action_meanings'):
+                actions_description = self.env.unwrapped.get_action_meanings()
+            self.action_space = DiscreteActionSpace(
+                num_actions=self.env.action_space.n,
+                descriptions=actions_description
+            )
+
+        if self.human_control:
+            # TODO: add this to the action space
+            # map keyboard keys to actions
+            self.key_to_action = {}
+            if hasattr(self.env.unwrapped, 'get_keys_to_action'):
+                self.key_to_action = self.env.unwrapped.get_keys_to_action()
+            else:
+                screen.error("Error: Environment {} does not support human control.".format(self.env), crash=True)
+
+        # initialize the state by getting a new state from the environment
+        self.reset_internal_state(True)
+
+        # render
+        if self.is_rendered:
+            image = self.get_rendered_image()
+            scale = 1
+            if self.human_control:
+                scale = 2
+            if not self.native_rendering:
+                self.renderer.create_screen(image.shape[1]*scale, image.shape[0]*scale)
+
+        # measurements
+        if self.env.spec is not None:
+            self.timestep_limit = self.env.spec.timestep_limit
+        else:
+            self.timestep_limit = None
+
+        # the info is only updated after the first step
+        self.state = self.step(self.action_space.default_action).next_state
+        self.state_space['measurements'] = VectorObservationSpace(shape=len(self.info.keys()))
+
+        if self.env.spec and custom_reward_threshold is None:
+                self.reward_success_threshold = self.env.spec.reward_threshold
+                self.reward_space = RewardSpace(1, reward_success_threshold=self.reward_success_threshold)
+
+        self.target_success_rate = target_success_rate
+
+    def _wrap_state(self, state):
+        if not isinstance(self.env.observation_space, gym.spaces.Dict):
+            return {'observation': state}
+        return state
+
+    def _update_state(self):
+        if self.is_atari_env and hasattr(self, 'current_ale_lives') \
+                and self.current_ale_lives != self.env.unwrapped.ale.lives():
+            if self.phase == RunPhase.TRAIN or self.phase == RunPhase.HEATUP:
+                # signal termination for life loss
+                self.done = True
+            elif self.phase == RunPhase.TEST and not self.done:
+                # the episode is not terminated in evaluation, but we need to press fire again
+                self._press_fire()
+            self._update_ale_lives()
+        # TODO: update the measurements
+        if self.state and "desired_goal" in self.state.keys():
+            self.goal = self.state['desired_goal']
+
+    def _take_action(self, action):
+        if type(self.action_space) == BoxActionSpace:
+            action = self.action_space.clip_action_to_space(action)
+
+        self.state, self.reward, self.done, self.info = self.env.step(action)
+        self.state = self._wrap_state(self.state)
+
+    def _random_noop(self):
+        # simulate a random initial environment state by stepping for a random number of times between 0 and 30
+        step_count = 0
+        random_initialization_steps = random.randint(0, self.random_initialization_steps)
+        while self.action_space is not None and (self.state is None or step_count < random_initialization_steps):
+            step_count += 1
+            self.step(self.action_space.default_action)
+
+    def _press_fire(self):
+        fire_action = 1
+        if self.is_atari_env and self.env.unwrapped.get_action_meanings()[fire_action] == 'FIRE':
+            self.current_ale_lives = self.env.unwrapped.ale.lives()
+            self.step(fire_action)
+            if self.done:
+                self.reset_internal_state()
+
+    def _update_ale_lives(self):
+        if self.is_atari_env:
+            self.current_ale_lives = self.env.unwrapped.ale.lives()
+
+    def _restart_environment_episode(self, force_environment_reset=False):
+        # prevent reset of environment if there are ale lives left
+        if (self.is_atari_env and self.env.unwrapped.ale.lives() > 0) \
+                and not force_environment_reset and not self.timelimit_env_wrapper._past_limit():
+            self.step(self.action_space.default_action)
+        else:
+            self.state = self.env.reset()
+            self.state = self._wrap_state(self.state)
+            self._update_ale_lives()
+
+        if self.is_atari_env:
+            self._random_noop()
+            self._press_fire()
+
+        # initialize the number of lives
+        self._update_ale_lives()
+
+    def _set_mujoco_camera(self, camera_idx: int):
+        """
+        This function can be used to set the camera for rendering the mujoco simulator
+        :param camera_idx: The index of the camera to use. Should be defined in the model
+        :return: None
+        """
+        if self.env.unwrapped.viewer is not None and self.env.unwrapped.viewer.cam.fixedcamid != camera_idx and\
+                self.env.unwrapped.viewer._ncam > camera_idx:
+            from mujoco_py.generated import const
+            self.env.unwrapped.viewer.cam.type = const.CAMERA_FIXED
+            self.env.unwrapped.viewer.cam.fixedcamid = camera_idx
+
+    def _get_robotics_image(self):
+        self.env.render()
+        image = self.env.unwrapped._get_viewer().read_pixels(1600, 900, depth=False)[::-1, :, :]
+        image = scipy.misc.imresize(image, (270, 480, 3))
+        return image
+
+    def _render(self):
+        self.env.render(mode='human')
+        # required for setting up a fixed camera for mujoco
+        if self.is_mujoco_env:
+            self._set_mujoco_camera(0)
+
+    def get_rendered_image(self):
+        if self.is_robotics_env:
+            # necessary for fetch since the rendered image is cropped to an irrelevant part of the simulator
+            image = self._get_robotics_image()
+        else:
+            image = self.env.render(mode='rgb_array')
+        # required for setting up a fixed camera for mujoco
+        if self.is_mujoco_env:
+            self._set_mujoco_camera(0)
+        return image
+
+    def get_target_success_rate(self) -> float:
+        return self.target_success_rate

+
+ +
+ +
+ + +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_modules/rl_coach/environments/starcraft2_environment.html b/docs/_modules/rl_coach/environments/starcraft2_environment.html new file mode 100644 index 0000000..3e3d8c8 --- /dev/null +++ b/docs/_modules/rl_coach/environments/starcraft2_environment.html @@ -0,0 +1,478 @@ + + + + + + + + + + + rl_coach.environments.starcraft2_environment — Reinforcement Learning Coach 0.11.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + +
+ +
+ + + + + + + + + + + + + + + + + +
+ +
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    • + +
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    • + +
  • rl_coach.environments.starcraft2_environment
    • + + +
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+ + +
+
+
+
+ +

Source code for rl_coach.environments.starcraft2_environment

+#
+# Copyright (c) 2017 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+
+from enum import Enum
+from typing import Union, List
+
+import numpy as np
+
+from rl_coach.filters.observation.observation_move_axis_filter import ObservationMoveAxisFilter
+
+try:
+    from pysc2 import maps
+    from pysc2.env import sc2_env
+    from pysc2.env import available_actions_printer
+    from pysc2.lib import actions
+    from pysc2.lib import features
+    from pysc2.env import environment
+    from absl import app
+    from absl import flags
+except ImportError:
+    from rl_coach.logger import failed_imports
+    failed_imports.append("PySc2")
+
+from rl_coach.environments.environment import Environment, EnvironmentParameters, LevelSelection
+from rl_coach.base_parameters import VisualizationParameters
+from rl_coach.spaces import BoxActionSpace, VectorObservationSpace, PlanarMapsObservationSpace, StateSpace, CompoundActionSpace, \
+    DiscreteActionSpace
+from rl_coach.filters.filter import InputFilter, OutputFilter
+from rl_coach.filters.observation.observation_rescale_to_size_filter import ObservationRescaleToSizeFilter
+from rl_coach.filters.action.linear_box_to_box_map import LinearBoxToBoxMap
+from rl_coach.filters.observation.observation_to_uint8_filter import ObservationToUInt8Filter
+
+FLAGS = flags.FLAGS
+FLAGS(['coach.py'])
+
+SCREEN_SIZE = 84  # will also impact the action space size
+
+# Starcraft Constants
+_NOOP = actions.FUNCTIONS.no_op.id
+_MOVE_SCREEN = actions.FUNCTIONS.Move_screen.id
+_SELECT_ARMY = actions.FUNCTIONS.select_army.id
+_PLAYER_RELATIVE = features.SCREEN_FEATURES.player_relative.index
+_NOT_QUEUED = [0]
+_SELECT_ALL = [0]
+
+
+class StarcraftObservationType(Enum):
+    Features = 0
+    RGB = 1
+
+
+StarcraftInputFilter = InputFilter(is_a_reference_filter=True)
+StarcraftInputFilter.add_observation_filter('screen', 'move_axis', ObservationMoveAxisFilter(0, -1))
+StarcraftInputFilter.add_observation_filter('screen', 'rescaling',
+                                            ObservationRescaleToSizeFilter(
+                                                PlanarMapsObservationSpace(np.array([84, 84, 1]),
+                                                                           low=0, high=255, channels_axis=-1)))
+StarcraftInputFilter.add_observation_filter('screen', 'to_uint8', ObservationToUInt8Filter(0, 255))
+
+StarcraftInputFilter.add_observation_filter('minimap', 'move_axis', ObservationMoveAxisFilter(0, -1))
+StarcraftInputFilter.add_observation_filter('minimap', 'rescaling',
+                                            ObservationRescaleToSizeFilter(
+                                                PlanarMapsObservationSpace(np.array([64, 64, 1]),
+                                                                           low=0, high=255, channels_axis=-1)))
+StarcraftInputFilter.add_observation_filter('minimap', 'to_uint8', ObservationToUInt8Filter(0, 255))
+
+
+StarcraftNormalizingOutputFilter = OutputFilter(is_a_reference_filter=True)
+StarcraftNormalizingOutputFilter.add_action_filter(
+    'normalization', LinearBoxToBoxMap(input_space_low=-SCREEN_SIZE / 2, input_space_high=SCREEN_SIZE / 2 - 1))
+
+
+class StarCraft2EnvironmentParameters(EnvironmentParameters):
+    def __init__(self, level=None):
+        super().__init__(level=level)
+        self.screen_size = 84
+        self.minimap_size = 64
+        self.feature_minimap_maps_to_use = range(7)
+        self.feature_screen_maps_to_use = range(17)
+        self.observation_type = StarcraftObservationType.Features
+        self.disable_fog = False
+        self.auto_select_all_army = True
+        self.default_input_filter = StarcraftInputFilter
+        self.default_output_filter = StarcraftNormalizingOutputFilter
+        self.use_full_action_space = False
+
+
+    @property
+    def path(self):
+        return 'rl_coach.environments.starcraft2_environment:StarCraft2Environment'
+
+
+# Environment
+
[docs]class StarCraft2Environment(Environment):
+    def __init__(self, level: LevelSelection, frame_skip: int, visualization_parameters: VisualizationParameters,
+                 target_success_rate: float=1.0, seed: Union[None, int]=None, human_control: bool=False,
+                 custom_reward_threshold: Union[int, float]=None,
+                 screen_size: int=84, minimap_size: int=64,
+                 feature_minimap_maps_to_use: List=range(7), feature_screen_maps_to_use: List=range(17),
+                 observation_type: StarcraftObservationType=StarcraftObservationType.Features,
+                 disable_fog: bool=False, auto_select_all_army: bool=True,
+                 use_full_action_space: bool=False, **kwargs):
+        super().__init__(level, seed, frame_skip, human_control, custom_reward_threshold, visualization_parameters, target_success_rate)
+
+        self.screen_size = screen_size
+        self.minimap_size = minimap_size
+        self.feature_minimap_maps_to_use = feature_minimap_maps_to_use
+        self.feature_screen_maps_to_use = feature_screen_maps_to_use
+        self.observation_type = observation_type
+        self.features_screen_size = None
+        self.feature_minimap_size = None
+        self.rgb_screen_size = None
+        self.rgb_minimap_size = None
+        if self.observation_type == StarcraftObservationType.Features:
+            self.features_screen_size = screen_size
+            self.feature_minimap_size = minimap_size
+        elif self.observation_type == StarcraftObservationType.RGB:
+            self.rgb_screen_size = screen_size
+            self.rgb_minimap_size = minimap_size
+        self.disable_fog = disable_fog
+        self.auto_select_all_army = auto_select_all_army
+        self.use_full_action_space = use_full_action_space
+
+        # step_mul is the equivalent to frame skipping. Not sure if it repeats actions in between or not though.
+        self.env = sc2_env.SC2Env(map_name=self.env_id, step_mul=frame_skip,
+                                  visualize=self.is_rendered,
+                                  agent_interface_format=sc2_env.AgentInterfaceFormat(
+                                      feature_dimensions=sc2_env.Dimensions(
+                                          screen=self.features_screen_size,
+                                          minimap=self.feature_minimap_size
+                                      )
+                                      # rgb_dimensions=sc2_env.Dimensions(
+                                      #     screen=self.rgb_screen_size,
+                                      #     minimap=self.rgb_screen_size
+                                      # )
+                                  ),
+                                  # feature_screen_size=self.features_screen_size,
+                                  # feature_minimap_size=self.feature_minimap_size,
+                                  # rgb_screen_size=self.rgb_screen_size,
+                                  # rgb_minimap_size=self.rgb_screen_size,
+                                  disable_fog=disable_fog,
+                                  random_seed=self.seed
+                                  )
+
+        # print all the available actions
+        # self.env = available_actions_printer.AvailableActionsPrinter(self.env)
+
+        self.reset_internal_state(True)
+
+        """
+        feature_screen:  [height_map, visibility_map, creep, power, player_id, player_relative, unit_type, selected,
+                          unit_hit_points, unit_hit_points_ratio, unit_energy, unit_energy_ratio, unit_shields,
+                          unit_shields_ratio, unit_density, unit_density_aa, effects]
+        feature_minimap: [height_map, visibility_map, creep, camera, player_id, player_relative, selecte
+        d]
+        player:          [player_id, minerals, vespene, food_cap, food_army, food_workers, idle_worker_dount,
+                          army_count, warp_gate_count, larva_count]
+        """
+        self.screen_shape = np.array(self.env.observation_spec()[0]['feature_screen'])
+        self.screen_shape[0] = len(self.feature_screen_maps_to_use)
+        self.minimap_shape = np.array(self.env.observation_spec()[0]['feature_minimap'])
+        self.minimap_shape[0] = len(self.feature_minimap_maps_to_use)
+        self.state_space = StateSpace({
+            "screen": PlanarMapsObservationSpace(shape=self.screen_shape, low=0, high=255, channels_axis=0),
+            "minimap": PlanarMapsObservationSpace(shape=self.minimap_shape, low=0, high=255, channels_axis=0),
+            "measurements": VectorObservationSpace(self.env.observation_spec()[0]["player"][0])
+        })
+        if self.use_full_action_space:
+            action_identifiers = list(self.env.action_spec()[0].functions)
+            num_action_identifiers = len(action_identifiers)
+            action_arguments = [(arg.name, arg.sizes) for arg in self.env.action_spec()[0].types]
+            sub_action_spaces = [DiscreteActionSpace(num_action_identifiers)]
+            for argument in action_arguments:
+                for dimension in argument[1]:
+                    sub_action_spaces.append(DiscreteActionSpace(dimension))
+            self.action_space = CompoundActionSpace(sub_action_spaces)
+        else:
+            self.action_space = BoxActionSpace(2, 0, self.screen_size - 1, ["X-Axis, Y-Axis"],
+                                               default_action=np.array([self.screen_size/2, self.screen_size/2]))
+
+        self.target_success_rate = target_success_rate
+
+    def _update_state(self):
+        timestep = 0
+        self.screen = self.last_result[timestep].observation.feature_screen
+        # extract only the requested segmentation maps from the observation
+        self.screen = np.take(self.screen, self.feature_screen_maps_to_use, axis=0)
+        self.minimap = self.last_result[timestep].observation.feature_minimap
+        self.measurements = self.last_result[timestep].observation.player
+        self.reward = self.last_result[timestep].reward
+        self.done = self.last_result[timestep].step_type == environment.StepType.LAST
+        self.state = {
+            'screen': self.screen,
+            'minimap': self.minimap,
+            'measurements': self.measurements
+        }
+
+    def _take_action(self, action):
+        if self.use_full_action_space:
+            action_identifier = action[0]
+            action_arguments = action[1:]
+            action = actions.FunctionCall(action_identifier, action_arguments)
+        else:
+            coord = np.array(action[0:2])
+            noop = False
+            coord = coord.round()
+            coord = np.clip(coord, 0, SCREEN_SIZE - 1)
+            self.last_action_idx = coord
+
+            if noop:
+                action = actions.FunctionCall(_NOOP, [])
+            else:
+                action = actions.FunctionCall(_MOVE_SCREEN, [_NOT_QUEUED, coord])
+
+        self.last_result = self.env.step(actions=[action])
+
+    def _restart_environment_episode(self, force_environment_reset=False):
+        # reset the environment
+        self.last_result = self.env.reset()
+
+        # select all the units on the screen
+        if self.auto_select_all_army:
+            self.env.step(actions=[actions.FunctionCall(_SELECT_ARMY, [_SELECT_ALL])])
+
+    def get_rendered_image(self):
+        screen = np.squeeze(np.tile(np.expand_dims(self.screen, -1), (1, 1, 3)))
+        screen = screen / np.max(screen) * 255
+        return screen.astype('uint8')
+
+    def dump_video_of_last_episode(self):
+        from rl_coach.logger import experiment_path
+        self.env._run_config.replay_dir = experiment_path
+        self.env.save_replay('replays')
+        super().dump_video_of_last_episode()
+
+    def get_target_success_rate(self):
+        return self.target_success_rate

+
+ +
+ +
+ + +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_modules/rl_coach/exploration_policies/additive_noise.html b/docs/_modules/rl_coach/exploration_policies/additive_noise.html new file mode 100644 index 0000000..a27fc99 --- /dev/null +++ b/docs/_modules/rl_coach/exploration_policies/additive_noise.html @@ -0,0 +1,330 @@ + + + + + + + + + + + rl_coach.exploration_policies.additive_noise — Reinforcement Learning Coach 0.11.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + +
+ +
+ + + + + + + + + + + + + + + + + +
+ +
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  • Docs »
    • + +
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    • + +
  • rl_coach.exploration_policies.additive_noise
    • + + +
  • + +
    • + +
+ + +
+
+
+
+ +

Source code for rl_coach.exploration_policies.additive_noise

+#
+# Copyright (c) 2017 Intel Corporation 
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from typing import List
+
+import numpy as np
+
+from rl_coach.core_types import RunPhase, ActionType
+from rl_coach.exploration_policies.exploration_policy import ExplorationPolicy, ExplorationParameters
+from rl_coach.schedules import Schedule, LinearSchedule
+from rl_coach.spaces import ActionSpace, BoxActionSpace
+
+
+# TODO: consider renaming to gaussian sampling
+class AdditiveNoiseParameters(ExplorationParameters):
+    def __init__(self):
+        super().__init__()
+        self.noise_percentage_schedule = LinearSchedule(0.1, 0.1, 50000)
+        self.evaluation_noise_percentage = 0.05
+
+    @property
+    def path(self):
+        return 'rl_coach.exploration_policies.additive_noise:AdditiveNoise'
+
+
+
[docs]class AdditiveNoise(ExplorationPolicy):
+    """
+    AdditiveNoise is an exploration policy intended for continuous action spaces. It takes the action from the agent
+    and adds a Gaussian distributed noise to it. The amount of noise added to the action follows the noise amount that
+    can be given in two different ways:
+    1. Specified by the user as a noise schedule which is taken in percentiles out of the action space size
+    2. Specified by the agents action. In case the agents action is a list with 2 values, the 1st one is assumed to
+    be the mean of the action, and 2nd is assumed to be its standard deviation.
+    """
+    def __init__(self, action_space: ActionSpace, noise_percentage_schedule: Schedule,
+                 evaluation_noise_percentage: float):
+        """
+        :param action_space: the action space used by the environment
+        :param noise_percentage_schedule: the schedule for the noise variance percentage relative to the absolute range
+                                          of the action space
+        :param evaluation_noise_percentage: the noise variance percentage that will be used during evaluation phases
+        """
+        super().__init__(action_space)
+        self.noise_percentage_schedule = noise_percentage_schedule
+        self.evaluation_noise_percentage = evaluation_noise_percentage
+
+        if not isinstance(action_space, BoxActionSpace):
+            raise ValueError("Additive noise exploration works only for continuous controls."
+                             "The given action space is of type: {}".format(action_space.__class__.__name__))
+
+        if not np.all(-np.inf < action_space.high) or not np.all(action_space.high < np.inf)\
+                or not np.all(-np.inf < action_space.low) or not np.all(action_space.low < np.inf):
+            raise ValueError("Additive noise exploration requires bounded actions")
+
+        # TODO: allow working with unbounded actions by defining the noise in terms of range and not percentage
+
+    def get_action(self, action_values: List[ActionType]) -> ActionType:
+        # TODO-potential-bug consider separating internally defined stdev and externally defined stdev into 2 policies
+
+        # set the current noise percentage
+        if self.phase == RunPhase.TEST:
+            current_noise_precentage = self.evaluation_noise_percentage
+        else:
+            current_noise_precentage = self.noise_percentage_schedule.current_value
+
+        # scale the noise to the action space range
+        action_values_std = current_noise_precentage * (self.action_space.high - self.action_space.low)
+
+        # extract the mean values
+        if isinstance(action_values, list):
+            # the action values are expected to be a list with the action mean and optionally the action stdev
+            action_values_mean = action_values[0].squeeze()
+        else:
+            # the action values are expected to be a numpy array representing the action mean
+            action_values_mean = action_values.squeeze()
+
+        # step the noise schedule
+        if self.phase == RunPhase.TRAIN:
+            self.noise_percentage_schedule.step()
+            # the second element of the list is assumed to be the standard deviation
+            if isinstance(action_values, list) and len(action_values) > 1:
+                action_values_std = action_values[1].squeeze()
+
+        # add noise to the action means
+        action = np.random.normal(action_values_mean, action_values_std)
+
+        return action
+
+    def get_control_param(self):
+        return np.ones(self.action_space.shape)*self.noise_percentage_schedule.current_value

+
+ +
+ +
+ + +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_modules/rl_coach/exploration_policies/boltzmann.html b/docs/_modules/rl_coach/exploration_policies/boltzmann.html new file mode 100644 index 0000000..4affe8f --- /dev/null +++ b/docs/_modules/rl_coach/exploration_policies/boltzmann.html @@ -0,0 +1,292 @@ + + + + + + + + + + + rl_coach.exploration_policies.boltzmann — Reinforcement Learning Coach 0.11.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + +
+ +
+ + + + + + + + + + + + + + + + + +
+ +
    + +
  • Docs »
    • + +
  • Module code »
    • + +
  • rl_coach.exploration_policies.boltzmann
    • + + +
  • + +
    • + +
+ + +
+
+
+
+ +

Source code for rl_coach.exploration_policies.boltzmann

+#
+# Copyright (c) 2017 Intel Corporation 
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from typing import List
+
+import numpy as np
+
+from rl_coach.core_types import RunPhase, ActionType
+from rl_coach.exploration_policies.exploration_policy import ExplorationPolicy, ExplorationParameters
+from rl_coach.schedules import Schedule
+from rl_coach.spaces import ActionSpace
+
+
+class BoltzmannParameters(ExplorationParameters):
+    def __init__(self):
+        super().__init__()
+        self.temperature_schedule = None
+
+    @property
+    def path(self):
+        return 'rl_coach.exploration_policies.boltzmann:Boltzmann'
+
+
+
+
[docs]class Boltzmann(ExplorationPolicy):
+    """
+    The Boltzmann exploration policy is intended for discrete action spaces. It assumes that each of the possible
+    actions has some value assigned to it (such as the Q value), and uses a softmax function to convert these values
+    into a distribution over the actions. It then samples the action for playing out of the calculated distribution.
+    An additional temperature schedule can be given by the user, and will control the steepness of the softmax function.
+    """
+    def __init__(self, action_space: ActionSpace, temperature_schedule: Schedule):
+        """
+        :param action_space: the action space used by the environment
+        :param temperature_schedule: the schedule for the temperature parameter of the softmax
+        """
+        super().__init__(action_space)
+        self.temperature_schedule = temperature_schedule
+
+    def get_action(self, action_values: List[ActionType]) -> ActionType:
+        if self.phase == RunPhase.TRAIN:
+            self.temperature_schedule.step()
+        # softmax calculation
+        exp_probabilities = np.exp(action_values / self.temperature_schedule.current_value)
+        probabilities = exp_probabilities / np.sum(exp_probabilities)
+        # make sure probs sum to 1
+        probabilities[-1] = 1 - np.sum(probabilities[:-1])
+        # choose actions according to the probabilities
+        return np.random.choice(range(self.action_space.shape), p=probabilities)
+
+    def get_control_param(self):
+        return self.temperature_schedule.current_value

+
+ +
+ +
+ + +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_modules/rl_coach/exploration_policies/bootstrapped.html b/docs/_modules/rl_coach/exploration_policies/bootstrapped.html new file mode 100644 index 0000000..d1d3821 --- /dev/null +++ b/docs/_modules/rl_coach/exploration_policies/bootstrapped.html @@ -0,0 +1,315 @@ + + + + + + + + + + + rl_coach.exploration_policies.bootstrapped — Reinforcement Learning Coach 0.11.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + +
+ +
+ + + + + + + + + + + + + + + + + +
+ +
    + +
  • Docs »
    • + +
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    • + +
  • rl_coach.exploration_policies.bootstrapped
    • + + +
  • + +
    • + +
+ + +
+
+
+
+ +

Source code for rl_coach.exploration_policies.bootstrapped

+#
+# Copyright (c) 2017 Intel Corporation 
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from typing import List
+
+import numpy as np
+
+from rl_coach.core_types import RunPhase, ActionType
+from rl_coach.exploration_policies.additive_noise import AdditiveNoiseParameters
+from rl_coach.exploration_policies.e_greedy import EGreedy, EGreedyParameters
+from rl_coach.exploration_policies.exploration_policy import ExplorationParameters
+from rl_coach.schedules import Schedule, LinearSchedule
+from rl_coach.spaces import ActionSpace
+
+
+class BootstrappedParameters(EGreedyParameters):
+    def __init__(self):
+        super().__init__()
+        self.architecture_num_q_heads = 10
+        self.bootstrapped_data_sharing_probability = 1.0
+        self.epsilon_schedule = LinearSchedule(1, 0.01, 1000000)
+
+    @property
+    def path(self):
+        return 'rl_coach.exploration_policies.bootstrapped:Bootstrapped'
+
+
+
[docs]class Bootstrapped(EGreedy):
+    """
+    Bootstrapped exploration policy is currently only used for discrete action spaces along with the
+    Bootstrapped DQN agent. It assumes that there is an ensemble of network heads, where each one predicts the
+    values for all the possible actions. For each episode, a single head is selected to lead the agent, according
+    to its value predictions. In evaluation, the action is selected using a majority vote over all the heads
+    predictions.
+
+    .. note::
+       This exploration policy will only work for Discrete action spaces with Bootstrapped DQN style agents,
+       since it requires the agent to have a network with multiple heads.
+    """
+    def __init__(self, action_space: ActionSpace, epsilon_schedule: Schedule, evaluation_epsilon: float,
+                 architecture_num_q_heads: int,
+                 continuous_exploration_policy_parameters: ExplorationParameters = AdditiveNoiseParameters(),):
+        """
+        :param action_space: the action space used by the environment
+        :param epsilon_schedule: a schedule for the epsilon values
+        :param evaluation_epsilon: the epsilon value to use for evaluation phases
+        :param continuous_exploration_policy_parameters: the parameters of the continuous exploration policy to use
+                                                         if the e-greedy is used for a continuous policy
+        :param architecture_num_q_heads: the number of q heads to select from
+        """
+        super().__init__(action_space, epsilon_schedule, evaluation_epsilon, continuous_exploration_policy_parameters)
+        self.num_heads = architecture_num_q_heads
+        self.selected_head = 0
+        self.last_action_values = 0
+
+    def select_head(self):
+        self.selected_head = np.random.randint(self.num_heads)
+
+    def get_action(self, action_values: List[ActionType]) -> ActionType:
+        # action values are none in case the exploration policy is going to select a random action
+        if action_values is not None:
+            if self.phase == RunPhase.TRAIN:
+                action_values = action_values[self.selected_head]
+            else:
+                # ensemble voting for evaluation
+                top_action_votings = np.argmax(action_values, axis=-1)
+                counts = np.bincount(top_action_votings.squeeze())
+                top_action = np.argmax(counts)
+                # convert the top action to a one hot vector and pass it to e-greedy
+                action_values = np.eye(len(self.action_space.actions))[[top_action]]
+        self.last_action_values = action_values
+        return super().get_action(action_values)
+
+    def get_control_param(self):
+        return self.selected_head

+
+ +
+ +
+ + +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_modules/rl_coach/exploration_policies/categorical.html b/docs/_modules/rl_coach/exploration_policies/categorical.html new file mode 100644 index 0000000..e901c8e --- /dev/null +++ b/docs/_modules/rl_coach/exploration_policies/categorical.html @@ -0,0 +1,281 @@ + + + + + + + + + + + rl_coach.exploration_policies.categorical — Reinforcement Learning Coach 0.11.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + +
+ +
+ + + + + + + + + + + + + + + + + +
+ +
    + +
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    • + +
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    • + +
  • rl_coach.exploration_policies.categorical
    • + + +
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    • + +
+ + +
+
+
+
+ +

Source code for rl_coach.exploration_policies.categorical

+#
+# Copyright (c) 2017 Intel Corporation 
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from typing import List
+
+import numpy as np
+
+from rl_coach.core_types import RunPhase, ActionType
+from rl_coach.exploration_policies.exploration_policy import ExplorationPolicy, ExplorationParameters
+from rl_coach.spaces import ActionSpace
+
+
+class CategoricalParameters(ExplorationParameters):
+    @property
+    def path(self):
+        return 'rl_coach.exploration_policies.categorical:Categorical'
+
+
+
[docs]class Categorical(ExplorationPolicy):
+    """
+    Categorical exploration policy is intended for discrete action spaces. It expects the action values to
+    represent a probability distribution over the action, from which a single action will be sampled.
+    In evaluation, the action that has the highest probability will be selected. This is particularly useful for
+    actor-critic schemes, where the actors output is a probability distribution over the actions.
+    """
+    def __init__(self, action_space: ActionSpace):
+        """
+        :param action_space: the action space used by the environment
+        """
+        super().__init__(action_space)
+
+    def get_action(self, action_values: List[ActionType]) -> ActionType:
+        if self.phase == RunPhase.TRAIN:
+            # choose actions according to the probabilities
+            return np.random.choice(self.action_space.actions, p=action_values)
+        else:
+            # take the action with the highest probability
+            return np.argmax(action_values)
+
+    def get_control_param(self):
+        return 0

+
+ +
+ +
+ + +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_modules/rl_coach/exploration_policies/continuous_entropy.html b/docs/_modules/rl_coach/exploration_policies/continuous_entropy.html new file mode 100644 index 0000000..71451b9 --- /dev/null +++ b/docs/_modules/rl_coach/exploration_policies/continuous_entropy.html @@ -0,0 +1,265 @@ + + + + + + + + + + + rl_coach.exploration_policies.continuous_entropy — Reinforcement Learning Coach 0.11.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + +
+ +
+ + + + + + + + + + + + + + + + + +
+ +
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  • rl_coach.exploration_policies.continuous_entropy
    • + + +
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    • + +
+ + +
+
+
+
+ +

Source code for rl_coach.exploration_policies.continuous_entropy

+#
+# Copyright (c) 2017 Intel Corporation 
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from rl_coach.exploration_policies.additive_noise import AdditiveNoise, AdditiveNoiseParameters
+
+
+class ContinuousEntropyParameters(AdditiveNoiseParameters):
+    @property
+    def path(self):
+        return 'rl_coach.exploration_policies.continuous_entropy:ContinuousEntropy'
+
+
+
[docs]class ContinuousEntropy(AdditiveNoise):
+    """
+    Continuous entropy is an exploration policy that is actually implemented as part of the network.
+    The exploration policy class is only a placeholder for choosing this policy. The exploration policy is
+    implemented by adding a regularization factor to the network loss, which regularizes the entropy of the action.
+    This exploration policy is only intended for continuous action spaces, and assumes that the entire calculation
+    is implemented as part of the head.
+
+    .. warning::
+       This exploration policy expects the agent or the network to implement the exploration functionality.
+       There are only a few heads that actually are relevant and implement the entropy regularization factor.
+    """
+    pass

+
+ +
+ +
+ + +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_modules/rl_coach/exploration_policies/e_greedy.html b/docs/_modules/rl_coach/exploration_policies/e_greedy.html new file mode 100644 index 0000000..66112ff --- /dev/null +++ b/docs/_modules/rl_coach/exploration_policies/e_greedy.html @@ -0,0 +1,342 @@ + + + + + + + + + + + rl_coach.exploration_policies.e_greedy — Reinforcement Learning Coach 0.11.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + +
+ +
+ + + + + + + + + + + + + + + + + +
+ +
    + +
  • Docs »
    • + +
  • Module code »
    • + +
  • rl_coach.exploration_policies.e_greedy
    • + + +
  • + +
    • + +
+ + +
+
+
+
+ +

Source code for rl_coach.exploration_policies.e_greedy

+#
+# Copyright (c) 2017 Intel Corporation 
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from typing import List
+
+import numpy as np
+
+from rl_coach.core_types import RunPhase, ActionType
+from rl_coach.exploration_policies.additive_noise import AdditiveNoiseParameters
+from rl_coach.exploration_policies.exploration_policy import ExplorationParameters
+from rl_coach.exploration_policies.exploration_policy import ExplorationPolicy
+from rl_coach.schedules import Schedule, LinearSchedule
+from rl_coach.spaces import ActionSpace, DiscreteActionSpace, BoxActionSpace
+from rl_coach.utils import dynamic_import_and_instantiate_module_from_params
+
+
+class EGreedyParameters(ExplorationParameters):
+    def __init__(self):
+        super().__init__()
+        self.epsilon_schedule = LinearSchedule(0.5, 0.01, 50000)
+        self.evaluation_epsilon = 0.05
+        self.continuous_exploration_policy_parameters = AdditiveNoiseParameters()
+        self.continuous_exploration_policy_parameters.noise_percentage_schedule = LinearSchedule(0.1, 0.1, 50000)
+        # for continuous control -
+        # (see http://www.cs.ubc.ca/~van/papers/2017-TOG-deepLoco/2017-TOG-deepLoco.pdf)
+
+    @property
+    def path(self):
+        return 'rl_coach.exploration_policies.e_greedy:EGreedy'
+
+
+
[docs]class EGreedy(ExplorationPolicy):
+    """
+    e-greedy is an exploration policy that is intended for both discrete and continuous action spaces.
+
+    For discrete action spaces, it assumes that each action is assigned a value, and it selects the action with the
+    highest value with probability 1 - epsilon. Otherwise, it selects a action sampled uniformly out of all the
+    possible actions. The epsilon value is given by the user and can be given as a schedule.
+    In evaluation, a different epsilon value can be specified.
+
+    For continuous action spaces, it assumes that the mean action is given by the agent. With probability epsilon,
+    it samples a random action out of the action space bounds. Otherwise, it selects the action according to a
+    given continuous exploration policy, which is set to AdditiveNoise by default. In evaluation, the action is
+    always selected according to the given continuous exploration policy (where its phase is set to evaluation as well).
+    """
+    def __init__(self, action_space: ActionSpace, epsilon_schedule: Schedule,
+                 evaluation_epsilon: float,
+                 continuous_exploration_policy_parameters: ExplorationParameters=AdditiveNoiseParameters()):
+        """
+        :param action_space: the action space used by the environment
+        :param epsilon_schedule: a schedule for the epsilon values
+        :param evaluation_epsilon: the epsilon value to use for evaluation phases
+        :param continuous_exploration_policy_parameters: the parameters of the continuous exploration policy to use
+                                                         if the e-greedy is used for a continuous policy
+        """
+        super().__init__(action_space)
+        self.epsilon_schedule = epsilon_schedule
+        self.evaluation_epsilon = evaluation_epsilon
+
+        if isinstance(self.action_space, BoxActionSpace):
+            # for continuous e-greedy (see http://www.cs.ubc.ca/~van/papers/2017-TOG-deepLoco/2017-TOG-deepLoco.pdf)
+            continuous_exploration_policy_parameters.action_space = action_space
+            self.continuous_exploration_policy = \
+                dynamic_import_and_instantiate_module_from_params(continuous_exploration_policy_parameters)
+
+        self.current_random_value = np.random.rand()
+
+    def requires_action_values(self):
+        epsilon = self.evaluation_epsilon if self.phase == RunPhase.TEST else self.epsilon_schedule.current_value
+        return self.current_random_value >= epsilon
+
+    def get_action(self, action_values: List[ActionType]) -> ActionType:
+        epsilon = self.evaluation_epsilon if self.phase == RunPhase.TEST else self.epsilon_schedule.current_value
+
+        if isinstance(self.action_space, DiscreteActionSpace):
+            top_action = np.argmax(action_values)
+            if self.current_random_value < epsilon:
+                chosen_action = self.action_space.sample()
+            else:
+                chosen_action = top_action
+        else:
+            if self.current_random_value < epsilon and self.phase == RunPhase.TRAIN:
+                chosen_action = self.action_space.sample()
+            else:
+                chosen_action = self.continuous_exploration_policy.get_action(action_values)
+
+        # step the epsilon schedule and generate a new random value for next time
+        if self.phase == RunPhase.TRAIN:
+            self.epsilon_schedule.step()
+        self.current_random_value = np.random.rand()
+        return chosen_action
+
+    def get_control_param(self):
+        if isinstance(self.action_space, DiscreteActionSpace):
+            return self.evaluation_epsilon if self.phase == RunPhase.TEST else self.epsilon_schedule.current_value
+        elif isinstance(self.action_space, BoxActionSpace):
+            return self.continuous_exploration_policy.get_control_param()
+
+    def change_phase(self, phase):
+        super().change_phase(phase)
+        if isinstance(self.action_space, BoxActionSpace):
+            self.continuous_exploration_policy.change_phase(phase)

+
+ +
+ +
+ + +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_modules/rl_coach/exploration_policies/exploration_policy.html b/docs/_modules/rl_coach/exploration_policies/exploration_policy.html new file mode 100644 index 0000000..df2bf93 --- /dev/null +++ b/docs/_modules/rl_coach/exploration_policies/exploration_policy.html @@ -0,0 +1,311 @@ + + + + + + + + + + + rl_coach.exploration_policies.exploration_policy — Reinforcement Learning Coach 0.11.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + +
+ +
+ + + + + + + + + + + + + + + + + +
+ +
    + +
  • Docs »
    • + +
  • Module code »
    • + +
  • rl_coach.exploration_policies.exploration_policy
    • + + +
  • + +
    • + +
+ + +
+
+
+
+ +

Source code for rl_coach.exploration_policies.exploration_policy

+#
+# Copyright (c) 2017 Intel Corporation 
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from typing import List
+
+from rl_coach.base_parameters import Parameters
+from rl_coach.core_types import RunPhase, ActionType
+from rl_coach.spaces import ActionSpace
+
+
+class ExplorationParameters(Parameters):
+    def __init__(self):
+        self.action_space = None
+
+    @property
+    def path(self):
+        return 'rl_coach.exploration_policies.exploration_policy:ExplorationPolicy'
+
+
+
[docs]class ExplorationPolicy(object):
+    """
+    An exploration policy takes the predicted actions or action values from the agent, and selects the action to
+    actually apply to the environment using some predefined algorithm.
+    """
+    def __init__(self, action_space: ActionSpace):
+        """
+        :param action_space: the action space used by the environment
+        """
+        self.phase = RunPhase.HEATUP
+        self.action_space = action_space
+
+
[docs]    def reset(self):
+        """
+        Used for resetting the exploration policy parameters when needed
+        :return: None
+        """
+        pass

+
+
[docs]    def get_action(self, action_values: List[ActionType]) -> ActionType:
+        """
+        Given a list of values corresponding to each action, 
+        choose one actions according to the exploration policy
+        :param action_values: A list of action values
+        :return: The chosen action
+        """
+        if self.__class__ == ExplorationPolicy:
+            raise ValueError("The ExplorationPolicy class is an abstract class and should not be used directly. "
+                             "Please set the exploration parameters to point to an inheriting class like EGreedy or "
+                             "AdditiveNoise")
+        else:
+            raise ValueError("The get_action function should be overridden in the inheriting exploration class")

+
+
[docs]    def change_phase(self, phase):
+        """
+        Change between running phases of the algorithm
+        :param phase: Either Heatup or Train
+        :return: none
+        """
+        self.phase = phase

+
+
[docs]    def requires_action_values(self) -> bool:
+        """
+        Allows exploration policies to define if they require the action values for the current step.
+        This can save up a lot of computation. For example in e-greedy, if the random value generated is smaller
+        than epsilon, the action is completely random, and the action values don't need to be calculated
+        :return: True if the action values are required. False otherwise
+        """
+        return True

+
+    def get_control_param(self):
+        return 0

+
+ +
+ +
+ + +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_modules/rl_coach/exploration_policies/greedy.html b/docs/_modules/rl_coach/exploration_policies/greedy.html new file mode 100644 index 0000000..b031dbe --- /dev/null +++ b/docs/_modules/rl_coach/exploration_policies/greedy.html @@ -0,0 +1,278 @@ + + + + + + + + + + + rl_coach.exploration_policies.greedy — Reinforcement Learning Coach 0.11.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + +
+ +
+ + + + + + + + + + + + + + + + + +
+ +
    + +
  • Docs »
    • + +
  • Module code »
    • + +
  • rl_coach.exploration_policies.greedy
    • + + +
  • + +
    • + +
+ + +
+
+
+
+ +

Source code for rl_coach.exploration_policies.greedy

+#
+# Copyright (c) 2017 Intel Corporation 
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from typing import List
+
+import numpy as np
+
+from rl_coach.core_types import ActionType
+from rl_coach.exploration_policies.exploration_policy import ExplorationPolicy, ExplorationParameters
+from rl_coach.spaces import ActionSpace, DiscreteActionSpace, BoxActionSpace
+
+
+class GreedyParameters(ExplorationParameters):
+    @property
+    def path(self):
+        return 'rl_coach.exploration_policies.greedy:Greedy'
+
+
+
[docs]class Greedy(ExplorationPolicy):
+    """
+    The Greedy exploration policy is intended for both discrete and continuous action spaces.
+    For discrete action spaces, it always selects the action with the maximum value, as given by the agent.
+    For continuous action spaces, it always return the exact action, as it was given by the agent.
+    """
+    def __init__(self, action_space: ActionSpace):
+        """
+        :param action_space: the action space used by the environment
+        """
+        super().__init__(action_space)
+
+    def get_action(self, action_values: List[ActionType]) -> ActionType:
+        if type(self.action_space) == DiscreteActionSpace:
+            return np.argmax(action_values)
+        if type(self.action_space) == BoxActionSpace:
+            return action_values
+
+    def get_control_param(self):
+        return 0

+
+ +
+ +
+ + +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_modules/rl_coach/exploration_policies/ou_process.html b/docs/_modules/rl_coach/exploration_policies/ou_process.html new file mode 100644 index 0000000..390dcba --- /dev/null +++ b/docs/_modules/rl_coach/exploration_policies/ou_process.html @@ -0,0 +1,313 @@ + + + + + + + + + + + rl_coach.exploration_policies.ou_process — Reinforcement Learning Coach 0.11.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + +
+ +
+ + + + + + + + + + + + + + + + + +
+ +
    + +
  • Docs »
    • + +
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    • + +
  • rl_coach.exploration_policies.ou_process
    • + + +
  • + +
    • + +
+ + +
+
+
+
+ +

Source code for rl_coach.exploration_policies.ou_process

+#
+# Copyright (c) 2017 Intel Corporation 
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from typing import List
+
+import numpy as np
+
+from rl_coach.core_types import RunPhase, ActionType
+from rl_coach.exploration_policies.exploration_policy import ExplorationPolicy, ExplorationParameters
+from rl_coach.spaces import ActionSpace, BoxActionSpace, GoalsSpace
+
+
+# Based on on the description in:
+# https://math.stackexchange.com/questions/1287634/implementing-ornstein-uhlenbeck-in-matlab
+class OUProcessParameters(ExplorationParameters):
+    def __init__(self):
+        super().__init__()
+        self.mu = 0
+        self.theta = 0.15
+        self.sigma = 0.2
+        self.dt = 0.01
+
+    @property
+    def path(self):
+        return 'rl_coach.exploration_policies.ou_process:OUProcess'
+
+
+# Ornstein-Uhlenbeck process
+
[docs]class OUProcess(ExplorationPolicy):
+    """
+    OUProcess exploration policy is intended for continuous action spaces, and selects the action according to
+    an Ornstein-Uhlenbeck process. The Ornstein-Uhlenbeck process implements the action as a Gaussian process, where
+    the samples are correlated between consequent time steps.
+    """
+    def __init__(self, action_space: ActionSpace, mu: float=0, theta: float=0.15, sigma: float=0.2, dt: float=0.01):
+        """
+        :param action_space: the action space used by the environment
+        """
+        super().__init__(action_space)
+        self.mu = float(mu) * np.ones(self.action_space.shape)
+        self.theta = float(theta)
+        self.sigma = float(sigma) * np.ones(self.action_space.shape)
+        self.state = np.zeros(self.action_space.shape)
+        self.dt = dt
+
+        if not (isinstance(action_space, BoxActionSpace) or isinstance(action_space, GoalsSpace)):
+            raise ValueError("OU process exploration works only for continuous controls."
+                             "The given action space is of type: {}".format(action_space.__class__.__name__))
+
+    def reset(self):
+        self.state = np.zeros(self.action_space.shape)
+
+    def noise(self):
+        x = self.state
+        dx = self.theta * (self.mu - x) * self.dt + self.sigma * np.random.randn(len(x)) * np.sqrt(self.dt)
+        self.state = x + dx
+        return self.state
+
+    def get_action(self, action_values: List[ActionType]) -> ActionType:
+        if self.phase == RunPhase.TRAIN:
+            noise = self.noise()
+        else:
+            noise = np.zeros(self.action_space.shape)
+
+        action = action_values.squeeze() + noise
+
+        return action
+
+    def get_control_param(self):
+        if self.phase == RunPhase.TRAIN:
+            return self.state
+        else:
+            return np.zeros(self.action_space.shape)

+
+ +
+ +
+ + +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_modules/rl_coach/exploration_policies/parameter_noise.html b/docs/_modules/rl_coach/exploration_policies/parameter_noise.html new file mode 100644 index 0000000..79a24c8 --- /dev/null +++ b/docs/_modules/rl_coach/exploration_policies/parameter_noise.html @@ -0,0 +1,314 @@ + + + + + + + + + + + rl_coach.exploration_policies.parameter_noise — Reinforcement Learning Coach 0.11.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + +
+ +
+ + + + + + + + + + + + + + + + + +
+ +
    + +
  • Docs »
    • + +
  • Module code »
    • + +
  • rl_coach.exploration_policies.parameter_noise
    • + + +
  • + +
    • + +
+ + +
+
+
+
+ +

Source code for rl_coach.exploration_policies.parameter_noise

+#
+# Copyright (c) 2017 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from typing import List, Dict
+
+import numpy as np
+
+from rl_coach.agents.dqn_agent import DQNAgentParameters
+from rl_coach.architectures.tensorflow_components.layers import NoisyNetDense
+from rl_coach.base_parameters import AgentParameters, NetworkParameters
+from rl_coach.spaces import ActionSpace, BoxActionSpace, DiscreteActionSpace
+
+from rl_coach.core_types import ActionType
+from rl_coach.exploration_policies.exploration_policy import ExplorationPolicy, ExplorationParameters
+
+
+class ParameterNoiseParameters(ExplorationParameters):
+    def __init__(self, agent_params: AgentParameters):
+        super().__init__()
+        if not isinstance(agent_params, DQNAgentParameters):
+            raise ValueError("Currently only DQN variants are supported for using an exploration type of "
+                             "ParameterNoise.")
+
+        self.network_params = agent_params.network_wrappers
+
+    @property
+    def path(self):
+        return 'rl_coach.exploration_policies.parameter_noise:ParameterNoise'
+
+
+
[docs]class ParameterNoise(ExplorationPolicy):
+    """
+    The ParameterNoise exploration policy is intended for both discrete and continuous action spaces.
+    It applies the exploration policy by replacing all the dense network layers with noisy layers.
+    The noisy layers have both weight means and weight standard deviations, and for each forward pass of the network
+    the weights are sampled from a normal distribution that follows the learned weights mean and standard deviation
+    values.
+
+    Warning: currently supported only by DQN variants
+    """
+    def __init__(self, network_params: Dict[str, NetworkParameters], action_space: ActionSpace):
+        """
+        :param action_space: the action space used by the environment
+        """
+        super().__init__(action_space)
+        self.network_params = network_params
+        self._replace_network_dense_layers()
+
+    def get_action(self, action_values: List[ActionType]) -> ActionType:
+        if type(self.action_space) == DiscreteActionSpace:
+            return np.argmax(action_values)
+        elif type(self.action_space) == BoxActionSpace:
+            action_values_mean = action_values[0].squeeze()
+            action_values_std = action_values[1].squeeze()
+            return np.random.normal(action_values_mean, action_values_std)
+        else:
+            raise ValueError("ActionSpace type {} is not supported for ParameterNoise.".format(type(self.action_space)))
+
+    def get_control_param(self):
+        return 0
+
+    def _replace_network_dense_layers(self):
+        # replace the dense type for all the networks components (embedders, mw, heads) with a NoisyNetDense
+
+        # NOTE: we are changing network params in a non-params class (an already instantiated class), this could have
+        #       been prone to a bug, but since the networks are created very late in the game
+        #       (after agent.init_environment_dependent()_modules is called) - then we are fine.
+
+        for network_wrapper_params in self.network_params.values():
+            for component_params in list(network_wrapper_params.input_embedders_parameters.values()) + \
+                                    [network_wrapper_params.middleware_parameters] + \
+                                    network_wrapper_params.heads_parameters:
+                component_params.dense_layer = NoisyNetDense

+
+
+ +
+ +
+ + +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_modules/rl_coach/exploration_policies/truncated_normal.html b/docs/_modules/rl_coach/exploration_policies/truncated_normal.html new file mode 100644 index 0000000..62b5033 --- /dev/null +++ b/docs/_modules/rl_coach/exploration_policies/truncated_normal.html @@ -0,0 +1,337 @@ + + + + + + + + + + + rl_coach.exploration_policies.truncated_normal — Reinforcement Learning Coach 0.11.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + +
+ +
+ + + + + + + + + + + + + + + + + +
+ +
    + +
  • Docs »
    • + +
  • Module code »
    • + +
  • rl_coach.exploration_policies.truncated_normal
    • + + +
  • + +
    • + +
+ + +
+
+
+
+ +

Source code for rl_coach.exploration_policies.truncated_normal

+#
+# Copyright (c) 2017 Intel Corporation 
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from typing import List
+
+import numpy as np
+from scipy.stats import truncnorm
+
+from rl_coach.core_types import RunPhase, ActionType
+from rl_coach.exploration_policies.exploration_policy import ExplorationPolicy, ExplorationParameters
+from rl_coach.schedules import Schedule, LinearSchedule
+from rl_coach.spaces import ActionSpace, BoxActionSpace
+
+
+class TruncatedNormalParameters(ExplorationParameters):
+    def __init__(self):
+        super().__init__()
+        self.noise_percentage_schedule = LinearSchedule(0.1, 0.1, 50000)
+        self.evaluation_noise_percentage = 0.05
+        self.clip_low = 0
+        self.clip_high = 1
+
+    @property
+    def path(self):
+        return 'rl_coach.exploration_policies.truncated_normal:TruncatedNormal'
+
+
+
[docs]class TruncatedNormal(ExplorationPolicy):
+    """
+    The TruncatedNormal exploration policy is intended for continuous action spaces. It samples the action from a
+    normal distribution, where the mean action is given by the agent, and the standard deviation can be given in t
+    wo different ways:
+    1. Specified by the user as a noise schedule which is taken in percentiles out of the action space size
+    2. Specified by the agents action. In case the agents action is a list with 2 values, the 1st one is assumed to
+    be the mean of the action, and 2nd is assumed to be its standard deviation.
+    When the sampled action is outside of the action bounds given by the user, it is sampled again and again, until it
+    is within the bounds.
+    """
+    def __init__(self, action_space: ActionSpace, noise_percentage_schedule: Schedule,
+                 evaluation_noise_percentage: float, clip_low: float, clip_high: float):
+        """
+        :param action_space: the action space used by the environment
+        :param noise_percentage_schedule: the schedule for the noise variance percentage relative to the absolute range
+                                          of the action space
+        :param evaluation_noise_percentage: the noise variance percentage that will be used during evaluation phases
+        """
+        super().__init__(action_space)
+        self.noise_percentage_schedule = noise_percentage_schedule
+        self.evaluation_noise_percentage = evaluation_noise_percentage
+        self.clip_low = clip_low
+        self.clip_high = clip_high
+
+        if not isinstance(action_space, BoxActionSpace):
+            raise ValueError("Truncated normal exploration works only for continuous controls."
+                             "The given action space is of type: {}".format(action_space.__class__.__name__))
+
+        if not np.all(-np.inf < action_space.high) or not np.all(action_space.high < np.inf)\
+                or not np.all(-np.inf < action_space.low) or not np.all(action_space.low < np.inf):
+            raise ValueError("Additive noise exploration requires bounded actions")
+
+        # TODO: allow working with unbounded actions by defining the noise in terms of range and not percentage
+
+    def get_action(self, action_values: List[ActionType]) -> ActionType:
+        # set the current noise percentage
+        if self.phase == RunPhase.TEST:
+            current_noise_precentage = self.evaluation_noise_percentage
+        else:
+            current_noise_precentage = self.noise_percentage_schedule.current_value
+
+        # scale the noise to the action space range
+        action_values_std = current_noise_precentage * (self.action_space.high - self.action_space.low)
+
+        # extract the mean values
+        if isinstance(action_values, list):
+            # the action values are expected to be a list with the action mean and optionally the action stdev
+            action_values_mean = action_values[0].squeeze()
+        else:
+            # the action values are expected to be a numpy array representing the action mean
+            action_values_mean = action_values.squeeze()
+
+        # step the noise schedule
+        if self.phase == RunPhase.TRAIN:
+            self.noise_percentage_schedule.step()
+            # the second element of the list is assumed to be the standard deviation
+            if isinstance(action_values, list) and len(action_values) > 1:
+                action_values_std = action_values[1].squeeze()
+
+        # sample from truncated normal distribution
+        normalized_low = (self.clip_low - action_values_mean) / action_values_std
+        normalized_high = (self.clip_high - action_values_mean) / action_values_std
+        distribution = truncnorm(normalized_low, normalized_high, loc=action_values_mean, scale=action_values_std)
+        action = distribution.rvs(1)
+
+        return action
+
+    def get_control_param(self):
+        return np.ones(self.action_space.shape)*self.noise_percentage_schedule.current_value

+
+ +
+ +
+ + +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_modules/rl_coach/exploration_policies/ucb.html b/docs/_modules/rl_coach/exploration_policies/ucb.html new file mode 100644 index 0000000..b340ae9 --- /dev/null +++ b/docs/_modules/rl_coach/exploration_policies/ucb.html @@ -0,0 +1,319 @@ + + + + + + + + + + + rl_coach.exploration_policies.ucb — Reinforcement Learning Coach 0.11.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + +
+ +
+ + + + + + + + + + + + + + + + + +
+ +
    + +
  • Docs »
    • + +
  • Module code »
    • + +
  • rl_coach.exploration_policies.ucb
    • + + +
  • + +
    • + +
+ + +
+
+
+
+ +

Source code for rl_coach.exploration_policies.ucb

+#
+# Copyright (c) 2017 Intel Corporation 
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from typing import List
+
+import numpy as np
+
+from rl_coach.core_types import RunPhase, ActionType, EnvironmentSteps
+from rl_coach.exploration_policies.additive_noise import AdditiveNoiseParameters
+from rl_coach.exploration_policies.e_greedy import EGreedy, EGreedyParameters
+from rl_coach.exploration_policies.exploration_policy import ExplorationParameters
+from rl_coach.schedules import Schedule, LinearSchedule, PieceWiseSchedule
+from rl_coach.spaces import ActionSpace
+
+
+class UCBParameters(EGreedyParameters):
+    def __init__(self):
+        super().__init__()
+        self.architecture_num_q_heads = 10
+        self.bootstrapped_data_sharing_probability = 1.0
+        self.epsilon_schedule = PieceWiseSchedule([
+            (LinearSchedule(1, 0.1, 1000000), EnvironmentSteps(1000000)),
+            (LinearSchedule(0.1, 0.01, 4000000), EnvironmentSteps(4000000))
+        ])
+        self.lamb = 0.1
+
+    @property
+    def path(self):
+        return 'rl_coach.exploration_policies.ucb:UCB'
+
+
+
[docs]class UCB(EGreedy):
+    """
+    UCB exploration policy is following the upper confidence bound heuristic to sample actions in discrete action spaces.
+    It assumes that there are multiple network heads that are predicting action values, and that the standard deviation
+    between the heads predictions represents the uncertainty of the agent in each of the actions.
+    It then updates the action value estimates to by mean(actions)+lambda*stdev(actions), where lambda is
+    given by the user. This exploration policy aims to take advantage of the uncertainty of the agent in its predictions,
+    and select the action according to the tradeoff between how uncertain the agent is, and how large it predicts
+    the outcome from those actions to be.
+    """
+    def __init__(self, action_space: ActionSpace, epsilon_schedule: Schedule, evaluation_epsilon: float,
+                 architecture_num_q_heads: int, lamb: int,
+                 continuous_exploration_policy_parameters: ExplorationParameters = AdditiveNoiseParameters()):
+        """
+        :param action_space: the action space used by the environment
+        :param epsilon_schedule: a schedule for the epsilon values
+        :param evaluation_epsilon: the epsilon value to use for evaluation phases
+        :param architecture_num_q_heads: the number of q heads to select from
+        :param lamb: lambda coefficient for taking the standard deviation into account
+        :param continuous_exploration_policy_parameters: the parameters of the continuous exploration policy to use
+                                                         if the e-greedy is used for a continuous policy
+        """
+        super().__init__(action_space, epsilon_schedule, evaluation_epsilon, continuous_exploration_policy_parameters)
+        self.num_heads = architecture_num_q_heads
+        self.lamb = lamb
+        self.std = 0
+        self.last_action_values = 0
+
+    def select_head(self):
+        pass
+
+    def get_action(self, action_values: List[ActionType]) -> ActionType:
+        # action values are none in case the exploration policy is going to select a random action
+        if action_values is not None:
+            if self.requires_action_values():
+                mean = np.mean(action_values, axis=0)
+                if self.phase == RunPhase.TRAIN:
+                    self.std = np.std(action_values, axis=0)
+                    self.last_action_values = mean + self.lamb * self.std
+                else:
+                    self.last_action_values = mean
+        return super().get_action(self.last_action_values)
+
+    def get_control_param(self):
+        if self.phase == RunPhase.TRAIN:
+            return np.mean(self.std)
+        else:
+            return 0

+
+ +
+ +
+ + +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_modules/rl_coach/filters/action/attention_discretization.html b/docs/_modules/rl_coach/filters/action/attention_discretization.html new file mode 100644 index 0000000..7c11399 --- /dev/null +++ b/docs/_modules/rl_coach/filters/action/attention_discretization.html @@ -0,0 +1,300 @@ + + + + + + + + + + + rl_coach.filters.action.attention_discretization — Reinforcement Learning Coach 0.11.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + +
+ +
+ + + + + + + + + + + + + + + + + +
+ +
    + +
  • Docs »
    • + +
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    • + +
  • rl_coach.filters.action.attention_discretization
    • + + +
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    • + +
+ + +
+
+
+
+ +

Source code for rl_coach.filters.action.attention_discretization

+#
+# Copyright (c) 2017 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from typing import Union, List
+
+import numpy as np
+
+from rl_coach.filters.action.box_discretization import BoxDiscretization
+from rl_coach.filters.action.partial_discrete_action_space_map import PartialDiscreteActionSpaceMap
+from rl_coach.spaces import AttentionActionSpace, BoxActionSpace, DiscreteActionSpace
+
+
+
[docs]class AttentionDiscretization(PartialDiscreteActionSpaceMap):
+    """
+    Discretizes an **AttentionActionSpace**. The attention action space defines the actions
+    as choosing sub-boxes in a given box. For example, consider an image of size 100x100, where the action is choosing
+    a crop window of size 20x20 to attend to in the image. AttentionDiscretization allows discretizing the possible crop
+    windows to choose into a finite number of options, and map a discrete action space into those crop windows.
+
+    Warning! this will currently only work for attention spaces with 2 dimensions.
+    """
+    def __init__(self, num_bins_per_dimension: Union[int, List[int]], force_int_bins=False):
+        """
+        :param num_bins_per_dimension: Number of discrete bins to use for each dimension of the action space
+        :param force_int_bins: If set to True, all the bins will represent integer coordinates in space.
+        """
+        # we allow specifying either a single number for all dimensions, or a single number per dimension in the target
+        # action space
+        self.num_bins_per_dimension = num_bins_per_dimension
+
+        self.force_int_bins = force_int_bins
+
+        # TODO: this will currently only work for attention spaces with 2 dimensions. generalize it.
+
+        super().__init__()
+
+    def validate_output_action_space(self, output_action_space: AttentionActionSpace):
+        if not isinstance(output_action_space, AttentionActionSpace):
+            raise ValueError("AttentionActionSpace discretization only works with an output space of type AttentionActionSpace. "
+                             "The given output space is {}".format(output_action_space))
+
+    def get_unfiltered_action_space(self, output_action_space: AttentionActionSpace) -> DiscreteActionSpace:
+        if isinstance(self.num_bins_per_dimension, int):
+            self.num_bins_per_dimension = [self.num_bins_per_dimension] * output_action_space.shape[0]
+
+        # create a discrete to linspace map to ease the extraction of attention actions
+        discrete_to_box = BoxDiscretization([n+1 for n in self.num_bins_per_dimension],
+                                            self.force_int_bins)
+        discrete_to_box.get_unfiltered_action_space(BoxActionSpace(output_action_space.shape,
+                                                                   output_action_space.low,
+                                                                   output_action_space.high), )
+
+        rows, cols = self.num_bins_per_dimension
+        start_ind = [i * (cols + 1) + j for i in range(rows + 1) if i < rows for j in range(cols + 1) if j < cols]
+        end_ind = [i + cols + 2 for i in start_ind]
+        self.target_actions = [np.array([discrete_to_box.target_actions[start],
+                                         discrete_to_box.target_actions[end]])
+                               for start, end in zip(start_ind, end_ind)]
+
+        return super().get_unfiltered_action_space(output_action_space)

+
+ +
+ +
+ + +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_modules/rl_coach/filters/action/box_discretization.html b/docs/_modules/rl_coach/filters/action/box_discretization.html new file mode 100644 index 0000000..553dbd2 --- /dev/null +++ b/docs/_modules/rl_coach/filters/action/box_discretization.html @@ -0,0 +1,300 @@ + + + + + + + + + + + rl_coach.filters.action.box_discretization — Reinforcement Learning Coach 0.11.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + +
+ +
+ + + + + + + + + + + + + + + + + +
+ +
    + +
  • Docs »
    • + +
  • Module code »
    • + +
  • rl_coach.filters.action.box_discretization
    • + + +
  • + +
    • + +
+ + +
+
+
+
+ +

Source code for rl_coach.filters.action.box_discretization

+#
+# Copyright (c) 2017 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from itertools import product
+from typing import Union, List
+
+import numpy as np
+
+from rl_coach.filters.action.partial_discrete_action_space_map import PartialDiscreteActionSpaceMap
+from rl_coach.spaces import BoxActionSpace, DiscreteActionSpace
+
+
+
[docs]class BoxDiscretization(PartialDiscreteActionSpaceMap):
+    """
+    Discretizes a continuous action space into a discrete action space, allowing the usage of
+    agents such as DQN for continuous environments such as MuJoCo. Given the number of bins to discretize into, the
+    original continuous action space is uniformly separated into the given number of bins, each mapped to a discrete
+    action index. Each discrete action is mapped to a single N dimensional action in the BoxActionSpace action space.
+    For example, if the original actions space is between -1 and 1 and 5 bins were selected, the new action
+    space will consist of 5 actions mapped to -1, -0.5, 0, 0.5 and 1.
+    """
+    def __init__(self, num_bins_per_dimension: Union[int, List[int]], force_int_bins=False):
+        """
+        :param num_bins_per_dimension: The number of bins to use for each dimension of the target action space.
+                                       The bins will be spread out uniformly over this space
+        :param force_int_bins: force the bins to represent only integer actions. for example, if the action space is in
+                               the range 0-10 and there are 5 bins, then the bins will be placed at 0, 2, 5, 7, 10,
+                               instead of 0, 2.5, 5, 7.5, 10.
+        """
+        # we allow specifying either a single number for all dimensions, or a single number per dimension in the target
+        # action space
+        self.num_bins_per_dimension = num_bins_per_dimension
+        self.force_int_bins = force_int_bins
+        super().__init__()
+
+    def validate_output_action_space(self, output_action_space: BoxActionSpace):
+        if not isinstance(output_action_space, BoxActionSpace):
+            raise ValueError("BoxActionSpace discretization only works with an output space of type BoxActionSpace. "
+                             "The given output space is {}".format(output_action_space))
+
+        if len(self.num_bins_per_dimension) != output_action_space.shape:
+            # TODO: this check is not sufficient. it does not deal with actions spaces with more than one axis
+            raise ValueError("The length of the list of bins per dimension ({}) does not match the number of "
+                             "dimensions in the action space ({})"
+                             .format(len(self.num_bins_per_dimension), output_action_space))
+
+    def get_unfiltered_action_space(self, output_action_space: BoxActionSpace) -> DiscreteActionSpace:
+        if isinstance(self.num_bins_per_dimension, int):
+            self.num_bins_per_dimension = np.ones(output_action_space.shape) * self.num_bins_per_dimension
+
+        bins = []
+        for i in range(len(output_action_space.low)):
+            dim_bins = np.linspace(output_action_space.low[i], output_action_space.high[i],
+                                   self.num_bins_per_dimension[i])
+            if self.force_int_bins:
+                dim_bins = dim_bins.astype(int)
+            bins.append(dim_bins)
+        self.target_actions = [list(action) for action in list(product(*bins))]
+
+        return super().get_unfiltered_action_space(output_action_space)

+
+ +
+ +
+ + +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_modules/rl_coach/filters/action/box_masking.html b/docs/_modules/rl_coach/filters/action/box_masking.html new file mode 100644 index 0000000..552de5d --- /dev/null +++ b/docs/_modules/rl_coach/filters/action/box_masking.html @@ -0,0 +1,308 @@ + + + + + + + + + + + rl_coach.filters.action.box_masking — Reinforcement Learning Coach 0.11.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + +
+ +
+ + + + + + + + + + + + + + + + + +
+ +
    + +
  • Docs »
    • + +
  • Module code »
    • + +
  • rl_coach.filters.action.box_masking
    • + + +
  • + +
    • + +
+ + +
+
+
+
+ +

Source code for rl_coach.filters.action.box_masking

+#
+# Copyright (c) 2017 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from typing import Union
+
+import numpy as np
+
+from rl_coach.core_types import ActionType
+from rl_coach.filters.action.action_filter import ActionFilter
+from rl_coach.spaces import BoxActionSpace
+
+
+
[docs]class BoxMasking(ActionFilter):
+    """
+    Masks part of the action space to enforce the agent to work in a defined space. For example,
+    if the original action space is between -1 and 1, then this filter can be used in order to constrain the agent actions
+    to the range 0 and 1 instead. This essentially masks the range -1 and 0 from the agent.
+    The resulting action space will be shifted and will always start from 0 and have the size of the unmasked area.
+    """
+    def __init__(self,
+                 masked_target_space_low: Union[None, int, float, np.ndarray],
+                 masked_target_space_high: Union[None, int, float, np.ndarray]):
+        """
+        :param masked_target_space_low: the lowest values that can be chosen in the target action space
+        :param masked_target_space_high: the highest values that can be chosen in the target action space
+        """
+        self.masked_target_space_low = masked_target_space_low
+        self.masked_target_space_high = masked_target_space_high
+        self.offset = masked_target_space_low
+        super().__init__()
+
+    def set_masking(self, masked_target_space_low: Union[None, int, float, np.ndarray],
+                    masked_target_space_high: Union[None, int, float, np.ndarray]):
+        self.masked_target_space_low = masked_target_space_low
+        self.masked_target_space_high = masked_target_space_high
+        self.offset = masked_target_space_low
+        if self.output_action_space:
+            self.validate_output_action_space(self.output_action_space)
+            self.input_action_space = BoxActionSpace(self.output_action_space.shape,
+                                                     low=0,
+                                                     high=self.masked_target_space_high - self.masked_target_space_low)
+
+    def validate_output_action_space(self, output_action_space: BoxActionSpace):
+        if not isinstance(output_action_space, BoxActionSpace):
+            raise ValueError("BoxActionSpace discretization only works with an output space of type BoxActionSpace. "
+                             "The given output space is {}".format(output_action_space))
+        if self.masked_target_space_low is None or self.masked_target_space_high is None:
+            raise ValueError("The masking target space size was not set. Please call set_masking.")
+        if not (np.all(output_action_space.low <= self.masked_target_space_low)
+                and np.all(self.masked_target_space_low <= output_action_space.high)):
+            raise ValueError("The low values for masking the action space ({}) are not within the range of the "
+                             "target space (low = {}, high = {})"
+                             .format(self.masked_target_space_low, output_action_space.low, output_action_space.high))
+        if not (np.all(output_action_space.low <= self.masked_target_space_high)
+                and np.all(self.masked_target_space_high <= output_action_space.high)):
+            raise ValueError("The high values for masking the action space ({}) are not within the range of the "
+                             "target space (low = {}, high = {})"
+                             .format(self.masked_target_space_high, output_action_space.low, output_action_space.high))
+
+    def get_unfiltered_action_space(self, output_action_space: BoxActionSpace) -> BoxActionSpace:
+        self.output_action_space = output_action_space
+        self.input_action_space = BoxActionSpace(output_action_space.shape,
+                                                 low=0,
+                                                 high=self.masked_target_space_high - self.masked_target_space_low)
+        return self.input_action_space
+
+    def filter(self, action: ActionType) -> ActionType:
+        return action + self.offset

+
+ +
+ +
+ + +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_modules/rl_coach/filters/action/full_discrete_action_space_map.html b/docs/_modules/rl_coach/filters/action/full_discrete_action_space_map.html new file mode 100644 index 0000000..a6c6e9c --- /dev/null +++ b/docs/_modules/rl_coach/filters/action/full_discrete_action_space_map.html @@ -0,0 +1,261 @@ + + + + + + + + + + + rl_coach.filters.action.full_discrete_action_space_map — Reinforcement Learning Coach 0.11.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + +
+ +
+ + + + + + + + + + + + + + + + + +
+ +
    + +
  • Docs »
    • + +
  • Module code »
    • + +
  • rl_coach.filters.action.full_discrete_action_space_map
    • + + +
  • + +
    • + +
+ + +
+
+
+
+ +

Source code for rl_coach.filters.action.full_discrete_action_space_map

+#
+# Copyright (c) 2017 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from rl_coach.filters.action.partial_discrete_action_space_map import PartialDiscreteActionSpaceMap
+from rl_coach.spaces import ActionSpace, DiscreteActionSpace
+
+
+
[docs]class FullDiscreteActionSpaceMap(PartialDiscreteActionSpaceMap):
+    """
+    Full map of two countable action spaces. This works in a similar way to the
+    PartialDiscreteActionSpaceMap, but maps the entire source action space into the entire target action space, without
+    masking any actions.
+    For example, if there are 10 multiselect actions in the output space, the actions 0-9 will be mapped to those
+    multiselect actions.
+    """
+    def __init__(self):
+        super().__init__()
+
+    def get_unfiltered_action_space(self, output_action_space: ActionSpace) -> DiscreteActionSpace:
+        self.target_actions = output_action_space.actions
+        return super().get_unfiltered_action_space(output_action_space)

+
+ +
+ +
+ + +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_modules/rl_coach/filters/action/linear_box_to_box_map.html b/docs/_modules/rl_coach/filters/action/linear_box_to_box_map.html new file mode 100644 index 0000000..1ae9263 --- /dev/null +++ b/docs/_modules/rl_coach/filters/action/linear_box_to_box_map.html @@ -0,0 +1,289 @@ + + + + + + + + + + + rl_coach.filters.action.linear_box_to_box_map — Reinforcement Learning Coach 0.11.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + +
+ +
+ + + + + + + + + + + + + + + + + +
+ +
    + +
  • Docs »
    • + +
  • Module code »
    • + +
  • rl_coach.filters.action.linear_box_to_box_map
    • + + +
  • + +
    • + +
+ + +
+
+
+
+ +

Source code for rl_coach.filters.action.linear_box_to_box_map

+#
+# Copyright (c) 2017 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from typing import Union
+
+import numpy as np
+
+from rl_coach.core_types import ActionType
+from rl_coach.filters.action.action_filter import ActionFilter
+from rl_coach.spaces import BoxActionSpace
+
+
+
[docs]class LinearBoxToBoxMap(ActionFilter):
+    """
+    A linear mapping of two box action spaces. For example, if the action space of the
+    environment consists of continuous actions between 0 and 1, and we want the agent to choose actions between -1 and 1,
+    the LinearBoxToBoxMap can be used to map the range -1 and 1 to the range 0 and 1 in a linear way. This means that the
+    action -1 will be mapped to 0, the action 1 will be mapped to 1, and the rest of the actions will be linearly mapped
+    between those values.
+    """
+    def __init__(self,
+                 input_space_low: Union[None, int, float, np.ndarray],
+                 input_space_high: Union[None, int, float, np.ndarray]):
+        """
+        :param input_space_low: the low values of the desired action space
+        :param input_space_high: the high values of the desired action space
+        """
+        self.input_space_low = input_space_low
+        self.input_space_high = input_space_high
+        self.rescale = None
+        self.offset = None
+        super().__init__()
+
+    def validate_output_action_space(self, output_action_space: BoxActionSpace):
+        if not isinstance(output_action_space, BoxActionSpace):
+            raise ValueError("BoxActionSpace discretization only works with an output space of type BoxActionSpace. "
+                             "The given output space is {}".format(output_action_space))
+
+    def get_unfiltered_action_space(self, output_action_space: BoxActionSpace) -> BoxActionSpace:
+        self.input_action_space = BoxActionSpace(output_action_space.shape, self.input_space_low, self.input_space_high)
+        self.rescale = \
+            (output_action_space.high - output_action_space.low) / (self.input_space_high - self.input_space_low)
+        self.offset = output_action_space.low - self.input_space_low
+        self.output_action_space = output_action_space
+        return self.input_action_space
+
+    def filter(self, action: ActionType) -> ActionType:
+        return self.output_action_space.low + (action - self.input_space_low) * self.rescale

+
+
+ +
+ +
+ + +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_modules/rl_coach/filters/action/partial_discrete_action_space_map.html b/docs/_modules/rl_coach/filters/action/partial_discrete_action_space_map.html new file mode 100644 index 0000000..acd17e2 --- /dev/null +++ b/docs/_modules/rl_coach/filters/action/partial_discrete_action_space_map.html @@ -0,0 +1,286 @@ + + + + + + + + + + + rl_coach.filters.action.partial_discrete_action_space_map — Reinforcement Learning Coach 0.11.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + +
+ +
+ + + + + + + + + + + + + + + + + +
+ +
    + +
  • Docs »
    • + +
  • Module code »
    • + +
  • rl_coach.filters.action.partial_discrete_action_space_map
    • + + +
  • + +
    • + +
+ + +
+
+
+
+ +

Source code for rl_coach.filters.action.partial_discrete_action_space_map

+#
+# Copyright (c) 2017 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from typing import List
+
+from rl_coach.core_types import ActionType
+from rl_coach.filters.action.action_filter import ActionFilter
+from rl_coach.spaces import DiscreteActionSpace, ActionSpace
+
+
+
[docs]class PartialDiscreteActionSpaceMap(ActionFilter):
+    """
+    Partial map of two countable action spaces. For example, consider an environment
+    with a MultiSelect action space (select multiple actions at the same time, such as jump and go right), with 8 actual
+    MultiSelect actions. If we want the agent to be able to select only 5 of those actions by their index (0-4), we can
+    map a discrete action space with 5 actions into the 5 selected MultiSelect actions. This will both allow the agent to
+    use regular discrete actions, and mask 3 of the actions from the agent.
+    """
+    def __init__(self, target_actions: List[ActionType]=None, descriptions: List[str]=None):
+        """
+        :param target_actions: A partial list of actions from the target space to map to.
+        :param descriptions: a list of descriptions of each of the actions
+        """
+        self.target_actions = target_actions
+        self.descriptions = descriptions
+        super().__init__()
+
+    def validate_output_action_space(self, output_action_space: ActionSpace):
+        if not self.target_actions:
+            raise ValueError("The target actions were not set")
+        for v in self.target_actions:
+            if not output_action_space.val_matches_space_definition(v):
+                raise ValueError("The values in the output actions ({}) do not match the output action "
+                                 "space definition ({})".format(v, output_action_space))
+
+    def get_unfiltered_action_space(self, output_action_space: ActionSpace) -> DiscreteActionSpace:
+        self.output_action_space = output_action_space
+        self.input_action_space = DiscreteActionSpace(len(self.target_actions), self.descriptions)
+        return self.input_action_space
+
+    def filter(self, action: ActionType) -> ActionType:
+        return self.target_actions[action]
+
+    def reverse_filter(self, action: ActionType) -> ActionType:
+        return [(action == x).all() for x in self.target_actions].index(True)

+
+
+ +
+ +
+ + +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_modules/rl_coach/filters/observation/observation_clipping_filter.html b/docs/_modules/rl_coach/filters/observation/observation_clipping_filter.html new file mode 100644 index 0000000..f750adc --- /dev/null +++ b/docs/_modules/rl_coach/filters/observation/observation_clipping_filter.html @@ -0,0 +1,274 @@ + + + + + + + + + + + rl_coach.filters.observation.observation_clipping_filter — Reinforcement Learning Coach 0.11.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + +
+ +
+ + + + + + + + + + + + + + + + + +
+ +
    + +
  • Docs »
    • + +
  • Module code »
    • + +
  • rl_coach.filters.observation.observation_clipping_filter
    • + + +
  • + +
    • + +
+ + +
+
+
+
+ +

Source code for rl_coach.filters.observation.observation_clipping_filter

+#
+# Copyright (c) 2017 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+
+import numpy as np
+
+from rl_coach.core_types import ObservationType
+from rl_coach.filters.observation.observation_filter import ObservationFilter
+from rl_coach.spaces import ObservationSpace
+
+
+
[docs]class ObservationClippingFilter(ObservationFilter):
+    """
+    Clips the observation values to a given range of values.
+    For example, if the observation consists of measurements in an arbitrary range,
+    and we want to control the minimum and maximum values of these observations,
+    we can define a range and clip the values of the measurements.
+    """
+    def __init__(self, clipping_low: float=-np.inf, clipping_high: float=np.inf):
+        """
+        :param clipping_low: The minimum value to allow after normalizing the observation
+        :param clipping_high: The maximum value to allow after normalizing the observation
+        """
+        super().__init__()
+        self.clip_min = clipping_low
+        self.clip_max = clipping_high
+
+    def filter(self, observation: ObservationType, update_internal_state: bool=True) -> ObservationType:
+        observation = np.clip(observation, self.clip_min, self.clip_max)
+
+        return observation
+
+    def get_filtered_observation_space(self, input_observation_space: ObservationSpace) -> ObservationSpace:
+        return input_observation_space

+
+ +
+ +
+ + +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_modules/rl_coach/filters/observation/observation_crop_filter.html b/docs/_modules/rl_coach/filters/observation/observation_crop_filter.html new file mode 100644 index 0000000..b8f926c --- /dev/null +++ b/docs/_modules/rl_coach/filters/observation/observation_crop_filter.html @@ -0,0 +1,321 @@ + + + + + + + + + + + rl_coach.filters.observation.observation_crop_filter — Reinforcement Learning Coach 0.11.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + +
+ +
+ + + + + + + + + + + + + + + + + +
+ +
    + +
  • Docs »
    • + +
  • Module code »
    • + +
  • rl_coach.filters.observation.observation_crop_filter
    • + + +
  • + +
    • + +
+ + +
+
+
+
+ +

Source code for rl_coach.filters.observation.observation_crop_filter

+#
+# Copyright (c) 2017 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+from typing import Union, Tuple
+
+import numpy as np
+
+from rl_coach.core_types import ObservationType
+from rl_coach.filters.observation.observation_filter import ObservationFilter
+from rl_coach.spaces import ObservationSpace
+
+
+
[docs]class ObservationCropFilter(ObservationFilter):
+    """
+    Crops the size of the observation to a given crop window. For example, in Atari, the
+    observations are images with a shape of 210x160. Usually, we will want to crop the size of the observation to a
+    square of 160x160 before rescaling them.
+    """
+    def __init__(self, crop_low: np.ndarray=None, crop_high: np.ndarray=None):
+        """
+        :param crop_low: a vector where each dimension describes the start index for cropping the observation in the
+                         corresponding dimension. a negative value of -1 will be mapped to the max size
+        :param crop_high: a vector where each dimension describes the end index for cropping the observation in the
+                          corresponding dimension. a negative value of -1 will be mapped to the max size
+        """
+        super().__init__()
+        if crop_low is None and crop_high is None:
+            raise ValueError("At least one of crop_low and crop_high should be set to a real value. ")
+        if crop_low is None:
+            crop_low = np.array([0] * len(crop_high))
+        if crop_high is None:
+            crop_high = np.array([-1] * len(crop_low))
+
+        self.crop_low = crop_low
+        self.crop_high = crop_high
+
+        for h, l in zip(crop_high, crop_low):
+            if h < l and h != -1:
+                raise ValueError("Some of the cropping low values are higher than cropping high values")
+        if np.any(crop_high < -1) or np.any(crop_low < -1):
+            raise ValueError("Cropping values cannot be negative")
+        if crop_low.shape != crop_high.shape:
+            raise ValueError("The low values and high values for cropping must have the same number of dimensions")
+        if crop_low.dtype != int or crop_high.dtype != int:
+            raise ValueError("The crop values should be int values, instead they are defined as: {} and {}"
+                             .format(crop_low.dtype, crop_high.dtype))
+
+    def _replace_negative_one_in_crop_size(self, crop_size: np.ndarray, observation_shape: Union[Tuple, np.ndarray]):
+        # replace -1 with the max size
+        crop_size = crop_size.copy()
+        for i in range(len(observation_shape)):
+            if crop_size[i] == -1:
+                crop_size[i] = observation_shape[i]
+        return crop_size
+
+    def validate_input_observation_space(self, input_observation_space: ObservationSpace):
+        crop_high = self._replace_negative_one_in_crop_size(self.crop_high, input_observation_space.shape)
+        crop_low = self._replace_negative_one_in_crop_size(self.crop_low, input_observation_space.shape)
+        if np.any(crop_high > input_observation_space.shape) or \
+                np.any(crop_low > input_observation_space.shape):
+            raise ValueError("The cropping values are outside of the observation space")
+        if not input_observation_space.is_point_in_space_shape(crop_low) or \
+                not input_observation_space.is_point_in_space_shape(crop_high - 1):
+            raise ValueError("The cropping indices are outside of the observation space")
+
+    def filter(self, observation: ObservationType, update_internal_state: bool=True) -> ObservationType:
+        # replace -1 with the max size
+        crop_high = self._replace_negative_one_in_crop_size(self.crop_high, observation.shape)
+        crop_low = self._replace_negative_one_in_crop_size(self.crop_low, observation.shape)
+
+        # crop
+        indices = [slice(i, j) for i, j in zip(crop_low, crop_high)]
+        observation = observation[indices]
+        return observation
+
+    def get_filtered_observation_space(self, input_observation_space: ObservationSpace) -> ObservationSpace:
+        # replace -1 with the max size
+        crop_high = self._replace_negative_one_in_crop_size(self.crop_high, input_observation_space.shape)
+        crop_low = self._replace_negative_one_in_crop_size(self.crop_low, input_observation_space.shape)
+
+        input_observation_space.shape = crop_high - crop_low
+        return input_observation_space

+
+ +
+ +
+ + +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_modules/rl_coach/filters/observation/observation_move_axis_filter.html b/docs/_modules/rl_coach/filters/observation/observation_move_axis_filter.html new file mode 100644 index 0000000..8a3c193 --- /dev/null +++ b/docs/_modules/rl_coach/filters/observation/observation_move_axis_filter.html @@ -0,0 +1,294 @@ + + + + + + + + + + + rl_coach.filters.observation.observation_move_axis_filter — Reinforcement Learning Coach 0.11.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + +
+ +
+ + + + + + + + + + + + + + + + + +
+ +
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    • + +
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    • + +
  • rl_coach.filters.observation.observation_move_axis_filter
    • + + +
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    • + +
+ + +
+
+
+
+ +

Source code for rl_coach.filters.observation.observation_move_axis_filter

+#
+# Copyright (c) 2017 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import numpy as np
+
+from rl_coach.core_types import ObservationType
+from rl_coach.filters.observation.observation_filter import ObservationFilter
+from rl_coach.spaces import ObservationSpace, PlanarMapsObservationSpace
+
+
+
[docs]class ObservationMoveAxisFilter(ObservationFilter):
+    """
+    Reorders the axes of the observation. This can be useful when the observation is an
+    image, and we want to move the channel axis to be the last axis instead of the first axis.
+    """
+    def __init__(self, axis_origin: int = None, axis_target: int=None):
+        """
+        :param axis_origin: The axis to move
+        :param axis_target: Where to move the selected axis to
+        """
+        super().__init__()
+        self.axis_origin = axis_origin
+        self.axis_target = axis_target
+
+    def validate_input_observation_space(self, input_observation_space: ObservationSpace):
+        shape = input_observation_space.shape
+        if not -len(shape) <= self.axis_origin < len(shape) or not -len(shape) <= self.axis_target < len(shape):
+            raise ValueError("The given axis does not exist in the context of the input observation shape. ")
+
+    def filter(self, observation: ObservationType, update_internal_state: bool=True) -> ObservationType:
+        return np.moveaxis(observation, self.axis_origin, self.axis_target)
+
+    def get_filtered_observation_space(self, input_observation_space: ObservationSpace) -> ObservationSpace:
+        axis_size = input_observation_space.shape[self.axis_origin]
+        input_observation_space.shape = np.delete(input_observation_space.shape, self.axis_origin)
+        if self.axis_target == -1:
+            input_observation_space.shape = np.append(input_observation_space.shape, axis_size)
+        elif self.axis_target < -1:
+            input_observation_space.shape = np.insert(input_observation_space.shape, self.axis_target+1, axis_size)
+        else:
+            input_observation_space.shape = np.insert(input_observation_space.shape, self.axis_target, axis_size)
+
+        # move the channels axis according to the axis change
+        if isinstance(input_observation_space, PlanarMapsObservationSpace):
+            if input_observation_space.channels_axis == self.axis_origin:
+                input_observation_space.channels_axis = self.axis_target
+            elif input_observation_space.channels_axis == self.axis_target:
+                input_observation_space.channels_axis = self.axis_origin
+            elif self.axis_origin < input_observation_space.channels_axis < self.axis_target:
+                input_observation_space.channels_axis -= 1
+            elif self.axis_target < input_observation_space.channels_axis < self.axis_origin:
+                input_observation_space.channels_axis += 1
+
+        return input_observation_space

+
+ +
+ +
+ + +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_modules/rl_coach/filters/observation/observation_normalization_filter.html b/docs/_modules/rl_coach/filters/observation/observation_normalization_filter.html new file mode 100644 index 0000000..bc57068 --- /dev/null +++ b/docs/_modules/rl_coach/filters/observation/observation_normalization_filter.html @@ -0,0 +1,302 @@ + + + + + + + + + + + rl_coach.filters.observation.observation_normalization_filter — Reinforcement Learning Coach 0.11.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + +
+ +
+ + + + + + + + + + + + + + + + + +
+ +
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    • + +
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    • + +
  • rl_coach.filters.observation.observation_normalization_filter
    • + + +
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    • + +
+ + +
+
+
+
+ +

Source code for rl_coach.filters.observation.observation_normalization_filter

+#
+# Copyright (c) 2017 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+from typing import List
+
+import numpy as np
+
+from rl_coach.architectures.tensorflow_components.shared_variables import SharedRunningStats
+from rl_coach.core_types import ObservationType
+from rl_coach.filters.observation.observation_filter import ObservationFilter
+from rl_coach.spaces import ObservationSpace
+
+
+
[docs]class ObservationNormalizationFilter(ObservationFilter):
+    """
+    Normalizes the observation values with a running mean and standard deviation of
+    all the observations seen so far. The normalization is performed element-wise. Additionally, when working with
+    multiple workers, the statistics used for the normalization operation are accumulated over all the workers.
+    """
+    def __init__(self, clip_min: float=-5.0, clip_max: float=5.0, name='observation_stats'):
+        """
+        :param clip_min: The minimum value to allow after normalizing the observation
+        :param clip_max: The maximum value to allow after normalizing the observation
+        """
+        super().__init__()
+        self.clip_min = clip_min
+        self.clip_max = clip_max
+        self.running_observation_stats = None
+        self.name = name
+        self.supports_batching = True
+        self.observation_space = None
+
+    def set_device(self, device, memory_backend_params=None) -> None:
+        """
+        An optional function that allows the filter to get the device if it is required to use tensorflow ops
+        :param device: the device to use
+        :return: None
+        """
+        self.running_observation_stats = SharedRunningStats(device, name=self.name, create_ops=False,
+                                                            pubsub_params=memory_backend_params)
+
+    def set_session(self, sess) -> None:
+        """
+        An optional function that allows the filter to get the session if it is required to use tensorflow ops
+        :param sess: the session
+        :return: None
+        """
+        self.running_observation_stats.set_session(sess)
+
+    def filter(self, observations: List[ObservationType], update_internal_state: bool=True) -> ObservationType:
+        observations = np.array(observations)
+        if update_internal_state:
+            self.running_observation_stats.push(observations)
+            self.last_mean = self.running_observation_stats.mean
+            self.last_stdev = self.running_observation_stats.std
+
+        # TODO: make sure that a batch is given here
+        return self.running_observation_stats.normalize(observations)
+
+    def get_filtered_observation_space(self, input_observation_space: ObservationSpace) -> ObservationSpace:
+        self.running_observation_stats.create_ops(shape=input_observation_space.shape,
+                                                  clip_values=(self.clip_min, self.clip_max))
+        return input_observation_space

+
+ +
+ +
+ + +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_modules/rl_coach/filters/observation/observation_reduction_by_sub_parts_name_filter.html b/docs/_modules/rl_coach/filters/observation/observation_reduction_by_sub_parts_name_filter.html new file mode 100644 index 0000000..f997686 --- /dev/null +++ b/docs/_modules/rl_coach/filters/observation/observation_reduction_by_sub_parts_name_filter.html @@ -0,0 +1,308 @@ + + + + + + + + + + + rl_coach.filters.observation.observation_reduction_by_sub_parts_name_filter — Reinforcement Learning Coach 0.11.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + +
+ +
+ + + + + + + + + + + + + + + + + +
+ +
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    • + + +
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    • + +
+ + +
+
+
+
+ +

Source code for rl_coach.filters.observation.observation_reduction_by_sub_parts_name_filter

+#
+# Copyright (c) 2017 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+import copy
+from enum import Enum
+from typing import List
+
+import numpy as np
+
+from rl_coach.core_types import ObservationType
+from rl_coach.filters.observation.observation_filter import ObservationFilter
+from rl_coach.spaces import ObservationSpace, VectorObservationSpace
+
+
+
[docs]class ObservationReductionBySubPartsNameFilter(ObservationFilter):
+    """
+    Allows keeping only parts of the observation, by specifying their
+    name. This is useful when the environment has a measurements vector as observation which includes several different
+    measurements, but you want the agent to only see some of the measurements and not all.
+    For example, the CARLA environment extracts multiple measurements that can be used by the agent, such as
+    speed and location. If we want to only use the speed, it can be done using this filter.
+    This will currently work only for VectorObservationSpace observations
+    """
+    class ReductionMethod(Enum):
+        Keep = 0
+        Discard = 1
+
+    def __init__(self, part_names: List[str], reduction_method: ReductionMethod):
+        """
+        :param part_names: A list of part names to reduce
+        :param reduction_method: A reduction method to use - keep or discard the given parts
+        """
+        super().__init__()
+        self.part_names = part_names
+        self.reduction_method = reduction_method
+        self.measurement_names = None
+        self.indices_to_keep = None
+
+    def filter(self, observation: ObservationType, update_internal_state: bool=True) -> ObservationType:
+        if not isinstance(observation, np.ndarray):
+            raise ValueError("All the state values are expected to be numpy arrays")
+        if self.indices_to_keep is None:
+            raise ValueError("To use ObservationReductionBySubPartsNameFilter, the get_filtered_observation_space "
+                             "function should be called before filtering an observation")
+        observation = observation[..., self.indices_to_keep]
+        return observation
+
+    def validate_input_observation_space(self, input_observation_space: ObservationSpace):
+        if not isinstance(input_observation_space, VectorObservationSpace):
+            raise ValueError("The ObservationReductionBySubPartsNameFilter support only VectorObservationSpace "
+                             "observations. The given observation space was: {}"
+                             .format(input_observation_space.__class__))
+
+    def get_filtered_observation_space(self, input_observation_space: VectorObservationSpace) -> ObservationSpace:
+        self.measurement_names = copy.copy(input_observation_space.measurements_names)
+
+        if self.reduction_method == self.ReductionMethod.Keep:
+            input_observation_space.shape[-1] = len(self.part_names)
+            self.indices_to_keep = [idx for idx, val in enumerate(self.measurement_names) if val in self.part_names]
+            input_observation_space.measurements_names = copy.copy(self.part_names)
+        elif self.reduction_method == self.ReductionMethod.Discard:
+            input_observation_space.shape[-1] -= len(self.part_names)
+            self.indices_to_keep = [idx for idx, val in enumerate(self.measurement_names) if val not in self.part_names]
+            input_observation_space.measurements_names = [val for val in input_observation_space.measurements_names if
+                                                          val not in self.part_names]
+        else:
+            raise ValueError("The given reduction method is not supported")
+
+        return input_observation_space

+
+ +
+ +
+ + +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_modules/rl_coach/filters/observation/observation_rescale_size_by_factor_filter.html b/docs/_modules/rl_coach/filters/observation/observation_rescale_size_by_factor_filter.html new file mode 100644 index 0000000..3e7f311 --- /dev/null +++ b/docs/_modules/rl_coach/filters/observation/observation_rescale_size_by_factor_filter.html @@ -0,0 +1,300 @@ + + + + + + + + + + + rl_coach.filters.observation.observation_rescale_size_by_factor_filter — Reinforcement Learning Coach 0.11.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + +
+ +
+ + + + + + + + + + + + + + + + + +
+ +
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  • rl_coach.filters.observation.observation_rescale_size_by_factor_filter
    • + + +
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    • + +
+ + +
+
+
+
+ +

Source code for rl_coach.filters.observation.observation_rescale_size_by_factor_filter

+#
+# Copyright (c) 2017 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from enum import Enum
+
+import scipy.ndimage
+
+from rl_coach.core_types import ObservationType
+from rl_coach.filters.observation.observation_filter import ObservationFilter
+from rl_coach.spaces import ObservationSpace
+
+
+# imresize interpolation types as defined by scipy here:
+# https://docs.scipy.org/doc/scipy-0.18.1/reference/generated/scipy.misc.imresize.html
+class RescaleInterpolationType(Enum):
+    NEAREST = 'nearest'
+    LANCZOS = 'lanczos'
+    BILINEAR = 'bilinear'
+    BICUBIC = 'bicubic'
+    CUBIC = 'cubic'
+
+
+
[docs]class ObservationRescaleSizeByFactorFilter(ObservationFilter):
+    """
+    Rescales an image observation by some factor. For example, the image size
+    can be reduced by a factor of 2.
+    Warning: this requires the input observation to be of type uint8 due to scipy requirements!
+    """
+    def __init__(self, rescale_factor: float, rescaling_interpolation_type: RescaleInterpolationType):
+        """
+        :param rescale_factor: the factor by which the observation will be rescaled
+        :param rescaling_interpolation_type: the interpolation type for rescaling
+        """
+        super().__init__()
+        self.rescale_factor = float(rescale_factor)  # scipy requires float scale factors
+        self.rescaling_interpolation_type = rescaling_interpolation_type
+        # TODO: allow selecting the channels dim
+
+    def validate_input_observation_space(self, input_observation_space: ObservationSpace):
+        if not 2 <= input_observation_space.num_dimensions <= 3:
+            raise ValueError("The rescale filter only applies to image observations where the number of dimensions is"
+                             "either 2 (grayscale) or 3 (RGB). The number of dimensions defined for the "
+                             "output observation was {}".format(input_observation_space.num_dimensions))
+        if input_observation_space.num_dimensions == 3 and input_observation_space.shape[-1] != 3:
+            raise ValueError("Observations with 3 dimensions must have 3 channels in the last axis (RGB)")
+
+    def filter(self, observation: ObservationType, update_internal_state: bool=True) -> ObservationType:
+        # scipy works only with uint8
+        observation = observation.astype('uint8')
+
+        # rescale
+        observation = scipy.misc.imresize(observation,
+                                          self.rescale_factor,
+                                          interp=self.rescaling_interpolation_type.value)
+
+        return observation
+
+    def get_filtered_observation_space(self, input_observation_space: ObservationSpace) -> ObservationSpace:
+        input_observation_space.shape[:2] = (input_observation_space.shape[:2] * self.rescale_factor).astype('int')
+        return input_observation_space

+
+ +
+ +
+ + +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_modules/rl_coach/filters/observation/observation_rescale_to_size_filter.html b/docs/_modules/rl_coach/filters/observation/observation_rescale_to_size_filter.html new file mode 100644 index 0000000..922f0e5 --- /dev/null +++ b/docs/_modules/rl_coach/filters/observation/observation_rescale_to_size_filter.html @@ -0,0 +1,326 @@ + + + + + + + + + + + rl_coach.filters.observation.observation_rescale_to_size_filter — Reinforcement Learning Coach 0.11.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + +
+ +
+ + + + + + + + + + + + + + + + + +
+ +
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  • Module code »
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  • rl_coach.filters.observation.observation_rescale_to_size_filter
    • + + +
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    • + +
+ + +
+
+
+
+ +

Source code for rl_coach.filters.observation.observation_rescale_to_size_filter

+#
+# Copyright (c) 2017 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import copy
+from enum import Enum
+
+import numpy as np
+import scipy.ndimage
+
+from rl_coach.core_types import ObservationType
+from rl_coach.filters.observation.observation_filter import ObservationFilter
+from rl_coach.spaces import ObservationSpace, PlanarMapsObservationSpace, ImageObservationSpace
+
+
+# imresize interpolation types as defined by scipy here:
+# https://docs.scipy.org/doc/scipy-0.18.1/reference/generated/scipy.misc.imresize.html
+class RescaleInterpolationType(Enum):
+    NEAREST = 'nearest'
+    LANCZOS = 'lanczos'
+    BILINEAR = 'bilinear'
+    BICUBIC = 'bicubic'
+    CUBIC = 'cubic'
+
+
+
[docs]class ObservationRescaleToSizeFilter(ObservationFilter):
+    """
+    Rescales an image observation to a given size. The target size does not
+    necessarily keep the aspect ratio of the original observation.
+    Warning: this requires the input observation to be of type uint8 due to scipy requirements!
+    """
+    def __init__(self, output_observation_space: PlanarMapsObservationSpace,
+                 rescaling_interpolation_type: RescaleInterpolationType=RescaleInterpolationType.BILINEAR):
+        """
+        :param output_observation_space: the output observation space
+        :param rescaling_interpolation_type: the interpolation type for rescaling
+        """
+        super().__init__()
+        self.output_observation_space = output_observation_space
+        self.rescaling_interpolation_type = rescaling_interpolation_type
+
+        if not isinstance(output_observation_space, PlanarMapsObservationSpace):
+            raise ValueError("The rescale filter only applies to observation spaces that inherit from "
+                             "PlanarMapsObservationSpace. This includes observations which consist of a set of 2D "
+                             "images or an RGB image. Instead the output observation space was defined as: {}"
+                             .format(output_observation_space.__class__))
+
+        self.planar_map_output_shape = copy.copy(self.output_observation_space.shape)
+        self.planar_map_output_shape = np.delete(self.planar_map_output_shape,
+                                                 self.output_observation_space.channels_axis)
+
+    def validate_input_observation_space(self, input_observation_space: ObservationSpace):
+        if not isinstance(input_observation_space, PlanarMapsObservationSpace):
+            raise ValueError("The rescale filter only applies to observation spaces that inherit from "
+                             "PlanarMapsObservationSpace. This includes observations which consist of a set of 2D "
+                             "images or an RGB image. Instead the input observation space was defined as: {}"
+                             .format(input_observation_space.__class__))
+        if input_observation_space.shape[input_observation_space.channels_axis] \
+                != self.output_observation_space.shape[self.output_observation_space.channels_axis]:
+            raise ValueError("The number of channels between the input and output observation spaces must match. "
+                             "Instead the number of channels were: {}, {}"
+                             .format(input_observation_space.shape[input_observation_space.channels_axis],
+                             self.output_observation_space.shape[self.output_observation_space.channels_axis]))
+
+    def filter(self, observation: ObservationType, update_internal_state: bool=True) -> ObservationType:
+        # scipy works only with uint8
+        observation = observation.astype('uint8')
+
+        # rescale
+        if isinstance(self.output_observation_space, ImageObservationSpace):
+            observation = scipy.misc.imresize(observation,
+                                              tuple(self.output_observation_space.shape),
+                                              interp=self.rescaling_interpolation_type.value)
+        else:
+            new_observation = []
+            for i in range(self.output_observation_space.shape[self.output_observation_space.channels_axis]):
+                new_observation.append(scipy.misc.imresize(observation.take(i, self.output_observation_space.channels_axis),
+                                                  tuple(self.planar_map_output_shape),
+                                                  interp=self.rescaling_interpolation_type.value))
+            new_observation = np.array(new_observation)
+            observation = new_observation.swapaxes(0, self.output_observation_space.channels_axis)
+
+        return observation
+
+    def get_filtered_observation_space(self, input_observation_space: ObservationSpace) -> ObservationSpace:
+        input_observation_space.shape = self.output_observation_space.shape
+        return input_observation_space

+
+ +
+ +
+ + +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_modules/rl_coach/filters/observation/observation_rgb_to_y_filter.html b/docs/_modules/rl_coach/filters/observation/observation_rgb_to_y_filter.html new file mode 100644 index 0000000..cf4081b --- /dev/null +++ b/docs/_modules/rl_coach/filters/observation/observation_rgb_to_y_filter.html @@ -0,0 +1,278 @@ + + + + + + + + + + + rl_coach.filters.observation.observation_rgb_to_y_filter — Reinforcement Learning Coach 0.11.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + +
+ +
+ + + + + + + + + + + + + + + + + +
+ +
    + +
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    • + +
  • Module code »
    • + +
  • rl_coach.filters.observation.observation_rgb_to_y_filter
    • + + +
  • + +
    • + +
+ + +
+
+
+
+ +

Source code for rl_coach.filters.observation.observation_rgb_to_y_filter

+#
+# Copyright (c) 2017 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from rl_coach.core_types import ObservationType
+from rl_coach.filters.observation.observation_filter import ObservationFilter
+from rl_coach.spaces import ObservationSpace
+
+
+
[docs]class ObservationRGBToYFilter(ObservationFilter):
+    """
+    Converts a color image observation specified using the RGB encoding into a grayscale
+    image observation, by keeping only the luminance (Y) channel of the YUV encoding. This can be useful if the colors
+    in the original image are not relevant for solving the task at hand.
+    The channels axis is assumed to be the last axis
+    """
+    def __init__(self):
+        super().__init__()
+
+    def validate_input_observation_space(self, input_observation_space: ObservationSpace):
+        if input_observation_space.num_dimensions != 3:
+            raise ValueError("The rescale filter only applies to image observations where the number of dimensions is"
+                             "3 (RGB). The number of dimensions defined for the input observation was {}"
+                             .format(input_observation_space.num_dimensions))
+        if input_observation_space.shape[-1] != 3:
+            raise ValueError("The observation space is expected to have 3 channels in the 1st dimension. The number of "
+                             "dimensions received is {}".format(input_observation_space.shape[-1]))
+
+    def filter(self, observation: ObservationType, update_internal_state: bool=True) -> ObservationType:
+
+        # rgb to y
+        r, g, b = observation[:, :, 0], observation[:, :, 1], observation[:, :, 2]
+        observation = 0.2989 * r + 0.5870 * g + 0.1140 * b
+
+        return observation
+
+    def get_filtered_observation_space(self, input_observation_space: ObservationSpace) -> ObservationSpace:
+        input_observation_space.shape = input_observation_space.shape[:-1]
+        return input_observation_space

+
+ +
+ +
+ + +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_modules/rl_coach/filters/observation/observation_squeeze_filter.html b/docs/_modules/rl_coach/filters/observation/observation_squeeze_filter.html new file mode 100644 index 0000000..d9c0445 --- /dev/null +++ b/docs/_modules/rl_coach/filters/observation/observation_squeeze_filter.html @@ -0,0 +1,276 @@ + + + + + + + + + + + rl_coach.filters.observation.observation_squeeze_filter — Reinforcement Learning Coach 0.11.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + +
+ +
+ + + + + + + + + + + + + + + + + +
+ +
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    • + +
  • Module code »
    • + +
  • rl_coach.filters.observation.observation_squeeze_filter
    • + + +
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    • + +
+ + +
+
+
+
+ +

Source code for rl_coach.filters.observation.observation_squeeze_filter

+#
+# Copyright (c) 2017 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import numpy as np
+
+from rl_coach.core_types import ObservationType
+from rl_coach.filters.observation.observation_filter import ObservationFilter
+from rl_coach.spaces import ObservationSpace
+
+
+
[docs]class ObservationSqueezeFilter(ObservationFilter):
+    """
+    Removes redundant axes from the observation, which are axes with a dimension of 1.
+    """
+    def __init__(self, axis: int = None):
+        """
+        :param axis: Specifies which axis to remove. If set to None, all the axes of size 1 will be removed.
+        """
+        super().__init__()
+        self.axis = axis
+
+    def validate_input_observation_space(self, input_observation_space: ObservationSpace):
+        if self.axis is None:
+            return
+
+        shape = input_observation_space.shape
+        if self.axis >= len(shape) or self.axis < -len(shape):
+            raise ValueError("The given axis does not exist in the context of the input observation shape. ")
+
+    def filter(self, observation: ObservationType, update_internal_state: bool=True) -> ObservationType:
+        return observation.squeeze(axis=self.axis)
+
+    def get_filtered_observation_space(self, input_observation_space: ObservationSpace) -> ObservationSpace:
+        dummy_tensor = np.random.rand(*tuple(input_observation_space.shape))
+        input_observation_space.shape = dummy_tensor.squeeze(axis=self.axis).shape
+        return input_observation_space

+
+ +
+ +
+ + +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_modules/rl_coach/filters/observation/observation_stacking_filter.html b/docs/_modules/rl_coach/filters/observation/observation_stacking_filter.html new file mode 100644 index 0000000..3f6764e --- /dev/null +++ b/docs/_modules/rl_coach/filters/observation/observation_stacking_filter.html @@ -0,0 +1,335 @@ + + + + + + + + + + + rl_coach.filters.observation.observation_stacking_filter — Reinforcement Learning Coach 0.11.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + +
+ +
+ + + + + + + + + + + + + + + + + +
+ +
    + +
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    • + +
  • Module code »
    • + +
  • rl_coach.filters.observation.observation_stacking_filter
    • + + +
  • + +
    • + +
+ + +
+
+
+
+ +

Source code for rl_coach.filters.observation.observation_stacking_filter

+#
+# Copyright (c) 2017 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import copy
+from collections import deque
+
+import numpy as np
+
+from rl_coach.core_types import ObservationType
+from rl_coach.filters.observation.observation_filter import ObservationFilter
+from rl_coach.spaces import ObservationSpace
+
+
+class LazyStack(object):
+    """
+    A lazy version of np.stack which avoids copying the memory until it is
+    needed.
+    """
+
+    def __init__(self, history, axis=None):
+        self.history = copy.copy(history)
+        self.axis = axis
+
+    def __array__(self, dtype=None):
+        array = np.stack(self.history, axis=self.axis)
+        if dtype is not None:
+            array = array.astype(dtype)
+        return array
+
+
+
[docs]class ObservationStackingFilter(ObservationFilter):
+    """
+    Stacks several observations on top of each other. For image observation this will
+    create a 3D blob. The stacking is done in a lazy manner in order to reduce memory consumption. To achieve this,
+    a LazyStack object is used in order to wrap the observations in the stack. For this reason, the
+    ObservationStackingFilter **must** be the last filter in the inputs filters stack.
+    This filter is stateful since it stores the previous step result and depends on it.
+    The filter adds an additional dimension to the output observation.
+
+    Warning!!! The filter replaces the observation with a LazyStack object, so no filters should be
+    applied after this filter. applying more filters will cause the LazyStack object to be converted to a numpy array
+    and increase the memory footprint.
+    """
+    def __init__(self, stack_size: int, stacking_axis: int=-1):
+        """
+        :param stack_size: the number of previous observations in the stack
+        :param stacking_axis: the axis on which to stack the observation on
+        """
+        super().__init__()
+        self.stack_size = stack_size
+        self.stacking_axis = stacking_axis
+        self.stack = []
+
+        if stack_size <= 0:
+            raise ValueError("The stack shape must be a positive number")
+        if type(stack_size) != int:
+            raise ValueError("The stack shape must be of int type")
+
+    @property
+    def next_filter(self) -> 'InputFilter':
+        return self._next_filter
+
+    @next_filter.setter
+    def next_filter(self, val: 'InputFilter'):
+        raise ValueError("ObservationStackingFilter can have no other filters after it since they break its "
+                         "functionality")
+
+    def validate_input_observation_space(self, input_observation_space: ObservationSpace):
+        if len(self.stack) > 0 and not input_observation_space.val_matches_space_definition(self.stack[-1]):
+            raise ValueError("The given input observation space is different than the observations already stored in"
+                             "the filters memory")
+        if input_observation_space.num_dimensions <= self.stacking_axis:
+            raise ValueError("The stacking axis is larger than the number of dimensions in the observation space")
+
+    def filter(self, observation: ObservationType, update_internal_state: bool=True) -> ObservationType:
+
+        if len(self.stack) == 0:
+            self.stack = deque([observation] * self.stack_size, maxlen=self.stack_size)
+        else:
+            if update_internal_state:
+                self.stack.append(observation)
+        observation = LazyStack(self.stack, self.stacking_axis)
+
+        return observation
+
+    def get_filtered_observation_space(self, input_observation_space: ObservationSpace) -> ObservationSpace:
+        if self.stacking_axis == -1:
+            input_observation_space.shape = np.append(input_observation_space.shape, values=[self.stack_size], axis=0)
+        else:
+            input_observation_space.shape = np.insert(input_observation_space.shape, obj=self.stacking_axis,
+                                                     values=[self.stack_size], axis=0)
+        return input_observation_space
+
+    def reset(self) -> None:
+        self.stack = []

+
+ +
+ +
+ + +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_modules/rl_coach/filters/observation/observation_to_uint8_filter.html b/docs/_modules/rl_coach/filters/observation/observation_to_uint8_filter.html new file mode 100644 index 0000000..30f9317 --- /dev/null +++ b/docs/_modules/rl_coach/filters/observation/observation_to_uint8_filter.html @@ -0,0 +1,292 @@ + + + + + + + + + + + rl_coach.filters.observation.observation_to_uint8_filter — Reinforcement Learning Coach 0.11.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + +
+ +
+ + + + + + + + + + + + + + + + + +
+ +
    + +
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    • + +
  • Module code »
    • + +
  • rl_coach.filters.observation.observation_to_uint8_filter
    • + + +
  • + +
    • + +
+ + +
+
+
+
+ +

Source code for rl_coach.filters.observation.observation_to_uint8_filter

+#
+# Copyright (c) 2017 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import numpy as np
+
+from rl_coach.core_types import ObservationType
+from rl_coach.filters.observation.observation_filter import ObservationFilter
+from rl_coach.spaces import ObservationSpace
+
+
+
[docs]class ObservationToUInt8Filter(ObservationFilter):
+    """
+    Converts a floating point observation into an unsigned int 8 bit observation. This is
+    mostly useful for reducing memory consumption and is usually used for image observations. The filter will first
+    spread the observation values over the range 0-255 and then discretize them into integer values.
+    """
+    def __init__(self, input_low: float, input_high: float):
+        """
+        :param input_low: The lowest value currently present in the observation
+        :param input_high: The highest value currently present in the observation
+        """
+        super().__init__()
+        self.input_low = input_low
+        self.input_high = input_high
+
+        if input_high <= input_low:
+            raise ValueError("The input observation space high values can be less or equal to the input observation "
+                             "space low values")
+
+    def validate_input_observation_space(self, input_observation_space: ObservationSpace):
+        if np.all(input_observation_space.low != self.input_low) or \
+                np.all(input_observation_space.high != self.input_high):
+            raise ValueError("The observation space values range don't match the configuration of the filter."
+                             "The configuration is: low = {}, high = {}. The actual values are: low = {}, high = {}"
+                             .format(self.input_low, self.input_high,
+                                     input_observation_space.low, input_observation_space.high))
+
+    def filter(self, observation: ObservationType, update_internal_state: bool=True) -> ObservationType:
+        # scale to 0-1
+        observation = (observation - self.input_low) / (self.input_high - self.input_low)
+
+        # scale to 0-255
+        observation *= 255
+
+        observation = observation.astype('uint8')
+
+        return observation
+
+    def get_filtered_observation_space(self, input_observation_space: ObservationSpace) -> ObservationSpace:
+        input_observation_space.low = 0
+        input_observation_space.high = 255
+        return input_observation_space

+
+ +
+ +
+ + +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_modules/rl_coach/filters/reward/reward_clipping_filter.html b/docs/_modules/rl_coach/filters/reward/reward_clipping_filter.html new file mode 100644 index 0000000..9bc744f --- /dev/null +++ b/docs/_modules/rl_coach/filters/reward/reward_clipping_filter.html @@ -0,0 +1,281 @@ + + + + + + + + + + + rl_coach.filters.reward.reward_clipping_filter — Reinforcement Learning Coach 0.11.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + +
+ +
+ + + + + + + + + + + + + + + + + +
+ +
    + +
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    • + +
  • Module code »
    • + +
  • rl_coach.filters.reward.reward_clipping_filter
    • + + +
  • + +
    • + +
+ + +
+
+
+
+ +

Source code for rl_coach.filters.reward.reward_clipping_filter

+#
+# Copyright (c) 2017 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import numpy as np
+
+from rl_coach.core_types import RewardType
+from rl_coach.filters.reward.reward_filter import RewardFilter
+from rl_coach.spaces import RewardSpace
+
+
+
[docs]class RewardClippingFilter(RewardFilter):
+    """
+    Clips the reward values into a given range. For example, in DQN, the Atari rewards are
+    clipped into the range -1 and 1 in order to control the scale of the returns.
+    """
+    def __init__(self, clipping_low: float=-np.inf, clipping_high: float=np.inf):
+        """
+        :param clipping_low: The low threshold for reward clipping
+        :param clipping_high: The high threshold for reward clipping
+        """
+        super().__init__()
+        self.clipping_low = clipping_low
+        self.clipping_high = clipping_high
+
+        if clipping_low > clipping_high:
+            raise ValueError("The reward clipping low must be lower than the reward clipping max")
+
+    def filter(self, reward: RewardType, update_internal_state: bool=True) -> RewardType:
+        reward = float(reward)
+
+        if self.clipping_high:
+            reward = min(reward, self.clipping_high)
+        if self.clipping_low:
+            reward = max(reward, self.clipping_low)
+
+        return reward
+
+    def get_filtered_reward_space(self, input_reward_space: RewardSpace) -> RewardSpace:
+        input_reward_space.high = min(self.clipping_high, input_reward_space.high)
+        input_reward_space.low = max(self.clipping_low, input_reward_space.low)
+        return input_reward_space

+
+ +
+ +
+ + +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_modules/rl_coach/filters/reward/reward_normalization_filter.html b/docs/_modules/rl_coach/filters/reward/reward_normalization_filter.html new file mode 100644 index 0000000..a391dca --- /dev/null +++ b/docs/_modules/rl_coach/filters/reward/reward_normalization_filter.html @@ -0,0 +1,297 @@ + + + + + + + + + + + rl_coach.filters.reward.reward_normalization_filter — Reinforcement Learning Coach 0.11.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + +
+ +
+ + + + + + + + + + + + + + + + + +
+ +
    + +
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    • + +
  • Module code »
    • + +
  • rl_coach.filters.reward.reward_normalization_filter
    • + + +
  • + +
    • + +
+ + +
+
+
+
+ +

Source code for rl_coach.filters.reward.reward_normalization_filter

+#
+# Copyright (c) 2017 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+
+import numpy as np
+
+from rl_coach.architectures.tensorflow_components.shared_variables import SharedRunningStats
+from rl_coach.core_types import RewardType
+from rl_coach.filters.reward.reward_filter import RewardFilter
+from rl_coach.spaces import RewardSpace
+
+
+
[docs]class RewardNormalizationFilter(RewardFilter):
+    """
+    Normalizes the reward values with a running mean and standard deviation of
+    all the rewards seen so far. When working with multiple workers, the statistics used for the normalization operation
+    are accumulated over all the workers.
+    """
+    def __init__(self, clip_min: float=-5.0, clip_max: float=5.0):
+        """
+        :param clip_min: The minimum value to allow after normalizing the reward
+        :param clip_max: The maximum value to allow after normalizing the reward
+        """
+        super().__init__()
+        self.clip_min = clip_min
+        self.clip_max = clip_max
+        self.running_rewards_stats = None
+
+    def set_device(self, device, memory_backend_params=None) -> None:
+        """
+        An optional function that allows the filter to get the device if it is required to use tensorflow ops
+        :param device: the device to use
+        :return: None
+        """
+        self.running_rewards_stats = SharedRunningStats(device, name='rewards_stats',
+                                                        pubsub_params=memory_backend_params)
+
+    def set_session(self, sess) -> None:
+        """
+        An optional function that allows the filter to get the session if it is required to use tensorflow ops
+        :param sess: the session
+        :return: None
+        """
+        self.running_rewards_stats.set_session(sess)
+
+    def filter(self, reward: RewardType, update_internal_state: bool=True) -> RewardType:
+        if update_internal_state:
+            self.running_rewards_stats.push(reward)
+
+        reward = (reward - self.running_rewards_stats.mean) / \
+                      (self.running_rewards_stats.std + 1e-15)
+        reward = np.clip(reward, self.clip_min, self.clip_max)
+
+        return reward
+
+    def get_filtered_reward_space(self, input_reward_space: RewardSpace) -> RewardSpace:
+        return input_reward_space

+
+ +
+ +
+ + +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_modules/rl_coach/filters/reward/reward_rescale_filter.html b/docs/_modules/rl_coach/filters/reward/reward_rescale_filter.html new file mode 100644 index 0000000..f24abb6 --- /dev/null +++ b/docs/_modules/rl_coach/filters/reward/reward_rescale_filter.html @@ -0,0 +1,271 @@ + + + + + + + + + + + rl_coach.filters.reward.reward_rescale_filter — Reinforcement Learning Coach 0.11.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + +
+ +
+ + + + + + + + + + + + + + + + + +
+ +
    + +
  • Docs »
    • + +
  • Module code »
    • + +
  • rl_coach.filters.reward.reward_rescale_filter
    • + + +
  • + +
    • + +
+ + +
+
+
+
+ +

Source code for rl_coach.filters.reward.reward_rescale_filter

+#
+# Copyright (c) 2017 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from rl_coach.core_types import RewardType
+from rl_coach.filters.reward.reward_filter import RewardFilter
+from rl_coach.spaces import RewardSpace
+
+
+
[docs]class RewardRescaleFilter(RewardFilter):
+    """
+    Rescales the reward by a given factor. Rescaling the rewards of the environment has been
+    observed to have a large effect (negative or positive) on the behavior of the learning process.
+    """
+    def __init__(self, rescale_factor: float):
+        """
+        :param rescale_factor: The reward rescaling factor by which the reward will be multiplied
+        """
+        super().__init__()
+        self.rescale_factor = rescale_factor
+
+        if rescale_factor == 0:
+            raise ValueError("The reward rescale value can not be set to 0")
+
+    def filter(self, reward: RewardType, update_internal_state: bool=True) -> RewardType:
+        reward = float(reward) * self.rescale_factor
+        return reward
+
+    def get_filtered_reward_space(self, input_reward_space: RewardSpace) -> RewardSpace:
+        input_reward_space.high = input_reward_space.high * self.rescale_factor
+        input_reward_space.low = input_reward_space.low * self.rescale_factor
+        return input_reward_space

+
+ +
+ +
+ + +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_modules/rl_coach/memories/episodic/episodic_experience_replay.html b/docs/_modules/rl_coach/memories/episodic/episodic_experience_replay.html new file mode 100644 index 0000000..0461fc3 --- /dev/null +++ b/docs/_modules/rl_coach/memories/episodic/episodic_experience_replay.html @@ -0,0 +1,535 @@ + + + + + + + + + + + rl_coach.memories.episodic.episodic_experience_replay — Reinforcement Learning Coach 0.11.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + +
+ +
+ + + + + + + + + + + + + + + + + +
+ +
    + +
  • Docs »
    • + +
  • Module code »
    • + +
  • rl_coach.memories.episodic.episodic_experience_replay
    • + + +
  • + +
    • + +
+ + +
+
+
+
+ +

Source code for rl_coach.memories.episodic.episodic_experience_replay

+#
+# Copyright (c) 2017 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from typing import List, Tuple, Union, Dict, Any
+
+import numpy as np
+
+from rl_coach.core_types import Transition, Episode
+from rl_coach.memories.memory import Memory, MemoryGranularity, MemoryParameters
+from rl_coach.utils import ReaderWriterLock
+
+
+class EpisodicExperienceReplayParameters(MemoryParameters):
+    def __init__(self):
+        super().__init__()
+        self.max_size = (MemoryGranularity.Transitions, 1000000)
+        self.n_step = -1
+
+    @property
+    def path(self):
+        return 'rl_coach.memories.episodic.episodic_experience_replay:EpisodicExperienceReplay'
+
+
+
[docs]class EpisodicExperienceReplay(Memory):
+    """
+    A replay buffer that stores episodes of transitions. The additional structure allows performing various
+    calculations of total return and other values that depend on the sequential behavior of the transitions
+    in the episode.
+    """
+    def __init__(self, max_size: Tuple[MemoryGranularity, int]=(MemoryGranularity.Transitions, 1000000), n_step=-1):
+        """
+        :param max_size: the maximum number of transitions or episodes to hold in the memory
+        """
+        super().__init__(max_size)
+        self.n_step = n_step
+        self._buffer = [Episode(n_step=self.n_step)]  # list of episodes
+        self.transitions = []
+        self._length = 1  # the episodic replay buffer starts with a single empty episode
+        self._num_transitions = 0
+        self._num_transitions_in_complete_episodes = 0
+        self.reader_writer_lock = ReaderWriterLock()
+
+    def length(self, lock: bool=False) -> int:
+        """
+        Get the number of episodes in the ER (even if they are not complete)
+        """
+        length = self._length
+        if self._length is not 0 and self._buffer[-1].is_empty():
+            length = self._length - 1
+
+        return length
+
+    def num_complete_episodes(self):
+        """ Get the number of complete episodes in ER """
+        length = self._length - 1
+
+        return length
+
+    def num_transitions(self):
+        return self._num_transitions
+
+    def num_transitions_in_complete_episodes(self):
+        return self._num_transitions_in_complete_episodes
+
+    def sample(self, size: int) -> List[Transition]:
+        """
+        Sample a batch of transitions form the replay buffer. If the requested size is larger than the number
+        of samples available in the replay buffer then the batch will return empty.
+        :param size: the size of the batch to sample
+        :return: a batch (list) of selected transitions from the replay buffer
+        """
+        self.reader_writer_lock.lock_writing()
+
+        if self.num_complete_episodes() >= 1:
+            transitions_idx = np.random.randint(self.num_transitions_in_complete_episodes(), size=size)
+            batch = [self.transitions[i] for i in transitions_idx]
+
+        else:
+            raise ValueError("The episodic replay buffer cannot be sampled since there are no complete episodes yet. "
+                             "There is currently 1 episodes with {} transitions".format(self._buffer[0].length()))
+
+        self.reader_writer_lock.release_writing()
+
+        return batch
+
+    def _enforce_max_length(self) -> None:
+        """
+        Make sure that the size of the replay buffer does not pass the maximum size allowed.
+        If it passes the max size, the oldest episode in the replay buffer will be removed.
+        :return: None
+        """
+        granularity, size = self.max_size
+        if granularity == MemoryGranularity.Transitions:
+            while size != 0 and self.num_transitions() > size:
+                self._remove_episode(0)
+        elif granularity == MemoryGranularity.Episodes:
+            while self.length() > size:
+                self._remove_episode(0)
+
+    def _update_episode(self, episode: Episode) -> None:
+        episode.update_transitions_rewards_and_bootstrap_data()
+
+    def verify_last_episode_is_closed(self) -> None:
+        """
+        Verify that there is no open episodes in the replay buffer
+        :return: None
+        """
+        self.reader_writer_lock.lock_writing_and_reading()
+
+        last_episode = self.get(-1, False)
+        if last_episode and last_episode.length() > 0:
+            self.close_last_episode(lock=False)
+
+        self.reader_writer_lock.release_writing_and_reading()
+
+    def close_last_episode(self, lock=True) -> None:
+        """
+        Close the last episode in the replay buffer and open a new one
+        :return: None
+        """
+        if lock:
+            self.reader_writer_lock.lock_writing_and_reading()
+
+        last_episode = self._buffer[-1]
+
+        self._num_transitions_in_complete_episodes += last_episode.length()
+        self._length += 1
+
+        # create a new Episode for the next transitions to be placed into
+        self._buffer.append(Episode(n_step=self.n_step))
+
+        # if update episode adds to the buffer, a new Episode needs to be ready first
+        # it would be better if this were less state full
+        self._update_episode(last_episode)
+
+        self._enforce_max_length()
+
+        if lock:
+            self.reader_writer_lock.release_writing_and_reading()
+
+    def store(self, transition: Transition) -> None:
+        """
+        Store a new transition in the memory. If the transition game_over flag is on, this closes the episode and
+        creates a new empty episode.
+        Warning! using the episodic memory by storing individual transitions instead of episodes will use the default
+        Episode class parameters in order to create new episodes.
+        :param transition: a transition to store
+        :return: None
+        """
+
+        # Calling super.store() so that in case a memory backend is used, the memory backend can store this transition.
+        super().store(transition)
+
+        self.reader_writer_lock.lock_writing_and_reading()
+
+        if len(self._buffer) == 0:
+            self._buffer.append(Episode(n_step=self.n_step))
+        last_episode = self._buffer[-1]
+        last_episode.insert(transition)
+        self.transitions.append(transition)
+        self._num_transitions += 1
+        if transition.game_over:
+            self.close_last_episode(False)
+
+        self._enforce_max_length()
+
+        self.reader_writer_lock.release_writing_and_reading()
+
+    def store_episode(self, episode: Episode, lock: bool=True) -> None:
+        """
+        Store a new episode in the memory.
+        :param episode: the new episode to store
+        :return: None
+        """
+        # Calling super.store() so that in case a memory backend is used, the memory backend can store this episode.
+        super().store_episode(episode)
+
+        if lock:
+            self.reader_writer_lock.lock_writing_and_reading()
+
+        if self._buffer[-1].length() == 0:
+            self._buffer[-1] = episode
+        else:
+            self._buffer.append(episode)
+        self.transitions.extend(episode.transitions)
+        self._num_transitions += episode.length()
+        self.close_last_episode(False)
+
+        if lock:
+            self.reader_writer_lock.release_writing_and_reading()
+
+    def get_episode(self, episode_index: int, lock: bool=True) -> Union[None, Episode]:
+        """
+        Returns the episode in the given index. If the episode does not exist, returns None instead.
+        :param episode_index: the index of the episode to return
+        :return: the corresponding episode
+        """
+        if lock:
+            self.reader_writer_lock.lock_writing()
+
+        if self.length() == 0 or episode_index >= self.length():
+            episode = None
+        else:
+            episode = self._buffer[episode_index]
+
+        if lock:
+            self.reader_writer_lock.release_writing()
+        return episode
+
+    def _remove_episode(self, episode_index: int) -> None:
+        """
+        Remove the episode in the given index (even if it is not complete yet)
+        :param episode_index: the index of the episode to remove
+        :return: None
+        """
+        if len(self._buffer) > episode_index:
+            episode_length = self._buffer[episode_index].length()
+            self._length -= 1
+            self._num_transitions -= episode_length
+            self._num_transitions_in_complete_episodes -= episode_length
+            del self.transitions[:episode_length]
+            del self._buffer[episode_index]
+
+    def remove_episode(self, episode_index: int) -> None:
+        """
+        Remove the episode in the given index (even if it is not complete yet)
+        :param episode_index: the index of the episode to remove
+        :return: None
+        """
+        self.reader_writer_lock.lock_writing_and_reading()
+
+        self._remove_episode(episode_index)
+
+        self.reader_writer_lock.release_writing_and_reading()
+
+    # for API compatibility
+    def get(self, episode_index: int, lock: bool=True) -> Union[None, Episode]:
+        """
+        Returns the episode in the given index. If the episode does not exist, returns None instead.
+        :param episode_index: the index of the episode to return
+        :return: the corresponding episode
+        """
+        return self.get_episode(episode_index, lock)
+
+    def get_last_complete_episode(self) -> Union[None, Episode]:
+        """
+        Returns the last complete episode in the memory or None if there are no complete episodes
+        :return: None or the last complete episode
+        """
+        self.reader_writer_lock.lock_writing()
+
+        last_complete_episode_index = self.num_complete_episodes() - 1
+        episode = None
+        if last_complete_episode_index >= 0:
+            episode = self.get(last_complete_episode_index)
+
+        self.reader_writer_lock.release_writing()
+
+        return episode
+
+    # for API compatibility
+    def remove(self, episode_index: int):
+        """
+        Remove the episode in the given index (even if it is not complete yet)
+        :param episode_index: the index of the episode to remove
+        :return: None
+        """
+        self.remove_episode(episode_index)
+
+    def clean(self) -> None:
+        """
+        Clean the memory by removing all the episodes
+        :return: None
+        """
+        self.reader_writer_lock.lock_writing_and_reading()
+
+        self.transitions = []
+        self._buffer = [Episode(n_step=self.n_step)]
+        self._length = 1
+        self._num_transitions = 0
+        self._num_transitions_in_complete_episodes = 0
+
+        self.reader_writer_lock.release_writing_and_reading()
+
+    def mean_reward(self) -> np.ndarray:
+        """
+        Get the mean reward in the replay buffer
+        :return: the mean reward
+        """
+        self.reader_writer_lock.lock_writing()
+
+        mean = np.mean([transition.reward for transition in self.transitions])
+
+        self.reader_writer_lock.release_writing()
+        return mean

+
+ +
+ +
+ + +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_modules/rl_coach/memories/episodic/episodic_hindsight_experience_replay.html b/docs/_modules/rl_coach/memories/episodic/episodic_hindsight_experience_replay.html new file mode 100644 index 0000000..84f03f5 --- /dev/null +++ b/docs/_modules/rl_coach/memories/episodic/episodic_hindsight_experience_replay.html @@ -0,0 +1,375 @@ + + + + + + + + + + + rl_coach.memories.episodic.episodic_hindsight_experience_replay — Reinforcement Learning Coach 0.11.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + +
+ +
+ + + + + + + + + + + + + + + + + +
+ +
    + +
  • Docs »
    • + +
  • Module code »
    • + +
  • rl_coach.memories.episodic.episodic_hindsight_experience_replay
    • + + +
  • + +
    • + +
+ + +
+
+
+
+ +

Source code for rl_coach.memories.episodic.episodic_hindsight_experience_replay

+#
+# Copyright (c) 2017 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import copy
+from enum import Enum
+from typing import Tuple, List
+
+import numpy as np
+
+from rl_coach.core_types import Episode, Transition
+from rl_coach.memories.episodic.episodic_experience_replay import EpisodicExperienceReplayParameters, \
+    EpisodicExperienceReplay
+from rl_coach.memories.non_episodic.experience_replay import MemoryGranularity
+from rl_coach.spaces import GoalsSpace
+
+
+class HindsightGoalSelectionMethod(Enum):
+    Future = 0
+    Final = 1
+    Episode = 2
+    Random = 3
+
+
+class EpisodicHindsightExperienceReplayParameters(EpisodicExperienceReplayParameters):
+    def __init__(self):
+        super().__init__()
+        self.hindsight_transitions_per_regular_transition = None
+        self.hindsight_goal_selection_method = None
+        self.goals_space = None
+
+    @property
+    def path(self):
+        return 'rl_coach.memories.episodic.episodic_hindsight_experience_replay:EpisodicHindsightExperienceReplay'
+
+
+
[docs]class EpisodicHindsightExperienceReplay(EpisodicExperienceReplay):
+    """
+    Implements Hindsight Experience Replay as described in the following paper: https://arxiv.org/pdf/1707.01495.pdf
+
+    """
+    def __init__(self, max_size: Tuple[MemoryGranularity, int],
+                 hindsight_transitions_per_regular_transition: int,
+                 hindsight_goal_selection_method: HindsightGoalSelectionMethod,
+                 goals_space: GoalsSpace):
+        """
+        :param max_size: The maximum size of the memory. should be defined in a granularity of Transitions
+        :param hindsight_transitions_per_regular_transition: The number of hindsight artificial transitions to generate
+                                                             for each actual transition
+        :param hindsight_goal_selection_method: The method that will be used for generating the goals for the
+                                                hindsight transitions. Should be one of HindsightGoalSelectionMethod
+        :param goals_space: A GoalsSpace which defines the base properties of the goals space
+        """
+        super().__init__(max_size)
+
+        self.hindsight_transitions_per_regular_transition = hindsight_transitions_per_regular_transition
+        self.hindsight_goal_selection_method = hindsight_goal_selection_method
+        self.goals_space = goals_space
+        self.last_episode_start_idx = 0
+
+    def _sample_goal(self, episode_transitions: List, transition_index: int):
+        """
+        Sample a single goal state according to the sampling method
+        :param episode_transitions: a list of all the transitions in the current episode
+        :param transition_index: the transition to start sampling from
+        :return: a goal corresponding to the sampled state
+        """
+        if self.hindsight_goal_selection_method == HindsightGoalSelectionMethod.Future:
+            # states that were observed in the same episode after the transition that is being replayed
+            selected_transition = np.random.choice(episode_transitions[transition_index+1:])
+        elif self.hindsight_goal_selection_method == HindsightGoalSelectionMethod.Final:
+            # the final state in the episode
+            selected_transition = episode_transitions[-1]
+        elif self.hindsight_goal_selection_method == HindsightGoalSelectionMethod.Episode:
+            # a random state from the episode
+            selected_transition = np.random.choice(episode_transitions)
+        elif self.hindsight_goal_selection_method == HindsightGoalSelectionMethod.Random:
+            # a random state from the entire replay buffer
+            selected_transition = np.random.choice(self.transitions)
+        else:
+            raise ValueError("Invalid goal selection method was used for the hindsight goal selection")
+        return self.goals_space.goal_from_state(selected_transition.state)
+
+    def _sample_goals(self, episode_transitions: List, transition_index: int):
+        """
+        Sample a batch of goal states according to the sampling method
+        :param episode_transitions: a list of all the transitions in the current episode
+        :param transition_index: the transition to start sampling from
+        :return: a goal corresponding to the sampled state
+        """
+        return [
+            self._sample_goal(episode_transitions, transition_index)
+            for _ in range(self.hindsight_transitions_per_regular_transition)
+        ]
+
+    def store_episode(self, episode: Episode, lock: bool=True) -> None:
+        # generate hindsight transitions only when an episode is finished
+        last_episode_transitions = copy.copy(episode.transitions)
+
+        # cannot create a future hindsight goal in the last transition of an episode
+        if self.hindsight_goal_selection_method == HindsightGoalSelectionMethod.Future:
+            relevant_base_transitions = last_episode_transitions[:-1]
+        else:
+            relevant_base_transitions = last_episode_transitions
+
+        # for each transition in the last episode, create a set of hindsight transitions
+        for transition_index, transition in enumerate(relevant_base_transitions):
+            sampled_goals = self._sample_goals(last_episode_transitions, transition_index)
+            for goal in sampled_goals:
+                hindsight_transition = copy.copy(transition)
+
+                if hindsight_transition.state['desired_goal'].shape != goal.shape:
+                    raise ValueError((
+                        'goal shape {goal_shape} already in transition is '
+                        'different than the one sampled as a hindsight goal '
+                        '{hindsight_goal_shape}.'
+                    ).format(
+                        goal_shape=hindsight_transition.state['desired_goal'].shape,
+                        hindsight_goal_shape=goal.shape,
+                    ))
+
+                # update the goal in the transition
+                hindsight_transition.state['desired_goal'] = goal
+                hindsight_transition.next_state['desired_goal'] = goal
+
+                # update the reward and terminal signal according to the goal
+                hindsight_transition.reward, hindsight_transition.game_over = \
+                    self.goals_space.get_reward_for_goal_and_state(goal, hindsight_transition.next_state)
+
+                hindsight_transition.n_step_discounted_rewards = None
+                episode.insert(hindsight_transition)
+
+        super().store_episode(episode)
+
+    def store(self, transition: Transition):
+        raise ValueError("An episodic HER cannot store a single transition. Only full episodes are to be stored.")

+
+ +
+ +
+ + +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_modules/rl_coach/memories/episodic/episodic_hrl_hindsight_experience_replay.html b/docs/_modules/rl_coach/memories/episodic/episodic_hrl_hindsight_experience_replay.html new file mode 100644 index 0000000..b0b1c77 --- /dev/null +++ b/docs/_modules/rl_coach/memories/episodic/episodic_hrl_hindsight_experience_replay.html @@ -0,0 +1,300 @@ + + + + + + + + + + + rl_coach.memories.episodic.episodic_hrl_hindsight_experience_replay — Reinforcement Learning Coach 0.11.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + +
+ +
+ + + + + + + + + + + + + + + + + +
+ +
    + +
  • Docs »
    • + +
  • Module code »
    • + +
  • rl_coach.memories.episodic.episodic_hrl_hindsight_experience_replay
    • + + +
  • + +
    • + +
+ + +
+
+
+
+ +

Source code for rl_coach.memories.episodic.episodic_hrl_hindsight_experience_replay

+#
+# Copyright (c) 2017 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from typing import Tuple
+
+from rl_coach.core_types import Episode, Transition
+from rl_coach.memories.episodic.episodic_hindsight_experience_replay import HindsightGoalSelectionMethod, \
+    EpisodicHindsightExperienceReplay, EpisodicHindsightExperienceReplayParameters
+from rl_coach.memories.non_episodic.experience_replay import MemoryGranularity
+from rl_coach.spaces import GoalsSpace
+
+
+class EpisodicHRLHindsightExperienceReplayParameters(EpisodicHindsightExperienceReplayParameters):
+    def __init__(self):
+        super().__init__()
+
+    @property
+    def path(self):
+        return 'rl_coach.memories.episodic.episodic_hrl_hindsight_experience_replay:EpisodicHRLHindsightExperienceReplay'
+
+
+
[docs]class EpisodicHRLHindsightExperienceReplay(EpisodicHindsightExperienceReplay):
+    """
+    Implements HRL Hindsight Experience Replay as described in the following paper:  https://arxiv.org/abs/1805.08180
+
+    This is the memory you should use if you want a shared hindsight experience replay buffer between multiple workers
+    """
+    def __init__(self, max_size: Tuple[MemoryGranularity, int],
+                 hindsight_transitions_per_regular_transition: int,
+                 hindsight_goal_selection_method: HindsightGoalSelectionMethod,
+                 goals_space: GoalsSpace,
+                 ):
+        """
+        :param max_size: The maximum size of the memory. should be defined in a granularity of Transitions
+        :param hindsight_transitions_per_regular_transition: The number of hindsight artificial transitions to generate
+                                                             for each actual transition
+        :param hindsight_goal_selection_method: The method that will be used for generating the goals for the
+                                                hindsight transitions. Should be one of HindsightGoalSelectionMethod
+        :param goals_space: A GoalsSpace  which defines the properties of the goals
+        :param do_action_hindsight: Replace the action (sub-goal) given to a lower layer, with the actual achieved goal
+        """
+        super().__init__(max_size, hindsight_transitions_per_regular_transition, hindsight_goal_selection_method,
+                         goals_space)
+
+    def store_episode(self, episode: Episode, lock: bool=True) -> None:
+        # for a layer producing sub-goals, we will replace in hindsight the action (sub-goal) given to the lower
+        # level with the actual achieved goal. the achieved goal (and observation) seen is assumed to be the same
+        # for all levels - we can use this level's achieved goal instead of the lower level's one
+
+        # Calling super.store() so that in case a memory backend is used, the memory backend can store this episode.
+        super().store_episode(episode)
+
+        for transition in episode.transitions:
+            new_achieved_goal = transition.next_state[self.goals_space.goal_name]
+            transition.action = new_achieved_goal
+
+        super().store_episode(episode)
+
+    def store(self, transition: Transition):
+        raise ValueError("An episodic HER cannot store a single transition. Only full episodes are to be stored.")

+
+ +
+ +
+ + +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_modules/rl_coach/memories/episodic/single_episode_buffer.html b/docs/_modules/rl_coach/memories/episodic/single_episode_buffer.html new file mode 100644 index 0000000..844f643 --- /dev/null +++ b/docs/_modules/rl_coach/memories/episodic/single_episode_buffer.html @@ -0,0 +1,260 @@ + + + + + + + + + + + rl_coach.memories.episodic.single_episode_buffer — Reinforcement Learning Coach 0.11.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + +
+ +
+ + + + + + + + + + + + + + + + + +
+ +
    + +
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    • + +
  • Module code »
    • + +
  • rl_coach.memories.episodic.single_episode_buffer
    • + + +
  • + +
    • + +
+ + +
+
+
+
+ +

Source code for rl_coach.memories.episodic.single_episode_buffer

+#
+# Copyright (c) 2017 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from rl_coach.memories.episodic.episodic_experience_replay import EpisodicExperienceReplay
+from rl_coach.memories.memory import MemoryGranularity, MemoryParameters
+
+
+class SingleEpisodeBufferParameters(MemoryParameters):
+    def __init__(self):
+        super().__init__()
+        del self.max_size
+
+    @property
+    def path(self):
+        return 'rl_coach.memories.episodic.single_episode_buffer:SingleEpisodeBuffer'
+
+
+
[docs]class SingleEpisodeBuffer(EpisodicExperienceReplay):
+    def __init__(self):
+        super().__init__((MemoryGranularity.Episodes, 1))

+
+ +
+ +
+ + +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_modules/rl_coach/memories/non_episodic/balanced_experience_replay.html b/docs/_modules/rl_coach/memories/non_episodic/balanced_experience_replay.html new file mode 100644 index 0000000..c1fdddc --- /dev/null +++ b/docs/_modules/rl_coach/memories/non_episodic/balanced_experience_replay.html @@ -0,0 +1,400 @@ + + + + + + + + + + + rl_coach.memories.non_episodic.balanced_experience_replay — Reinforcement Learning Coach 0.11.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + +
+ +
+ + + + + + + + + + + + + + + + + +
+ +
    + +
  • Docs »
    • + +
  • Module code »
    • + +
  • rl_coach.memories.non_episodic.balanced_experience_replay
    • + + +
  • + +
    • + +
+ + +
+
+
+
+ +

Source code for rl_coach.memories.non_episodic.balanced_experience_replay

+#
+# Copyright (c) 2017 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import operator
+import random
+from enum import Enum
+from typing import List, Tuple, Any, Union
+
+import numpy as np
+
+from rl_coach.core_types import Transition
+from rl_coach.memories.memory import MemoryGranularity
+from rl_coach.memories.non_episodic.experience_replay import ExperienceReplayParameters, ExperienceReplay
+from rl_coach.schedules import Schedule, ConstantSchedule
+
+
+class BalancedExperienceReplayParameters(ExperienceReplayParameters):
+    def __init__(self):
+        super().__init__()
+        self.max_size = (MemoryGranularity.Transitions, 1000000)
+        self.allow_duplicates_in_batch_sampling = False
+        self.num_classes = 0
+        self.state_key_with_the_class_index = 'class'
+
+    @property
+    def path(self):
+        return 'rl_coach.memories.non_episodic.balanced_experience_replay:BalancedExperienceReplay'
+
+
+"""
+A replay buffer which allows sampling batches which are balanced in terms of the classes that are sampled
+"""
+
[docs]class BalancedExperienceReplay(ExperienceReplay):
+    def __init__(self, max_size: Tuple[MemoryGranularity, int], allow_duplicates_in_batch_sampling: bool=True,
+                 num_classes: int=0, state_key_with_the_class_index: Any='class'):
+        """
+        :param max_size: the maximum number of transitions or episodes to hold in the memory
+        :param allow_duplicates_in_batch_sampling: allow having the same transition multiple times in a batch
+        :param num_classes: the number of classes in the replayed data
+        :param state_key_with_the_class_index: the class index is assumed to be a value in the state dictionary.
+                                           this parameter determines the key to retrieve the class index value
+        """
+        super().__init__(max_size, allow_duplicates_in_batch_sampling)
+        self.current_class_to_sample_from = 0
+        self.num_classes = num_classes
+        self.state_key_with_the_class_index = state_key_with_the_class_index
+        self.transitions = [[] for _ in range(self.num_classes)]
+        self.transitions_order = []
+
+        if self.num_classes < 2:
+            raise ValueError("The number of classes for a balanced replay buffer should be at least 2. "
+                             "The number of classes that were defined are: {}".format(self.num_classes))
+
+    def store(self, transition: Transition, lock: bool=True) -> None:
+        """
+        Store a new transition in the memory.
+        :param transition: a transition to store
+        :param lock: if true, will lock the readers writers lock. this can cause a deadlock if an inheriting class
+                     locks and then calls store with lock = True
+        :return: None
+        """
+        # Calling super.store() so that in case a memory backend is used, the memory backend can store this transition.
+        super().store(transition)
+        if lock:
+            self.reader_writer_lock.lock_writing_and_reading()
+
+        self._num_transitions += 1
+
+        if self.state_key_with_the_class_index not in transition.state.keys():
+            raise ValueError("The class index was not present in the state of the transition under the given key ({})"
+                             .format(self.state_key_with_the_class_index))
+
+        class_idx = transition.state[self.state_key_with_the_class_index]
+
+        if class_idx >= self.num_classes:
+            raise ValueError("The given class index is outside the defined number of classes for the replay buffer. "
+                             "The given class was: {} and the number of classes defined is: {}"
+                             .format(class_idx, self.num_classes))
+
+        self.transitions[class_idx].append(transition)
+        self.transitions_order.append(class_idx)
+        self._enforce_max_length()
+
+        if lock:
+            self.reader_writer_lock.release_writing_and_reading()
+
+    def sample(self, size: int) -> List[Transition]:
+        """
+        Sample a batch of transitions form the replay buffer. If the requested size is larger than the number
+        of samples available in the replay buffer then the batch will return empty.
+        :param size: the size of the batch to sample
+        :return: a batch (list) of selected transitions from the replay buffer
+        """
+        self.reader_writer_lock.lock_writing()
+
+        if size % self.num_classes != 0:
+            raise ValueError("Sampling batches from a balanced replay buffer should be done only using batch sizes "
+                             "which are a multiple of the number of classes. The number of classes defined is: {} "
+                             "and the batch size requested is: {}".format(self.num_classes, size))
+
+        batch_size_from_each_class = size // self.num_classes
+
+        if self.allow_duplicates_in_batch_sampling:
+            transitions_idx = [np.random.randint(len(class_transitions), size=batch_size_from_each_class)
+                               for class_transitions in self.transitions]
+
+        else:
+            for class_idx, class_transitions in enumerate(self.transitions):
+                if self.num_transitions() < batch_size_from_each_class:
+                    raise ValueError("The replay buffer cannot be sampled since there are not enough transitions yet. "
+                                     "There are currently {} transitions for class {}"
+                                     .format(len(class_transitions), class_idx))
+
+            transitions_idx = [np.random.choice(len(class_transitions), size=batch_size_from_each_class, replace=False)
+                               for class_transitions in self.transitions]
+
+        batch = []
+        for class_idx, class_transitions_idx in enumerate(transitions_idx):
+            batch += [self.transitions[class_idx][i] for i in class_transitions_idx]
+
+        self.reader_writer_lock.release_writing()
+
+        return batch
+
+    def remove_transition(self, transition_index: int, lock: bool=True) -> None:
+        raise ValueError("It is not possible to remove specific transitions with a balanced replay buffer")
+
+    def get_transition(self, transition_index: int, lock: bool=True) -> Union[None, Transition]:
+        raise ValueError("It is not possible to access specific transitions with a balanced replay buffer")
+
+    def _enforce_max_length(self) -> None:
+        """
+        Make sure that the size of the replay buffer does not pass the maximum size allowed.
+        If it passes the max size, the oldest transition in the replay buffer will be removed.
+        This function does not use locks since it is only called internally
+        :return: None
+        """
+        granularity, size = self.max_size
+        if granularity == MemoryGranularity.Transitions:
+            while size != 0 and self.num_transitions() > size:
+                self._num_transitions -= 1
+                del self.transitions[self.transitions_order[0]][0]
+                del self.transitions_order[0]
+        else:
+            raise ValueError("The granularity of the replay buffer can only be set in terms of transitions")
+
+    def clean(self, lock: bool=True) -> None:
+        """
+        Clean the memory by removing all the episodes
+        :return: None
+        """
+        if lock:
+            self.reader_writer_lock.lock_writing_and_reading()
+
+        self.transitions = [[] for _ in range(self.num_classes)]
+        self.transitions_order = []
+        self._num_transitions = 0
+
+        if lock:
+            self.reader_writer_lock.release_writing_and_reading()

+
+ +
+ +
+ + +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_modules/rl_coach/memories/non_episodic/differentiable_neural_dictionary.html b/docs/_modules/rl_coach/memories/non_episodic/differentiable_neural_dictionary.html new file mode 100644 index 0000000..6b5d69e --- /dev/null +++ b/docs/_modules/rl_coach/memories/non_episodic/differentiable_neural_dictionary.html @@ -0,0 +1,518 @@ + + + + + + + + + + + rl_coach.memories.non_episodic.differentiable_neural_dictionary — Reinforcement Learning Coach 0.11.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + +
+ +
+ + + + + + + + + + + + + + + + + +
+ +
    + +
  • Docs »
    • + +
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  • rl_coach.memories.non_episodic.differentiable_neural_dictionary
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+ + +
+
+
+
+ +

Source code for rl_coach.memories.non_episodic.differentiable_neural_dictionary

+#
+# Copyright (c) 2017 Intel Corporation 
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import os
+import pickle
+
+import numpy as np
+try:
+    import annoy
+    from annoy import AnnoyIndex
+except ImportError:
+    from rl_coach.logger import failed_imports
+    failed_imports.append("annoy")
+
+
+class AnnoyDictionary(object):
+    def __init__(self, dict_size, key_width, new_value_shift_coefficient=0.1, batch_size=100, key_error_threshold=0.01,
+                 num_neighbors=50, override_existing_keys=True, rebuild_on_every_update=False):
+        self.rebuild_on_every_update = rebuild_on_every_update
+        self.max_size = dict_size
+        self.curr_size = 0
+        self.new_value_shift_coefficient = new_value_shift_coefficient
+        self.num_neighbors = num_neighbors
+        self.override_existing_keys = override_existing_keys
+
+        self.index = AnnoyIndex(key_width, metric='euclidean')
+        self.index.set_seed(1)
+
+        self.embeddings = np.zeros((dict_size, key_width))
+        self.values = np.zeros(dict_size)
+        self.additional_data = [None] * dict_size
+
+        self.lru_timestamps = np.zeros(dict_size)
+        self.current_timestamp = 0.0
+
+        # keys that are in this distance will be considered as the same key
+        self.key_error_threshold = key_error_threshold
+
+        self.initial_update_size = batch_size
+        self.min_update_size = self.initial_update_size
+        self.key_dimension = key_width
+        self.value_dimension = 1
+        self._reset_buffer()
+
+        self.built_capacity = 0
+
+    def add(self, keys, values, additional_data=None):
+        if not additional_data:
+            additional_data = [None] * len(keys)
+
+        # Adds new embeddings and values to the dictionary
+        indices = []
+        indices_to_remove = []
+        for i in range(keys.shape[0]):
+            index = self._lookup_key_index(keys[i])
+            if index and self.override_existing_keys:
+                # update existing value
+                self.values[index] += self.new_value_shift_coefficient * (values[i] - self.values[index])
+                self.additional_data[index[0][0]] = additional_data[i]
+                self.lru_timestamps[index] = self.current_timestamp
+                indices_to_remove.append(i)
+            else:
+                # add new
+                if self.curr_size >= self.max_size:
+                    # find the LRU entry
+                    index = np.argmin(self.lru_timestamps)
+                else:
+                    index = self.curr_size
+                    self.curr_size += 1
+                self.lru_timestamps[index] = self.current_timestamp
+                indices.append(index)
+
+        for i in reversed(indices_to_remove):
+            keys = np.delete(keys, i, 0)
+            values = np.delete(values, i, 0)
+            del additional_data[i]
+
+        self.buffered_keys = np.vstack((self.buffered_keys, keys))
+        self.buffered_values = np.vstack((self.buffered_values, values))
+        self.buffered_indices = self.buffered_indices + indices
+        self.buffered_additional_data = self.buffered_additional_data + additional_data
+
+        if len(self.buffered_indices) >= self.min_update_size:
+            self.min_update_size = max(self.initial_update_size, int(self.curr_size * 0.02))
+            self._rebuild_index()
+        elif self.rebuild_on_every_update:
+            self._rebuild_index()
+
+        self.current_timestamp += 1
+
+    # Returns the stored embeddings and values of the closest embeddings
+    def query(self, keys, k):
+        if not self.has_enough_entries(k):
+            # this will only happen when the DND is not yet populated with enough entries, which is only during heatup
+            # these values won't be used and therefore they are meaningless
+            return [0.0], [0.0], [0], [None]
+
+        _, indices = self._get_k_nearest_neighbors_indices(keys, k)
+
+        embeddings = []
+        values = []
+        additional_data = []
+        for ind in indices:
+            self.lru_timestamps[ind] = self.current_timestamp
+            embeddings.append(self.embeddings[ind])
+            values.append(self.values[ind])
+            curr_additional_data = []
+            for sub_ind in ind:
+                curr_additional_data.append(self.additional_data[sub_ind])
+            additional_data.append(curr_additional_data)
+
+        self.current_timestamp += 1
+
+        return embeddings, values, indices, additional_data
+
+    def has_enough_entries(self, k):
+        return self.curr_size > k and (self.built_capacity > k)
+
+    def sample_embeddings(self, num_embeddings):
+        return self.embeddings[np.random.choice(self.curr_size, num_embeddings)]
+
+    def _get_k_nearest_neighbors_indices(self, keys, k):
+        distances = []
+        indices = []
+        for key in keys:
+            index, distance = self.index.get_nns_by_vector(key, k, include_distances=True)
+            distances.append(distance)
+            indices.append(index)
+        return distances, indices
+
+    def _rebuild_index(self):
+        self.index.unbuild()
+        self.embeddings[self.buffered_indices] = self.buffered_keys
+        self.values[self.buffered_indices] = np.squeeze(self.buffered_values)
+        for i, data in zip(self.buffered_indices, self.buffered_additional_data):
+            self.additional_data[i] = data
+        for idx, key in zip(self.buffered_indices, self.buffered_keys):
+            self.index.add_item(idx, key)
+
+        self._reset_buffer()
+
+        self.index.build(self.num_neighbors)
+        self.built_capacity = self.curr_size
+
+    def _reset_buffer(self):
+        self.buffered_keys = np.zeros((0, self.key_dimension))
+        self.buffered_values = np.zeros((0, self.value_dimension))
+        self.buffered_indices = []
+        self.buffered_additional_data = []
+
+    def _lookup_key_index(self, key):
+        distance, index = self._get_k_nearest_neighbors_indices([key], 1)
+        if distance != [[]] and distance[0][0] <= self.key_error_threshold:
+            return index
+        return None
+
+
+
[docs]class QDND(object):
+    def __init__(self, dict_size, key_width, num_actions, new_value_shift_coefficient=0.1, key_error_threshold=0.01,
+                 learning_rate=0.01, num_neighbors=50, return_additional_data=False, override_existing_keys=False,
+                 rebuild_on_every_update=False):
+        self.dict_size = dict_size
+        self.key_width = key_width
+        self.num_actions = num_actions
+        self.new_value_shift_coefficient = new_value_shift_coefficient
+        self.key_error_threshold = key_error_threshold
+        self.learning_rate = learning_rate
+        self.num_neighbors = num_neighbors
+        self.return_additional_data = return_additional_data
+        self.override_existing_keys = override_existing_keys
+        self.dicts = []
+
+        # create a dict for each action
+        for a in range(num_actions):
+            new_dict = AnnoyDictionary(dict_size, key_width, new_value_shift_coefficient,
+                                       key_error_threshold=key_error_threshold, num_neighbors=num_neighbors,
+                                       override_existing_keys=override_existing_keys,
+                                       rebuild_on_every_update=rebuild_on_every_update)
+            self.dicts.append(new_dict)
+
+    def add(self, embeddings, actions, values, additional_data=None):
+        # add a new set of embeddings and values to each of the underlining dictionaries
+        embeddings = np.array(embeddings)
+        actions = np.array(actions)
+        values = np.array(values)
+        for a in range(self.num_actions):
+            idx = np.where(actions == a)
+            curr_action_embeddings = embeddings[idx]
+            curr_action_values = np.expand_dims(values[idx], -1)
+            if additional_data:
+                curr_additional_data = []
+                for i in idx[0]:
+                    curr_additional_data.append(additional_data[i])
+            else:
+                curr_additional_data = None
+
+            self.dicts[a].add(curr_action_embeddings, curr_action_values, curr_additional_data)
+        return True
+
+    def query(self, embeddings, action, k):
+        # query for nearest neighbors to the given embeddings
+        dnd_embeddings = []
+        dnd_values = []
+        dnd_indices = []
+        dnd_additional_data = []
+        for i in range(len(embeddings)):
+            embedding, value, indices, additional_data = self.dicts[action].query([embeddings[i]], k)
+            dnd_embeddings.append(embedding[0])
+            dnd_values.append(value[0])
+            dnd_indices.append(indices[0])
+            dnd_additional_data.append(additional_data[0])
+
+        if self.return_additional_data:
+            return dnd_embeddings, dnd_values, dnd_indices, dnd_additional_data
+        else:
+            return dnd_embeddings, dnd_values, dnd_indices
+
+    def has_enough_entries(self, k):
+        # check if each of the action dictionaries has at least k entries
+        for a in range(self.num_actions):
+            if not self.dicts[a].has_enough_entries(k):
+                return False
+        return True
+
+    def update_keys_and_values(self, actions, key_gradients, value_gradients, indices):
+        # Update DND keys and values
+        for batch_action, batch_keys, batch_values, batch_indices in zip(actions, key_gradients, value_gradients, indices):
+            # Update keys (embeddings) and values in DND
+            for i, index in enumerate(batch_indices):
+                self.dicts[batch_action].embeddings[index, :] -= self.learning_rate * batch_keys[i, :]
+                self.dicts[batch_action].values[index] -= self.learning_rate * batch_values[i]
+
+    def sample_embeddings(self, num_embeddings):
+        num_actions = len(self.dicts)
+        embeddings = []
+        num_embeddings_per_action = int(num_embeddings/num_actions)
+        for action in range(num_actions):
+            embeddings.append(self.dicts[action].sample_embeddings(num_embeddings_per_action))
+        embeddings = np.vstack(embeddings)
+
+        # the numbers did not divide nicely, let's just randomly sample some more embeddings
+        if num_embeddings_per_action * num_actions < num_embeddings:
+            action = np.random.randint(0, num_actions)
+            extra_embeddings = self.dicts[action].sample_embeddings(num_embeddings -
+                                                                   num_embeddings_per_action * num_actions)
+            embeddings = np.vstack([embeddings, extra_embeddings])
+        return embeddings
+
+    def clean(self):
+        # create a new dict for each action
+        self.dicts = []
+        for a in range(self.num_actions):
+            new_dict = AnnoyDictionary(self.dict_size, self.key_width, self.new_value_shift_coefficient,
+                                       key_error_threshold=self.key_error_threshold, num_neighbors=self.num_neighbors)
+            self.dicts.append(new_dict)

+
+
+def load_dnd(model_dir):
+    max_id = 0
+
+    for f in [s for s in os.listdir(model_dir) if s.endswith('.dnd')]:
+        if int(f.split('.')[0]) > max_id:
+            max_id = int(f.split('.')[0])
+
+    model_path = str(max_id) + '.dnd'
+    with open(os.path.join(model_dir, model_path), 'rb') as f:
+        DND = pickle.load(f)
+
+        for a in range(DND.num_actions):
+            DND.dicts[a].index = AnnoyIndex(512, metric='euclidean')
+            DND.dicts[a].index.set_seed(1)
+
+            for idx, key in zip(range(DND.dicts[a].curr_size), DND.dicts[a].embeddings[:DND.dicts[a].curr_size]):
+                DND.dicts[a].index.add_item(idx, key)
+
+            DND.dicts[a].index.build(50)
+
+    return DND
+
+ +
+ +
+ + +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_modules/rl_coach/memories/non_episodic/experience_replay.html b/docs/_modules/rl_coach/memories/non_episodic/experience_replay.html new file mode 100644 index 0000000..660b17f --- /dev/null +++ b/docs/_modules/rl_coach/memories/non_episodic/experience_replay.html @@ -0,0 +1,467 @@ + + + + + + + + + + + rl_coach.memories.non_episodic.experience_replay — Reinforcement Learning Coach 0.11.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + +
+ +
+ + + + + + + + + + + + + + + + + +
+ +
    + +
  • Docs »
    • + +
  • Module code »
    • + +
  • rl_coach.memories.non_episodic.experience_replay
    • + + +
  • + +
    • + +
+ + +
+
+
+
+ +

Source code for rl_coach.memories.non_episodic.experience_replay

+#
+# Copyright (c) 2017 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from typing import List, Tuple, Union, Dict, Any
+import pickle
+import sys
+import time
+
+import numpy as np
+
+from rl_coach.core_types import Transition
+from rl_coach.logger import screen
+from rl_coach.memories.memory import Memory, MemoryGranularity, MemoryParameters
+from rl_coach.utils import ReaderWriterLock, ProgressBar
+
+
+class ExperienceReplayParameters(MemoryParameters):
+    def __init__(self):
+        super().__init__()
+        self.max_size = (MemoryGranularity.Transitions, 1000000)
+        self.allow_duplicates_in_batch_sampling = True
+
+    @property
+    def path(self):
+        return 'rl_coach.memories.non_episodic.experience_replay:ExperienceReplay'
+
+
+
[docs]class ExperienceReplay(Memory):
+    """
+    A regular replay buffer which stores transition without any additional structure
+    """
+    def __init__(self, max_size: Tuple[MemoryGranularity, int], allow_duplicates_in_batch_sampling: bool=True):
+        """
+        :param max_size: the maximum number of transitions or episodes to hold in the memory
+        :param allow_duplicates_in_batch_sampling: allow having the same transition multiple times in a batch
+        """
+        super().__init__(max_size)
+        if max_size[0] != MemoryGranularity.Transitions:
+            raise ValueError("Experience replay size can only be configured in terms of transitions")
+        self.transitions = []
+        self.allow_duplicates_in_batch_sampling = allow_duplicates_in_batch_sampling
+
+        self.reader_writer_lock = ReaderWriterLock()
+
+    def length(self) -> int:
+        """
+        Get the number of transitions in the ER
+        """
+        return self.num_transitions()
+
+    def num_transitions(self) -> int:
+        """
+        Get the number of transitions in the ER
+        """
+        return len(self.transitions)
+
+    def sample(self, size: int) -> List[Transition]:
+        """
+        Sample a batch of transitions form the replay buffer. If the requested size is larger than the number
+        of samples available in the replay buffer then the batch will return empty.
+        :param size: the size of the batch to sample
+        :param beta: the beta parameter used for importance sampling
+        :return: a batch (list) of selected transitions from the replay buffer
+        """
+        self.reader_writer_lock.lock_writing()
+
+        if self.allow_duplicates_in_batch_sampling:
+            transitions_idx = np.random.randint(self.num_transitions(), size=size)
+
+        else:
+            if self.num_transitions() >= size:
+                transitions_idx = np.random.choice(self.num_transitions(), size=size, replace=False)
+            else:
+                raise ValueError("The replay buffer cannot be sampled since there are not enough transitions yet. "
+                                 "There are currently {} transitions".format(self.num_transitions()))
+
+        batch = [self.transitions[i] for i in transitions_idx]
+
+        self.reader_writer_lock.release_writing()
+        return batch
+
+    def _enforce_max_length(self) -> None:
+        """
+        Make sure that the size of the replay buffer does not pass the maximum size allowed.
+        If it passes the max size, the oldest transition in the replay buffer will be removed.
+        This function does not use locks since it is only called internally
+        :return: None
+        """
+        granularity, size = self.max_size
+        if granularity == MemoryGranularity.Transitions:
+            while size != 0 and self.num_transitions() > size:
+                self.remove_transition(0, False)
+        else:
+            raise ValueError("The granularity of the replay buffer can only be set in terms of transitions")
+
+    def store(self, transition: Transition, lock: bool=True) -> None:
+        """
+        Store a new transition in the memory.
+        :param transition: a transition to store
+        :param lock: if true, will lock the readers writers lock. this can cause a deadlock if an inheriting class
+                     locks and then calls store with lock = True
+        :return: None
+        """
+        # Calling super.store() so that in case a memory backend is used, the memory backend can store this transition.
+        super().store(transition)
+        if lock:
+            self.reader_writer_lock.lock_writing_and_reading()
+
+        self.transitions.append(transition)
+        self._enforce_max_length()
+
+        if lock:
+            self.reader_writer_lock.release_writing_and_reading()
+
+    def get_transition(self, transition_index: int, lock: bool=True) -> Union[None, Transition]:
+        """
+        Returns the transition in the given index. If the transition does not exist, returns None instead.
+        :param transition_index: the index of the transition to return
+        :param lock: use write locking if this is a shared memory
+        :return: the corresponding transition
+        """
+        if lock:
+            self.reader_writer_lock.lock_writing()
+
+        if self.length() == 0 or transition_index >= self.length():
+            transition = None
+        else:
+            transition = self.transitions[transition_index]
+
+        if lock:
+            self.reader_writer_lock.release_writing()
+
+        return transition
+
+    def remove_transition(self, transition_index: int, lock: bool=True) -> None:
+        """
+        Remove the transition in the given index.
+
+        This does not remove the transition from the segment trees! it is just used to remove the transition
+        from the transitions list
+        :param transition_index: the index of the transition to remove
+        :return: None
+        """
+        if lock:
+            self.reader_writer_lock.lock_writing_and_reading()
+
+        if self.num_transitions() > transition_index:
+            del self.transitions[transition_index]
+
+        if lock:
+            self.reader_writer_lock.release_writing_and_reading()
+
+    # for API compatibility
+    def get(self, transition_index: int, lock: bool=True) -> Union[None, Transition]:
+        """
+        Returns the transition in the given index. If the transition does not exist, returns None instead.
+        :param transition_index: the index of the transition to return
+        :return: the corresponding transition
+        """
+        return self.get_transition(transition_index, lock)
+
+    # for API compatibility
+    def remove(self, transition_index: int, lock: bool=True):
+        """
+        Remove the transition in the given index
+        :param transition_index: the index of the transition to remove
+        :return: None
+        """
+        self.remove_transition(transition_index, lock)
+
+    def clean(self, lock: bool=True) -> None:
+        """
+        Clean the memory by removing all the episodes
+        :return: None
+        """
+        if lock:
+            self.reader_writer_lock.lock_writing_and_reading()
+
+        self.transitions = []
+
+        if lock:
+            self.reader_writer_lock.release_writing_and_reading()
+
+    def mean_reward(self) -> np.ndarray:
+        """
+        Get the mean reward in the replay buffer
+        :return: the mean reward
+        """
+        self.reader_writer_lock.lock_writing()
+
+        mean = np.mean([transition.reward for transition in self.transitions])
+
+        self.reader_writer_lock.release_writing()
+
+        return mean
+
+    def save(self, file_path: str) -> None:
+        """
+        Save the replay buffer contents to a pickle file
+        :param file_path: the path to the file that will be used to store the pickled transitions
+        """
+        with open(file_path, 'wb') as file:
+            pickle.dump(self.transitions, file)
+
+    def load(self, file_path: str) -> None:
+        """
+        Restore the replay buffer contents from a pickle file.
+        The pickle file is assumed to include a list of transitions.
+        :param file_path: The path to a pickle file to restore
+        """
+        with open(file_path, 'rb') as file:
+            transitions = pickle.load(file)
+            num_transitions = len(transitions)
+            if num_transitions > self.max_size[1]:
+                screen.warning("Warning! The number of transition to load into the replay buffer ({}) is "
+                               "bigger than the max size of the replay buffer ({}). The excessive transitions will "
+                               "not be stored.".format(num_transitions, self.max_size[1]))
+
+            progress_bar = ProgressBar(num_transitions)
+            for transition_idx, transition in enumerate(transitions):
+                self.store(transition)
+
+                # print progress
+                if transition_idx % 100 == 0:
+                    progress_bar.update(transition_idx)
+
+            progress_bar.close()

+
+ +
+ +
+ + +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_modules/rl_coach/memories/non_episodic/prioritized_experience_replay.html b/docs/_modules/rl_coach/memories/non_episodic/prioritized_experience_replay.html new file mode 100644 index 0000000..a516e7c --- /dev/null +++ b/docs/_modules/rl_coach/memories/non_episodic/prioritized_experience_replay.html @@ -0,0 +1,526 @@ + + + + + + + + + + + rl_coach.memories.non_episodic.prioritized_experience_replay — Reinforcement Learning Coach 0.11.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + +
+ +
+ + + + + + + + + + + + + + + + + +
+ +
    + +
  • Docs »
    • + +
  • Module code »
    • + +
  • rl_coach.memories.non_episodic.prioritized_experience_replay
    • + + +
  • + +
    • + +
+ + +
+
+
+
+ +

Source code for rl_coach.memories.non_episodic.prioritized_experience_replay

+#
+# Copyright (c) 2017 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import operator
+import random
+from enum import Enum
+from typing import List, Tuple, Any
+
+import numpy as np
+
+from rl_coach.core_types import Transition
+from rl_coach.memories.memory import MemoryGranularity
+from rl_coach.memories.non_episodic.experience_replay import ExperienceReplayParameters, ExperienceReplay
+from rl_coach.schedules import Schedule, ConstantSchedule
+
+
+class PrioritizedExperienceReplayParameters(ExperienceReplayParameters):
+    def __init__(self):
+        super().__init__()
+        self.max_size = (MemoryGranularity.Transitions, 1000000)
+        self.alpha = 0.6
+        self.beta = ConstantSchedule(0.4)
+        self.epsilon = 1e-6
+
+    @property
+    def path(self):
+        return 'rl_coach.memories.non_episodic.prioritized_experience_replay:PrioritizedExperienceReplay'
+
+
+class SegmentTree(object):
+    """
+    A tree which can be used as a min/max heap or a sum tree
+    Add or update item value - O(log N)
+    Sampling an item - O(log N)
+    """
+    class Operation(Enum):
+        MAX = {"operator": max, "initial_value": -float("inf")}
+        MIN = {"operator": min, "initial_value": float("inf")}
+        SUM = {"operator": operator.add, "initial_value": 0}
+
+    def __init__(self, size: int, operation: Operation):
+        self.next_leaf_idx_to_write = 0
+        self.size = size
+        if not (size > 0 and size & (size - 1) == 0):
+            raise ValueError("A segment tree size must be a positive power of 2. The given size is {}".format(self.size))
+        self.operation = operation
+        self.tree = np.ones(2 * size - 1) * self.operation.value['initial_value']
+        self.data = [None] * size
+
+    def _propagate(self, node_idx: int) -> None:
+        """
+        Propagate an update of a node's value to its parent node
+        :param node_idx: the index of the node that was updated
+        :return: None
+        """
+        parent = (node_idx - 1) // 2
+
+        self.tree[parent] = self.operation.value['operator'](self.tree[parent * 2 + 1], self.tree[parent * 2 + 2])
+
+        if parent != 0:
+            self._propagate(parent)
+
+    def _retrieve(self, root_node_idx: int, val: float)-> int:
+        """
+        Retrieve the first node that has a value larger than val and is a child of the node at index idx
+        :param root_node_idx: the index of the root node to search from
+        :param val: the value to query for
+        :return: the index of the resulting node
+        """
+        left = 2 * root_node_idx + 1
+        right = left + 1
+
+        if left >= len(self.tree):
+            return root_node_idx
+
+        if val <= self.tree[left]:
+            return self._retrieve(left, val)
+        else:
+            return self._retrieve(right, val-self.tree[left])
+
+    def total_value(self) -> float:
+        """
+        Return the total value of the tree according to the tree operation. For SUM for example, this will return
+        the total sum of the tree. for MIN, this will return the minimal value
+        :return: the total value of the tree
+        """
+        return self.tree[0]
+
+    def add(self, val: float, data: Any) -> None:
+        """
+        Add a new value to the tree with data assigned to it
+        :param val: the new value to add to the tree
+        :param data: the data that should be assigned to this value
+        :return: None
+        """
+        self.data[self.next_leaf_idx_to_write] = data
+        self.update(self.next_leaf_idx_to_write, val)
+
+        self.next_leaf_idx_to_write += 1
+        if self.next_leaf_idx_to_write >= self.size:
+            self.next_leaf_idx_to_write = 0
+
+    def update(self, leaf_idx: int, new_val: float) -> None:
+        """
+        Update the value of the node at index idx
+        :param leaf_idx: the index of the node to update
+        :param new_val: the new value of the node
+        :return: None
+        """
+        node_idx = leaf_idx + self.size - 1
+        if not 0 <= node_idx < len(self.tree):
+            raise ValueError("The given left index ({}) can not be found in the tree. The available leaves are: 0-{}"
+                             .format(leaf_idx, self.size - 1))
+
+        self.tree[node_idx] = new_val
+        self._propagate(node_idx)
+
+    def get_element_by_partial_sum(self, val: float) -> Tuple[int, float, Any]:
+        """
+        Given a value between 0 and the tree sum, return the object which this value is in it's range.
+        For example, if we have 3 leaves: 10, 20, 30, and val=35, this will return the 3rd leaf, by accumulating
+        leaves by their order until getting to 35. This allows sampling leaves according to their proportional
+        probability.
+        :param val: a value within the range 0 and the tree sum
+        :return: the index of the resulting leaf in the tree, its probability and
+                 the object itself
+        """
+        node_idx = self._retrieve(0, val)
+        leaf_idx = node_idx - self.size + 1
+        data_value = self.tree[node_idx]
+        data = self.data[leaf_idx]
+
+        return leaf_idx, data_value, data
+
+    def __str__(self):
+        result = ""
+        start = 0
+        size = 1
+        while size <= self.size:
+            result += "{}\n".format(self.tree[start:(start + size)])
+            start += size
+            size *= 2
+        return result
+
+
+
[docs]class PrioritizedExperienceReplay(ExperienceReplay):
+    """
+    This is the proportional sampling variant of the prioritized experience replay as described
+    in https://arxiv.org/pdf/1511.05952.pdf.
+    """
+    def __init__(self, max_size: Tuple[MemoryGranularity, int], alpha: float=0.6, beta: Schedule=ConstantSchedule(0.4),
+                 epsilon: float=1e-6, allow_duplicates_in_batch_sampling: bool=True):
+        """
+        :param max_size: the maximum number of transitions or episodes to hold in the memory
+        :param alpha: the alpha prioritization coefficient
+        :param beta: the beta parameter used for importance sampling
+        :param epsilon: a small value added to the priority of each transition
+        :param allow_duplicates_in_batch_sampling: allow having the same transition multiple times in a batch
+        """
+        if max_size[0] != MemoryGranularity.Transitions:
+            raise ValueError("Prioritized Experience Replay currently only support setting the memory size in "
+                             "transitions granularity.")
+        self.power_of_2_size = 1
+        while self.power_of_2_size < max_size[1]:
+            self.power_of_2_size *= 2
+        super().__init__((MemoryGranularity.Transitions, self.power_of_2_size), allow_duplicates_in_batch_sampling)
+        self.sum_tree = SegmentTree(self.power_of_2_size, SegmentTree.Operation.SUM)
+        self.min_tree = SegmentTree(self.power_of_2_size, SegmentTree.Operation.MIN)
+        self.max_tree = SegmentTree(self.power_of_2_size, SegmentTree.Operation.MAX)
+        self.alpha = alpha
+        self.beta = beta
+        self.epsilon = epsilon
+        self.maximal_priority = 1.0
+
+    def _update_priority(self, leaf_idx: int, error: float) -> None:
+        """
+        Update the priority of a given transition, using its index in the tree and its error
+        :param leaf_idx: the index of the transition leaf in the tree
+        :param error: the new error value
+        :return: None
+        """
+        if error < 0:
+            raise ValueError("The priorities must be non-negative values")
+        priority = (error + self.epsilon)
+        self.sum_tree.update(leaf_idx, priority ** self.alpha)
+        self.min_tree.update(leaf_idx, priority ** self.alpha)
+        self.max_tree.update(leaf_idx, priority)
+        self.maximal_priority = self.max_tree.total_value()
+
+    def update_priorities(self, indices: List[int], error_values: List[float]) -> None:
+        """
+        Update the priorities of a batch of transitions using their indices and their new TD error terms
+        :param indices: the indices of the transitions to update
+        :param error_values: the new error values
+        :return: None
+        """
+        self.reader_writer_lock.lock_writing_and_reading()
+
+        if len(indices) != len(error_values):
+            raise ValueError("The number of indexes requested for update don't match the number of error values given")
+        for transition_idx, error in zip(indices, error_values):
+            self._update_priority(transition_idx, error)
+
+        self.reader_writer_lock.release_writing_and_reading()
+
+    def sample(self, size: int) -> List[Transition]:
+        """
+        Sample a batch of transitions form the replay buffer. If the requested size is larger than the number
+        of samples available in the replay buffer then the batch will return empty.
+        :param size: the size of the batch to sample
+        :return: a batch (list) of selected transitions from the replay buffer
+        """
+
+        self.reader_writer_lock.lock_writing()
+
+        if self.num_transitions() >= size:
+            # split the tree leaves to equal segments and sample one transition from each segment
+            batch = []
+            segment_size = self.sum_tree.total_value() / size
+
+            # get the maximum weight in the memory
+            min_probability = self.min_tree.total_value() / self.sum_tree.total_value()  # min P(j) = min p^a / sum(p^a)
+            max_weight = (min_probability * self.num_transitions()) ** -self.beta.current_value  # max wi
+
+            # sample a batch
+            for i in range(size):
+                segment_start = segment_size * i
+                segment_end = segment_size * (i + 1)
+
+                # sample leaf and calculate its weight
+                val = random.uniform(segment_start, segment_end)
+                leaf_idx, priority, transition = self.sum_tree.get_element_by_partial_sum(val)
+                priority /= self.sum_tree.total_value()   # P(j) = p^a / sum(p^a)
+                weight = (self.num_transitions() * priority) ** -self.beta.current_value  # (N * P(j)) ^ -beta
+                normalized_weight = weight / max_weight  # wj = ((N * P(j)) ^ -beta) / max wi
+
+                transition.info['idx'] = leaf_idx
+                transition.info['weight'] = normalized_weight
+
+                batch.append(transition)
+
+            self.beta.step()
+
+        else:
+            raise ValueError("The replay buffer cannot be sampled since there are not enough transitions yet. "
+                             "There are currently {} transitions".format(self.num_transitions()))
+
+        self.reader_writer_lock.release_writing()
+        return batch
+
+    def store(self, transition: Transition, lock=True) -> None:
+        """
+        Store a new transition in the memory.
+        :param transition: a transition to store
+        :return: None
+        """
+        # Calling super.store() so that in case a memory backend is used, the memory backend can store this transition.
+        super().store(transition)
+
+        if lock:
+            self.reader_writer_lock.lock_writing_and_reading()
+
+        transition_priority = self.maximal_priority
+        self.sum_tree.add(transition_priority ** self.alpha, transition)
+        self.min_tree.add(transition_priority ** self.alpha, transition)
+        self.max_tree.add(transition_priority, transition)
+        super().store(transition, False)
+
+        if lock:
+            self.reader_writer_lock.release_writing_and_reading()
+
+    def clean(self, lock=True) -> None:
+        """
+        Clean the memory by removing all the episodes
+        :return: None
+        """
+        if lock:
+            self.reader_writer_lock.lock_writing_and_reading()
+
+        super().clean(lock=False)
+        self.sum_tree = SegmentTree(self.power_of_2_size, SegmentTree.Operation.SUM)
+        self.min_tree = SegmentTree(self.power_of_2_size, SegmentTree.Operation.MIN)
+        self.max_tree = SegmentTree(self.power_of_2_size, SegmentTree.Operation.MAX)
+
+        if lock:
+            self.reader_writer_lock.release_writing_and_reading()

+
+ +
+ +
+ + +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_modules/rl_coach/memories/non_episodic/transition_collection.html b/docs/_modules/rl_coach/memories/non_episodic/transition_collection.html new file mode 100644 index 0000000..1b18ee7 --- /dev/null +++ b/docs/_modules/rl_coach/memories/non_episodic/transition_collection.html @@ -0,0 +1,263 @@ + + + + + + + + + + + rl_coach.memories.non_episodic.transition_collection — Reinforcement Learning Coach 0.11.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + +
+ +
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+
+
+ +

Source code for rl_coach.memories.non_episodic.transition_collection

+from rl_coach.core_types import Transition
+
+
+
[docs]class TransitionCollection(object):
+    """
+    Simple python implementation of transitions collection non-episodic memories
+    are constructed on top of.
+    """
+    def __init__(self):
+        super(TransitionCollection, self).__init__()
+
+    def append(self, transition):
+        pass
+
+    def extend(self, transitions):
+        for transition in transitions:
+            self.append(transition)
+
+    def __len__(self):
+        pass
+
+    def __del__(self, range: slice):
+        # NOTE: the only slice used is the form: slice(None, n)
+        # NOTE: if it is easier, what we really want here is the ability to
+        # constrain the size of the collection. as new transitions are added,
+        # old transitions can be removed to maintain a maximum collection size.
+        pass
+
+    def __getitem__(self, key: int):
+        # NOTE: we can switch to a method which fetches multiple items at a time
+        # if that would significantly improve performance
+        pass
+
+    def __iter__(self):
+        # this is not high priority
+        pass

+
+ +
+ +
+ + +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_modules/rl_coach/spaces.html b/docs/_modules/rl_coach/spaces.html new file mode 100644 index 0000000..65f3bc5 --- /dev/null +++ b/docs/_modules/rl_coach/spaces.html @@ -0,0 +1,858 @@ + + + + + + + + + + + rl_coach.spaces — Reinforcement Learning Coach 0.11.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + +
+ +
+ + + + + + + + + + + + + + + + + +
+ + + + +
+
+
+
+ +

Source code for rl_coach.spaces

+#
+# Copyright (c) 2017 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import random
+from enum import Enum
+from itertools import product
+from typing import Union, List, Dict, Tuple, Callable
+
+import numpy as np
+import scipy
+import scipy.spatial
+
+from rl_coach.core_types import ActionType, ActionInfo
+from rl_coach.utils import eps
+
+
+
[docs]class Space(object):
+    """
+    A space defines a set of valid values
+    """
+    def __init__(self, shape: Union[int, tuple, list, np.ndarray], low: Union[None, int, float, np.ndarray]=-np.inf,
+                 high: Union[None, int, float, np.ndarray]=np.inf):
+        """
+        :param shape: the shape of the space
+        :param low: the lowest values possible in the space. can be an array defining the lowest values per point,
+                    or a single value defining the general lowest values
+        :param high: the highest values possible in the space. can be an array defining the highest values per point,
+                    or a single value defining the general highest values
+        """
+
+        # the number of dimensions is the number of axes in the shape. it will be set in the shape setter
+        self.num_dimensions = 0
+
+        # the number of elements is the number of possible actions if the action space was discrete.
+        # it will be set in the shape setter
+        self.num_elements = 0
+
+        self._low = self._high = None
+        self._shape = self.shape = shape
+        self._low = self.low = low
+        self._high = self.high = high
+
+        # we allow zero sized spaces which means that the space is empty. this is useful for environments with no
+        # measurements for example.
+        if type(shape) == int and shape < 0:
+            raise ValueError("The shape of the space must be a non-negative number")
+
+    @property
+    def shape(self):
+        return self._shape
+
+    @shape.setter
+    def shape(self, val: Union[int, tuple, list, np.ndarray]):
+        # convert the shape to an np.ndarray
+        self._shape = val
+        if type(self._shape) == int:
+            self._shape = np.array([self._shape])
+        if type(self._shape) == tuple or type(self._shape) == list:
+            self._shape = np.array(self._shape)
+
+        # the shape is now an np.ndarray
+        self.num_dimensions = len(self._shape)
+        self.num_elements = int(np.prod(self._shape))
+
+    @property
+    def low(self):
+        if hasattr(self, '_low'):
+            return self._low
+        else:
+            return None
+
+    @low.setter
+    def low(self, val: Union[None, int, float, np.ndarray]):
+        if type(val) == np.ndarray and type(self.shape) == np.ndarray and np.all(val.shape != self.shape):
+            raise ValueError("The low values shape don't match the shape of the space")
+        elif self.high is not None and not np.all(self.high >= val):
+            raise ValueError("At least one of the axes-parallel lines defining the space has high values which "
+                             "are lower than the given low values")
+        else:
+            self._low = val
+            # we allow using a number to define the low values, but we immediately convert it to an array which defines
+            # the low values for all the space dimensions in order to expose a consistent value type
+            if type(self._low) == int or type(self._low) == float:
+                self._low = np.ones(self.shape)*self._low
+
+    @property
+    def high(self):
+        if hasattr(self, '_high'):
+            return self._high
+        else:
+            return None
+
+    @high.setter
+    def high(self, val: Union[None, int, float, np.ndarray]):
+        if type(val) == np.ndarray and type(self.shape) == np.ndarray and np.all(val.shape != self.shape):
+            raise ValueError("The high values shape don't match the shape of the space")
+        elif self.low is not None and not np.all(self.low <= val):
+            raise ValueError("At least one of the axes-parallel lines defining the space has low values which "
+                             "are higher than the given high values")
+        else:
+            self._high = val
+            # we allow using a number to define the high values, but we immediately convert it to an array which defines
+            # the high values for all the space dimensions in order to expose a consistent value type
+            if type(self._high) == int or type(self._high) == float:
+                self._high = np.ones(self.shape)*self._high
+
+
[docs]    def val_matches_space_definition(self, val: Union[int, float, np.ndarray]) -> bool:
+        """
+        Checks if the given value matches the space definition in terms of shape and values
+
+        :param val: a value to check
+        :return: True / False depending on if the val matches the space definition
+        """
+        if (type(val) == int or type(val) == float) and not np.all(self.shape == np.ones(1)):
+            return False
+        if type(val) == np.ndarray and not np.all(val.shape == self.shape):
+            return False
+        if (self.low is not None and not np.all(val >= self.low)) \
+                or (self.high is not None and not np.all(val <= self.high)):
+            # TODO: check the performance overhead this causes
+            return False
+        return True

+
+
[docs]    def is_point_in_space_shape(self, point: np.ndarray) -> bool:
+        """
+        Checks if a given multidimensional point is within the bounds of the shape of the space
+
+        :param point: a multidimensional point
+        :return: True if the point is within the shape of the space. False otherwise
+        """
+        if len(point) != self.num_dimensions:
+            return False
+        if np.any(point < np.zeros(self.num_dimensions)) or np.any(point >= self.shape):
+            return False
+        return True

+
+
[docs]    def sample(self) -> np.ndarray:
+        """
+        Sample the defined space, either uniformly, if space bounds are defined, or Normal distributed if no
+        bounds are defined
+
+        :return: A numpy array sampled from the space
+        """
+        # if there are infinite bounds, we sample using gaussian noise with mean 0 and std 1
+        if np.any(self.low == -np.inf) or np.any(self.high == np.inf):
+            return np.random.normal(0, 1, self.shape)
+        else:
+            return np.random.uniform(self.low, self.high, self.shape)

+
+
+class RewardSpace(Space):
+    def __init__(self, shape: Union[int, np.ndarray], low: Union[None, int, float, np.ndarray]=-np.inf,
+                 high: Union[None, int, float, np.ndarray]=np.inf,
+                 reward_success_threshold: Union[None, int, float]=None):
+        super().__init__(shape, low, high)
+        self.reward_success_threshold = reward_success_threshold
+
+
+"""
+Observation Spaces
+"""
+
+
+
[docs]class ObservationSpace(Space):
+    def __init__(self, shape: Union[int, np.ndarray], low: Union[None, int, float, np.ndarray]=-np.inf,
+                 high: Union[None, int, float, np.ndarray]=np.inf):
+        super().__init__(shape, low, high)

+
+
+
[docs]class VectorObservationSpace(ObservationSpace):
+    """
+    An observation space which is defined as a vector of elements. This can be particularly useful for environments
+    which return measurements, such as in robotic environmnets.
+    """
+    def __init__(self, shape: int, low: Union[None, int, float, np.ndarray]=-np.inf,
+                 high: Union[None, int, float, np.ndarray]=np.inf, measurements_names: List[str]=None):
+        if measurements_names is None:
+            measurements_names = []
+        if len(measurements_names) > shape:
+            raise ValueError("measurement_names size {} is larger than shape {}.".format(
+                len(measurements_names), shape))
+
+        self.measurements_names = measurements_names
+        super().__init__(shape, low, high)

+
+
+
[docs]class PlanarMapsObservationSpace(ObservationSpace):
+    """
+    An observation space which defines a stack of 2D observations. For example, an environment which returns
+    a stack of segmentation maps like in Starcraft.
+    """
+    def __init__(self, shape: Union[np.ndarray], low: int, high: int, channels_axis: int=-1):
+        super().__init__(shape, low, high)
+        self.channels_axis = channels_axis
+
+        if not 2 <= len(shape) <= 3:
+            raise ValueError("Planar maps observations must have 3 dimensions - a channels dimension and 2 maps "
+                             "dimensions, not {}".format(len(shape)))
+        if len(shape) == 2:
+            self.channels = 1
+        else:
+            self.channels = shape[channels_axis]

+
+
+
[docs]class ImageObservationSpace(PlanarMapsObservationSpace):
+    """
+    An observation space which is a private case of the PlanarMapsObservationSpace, where the stack of 2D observations
+    represent a RGB image, or a grayscale image.
+    """
+    def __init__(self, shape: Union[np.ndarray], high: int, channels_axis: int=-1):
+        # TODO: consider allowing arbitrary low values for images
+        super().__init__(shape, 0, high, channels_axis)
+        self.has_colors = self.channels == 3
+        if not self.channels == 3 and not self.channels == 1:
+            raise ValueError("Image observations must have 1 or 3 channels, not {}".format(self.channels))

+
+
+# TODO: mixed observation spaces (image + measurements, image + segmentation + depth map, etc.)
+class StateSpace(object):
+    def __init__(self, sub_spaces: Dict[str, Space]):
+        self.sub_spaces = sub_spaces
+
+    def __getitem__(self, item):
+        return self.sub_spaces[item]
+
+    def __setitem__(self, key, value):
+        self.sub_spaces[key] = value
+
+
+"""
+Action Spaces
+"""
+
+
+
[docs]class ActionSpace(Space):
+    def __init__(self, shape: Union[int, np.ndarray], low: Union[None, int, float, np.ndarray]=-np.inf,
+                 high: Union[None, int, float, np.ndarray]=np.inf, descriptions: Union[None, List, Dict]=None,
+                 default_action: ActionType=None):
+        super().__init__(shape, low, high)
+        # we allow a mismatch between the number of descriptions and the number of actions.
+        # in this case the descriptions for the actions that were not given will be the action index
+        if descriptions is not None:
+            self.descriptions = descriptions
+        else:
+            self.descriptions = {}
+        self.default_action = default_action
+
+    @property
+    def actions(self) -> List[ActionType]:
+        raise NotImplementedError("The action space does not have an explicit actions list")
+
+
[docs]    def sample_with_info(self) -> ActionInfo:
+        """
+        Get a random action with additional "fake" info
+
+        :return: An action info instance
+        """
+        return ActionInfo(self.sample())

+
+
[docs]    def clip_action_to_space(self, action: ActionType) -> ActionType:
+        """
+        Given an action, clip its values to fit to the action space ranges
+
+        :param action: a given action
+        :return: the clipped action
+        """
+        return action

+
+    def get_description(self, action: np.ndarray) -> str:
+        raise NotImplementedError("")
+
+    def __str__(self):
+        return "{}: shape = {}, low = {}, high = {}".format(self.__class__.__name__, self.shape, self.low, self.high)
+
+    def __repr__(self):
+        return self.__str__()

+
+
+
[docs]class AttentionActionSpace(ActionSpace):
+    """
+    A box selection continuous action space, meaning that the actions are defined as selecting a multidimensional box
+    from a given range.
+    The actions will be in the form:
+    [[low_x, low_y, ...], [high_x, high_y, ...]]
+    """
+    def __init__(self, shape: int, low: Union[None, int, float, np.ndarray]=-np.inf,
+                 high: Union[None, int, float, np.ndarray]=np.inf, descriptions: Union[None, List, Dict]=None,
+                 default_action: np.ndarray = None, forced_attention_size: Union[None, int, float, np.ndarray]=None):
+        super().__init__(shape, low, high, descriptions)
+
+        self.forced_attention_size = forced_attention_size
+        if isinstance(self.forced_attention_size, int) or isinstance(self.forced_attention_size, float):
+            self.forced_attention_size = np.ones(self.shape) * self.forced_attention_size
+
+        if self.forced_attention_size is not None and np.all(self.forced_attention_size > (self.high - self.low)):
+            raise ValueError("The forced attention size is larger than the action space")
+
+        # default action
+        if default_action is None:
+            if self.forced_attention_size is not None:
+                self.default_action = [self.low*np.ones(self.shape),
+                                       (self.low+self.forced_attention_size)*np.ones(self.shape)]
+            else:
+                self.default_action = [self.low*np.ones(self.shape), self.high*np.ones(self.shape)]
+        else:
+            self.default_action = default_action
+
+    def sample(self) -> List:
+        if self.forced_attention_size is not None:
+            sampled_low = np.random.uniform(self.low, self.high-self.forced_attention_size, self.shape)
+            sampled_high = sampled_low + self.forced_attention_size
+        else:
+            sampled_low = np.random.uniform(self.low, self.high, self.shape)
+            sampled_high = np.random.uniform(sampled_low, self.high, self.shape)
+        return [sampled_low, sampled_high]
+
+    def clip_action_to_space(self, action: ActionType) -> ActionType:
+        action = [np.clip(action[0], self.low, self.high), np.clip(action[1], self.low, self.high)]
+        return action

+
+
+
[docs]class BoxActionSpace(ActionSpace):
+    """
+    A multidimensional bounded or unbounded continuous action space
+    """
+    def __init__(self, shape: Union[int, np.ndarray], low: Union[None, int, float, np.ndarray]=-np.inf,
+                 high: Union[None, int, float, np.ndarray]=np.inf, descriptions: Union[None, List, Dict]=None,
+                 default_action: np.ndarray=None):
+        super().__init__(shape, low, high, descriptions)
+        self.max_abs_range = np.maximum(np.abs(self.low), np.abs(self.high))
+
+        # default action
+        if default_action is None:
+            if np.any(np.isinf(self.low)) or np.any(np.isinf(self.high)):
+                self.default_action = np.zeros(shape)
+            else:
+                self.default_action = self.low + (self.high - self.low) / 2
+        else:
+            self.default_action = default_action
+
+    def clip_action_to_space(self, action: ActionType) -> ActionType:
+        action = np.clip(action, self.low, self.high)
+        return action

+
+
+
[docs]class DiscreteActionSpace(ActionSpace):
+    """
+    A discrete action space with action indices as actions
+    """
+    def __init__(self, num_actions: int, descriptions: Union[None, List, Dict]=None, default_action: np.ndarray=None):
+        super().__init__(1, low=0, high=num_actions-1, descriptions=descriptions)
+        # the number of actions is mapped to high
+
+        # default action
+        if default_action is None:
+            self.default_action = 0
+        else:
+            self.default_action = default_action
+
+    @property
+    def actions(self) -> List[ActionType]:
+        return list(range(0, int(self.high[0]) + 1))
+
+    def sample(self) -> int:
+        return np.random.choice(self.actions)
+
+    def sample_with_info(self) -> ActionInfo:
+        return ActionInfo(self.sample(), action_probability=1. / (self.high[0] - self.low[0] + 1))
+
+    def get_description(self, action: int) -> str:
+        if type(self.descriptions) == list and 0 <= action < len(self.descriptions):
+            return self.descriptions[action]
+        elif type(self.descriptions) == dict and action in self.descriptions.keys():
+            return self.descriptions[action]
+        elif 0 <= action < self.shape:
+            return str(action)
+        else:
+            raise ValueError("The given action is outside of the action space")

+
+
+
[docs]class MultiSelectActionSpace(ActionSpace):
+    """
+    A discrete action space where multiple actions can be selected at once. The actions are encoded as multi-hot vectors
+    """
+    def __init__(self, size: int, max_simultaneous_selected_actions: int=1, descriptions: Union[None, List, Dict]=None,
+                 default_action: np.ndarray=None, allow_no_action_to_be_selected=True):
+        super().__init__(size, low=None, high=None, descriptions=descriptions)
+        self.max_simultaneous_selected_actions = max_simultaneous_selected_actions
+
+        if max_simultaneous_selected_actions > size:
+            raise ValueError("The maximum simultaneous selected actions can't be larger the max number of actions")
+
+        # create all combinations of actions as a list of actions
+        I = [np.eye(size)]*self.max_simultaneous_selected_actions
+        self._actions = []
+        if allow_no_action_to_be_selected:
+            self._actions.append(np.zeros(size))
+        self._actions.extend(list(np.unique([np.clip(np.sum(t, axis=0), 0, 1) for t in product(*I)], axis=0)))
+
+        # default action
+        if default_action is None:
+            self.default_action = self._actions[0]
+        else:
+            self.default_action = default_action
+
+    @property
+    def actions(self) -> List[ActionType]:
+        return self._actions
+
+    def sample(self) -> np.ndarray:
+        # samples a multi-hot vector
+        return random.choice(self.actions)
+
+    def sample_with_info(self) -> ActionInfo:
+        return ActionInfo(self.sample(), action_probability=1. / len(self.actions))
+
+    def get_description(self, action: np.ndarray) -> str:
+        if np.sum(len(np.where(action == 0)[0])) + np.sum(len(np.where(action == 1)[0])) != self.shape or \
+                        np.sum(len(np.where(action == 1)[0])) > self.max_simultaneous_selected_actions:
+            raise ValueError("The given action is not in the action space")
+        selected_actions = np.where(action == 1)[0]
+        description = [self.descriptions[a] for a in selected_actions]
+        if len(description) == 0:
+            description = ['no-op']
+        return ' + '.join(description)

+
+
+
[docs]class CompoundActionSpace(ActionSpace):
+    """
+    An action space which consists of multiple sub-action spaces.
+    For example, in Starcraft the agent should choose an action identifier from ~550 options (Discrete(550)),
+    but it also needs to choose 13 different arguments for the selected action identifier, where each argument is
+    by itself an action space. In Starcraft, the arguments are Discrete action spaces as well, but this is not mandatory.
+    """
+    def __init__(self, sub_spaces: List[ActionSpace]):
+        super().__init__(0)
+        self.sub_action_spaces = sub_spaces
+        # TODO: define the shape, low and high value in a better way
+
+    @property
+    def actions(self) -> List[ActionType]:
+        return [action_space.actions for action_space in self.sub_action_spaces]
+
+    def sample(self) -> ActionType:
+        return [action_space.sample() for action_space in self.sub_action_spaces]
+
+    def clip_action_to_space(self, actions: List[ActionType]) -> ActionType:
+        if not isinstance(actions, list) or len(actions) != len(self.sub_action_spaces):
+            raise ValueError("The actions to be clipped must be a list with the same number of sub-actions as "
+                             "defined in the compound action space.")
+        for idx in range(len(self.sub_action_spaces)):
+            actions[idx] = self.sub_action_spaces[idx].clip_action_to_space(actions[idx])
+        return actions
+
+    def get_description(self, actions: np.ndarray) -> str:
+        description = [action_space.get_description(action) for action_space, action in zip(self.sub_action_spaces, actions)]
+        return ' + '.join(description)

+
+
+"""
+Goals
+"""
+
+
+class GoalToRewardConversion(object):
+    def __init__(self, goal_reaching_reward: float=0):
+        self.goal_reaching_reward = goal_reaching_reward
+
+    def convert_distance_to_reward(self, distance: Union[float, np.ndarray]) -> Tuple[float, bool]:
+        """
+        Given a distance from the goal, return a reward and a flag representing if the goal was reached
+
+        :param distance: the distance from the goal
+        :return:
+        """
+        raise NotImplementedError("")
+
+
+class ReachingGoal(GoalToRewardConversion):
+    """
+    get a reward if the goal was reached and 0 otherwise
+    """
+    def __init__(self, distance_from_goal_threshold: Union[float, np.ndarray], goal_reaching_reward: float=0,
+                 default_reward: float=-1):
+        """
+        :param distance_from_goal_threshold: consider getting to this distance from the goal the same as getting
+                                             to the goal
+        :param goal_reaching_reward: the reward the agent will get when reaching the goal
+        :param default_reward: the reward the agent will get until it reaches the goal
+        """
+        super().__init__(goal_reaching_reward)
+        self.distance_from_goal_threshold = distance_from_goal_threshold
+        self.default_reward = default_reward
+
+    def convert_distance_to_reward(self, distance: Union[float, np.ndarray]) -> Tuple[float, bool]:
+        if np.all(distance <= self.distance_from_goal_threshold):
+            return self.goal_reaching_reward, True
+        else:
+            return self.default_reward, False
+
+
+class InverseDistanceFromGoal(GoalToRewardConversion):
+    """
+    get a reward inversely proportional to the distance from the goal
+    """
+    def __init__(self, distance_from_goal_threshold: Union[float, np.ndarray], max_reward: float=1):
+        """
+        :param distance_from_goal_threshold: consider getting to this distance from the goal the same as getting
+                                             to the goal
+        :param max_reward: the max reward the agent can get
+        """
+        super().__init__(goal_reaching_reward=max_reward)
+        self.distance_from_goal_threshold = distance_from_goal_threshold
+        self.max_reward = max_reward
+
+    def convert_distance_to_reward(self, distance: Union[float, np.ndarray]) -> Tuple[float, bool]:
+        return min(self.max_reward, 1 / (distance + eps)), distance <= self.distance_from_goal_threshold
+
+
+
[docs]class GoalsSpace(VectorObservationSpace, ActionSpace):
+    """
+    A multidimensional space with a goal type definition. It also behaves as an action space, so that hierarchical
+    agents can use it as an output action space.
+    The class acts as a wrapper to the target space. So after setting the target space, all the values of the class
+    will match the values of the target space (the shape, low, high, etc.)
+    """
+
[docs]    class DistanceMetric(Enum):
+        Euclidean = 0
+        Cosine = 1
+        Manhattan = 2

+
+    def __init__(self, goal_name: str, reward_type: GoalToRewardConversion,
+                 distance_metric: Union[DistanceMetric, Callable]):
+        """
+        :param goal_name: the name of the observation space to use as the achieved goal.
+        :param reward_type: the reward type to use for converting distances from goal to rewards
+        :param distance_metric: the distance metric to use. could be either one of the distances in the
+                                DistanceMetric enum, or a custom function that gets two vectors as input and
+                                returns the distance between them
+        """
+        super().__init__(0)
+        self.goal_name = goal_name
+        self.distance_metric = distance_metric
+        self.reward_type = reward_type
+        self.target_space = None
+        self.max_abs_range = None
+
+    def set_target_space(self, target_space: Space) -> None:
+        self.target_space = target_space
+        super().__init__(self.target_space.shape, self.target_space.low, self.target_space.high)
+        self.max_abs_range = np.maximum(np.abs(self.low), np.abs(self.high))
+
+
[docs]    def goal_from_state(self, state: Dict):
+        """
+        Given a state, extract an observation according to the goal_name
+
+        :param state: a dictionary of observations
+        :return: the observation corresponding to the goal_name
+        """
+        return state[self.goal_name]

+
+
[docs]    def distance_from_goal(self, goal: np.ndarray, state: dict) -> float:
+        """
+        Given a state, check its distance from the goal
+
+        :param goal: a numpy array representing the goal
+        :param state: a dict representing the state
+        :return: the distance from the goal
+        """
+        state_value = self.goal_from_state(state)
+
+        # calculate distance
+        if self.distance_metric == self.DistanceMetric.Cosine:
+            dist = scipy.spatial.distance.cosine(goal, state_value)
+        elif self.distance_metric == self.DistanceMetric.Euclidean:
+            dist = scipy.spatial.distance.euclidean(goal, state_value)
+        elif self.distance_metric == self.DistanceMetric.Manhattan:
+            dist = scipy.spatial.distance.cityblock(goal, state_value)
+        elif callable(self.distance_metric):
+            dist = self.distance_metric(goal, state_value)
+        else:
+            raise ValueError("The given distance metric for the goal is not valid.")
+
+        return dist

+
+
[docs]    def get_reward_for_goal_and_state(self, goal: np.ndarray, state: dict) -> Tuple[float, bool]:
+        """
+        Given a state, check if the goal was reached and return a reward accordingly
+
+        :param goal: a numpy array representing the goal
+        :param state: a dict representing the state
+        :return: the reward for the current goal and state pair and a boolean representing if the goal was reached
+        """
+        dist = self.distance_from_goal(goal, state)
+        return self.reward_type.convert_distance_to_reward(dist)

+
+
+class AgentSelection(DiscreteActionSpace):
+    """
+    An discrete action space which is bounded by the number of agents to select from
+    """
+    def __init__(self, num_agents: int):
+        super().__init__(num_agents)
+
+
+class SpacesDefinition(object):
+    """
+    A container class that allows passing the definitions of all the spaces at once
+    """
+    def __init__(self,
+                 state: StateSpace,
+                 goal: ObservationSpace,
+                 action: ActionSpace,
+                 reward: RewardSpace):
+        self.state = state
+        self.goal = goal
+        self.action = action
+        self.reward = reward
+
+ +
+ +
+ + +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_sources/components/additional_parameters.rst.txt b/docs/_sources/components/additional_parameters.rst.txt new file mode 100644 index 0000000..cfa8bb4 --- /dev/null +++ b/docs/_sources/components/additional_parameters.rst.txt @@ -0,0 +1,18 @@ +Additional Parameters +===================== + +VisualizationParameters +----------------------- +.. autoclass:: rl_coach.base_parameters.VisualizationParameters + +PresetValidationParameters +-------------------------- +.. autoclass:: rl_coach.base_parameters.PresetValidationParameters + +TaskParameters +-------------- +.. autoclass:: rl_coach.base_parameters.TaskParameters + +DistributedTaskParameters +------------------------- +.. autoclass:: rl_coach.base_parameters.DistributedTaskParameters diff --git a/docs/_sources/components/agents/imitation/bc.rst.txt b/docs/_sources/components/agents/imitation/bc.rst.txt new file mode 100644 index 0000000..3f9c06f --- /dev/null +++ b/docs/_sources/components/agents/imitation/bc.rst.txt @@ -0,0 +1,29 @@ +Behavioral Cloning +================== + +**Actions space:** Discrete | Continuous + +Network Structure +----------------- + +.. image:: /_static/img/design_imgs/pg.png + :align: center + + +Algorithm Description +--------------------- + +Training the network +++++++++++++++++++++ + +The replay buffer contains the expert demonstrations for the task. +These demonstrations are given as state, action tuples, and with no reward. +The training goal is to reduce the difference between the actions predicted by the network and the actions taken by +the expert for each state. + +1. Sample a batch of transitions from the replay buffer. +2. Use the current states as input to the network, and the expert actions as the targets of the network. +3. For the network head, we use the policy head, which uses the cross entropy loss function. + + +.. autoclass:: rl_coach.agents.bc_agent.BCAlgorithmParameters \ No newline at end of file diff --git a/docs/_sources/components/agents/imitation/cil.rst.txt b/docs/_sources/components/agents/imitation/cil.rst.txt new file mode 100644 index 0000000..b48c0d4 --- /dev/null +++ b/docs/_sources/components/agents/imitation/cil.rst.txt @@ -0,0 +1,36 @@ +Conditional Imitation Learning +============================== + +**Actions space:** Discrete | Continuous + +**References:** `End-to-end Driving via Conditional Imitation Learning `_ + +Network Structure +----------------- + +.. image:: /_static/img/design_imgs/cil.png + :align: center + + +Algorithm Description +--------------------- + +Training the network +++++++++++++++++++++ + +The replay buffer contains the expert demonstrations for the task. +These demonstrations are given as state, action tuples, and with no reward. +The training goal is to reduce the difference between the actions predicted by the network and the actions taken by +the expert for each state. +In conditional imitation learning, each transition is assigned a class, which determines the goal that was pursuit +in that transitions. For example, 3 possible classes can be: turn right, turn left and follow lane. + +1. Sample a batch of transitions from the replay buffer, where the batch is balanced, meaning that an equal number + of transitions will be sampled from each class index. +2. Use the current states as input to the network, and assign the expert actions as the targets of the network heads + corresponding to the state classes. For the other heads, set the targets to match the currently predicted values, + so that the loss for the other heads will be zeroed out. +3. We use a regression head, that minimizes the MSE loss between the network predicted values and the target values. + + +.. autoclass:: rl_coach.agents.cil_agent.CILAlgorithmParameters \ No newline at end of file diff --git a/docs/_sources/components/agents/index.rst.txt b/docs/_sources/components/agents/index.rst.txt new file mode 100644 index 0000000..1a5cd42 --- /dev/null +++ b/docs/_sources/components/agents/index.rst.txt @@ -0,0 +1,43 @@ +Agents +====== + +Coach supports many state-of-the-art reinforcement learning algorithms, which are separated into three main classes - +value optimization, policy optimization and imitation learning. +A detailed description of those algorithms can be found by navigating to each of the algorithm pages. + +.. image:: /_static/img/algorithms.png + :width: 600px + :align: center + +.. toctree:: + :maxdepth: 1 + :caption: Agents + + policy_optimization/ac + imitation/bc + value_optimization/bs_dqn + value_optimization/categorical_dqn + imitation/cil + policy_optimization/cppo + policy_optimization/ddpg + other/dfp + value_optimization/double_dqn + value_optimization/dqn + value_optimization/dueling_dqn + value_optimization/mmc + value_optimization/n_step + value_optimization/naf + value_optimization/nec + value_optimization/pal + policy_optimization/pg + policy_optimization/ppo + value_optimization/rainbow + value_optimization/qr_dqn + + +.. autoclass:: rl_coach.base_parameters.AgentParameters + +.. autoclass:: rl_coach.agents.agent.Agent + :members: + :inherited-members: + diff --git a/docs/_sources/components/agents/other/dfp.rst.txt b/docs/_sources/components/agents/other/dfp.rst.txt new file mode 100644 index 0000000..6640f56 --- /dev/null +++ b/docs/_sources/components/agents/other/dfp.rst.txt @@ -0,0 +1,39 @@ +Direct Future Prediction +======================== + +**Actions space:** Discrete + +**References:** `Learning to Act by Predicting the Future `_ + +Network Structure +----------------- + +.. image:: /_static/img/design_imgs/dfp.png + :width: 600px + :align: center + + +Algorithm Description +--------------------- +Choosing an action +++++++++++++++++++ + +1. The current states (observations and measurements) and the corresponding goal vector are passed as an input to the network. + The output of the network is the predicted future measurements for time-steps :math:`t+1,t+2,t+4,t+8,t+16` and + :math:`t+32` for each possible action. +2. For each action, the measurements of each predicted time-step are multiplied by the goal vector, + and the result is a single vector of future values for each action. +3. Then, a weighted sum of the future values of each action is calculated, and the result is a single value for each action. +4. The action values are passed to the exploration policy to decide on the action to use. + +Training the network +++++++++++++++++++++ + +Given a batch of transitions, run them through the network to get the current predictions of the future measurements +per action, and set them as the initial targets for training the network. For each transition +:math:`(s_t,a_t,r_t,s_{t+1} )` in the batch, the target of the network for the action that was taken, is the actual + measurements that were seen in time-steps :math:`t+1,t+2,t+4,t+8,t+16` and :math:`t+32`. + For the actions that were not taken, the targets are the current values. + + +.. autoclass:: rl_coach.agents.dfp_agent.DFPAlgorithmParameters diff --git a/docs/_sources/components/agents/policy_optimization/ac.rst.txt b/docs/_sources/components/agents/policy_optimization/ac.rst.txt new file mode 100644 index 0000000..c748e50 --- /dev/null +++ b/docs/_sources/components/agents/policy_optimization/ac.rst.txt @@ -0,0 +1,40 @@ +Actor-Critic +============ + +**Actions space:** Discrete | Continuous + +**References:** `Asynchronous Methods for Deep Reinforcement Learning `_ + +Network Structure +----------------- + +.. image:: /_static/img/design_imgs/ac.png + :width: 500px + :align: center + +Algorithm Description +--------------------- + +Choosing an action - Discrete actions ++++++++++++++++++++++++++++++++++++++ + +The policy network is used in order to predict action probabilites. While training, a sample is taken from a categorical +distribution assigned with these probabilities. When testing, the action with the highest probability is used. + +Training the network +++++++++++++++++++++ +A batch of :math:`T_{max}` transitions is used, and the advantages are calculated upon it. + +Advantages can be calculated by either of the following methods (configured by the selected preset) - + +1. **A_VALUE** - Estimating advantage directly: + :math:`A(s_t, a_t) = \underbrace{\sum_{i=t}^{i=t + k - 1} \gamma^{i-t}r_i +\gamma^{k} V(s_{t+k})}_{Q(s_t, a_t)} - V(s_t)` + where :math:`k` is :math:`T_{max} - State\_Index` for each state in the batch. + +2. **GAE** - By following the `Generalized Advantage Estimation `_ paper. + +The advantages are then used in order to accumulate gradients according to +:math:`L = -\mathop{\mathbb{E}} [log (\pi) \cdot A]` + + +.. autoclass:: rl_coach.agents.actor_critic_agent.ActorCriticAlgorithmParameters \ No newline at end of file diff --git a/docs/_sources/components/agents/policy_optimization/cppo.rst.txt b/docs/_sources/components/agents/policy_optimization/cppo.rst.txt new file mode 100644 index 0000000..b2cba5d --- /dev/null +++ b/docs/_sources/components/agents/policy_optimization/cppo.rst.txt @@ -0,0 +1,44 @@ +Clipped Proximal Policy Optimization +==================================== + +**Actions space:** Discrete | Continuous + +**References:** `Proximal Policy Optimization Algorithms `_ + +Network Structure +----------------- + +.. image:: /_static/img/design_imgs/ppo.png + :align: center + +Algorithm Description +--------------------- +Choosing an action - Continuous action +++++++++++++++++++++++++++++++++++++++ + +Same as in PPO. + +Training the network +++++++++++++++++++++ + +Very similar to PPO, with several small (but very simplifying) changes: + +1. Train both the value and policy networks, simultaneously, by defining a single loss function, + which is the sum of each of the networks loss functions. Then, back propagate gradients only once from this unified loss function. + +2. The unified network's optimizer is set to Adam (instead of L-BFGS for the value network as in PPO). + +3. Value targets are now also calculated based on the GAE advantages. + In this method, the :math:`V` values are predicted from the critic network, and then added to the GAE based advantages, + in order to get a :math:`Q` value for each action. Now, since our critic network is predicting a :math:`V` value for + each state, setting the :math:`Q` calculated action-values as a target, will on average serve as a :math:`V` state-value target. + +4. Instead of adapting the penalizing KL divergence coefficient used in PPO, the likelihood ratio + :math:`r_t(\theta) =\frac{\pi_{\theta}(a|s)}{\pi_{\theta_{old}}(a|s)}` is clipped, to achieve a similar effect. + This is done by defining the policy's loss function to be the minimum between the standard surrogate loss and an epsilon + clipped surrogate loss: + + :math:`L^{CLIP}(\theta)=E_{t}[min(r_t(\theta)\cdot \hat{A}_t, clip(r_t(\theta), 1-\epsilon, 1+\epsilon) \cdot \hat{A}_t)]` + + +.. autoclass:: rl_coach.agents.clipped_ppo_agent.ClippedPPOAlgorithmParameters \ No newline at end of file diff --git a/docs/_sources/components/agents/policy_optimization/ddpg.rst.txt b/docs/_sources/components/agents/policy_optimization/ddpg.rst.txt new file mode 100644 index 0000000..d136ab4 --- /dev/null +++ b/docs/_sources/components/agents/policy_optimization/ddpg.rst.txt @@ -0,0 +1,50 @@ +Deep Deterministic Policy Gradient +================================== + +**Actions space:** Continuous + +**References:** `Continuous control with deep reinforcement learning `_ + +Network Structure +----------------- + +.. image:: /_static/img/design_imgs/ddpg.png + :align: center + +Algorithm Description +--------------------- +Choosing an action +++++++++++++++++++ + +Pass the current states through the actor network, and get an action mean vector :math:`\mu`. +While in training phase, use a continuous exploration policy, such as the Ornstein-Uhlenbeck process, +to add exploration noise to the action. When testing, use the mean vector :math:`\mu` as-is. + +Training the network +++++++++++++++++++++ + +Start by sampling a batch of transitions from the experience replay. + +* To train the **critic network**, use the following targets: + + :math:`y_t=r(s_t,a_t )+\gamma \cdot Q(s_{t+1},\mu(s_{t+1} ))` + + First run the actor target network, using the next states as the inputs, and get :math:`\mu (s_{t+1} )`. + Next, run the critic target network using the next states and :math:`\mu (s_{t+1} )`, and use the output to + calculate :math:`y_t` according to the equation above. To train the network, use the current states and actions + as the inputs, and :math:`y_t` as the targets. + +* To train the **actor network**, use the following equation: + + :math:`\nabla_{\theta^\mu } J \approx E_{s_t \tilde{} \rho^\beta } [\nabla_a Q(s,a)|_{s=s_t,a=\mu (s_t ) } \cdot \nabla_{\theta^\mu} \mu(s)|_{s=s_t} ]` + + Use the actor's online network to get the action mean values using the current states as the inputs. + Then, use the critic online network in order to get the gradients of the critic output with respect to the + action mean values :math:`\nabla _a Q(s,a)|_{s=s_t,a=\mu(s_t ) }`. + Using the chain rule, calculate the gradients of the actor's output, with respect to the actor weights, + given :math:`\nabla_a Q(s,a)`. Finally, apply those gradients to the actor network. + +After every training step, do a soft update of the critic and actor target networks' weights from the online networks. + + +.. autoclass:: rl_coach.agents.ddpg_agent.DDPGAlgorithmParameters \ No newline at end of file diff --git a/docs/_sources/components/agents/policy_optimization/hac.rst.txt b/docs/_sources/components/agents/policy_optimization/hac.rst.txt new file mode 100644 index 0000000..b177b53 --- /dev/null +++ b/docs/_sources/components/agents/policy_optimization/hac.rst.txt @@ -0,0 +1,24 @@ +Hierarchical Actor Critic +========================= + +**Actions space:** Continuous + +**References:** `Hierarchical Reinforcement Learning with Hindsight `_ + +Network Structure +----------------- + +.. image:: /_static/img/design_imgs/ddpg.png + :align: center + +Algorithm Description +--------------------- +Choosing an action +++++++++++++++++++ + +Pass the current states through the actor network, and get an action mean vector :math:`\mu`. +While in training phase, use a continuous exploration policy, such as the Ornstein-Uhlenbeck process, +to add exploration noise to the action. When testing, use the mean vector :math:`\mu` as-is. + +Training the network +++++++++++++++++++++ diff --git a/docs/_sources/components/agents/policy_optimization/pg.rst.txt b/docs/_sources/components/agents/policy_optimization/pg.rst.txt new file mode 100644 index 0000000..ac0feaa --- /dev/null +++ b/docs/_sources/components/agents/policy_optimization/pg.rst.txt @@ -0,0 +1,39 @@ +Policy Gradient +=============== + +**Actions space:** Discrete | Continuous + +**References:** `Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning `_ + +Network Structure +----------------- + +.. image:: /_static/img/design_imgs/pg.png + :align: center + +Algorithm Description +--------------------- +Choosing an action - Discrete actions ++++++++++++++++++++++++++++++++++++++ +Run the current states through the network and get a policy distribution over the actions. +While training, sample from the policy distribution. When testing, take the action with the highest probability. + +Training the network +++++++++++++++++++++ +The policy head loss is defined as :math:`L=-log (\pi) \cdot PolicyGradientRescaler`. +The :code:`PolicyGradientRescaler` is used in order to reduce the policy gradient variance, which might be very noisy. +This is done in order to reduce the variance of the updates, since noisy gradient updates might destabilize the policy's +convergence. The rescaler is a configurable parameter and there are few options to choose from: + +* **Total Episode Return** - The sum of all the discounted rewards during the episode. +* **Future Return** - Return from each transition until the end of the episode. +* **Future Return Normalized by Episode** - Future returns across the episode normalized by the episode's mean and standard deviation. +* **Future Return Normalized by Timestep** - Future returns normalized using running means and standard deviations, + which are calculated seperately for each timestep, across different episodes. + +Gradients are accumulated over a number of full played episodes. The gradients accumulation over several episodes +serves the same purpose - reducing the update variance. After accumulating gradients for several episodes, +the gradients are then applied to the network. + + +.. autoclass:: rl_coach.agents.policy_gradients_agent.PolicyGradientAlgorithmParameters \ No newline at end of file diff --git a/docs/_sources/components/agents/policy_optimization/ppo.rst.txt b/docs/_sources/components/agents/policy_optimization/ppo.rst.txt new file mode 100644 index 0000000..ea4ee39 --- /dev/null +++ b/docs/_sources/components/agents/policy_optimization/ppo.rst.txt @@ -0,0 +1,45 @@ +Proximal Policy Optimization +============================ + +**Actions space:** Discrete | Continuous + +**References:** `Proximal Policy Optimization Algorithms `_ + +Network Structure +----------------- + +.. image:: /_static/img/design_imgs/ppo.png + :align: center + + +Algorithm Description +--------------------- +Choosing an action - Continuous actions ++++++++++++++++++++++++++++++++++++++++ +Run the observation through the policy network, and get the mean and standard deviation vectors for this observation. +While in training phase, sample from a multi-dimensional Gaussian distribution with these mean and standard deviation values. +When testing, just take the mean values predicted by the network. + +Training the network +++++++++++++++++++++ + +1. Collect a big chunk of experience (in the order of thousands of transitions, sampled from multiple episodes). + +2. Calculate the advantages for each transition, using the *Generalized Advantage Estimation* method (Schulman '2015). + +3. Run a single training iteration of the value network using an L-BFGS optimizer. Unlike first order optimizers, + the L-BFGS optimizer runs on the entire dataset at once, without batching. + It continues running until some low loss threshold is reached. To prevent overfitting to the current dataset, + the value targets are updated in a soft manner, using an Exponentially Weighted Moving Average, based on the total + discounted returns of each state in each episode. + +4. Run several training iterations of the policy network. This is done by using the previously calculated advantages as + targets. The loss function penalizes policies that deviate too far from the old policy (the policy that was used *before* + starting to run the current set of training iterations) using a regularization term. + +5. After training is done, the last sampled KL divergence value will be compared with the *target KL divergence* value, + in order to adapt the penalty coefficient used in the policy loss. If the KL divergence went too high, + increase the penalty, if it went too low, reduce it. Otherwise, leave it unchanged. + + +.. autoclass:: rl_coach.agents.ppo_agent.PPOAlgorithmParameters \ No newline at end of file diff --git a/docs/_sources/components/agents/value_optimization/bs_dqn.rst.txt b/docs/_sources/components/agents/value_optimization/bs_dqn.rst.txt new file mode 100644 index 0000000..0b92eae --- /dev/null +++ b/docs/_sources/components/agents/value_optimization/bs_dqn.rst.txt @@ -0,0 +1,43 @@ +Bootstrapped DQN +================ + +**Actions space:** Discrete + +**References:** `Deep Exploration via Bootstrapped DQN `_ + +Network Structure +----------------- + +.. image:: /_static/img/design_imgs/bs_dqn.png + :align: center + +Algorithm Description +--------------------- +Choosing an action +++++++++++++++++++ +The current states are used as the input to the network. The network contains several $Q$ heads, which are used +for returning different estimations of the action :math:`Q` values. For each episode, the bootstrapped exploration policy +selects a single head to play with during the episode. According to the selected head, only the relevant +output :math:`Q` values are used. Using those :math:`Q` values, the exploration policy then selects the action for acting. + +Storing the transitions ++++++++++++++++++++++++ +For each transition, a Binomial mask is generated according to a predefined probability, and the number of output heads. +The mask is a binary vector where each element holds a 0 for heads that shouldn't train on the specific transition, +and 1 for heads that should use the transition for training. The mask is stored as part of the transition info in +the replay buffer. + +Training the network +++++++++++++++++++++ +First, sample a batch of transitions from the replay buffer. Run the current states through the network and get the +current :math:`Q` value predictions for all the heads and all the actions. For each transition in the batch, +and for each output head, if the transition mask is 1 - change the targets of the played action to :math:`y_t`, +according to the standard DQN update rule: + +:math:`y_t=r(s_t,a_t )+\gamma\cdot max_a Q(s_{t+1},a)` + +Otherwise, leave it intact so that the transition does not affect the learning of this head. +Then, train the online network according to the calculated targets. + +As in DQN, once in every few thousand steps, copy the weights from the online network to the target network. + diff --git a/docs/_sources/components/agents/value_optimization/categorical_dqn.rst.txt b/docs/_sources/components/agents/value_optimization/categorical_dqn.rst.txt new file mode 100644 index 0000000..dc07872 --- /dev/null +++ b/docs/_sources/components/agents/value_optimization/categorical_dqn.rst.txt @@ -0,0 +1,39 @@ +Categorical DQN +=============== + +**Actions space:** Discrete + +**References:** `A Distributional Perspective on Reinforcement Learning `_ + +Network Structure +----------------- + +.. image:: /_static/img/design_imgs/distributional_dqn.png + :align: center + +Algorithm Description +--------------------- + +Training the network +++++++++++++++++++++ + +1. Sample a batch of transitions from the replay buffer. + +2. The Bellman update is projected to the set of atoms representing the :math:`Q` values distribution, such + that the :math:`i-th` component of the projected update is calculated as follows: + + :math:`(\Phi \hat{T} Z_{\theta}(s_t,a_t))_i=\sum_{j=0}^{N-1}\Big[1-\frac{\lvert[\hat{T}_{z_{j}}]^{V_{MAX}}_{V_{MIN}}-z_i\rvert}{\Delta z}\Big]^1_0 \ p_j(s_{t+1}, \pi(s_{t+1}))` + + where: + * :math:`[ \cdot ]` bounds its argument in the range :math:`[a, b]` + * :math:`\hat{T}_{z_{j}}` is the Bellman update for atom :math:`z_j`: :math:`\hat{T}_{z_{j}} := r+\gamma z_j` + + +3. Network is trained with the cross entropy loss between the resulting probability distribution and the target + probability distribution. Only the target of the actions that were actually taken is updated. + +4. Once in every few thousand steps, weights are copied from the online network to the target network. + + + +.. autoclass:: rl_coach.agents.categorical_dqn_agent.CategoricalDQNAlgorithmParameters diff --git a/docs/_sources/components/agents/value_optimization/double_dqn.rst.txt b/docs/_sources/components/agents/value_optimization/double_dqn.rst.txt new file mode 100644 index 0000000..cb29797 --- /dev/null +++ b/docs/_sources/components/agents/value_optimization/double_dqn.rst.txt @@ -0,0 +1,35 @@ +Double DQN +========== + +**Actions space:** Discrete + +**References:** `Deep Reinforcement Learning with Double Q-learning `_ + +Network Structure +----------------- + +.. image:: /_static/img/design_imgs/dqn.png + :align: center + +Algorithm Description +--------------------- + +Training the network +++++++++++++++++++++ + +1. Sample a batch of transitions from the replay buffer. + +2. Using the next states from the sampled batch, run the online network in order to find the $Q$ maximizing + action :math:`argmax_a Q(s_{t+1},a)`. For these actions, use the corresponding next states and run the target + network to calculate :math:`Q(s_{t+1},argmax_a Q(s_{t+1},a))`. + +3. In order to zero out the updates for the actions that were not played (resulting from zeroing the MSE loss), + use the current states from the sampled batch, and run the online network to get the current Q values predictions. + Set those values as the targets for the actions that were not actually played. + +4. For each action that was played, use the following equation for calculating the targets of the network: + :math:`y_t=r(s_t,a_t )+\gamma \cdot Q(s_{t+1},argmax_a Q(s_{t+1},a))` + +5. Finally, train the online network using the current states as inputs, and with the aforementioned targets. + +6. Once in every few thousand steps, copy the weights from the online network to the target network. diff --git a/docs/_sources/components/agents/value_optimization/dqn.rst.txt b/docs/_sources/components/agents/value_optimization/dqn.rst.txt new file mode 100644 index 0000000..4882e38 --- /dev/null +++ b/docs/_sources/components/agents/value_optimization/dqn.rst.txt @@ -0,0 +1,37 @@ +Deep Q Networks +=============== + +**Actions space:** Discrete + +**References:** `Playing Atari with Deep Reinforcement Learning `_ + +Network Structure +----------------- + +.. image:: /_static/img/design_imgs/dqn.png + :align: center + +Algorithm Description +--------------------- + +Training the network +++++++++++++++++++++ + +1. Sample a batch of transitions from the replay buffer. + +2. Using the next states from the sampled batch, run the target network to calculate the :math:`Q` values for each of + the actions :math:`Q(s_{t+1},a)`, and keep only the maximum value for each state. + +3. In order to zero out the updates for the actions that were not played (resulting from zeroing the MSE loss), + use the current states from the sampled batch, and run the online network to get the current Q values predictions. + Set those values as the targets for the actions that were not actually played. + +4. For each action that was played, use the following equation for calculating the targets of the network:​ $$ y_t=r(s_t,a_t)+γ\cdot max_a {Q(s_{t+1},a)} $$ + :math:`y_t=r(s_t,a_t )+\gamma \cdot max_a Q(s_{t+1})` + +5. Finally, train the online network using the current states as inputs, and with the aforementioned targets. + +6. Once in every few thousand steps, copy the weights from the online network to the target network. + + +.. autoclass:: rl_coach.agents.dqn_agent.DQNAlgorithmParameters diff --git a/docs/_sources/components/agents/value_optimization/dueling_dqn.rst.txt b/docs/_sources/components/agents/value_optimization/dueling_dqn.rst.txt new file mode 100644 index 0000000..d29b305 --- /dev/null +++ b/docs/_sources/components/agents/value_optimization/dueling_dqn.rst.txt @@ -0,0 +1,27 @@ +Dueling DQN +=========== + +**Actions space:** Discrete + +**References:** `Dueling Network Architectures for Deep Reinforcement Learning `_ + +Network Structure +----------------- + +.. image:: /_static/img/design_imgs/dueling_dqn.png + :align: center + +General Description +------------------- +Dueling DQN presents a change in the network structure comparing to DQN. + +Dueling DQN uses a specialized *Dueling Q Head* in order to separate :math:`Q` to an :math:`A` (advantage) +stream and a :math:`V` stream. Adding this type of structure to the network head allows the network to better differentiate +actions from one another, and significantly improves the learning. + +In many states, the values of the different actions are very similar, and it is less important which action to take. +This is especially important in environments where there are many actions to choose from. In DQN, on each training +iteration, for each of the states in the batch, we update the :ath:`Q` values only for the specific actions taken in +those states. This results in slower learning as we do not learn the :math:`Q` values for actions that were not taken yet. +On dueling architecture, on the other hand, learning is faster - as we start learning the state-value even if only a +single action has been taken at this state. \ No newline at end of file diff --git a/docs/_sources/components/agents/value_optimization/mmc.rst.txt b/docs/_sources/components/agents/value_optimization/mmc.rst.txt new file mode 100644 index 0000000..c96b4ca --- /dev/null +++ b/docs/_sources/components/agents/value_optimization/mmc.rst.txt @@ -0,0 +1,37 @@ +Mixed Monte Carlo +================= + +**Actions space:** Discrete + +**References:** `Count-Based Exploration with Neural Density Models `_ + +Network Structure +----------------- + +.. image:: /_static/img/design_imgs/dqn.png + :align: center + +Algorithm Description +--------------------- +Training the network +++++++++++++++++++++ + +In MMC, targets are calculated as a mixture between Double DQN targets and full Monte Carlo samples (total discounted returns). + +The DDQN targets are calculated in the same manner as in the DDQN agent: + +:math:`y_t^{DDQN}=r(s_t,a_t )+\gamma Q(s_{t+1},argmax_a Q(s_{t+1},a))` + +The Monte Carlo targets are calculated by summing up the discounted rewards across the entire episode: + +:math:`y_t^{MC}=\sum_{j=0}^T\gamma^j r(s_{t+j},a_{t+j} )` + +A mixing ratio $\alpha$ is then used to get the final targets: + +:math:`y_t=(1-\alpha)\cdot y_t^{DDQN}+\alpha \cdot y_t^{MC}` + +Finally, the online network is trained using the current states as inputs, and the calculated targets. +Once in every few thousand steps, copy the weights from the online network to the target network. + + +.. autoclass:: rl_coach.agents.mmc_agent.MixedMonteCarloAlgorithmParameters diff --git a/docs/_sources/components/agents/value_optimization/n_step.rst.txt b/docs/_sources/components/agents/value_optimization/n_step.rst.txt new file mode 100644 index 0000000..6ff0722 --- /dev/null +++ b/docs/_sources/components/agents/value_optimization/n_step.rst.txt @@ -0,0 +1,35 @@ +N-Step Q Learning +================= + +**Actions space:** Discrete + +**References:** `Asynchronous Methods for Deep Reinforcement Learning `_ + +Network Structure +----------------- + +.. image:: /_static/img/design_imgs/dqn.png + :align: center + +Algorithm Description +--------------------- + +Training the network +++++++++++++++++++++ + +The :math:`N`-step Q learning algorithm works in similar manner to DQN except for the following changes: + +1. No replay buffer is used. Instead of sampling random batches of transitions, the network is trained every + :math:`N` steps using the latest :math:`N` steps played by the agent. + +2. In order to stabilize the learning, multiple workers work together to update the network. + This creates the same effect as uncorrelating the samples used for training. + +3. Instead of using single-step Q targets for the network, the rewards from $N$ consequent steps are accumulated + to form the :math:`N`-step Q targets, according to the following equation: + :math:`R(s_t, a_t) = \sum_{i=t}^{i=t + k - 1} \gamma^{i-t}r_i +\gamma^{k} V(s_{t+k})` + where :math:`k` is :math:`T_{max} - State\_Index` for each state in the batch + + + +.. autoclass:: rl_coach.agents.n_step_q_agent.NStepQAlgorithmParameters diff --git a/docs/_sources/components/agents/value_optimization/naf.rst.txt b/docs/_sources/components/agents/value_optimization/naf.rst.txt new file mode 100644 index 0000000..8d7df05 --- /dev/null +++ b/docs/_sources/components/agents/value_optimization/naf.rst.txt @@ -0,0 +1,33 @@ +Normalized Advantage Functions +============================== + +**Actions space:** Continuous + +**References:** `Continuous Deep Q-Learning with Model-based Acceleration `_ + +Network Structure +----------------- + +.. image:: /_static/img/design_imgs/naf.png + :width: 600px + :align: center + +Algorithm Description +--------------------- +Choosing an action +++++++++++++++++++ +The current state is used as an input to the network. The action mean :math:`\mu(s_t )` is extracted from the output head. +It is then passed to the exploration policy which adds noise in order to encourage exploration. + +Training the network +++++++++++++++++++++ +The network is trained by using the following targets: +:math:`y_t=r(s_t,a_t )+\gamma\cdot V(s_{t+1})` +Use the next states as the inputs to the target network and extract the :math:`V` value, from within the head, +to get :math:`V(s_{t+1} )`. Then, update the online network using the current states and actions as inputs, +and :math:`y_t` as the targets. +After every training step, use a soft update in order to copy the weights from the online network to the target network. + + + +.. autoclass:: rl_coach.agents.naf_agent.NAFAlgorithmParameters diff --git a/docs/_sources/components/agents/value_optimization/nec.rst.txt b/docs/_sources/components/agents/value_optimization/nec.rst.txt new file mode 100644 index 0000000..7410a9e --- /dev/null +++ b/docs/_sources/components/agents/value_optimization/nec.rst.txt @@ -0,0 +1,50 @@ +Neural Episodic Control +======================= + +**Actions space:** Discrete + +**References:** `Neural Episodic Control `_ + +Network Structure +----------------- + +.. image:: /_static/img/design_imgs/nec.png + :width: 500px + :align: center + +Algorithm Description +--------------------- +Choosing an action +++++++++++++++++++ + +1. Use the current state as an input to the online network and extract the state embedding, which is the intermediate + output from the middleware. + +2. For each possible action :math:`a_i`, run the DND head using the state embedding and the selected action :math:`a_i` as inputs. + The DND is queried and returns the :math:`P` nearest neighbor keys and values. The keys and values are used to calculate + and return the action :math:`Q` value from the network. + +3. Pass all the :math:`Q` values to the exploration policy and choose an action accordingly. + +4. Store the state embeddings and actions taken during the current episode in a small buffer :math:`B`, in order to + accumulate transitions until it is possible to calculate the total discounted returns over the entire episode. + +Finalizing an episode ++++++++++++++++++++++ +For each step in the episode, the state embeddings and the taken actions are stored in the buffer :math:`B`. +When the episode is finished, the replay buffer calculates the :math:`N`-step total return of each transition in the +buffer, bootstrapped using the maximum :math:`Q` value of the :math:`N`-th transition. Those values are inserted +along with the total return into the DND, and the buffer :math:`B` is reset. + +Training the network +++++++++++++++++++++ +Train the network only when the DND has enough entries for querying. + +To train the network, the current states are used as the inputs and the :math:`N`-step returns are used as the targets. +The :math:`N`-step return used takes into account :math:`N` consecutive steps, and bootstraps the last value from +the network if necessary: +:math:`y_t=\sum_{j=0}^{N-1}\gamma^j r(s_{t+j},a_{t+j} ) +\gamma^N max_a Q(s_{t+N},a)` + + + +.. autoclass:: rl_coach.agents.nec_agent.NECAlgorithmParameters diff --git a/docs/_sources/components/agents/value_optimization/pal.rst.txt b/docs/_sources/components/agents/value_optimization/pal.rst.txt new file mode 100644 index 0000000..9ebcba6 --- /dev/null +++ b/docs/_sources/components/agents/value_optimization/pal.rst.txt @@ -0,0 +1,45 @@ +Persistent Advantage Learning +============================= + +**Actions space:** Discrete + +**References:** `Increasing the Action Gap: New Operators for Reinforcement Learning `_ + +Network Structure +----------------- + +.. image:: /_static/img/design_imgs/dqn.png + :align: center + +Algorithm Description +--------------------- +Training the network +++++++++++++++++++++ + +1. Sample a batch of transitions from the replay buffer. + +2. Start by calculating the initial target values in the same manner as they are calculated in DDQN + :math:`y_t^{DDQN}=r(s_t,a_t )+\gamma Q(s_{t+1},argmax_a Q(s_{t+1},a))` + +3. The action gap :math:`V(s_t )-Q(s_t,a_t)` should then be subtracted from each of the calculated targets. + To calculate the action gap, run the target network using the current states and get the :math:`Q` values + for all the actions. Then estimate :math:`V` as the maximum predicted :math:`Q` value for the current state: + :math:`V(s_t )=max_a Q(s_t,a)` + +4. For *advantage learning (AL)*, reduce the action gap weighted by a predefined parameter :math:`\alpha` from + the targets :math:`y_t^{DDQN}`: + :math:`y_t=y_t^{DDQN}-\alpha \cdot (V(s_t )-Q(s_t,a_t ))` + +5. For *persistent advantage learning (PAL)*, the target network is also used in order to calculate the action + gap for the next state: + :math:`V(s_{t+1} )-Q(s_{t+1},a_{t+1})` + where :math:`a_{t+1}` is chosen by running the next states through the online network and choosing the action that + has the highest predicted :math:`Q` value. Finally, the targets will be defined as - + :math:`y_t=y_t^{DDQN}-\alpha \cdot min(V(s_t )-Q(s_t,a_t ),V(s_{t+1} )-Q(s_{t+1},a_{t+1} ))` + +6. Train the online network using the current states as inputs, and with the aforementioned targets. + +7. Once in every few thousand steps, copy the weights from the online network to the target network. + + +.. autoclass:: rl_coach.agents.pal_agent.PALAlgorithmParameters diff --git a/docs/_sources/components/agents/value_optimization/qr_dqn.rst.txt b/docs/_sources/components/agents/value_optimization/qr_dqn.rst.txt new file mode 100644 index 0000000..88bb5c3 --- /dev/null +++ b/docs/_sources/components/agents/value_optimization/qr_dqn.rst.txt @@ -0,0 +1,33 @@ +Quantile Regression DQN +======================= + +**Actions space:** Discrete + +**References:** `Distributional Reinforcement Learning with Quantile Regression `_ + +Network Structure +----------------- + +.. image:: /_static/img/design_imgs/qr_dqn.png + :align: center + +Algorithm Description +--------------------- + +Training the network +++++++++++++++++++++ + +1. Sample a batch of transitions from the replay buffer. + +2. First, the next state quantiles are predicted. These are used in order to calculate the targets for the network, + by following the Bellman equation. + Next, the current quantile locations for the current states are predicted, sorted, and used for calculating the + quantile midpoints targets. + +3. The network is trained with the quantile regression loss between the resulting quantile locations and the target + quantile locations. Only the targets of the actions that were actually taken are updated. + +4. Once in every few thousand steps, weights are copied from the online network to the target network. + + +.. autoclass:: rl_coach.agents.qr_dqn_agent.QuantileRegressionDQNAlgorithmParameters \ No newline at end of file diff --git a/docs/_sources/components/agents/value_optimization/rainbow.rst.txt b/docs/_sources/components/agents/value_optimization/rainbow.rst.txt new file mode 100644 index 0000000..5c2b443 --- /dev/null +++ b/docs/_sources/components/agents/value_optimization/rainbow.rst.txt @@ -0,0 +1,51 @@ +Rainbow +======= + +**Actions space:** Discrete + +**References:** `Rainbow: Combining Improvements in Deep Reinforcement Learning `_ + +Network Structure +----------------- + +.. image:: /_static/img/design_imgs/rainbow.png + :align: center + +Algorithm Description +--------------------- + +Rainbow combines 6 recent advancements in reinforcement learning: + +* N-step returns +* Distributional state-action value learning +* Dueling networks +* Noisy Networks +* Double DQN +* Prioritized Experience Replay + +Training the network +++++++++++++++++++++ + +1. Sample a batch of transitions from the replay buffer. + +2. The Bellman update is projected to the set of atoms representing the :math:`Q` values distribution, such + that the :math:`i-th` component of the projected update is calculated as follows: + + :math:`(\Phi \hat{T} Z_{\theta}(s_t,a_t))_i=\sum_{j=0}^{N-1}\Big[1-\frac{\lvert[\hat{T}_{z_{j}}]^{V_{MAX}}_{V_{MIN}}-z_i\rvert}{\Delta z}\Big]^1_0 \ p_j(s_{t+1}, \pi(s_{t+1}))` + + where: + * :math:`[ \cdot ]` bounds its argument in the range :math:`[a, b]` + * :math:`\hat{T}_{z_{j}}` is the Bellman update for atom + :math:`z_j`: :math:`\hat{T}_{z_{j}} := r_t+\gamma r_{t+1} + ... + \gamma r_{t+n-1} + \gamma^{n-1} z_j` + + +3. Network is trained with the cross entropy loss between the resulting probability distribution and the target + probability distribution. Only the target of the actions that were actually taken is updated. + +4. Once in every few thousand steps, weights are copied from the online network to the target network. + +5. After every training step, the priorities of the batch transitions are updated in the prioritized replay buffer + using the KL divergence loss that is returned from the network. + + +.. autoclass:: rl_coach.agents.rainbow_dqn_agent.RainbowDQNAlgorithmParameters diff --git a/docs/_sources/components/architectures/index.rst.txt b/docs/_sources/components/architectures/index.rst.txt new file mode 100644 index 0000000..3e3fa83 --- /dev/null +++ b/docs/_sources/components/architectures/index.rst.txt @@ -0,0 +1,27 @@ +Architectures +============= + +Architectures contain all the classes that implement the neural network related stuff for the agent. +Since Coach is intended to work with multiple neural network frameworks, each framework will implement its +own components under a dedicated directory. For example, tensorflow components will contain all the neural network +parts that are implemented using TensorFlow. + +.. autoclass:: rl_coach.base_parameters.NetworkParameters + +Architecture +------------ +.. autoclass:: rl_coach.architectures.architecture.Architecture + :members: + :inherited-members: + +NetworkWrapper +-------------- + +.. image:: /_static/img/distributed.png + :width: 600px + :align: center + +.. autoclass:: rl_coach.architectures.network_wrapper.NetworkWrapper + :members: + :inherited-members: + diff --git a/docs/_sources/components/core_types.rst.txt b/docs/_sources/components/core_types.rst.txt new file mode 100644 index 0000000..5202c22 --- /dev/null +++ b/docs/_sources/components/core_types.rst.txt @@ -0,0 +1,33 @@ +Core Types +========== + +ActionInfo +---------- +.. autoclass:: rl_coach.core_types.ActionInfo + :members: + :inherited-members: + +Batch +----- +.. autoclass:: rl_coach.core_types.Batch + :members: + :inherited-members: + +EnvResponse +----------- +.. autoclass:: rl_coach.core_types.EnvResponse + :members: + :inherited-members: + +Episode +------- +.. autoclass:: rl_coach.core_types.Episode + :members: + :inherited-members: + +Transition +---------- +.. autoclass:: rl_coach.core_types.Transition + :members: + :inherited-members: + diff --git a/docs/_sources/components/environments/index.rst.txt b/docs/_sources/components/environments/index.rst.txt new file mode 100644 index 0000000..5f0d20f --- /dev/null +++ b/docs/_sources/components/environments/index.rst.txt @@ -0,0 +1,70 @@ +Environments +============ + +.. autoclass:: rl_coach.environments.environment.Environment + :members: + :inherited-members: + +DeepMind Control Suite +---------------------- + +A set of reinforcement learning environments powered by the MuJoCo physics engine. + +Website: `DeepMind Control Suite `_ + +.. autoclass:: rl_coach.environments.control_suite_environment.ControlSuiteEnvironment + + +Blizzard Starcraft II +--------------------- + +A popular strategy game which was wrapped with a python interface by DeepMind. + +Website: `Blizzard Starcraft II `_ + +.. autoclass:: rl_coach.environments.starcraft2_environment.StarCraft2Environment + + +ViZDoom +-------- + +A Doom-based AI research platform for reinforcement learning from raw visual information. + +Website: `ViZDoom `_ + +.. autoclass:: rl_coach.environments.doom_environment.DoomEnvironment + + +CARLA +----- + +An open-source simulator for autonomous driving research. + +Website: `CARLA `_ + +.. autoclass:: rl_coach.environments.carla_environment.CarlaEnvironment + +OpenAI Gym +---------- + +A library which consists of a set of environments, from games to robotics. +Additionally, it can be extended using the API defined by the authors. + +Website: `OpenAI Gym `_ + +In Coach, we support all the native environments in Gym, along with several extensions such as: + +* `Roboschool `_ - a set of environments powered by the PyBullet engine, + that offer a free alternative to MuJoCo. + +* `Gym Extensions `_ - a set of environments that extends Gym for + auxiliary tasks (multitask learning, transfer learning, inverse reinforcement learning, etc.) + +* `PyBullet `_ - a physics engine that + includes a set of robotics environments. + + +.. autoclass:: rl_coach.environments.gym_environment.GymEnvironment + + + diff --git a/docs/_sources/components/exploration_policies/index.rst.txt b/docs/_sources/components/exploration_policies/index.rst.txt new file mode 100644 index 0000000..10b6c77 --- /dev/null +++ b/docs/_sources/components/exploration_policies/index.rst.txt @@ -0,0 +1,87 @@ +Exploration Policies +==================== + +Exploration policies are a component that allow the agent to tradeoff exploration and exploitation according to a +predefined policy. This is one of the most important aspects of reinforcement learning agents, and can require some +tuning to get it right. Coach supports several pre-defined exploration policies, and it can be easily extended with +custom policies. Note that not all exploration policies are expected to work for both discrete and continuous action +spaces. + +.. role:: green +.. role:: red + ++----------------------+-----------------------+------------------+ +| Exploration Policy | Discrete Action Space | Box Action Space | ++======================+=======================+==================+ +| AdditiveNoise | :red:`X` | :green:`V` | ++----------------------+-----------------------+------------------+ +| Boltzmann | :green:`V` | :red:`X` | ++----------------------+-----------------------+------------------+ +| Bootstrapped | :green:`V` | :red:`X` | ++----------------------+-----------------------+------------------+ +| Categorical | :green:`V` | :red:`X` | ++----------------------+-----------------------+------------------+ +| ContinuousEntropy | :red:`X` | :green:`V` | ++----------------------+-----------------------+------------------+ +| EGreedy | :green:`V` | :green:`V` | ++----------------------+-----------------------+------------------+ +| Greedy | :green:`V` | :green:`V` | ++----------------------+-----------------------+------------------+ +| OUProcess | :red:`X` | :green:`V` | ++----------------------+-----------------------+------------------+ +| ParameterNoise | :green:`V` | :green:`V` | ++----------------------+-----------------------+------------------+ +| TruncatedNormal | :red:`X` | :green:`V` | ++----------------------+-----------------------+------------------+ +| UCB | :green:`V` | :red:`X` | ++----------------------+-----------------------+------------------+ + +ExplorationPolicy +----------------- +.. autoclass:: rl_coach.exploration_policies.ExplorationPolicy + :members: + :inherited-members: + +AdditiveNoise +------------- +.. autoclass:: rl_coach.exploration_policies.AdditiveNoise + +Boltzmann +--------- +.. autoclass:: rl_coach.exploration_policies.Boltzmann + +Bootstrapped +------------ +.. autoclass:: rl_coach.exploration_policies.Bootstrapped + +Categorical +----------- +.. autoclass:: rl_coach.exploration_policies.Categorical + +ContinuousEntropy +----------------- +.. autoclass:: rl_coach.exploration_policies.ContinuousEntropy + +EGreedy +------- +.. autoclass:: rl_coach.exploration_policies.EGreedy + +Greedy +------ +.. autoclass:: rl_coach.exploration_policies.Greedy + +OUProcess +--------- +.. autoclass:: rl_coach.exploration_policies.OUProcess + +ParameterNoise +-------------- +.. autoclass:: rl_coach.exploration_policies.ParameterNoise + +TruncatedNormal +--------------- +.. autoclass:: rl_coach.exploration_policies.TruncatedNormal + +UCB +--- +.. autoclass:: rl_coach.exploration_policies.UCB \ No newline at end of file diff --git a/docs/_sources/components/filters/index.rst.txt b/docs/_sources/components/filters/index.rst.txt new file mode 100644 index 0000000..1e4c7f5 --- /dev/null +++ b/docs/_sources/components/filters/index.rst.txt @@ -0,0 +1,28 @@ +Filters +======= + +.. toctree:: + :maxdepth: 1 + :caption: Filters + + input_filters + output_filters + +Filters are a mechanism in Coach that allows doing pre-processing and post-processing of the internal agent information. +There are two filter categories - + +* **Input filters** - these are filters that process the information passed **into** the agent from the environment. + This information includes the observation and the reward. Input filters therefore allow rescaling observations, + normalizing rewards, stack observations, etc. + +* **Output filters** - these are filters that process the information going **out** of the agent into the environment. + This information includes the action the agent chooses to take. Output filters therefore allow conversion of + actions from one space into another. For example, the agent can take :math:`N` discrete actions, that will be mapped by + the output filter onto :math:`N` continuous actions. + +Filters can be stacked on top of each other in order to build complex processing flows of the inputs or outputs. + +.. image:: /_static/img/filters.png + :width: 350px + :align: center + diff --git a/docs/_sources/components/filters/input_filters.rst.txt b/docs/_sources/components/filters/input_filters.rst.txt new file mode 100644 index 0000000..3b0d4b1 --- /dev/null +++ b/docs/_sources/components/filters/input_filters.rst.txt @@ -0,0 +1,67 @@ +Input Filters +============= + +The input filters are separated into two categories - **observation filters** and **reward filters**. + +Observation Filters +------------------- + +ObservationClippingFilter ++++++++++++++++++++++++++ +.. autoclass:: rl_coach.filters.observation.ObservationClippingFilter + +ObservationCropFilter ++++++++++++++++++++++ +.. autoclass:: rl_coach.filters.observation.ObservationCropFilter + +ObservationMoveAxisFilter ++++++++++++++++++++++++++ +.. autoclass:: rl_coach.filters.observation.ObservationMoveAxisFilter + +ObservationNormalizationFilter +++++++++++++++++++++++++++++++ +.. autoclass:: rl_coach.filters.observation.ObservationNormalizationFilter + +ObservationReductionBySubPartsNameFilter +++++++++++++++++++++++++++++++++++++++++ +.. autoclass:: rl_coach.filters.observation.ObservationReductionBySubPartsNameFilter + +ObservationRescaleSizeByFactorFilter +++++++++++++++++++++++++++++++++++++ +.. autoclass:: rl_coach.filters.observation.ObservationRescaleSizeByFactorFilter + +ObservationRescaleToSizeFilter +++++++++++++++++++++++++++++++ +.. autoclass:: rl_coach.filters.observation.ObservationRescaleToSizeFilter + +ObservationRGBToYFilter ++++++++++++++++++++++++ +.. autoclass:: rl_coach.filters.observation.ObservationRGBToYFilter + +ObservationSqueezeFilter +++++++++++++++++++++++++ +.. autoclass:: rl_coach.filters.observation.ObservationSqueezeFilter + +ObservationStackingFilter ++++++++++++++++++++++++++ +.. autoclass:: rl_coach.filters.observation.ObservationStackingFilter + +ObservationToUInt8Filter +++++++++++++++++++++++++ +.. autoclass:: rl_coach.filters.observation.ObservationToUInt8Filter + + +Reward Filters +-------------- + +RewardClippingFilter +++++++++++++++++++++ +.. autoclass:: rl_coach.filters.reward.RewardClippingFilter + +RewardNormalizationFilter ++++++++++++++++++++++++++ +.. autoclass:: rl_coach.filters.reward.RewardNormalizationFilter + +RewardRescaleFilter ++++++++++++++++++++ +.. autoclass:: rl_coach.filters.reward.RewardRescaleFilter diff --git a/docs/_sources/components/filters/output_filters.rst.txt b/docs/_sources/components/filters/output_filters.rst.txt new file mode 100644 index 0000000..1a2f460 --- /dev/null +++ b/docs/_sources/components/filters/output_filters.rst.txt @@ -0,0 +1,37 @@ +Output Filters +-------------- + +The output filters only process the actions. + +Action Filters +++++++++++++++ + +.. autoclass:: rl_coach.filters.action.AttentionDiscretization + +.. image:: /_static/img/attention_discretization.png + :align: center + +.. autoclass:: rl_coach.filters.action.BoxDiscretization + +.. image:: /_static/img/box_discretization.png + :align: center + +.. autoclass:: rl_coach.filters.action.BoxMasking + +.. image:: /_static/img/box_masking.png + :align: center + +.. autoclass:: rl_coach.filters.action.PartialDiscreteActionSpaceMap + +.. image:: /_static/img/partial_discrete_action_space_map.png + :align: center + +.. autoclass:: rl_coach.filters.action.FullDiscreteActionSpaceMap + +.. image:: /_static/img/full_discrete_action_space_map.png + :align: center + +.. autoclass:: rl_coach.filters.action.LinearBoxToBoxMap + +.. image:: /_static/img/linear_box_to_box_map.png + :align: center \ No newline at end of file diff --git a/docs/_sources/components/memories/index.rst.txt b/docs/_sources/components/memories/index.rst.txt new file mode 100644 index 0000000..2575a32 --- /dev/null +++ b/docs/_sources/components/memories/index.rst.txt @@ -0,0 +1,44 @@ +Memories +======== + +Episodic Memories +----------------- + +EpisodicExperienceReplay +++++++++++++++++++++++++ +.. autoclass:: rl_coach.memories.episodic.EpisodicExperienceReplay + +EpisodicHindsightExperienceReplay ++++++++++++++++++++++++++++++++++ +.. autoclass:: rl_coach.memories.episodic.EpisodicHindsightExperienceReplay + +EpisodicHRLHindsightExperienceReplay +++++++++++++++++++++++++++++++++++++ +.. autoclass:: rl_coach.memories.episodic.EpisodicHRLHindsightExperienceReplay + +SingleEpisodeBuffer ++++++++++++++++++++ +.. autoclass:: rl_coach.memories.episodic.SingleEpisodeBuffer + + +Non-Episodic Memories +--------------------- +BalancedExperienceReplay +++++++++++++++++++++++++ +.. autoclass:: rl_coach.memories.non_episodic.BalancedExperienceReplay + +QDND +++++ +.. autoclass:: rl_coach.memories.non_episodic.QDND + +ExperienceReplay +++++++++++++++++ +.. autoclass:: rl_coach.memories.non_episodic.ExperienceReplay + +PrioritizedExperienceReplay ++++++++++++++++++++++++++++ +.. autoclass:: rl_coach.memories.non_episodic.PrioritizedExperienceReplay + +TransitionCollection +++++++++++++++++++++ +.. autoclass:: rl_coach.memories.non_episodic.TransitionCollection diff --git a/docs/_sources/components/spaces.rst.txt b/docs/_sources/components/spaces.rst.txt new file mode 100644 index 0000000..4adf3f5 --- /dev/null +++ b/docs/_sources/components/spaces.rst.txt @@ -0,0 +1,64 @@ +Spaces +====== + +Space +----- +.. autoclass:: rl_coach.spaces.Space + :members: + :inherited-members: + + + +Observation Spaces +------------------ +.. autoclass:: rl_coach.spaces.ObservationSpace + :members: + :inherited-members: + +VectorObservationSpace +++++++++++++++++++++++ +.. autoclass:: rl_coach.spaces.VectorObservationSpace + +PlanarMapsObservationSpace +++++++++++++++++++++++++++ +.. autoclass:: rl_coach.spaces.PlanarMapsObservationSpace + +ImageObservationSpace ++++++++++++++++++++++ +.. autoclass:: rl_coach.spaces.ImageObservationSpace + + + +Action Spaces +------------- +.. autoclass:: rl_coach.spaces.ActionSpace + :members: + :inherited-members: + +AttentionActionSpace +++++++++++++++++++++ +.. autoclass:: rl_coach.spaces.AttentionActionSpace + +BoxActionSpace +++++++++++++++ +.. autoclass:: rl_coach.spaces.BoxActionSpace + +DiscreteActionSpace +++++++++++++++++++++ +.. autoclass:: rl_coach.spaces.DiscreteActionSpace + +MultiSelectActionSpace +++++++++++++++++++++++ +.. autoclass:: rl_coach.spaces.MultiSelectActionSpace + +CompoundActionSpace ++++++++++++++++++++ +.. autoclass:: rl_coach.spaces.CompoundActionSpace + + + +Goal Spaces +----------- +.. autoclass:: rl_coach.spaces.GoalsSpace + :members: + :inherited-members: diff --git a/docs/_sources/contributing/add_agent.rst.txt b/docs/_sources/contributing/add_agent.rst.txt new file mode 100644 index 0000000..52a95b4 --- /dev/null +++ b/docs/_sources/contributing/add_agent.rst.txt @@ -0,0 +1,80 @@ +Adding a New Agent +================== + +Coach's modularity makes adding an agent a simple and clean task. +We suggest using the following +`Jupyter notebook tutorial `_ +to ramp up on this process. In general, it involves the following steps: + +1. Implement your algorithm in a new file. The agent can inherit base classes such as **ValueOptimizationAgent** or + **ActorCriticAgent**, or the more generic **Agent** base class. + + .. note:: + **ValueOptimizationAgent**, **PolicyOptimizationAgent** and **Agent** are abstract classes. + :code:`learn_from_batch()` should be overriden with the desired behavior for the algorithm being implemented. + If deciding to inherit from **Agent**, also :code:`choose_action()` should be overriden. + + .. code-block:: python + + def learn_from_batch(self, batch) -> Tuple[float, List, List]: + """ + Given a batch of transitions, calculates their target values and updates the network. + :param batch: A list of transitions + :return: The total loss of the training, the loss per head and the unclipped gradients + """ + + def choose_action(self, curr_state): + """ + choose an action to act with in the current episode being played. Different behavior might be exhibited when training + or testing. + + :param curr_state: the current state to act upon. + :return: chosen action, some action value describing the action (q-value, probability, etc) + """ + +2. Implement your agent's specific network head, if needed, at the implementation for the framework of your choice. + For example **architectures/neon_components/heads.py**. The head will inherit the generic base class Head. + A new output type should be added to configurations.py, and a mapping between the new head and output type should + be defined in the get_output_head() function at **architectures/neon_components/general_network.py** + +3. Define a new parameters class that inherits AgentParameters. + The parameters class defines all the hyperparameters for the agent, and is initialized with 4 main components: + + * **algorithm**: A class inheriting AlgorithmParameters which defines any algorithm specific parameters + + * **exploration**: A class inheriting ExplorationParameters which defines the exploration policy parameters. + There are several common exploration policies built-in which you can use, and are defined under + the exploration sub directory. You can also define your own custom exploration policy. + + * **memory**: A class inheriting MemoryParameters which defined the memory parameters. + There are several common memory types built-in which you can use, and are defined under the memories + sub directory. You can also define your own custom memory. + + * **networks**: A dictionary defining all the networks that will be used by the agent. The keys of the dictionary + define the network name and will be used to access each network through the agent class. + The dictionary values are a class inheriting NetworkParameters, which define the network structure + and parameters. + + + Additionally, set the path property to return the path to your agent class in the following format: + + :code:`:` + + For example, + + .. code-block:: python + + class RainbowAgentParameters(AgentParameters): + def __init__(self): + super().__init__(algorithm=RainbowAlgorithmParameters(), + exploration=RainbowExplorationParameters(), + memory=RainbowMemoryParameters(), + networks={"main": RainbowNetworkParameters()}) + + @property + def path(self): + return 'rainbow.rainbow_agent:RainbowAgent' + +4. (Optional) Define a preset using the new agent type with a given environment, and the hyper-parameters that should + be used for training on that environment. + diff --git a/docs_raw/docs/contributing/add_env.md b/docs/_sources/contributing/add_env.rst.txt similarity index 68% rename from docs_raw/docs/contributing/add_env.md rename to docs/_sources/contributing/add_env.rst.txt index 25a7f2c..ed2777f 100644 --- a/docs_raw/docs/contributing/add_env.md +++ b/docs/_sources/contributing/add_env.rst.txt @@ -1,29 +1,41 @@ -Adding a new environment to Coach is as easy as solving CartPole. +Adding a New Environment +======================== + +Adding a new environment to Coach is as easy as solving CartPole. There are essentially two ways to integrate new environments to Coach: -## Using the OpenAI Gym API +Using the OpenAI Gym API +------------------------ If your environment is already using the OpenAI Gym API, you are already good to go. -When selecting the environment parameters in the preset, use GymEnvironmentParameters(), +When selecting the environment parameters in the preset, use :code:`GymEnvironmentParameters()`, and pass the path to your environment source code using the level parameter. You can specify additional parameters for your environment using the additional_simulator_parameters parameter. -Take for example the definition used in the Pendulum_HAC preset: +Take for example the definition used in the :code:`Pendulum_HAC` preset: + +.. code-block:: python env_params = GymEnvironmentParameters() env_params.level = "rl_coach.environments.mujoco.pendulum_with_goals:PendulumWithGoals" env_params.additional_simulator_parameters = {"time_limit": 1000} -## Using the Coach API +Using the Coach API +------------------- There are a few simple steps to follow, and we will walk through them one by one. +As an alternative, we highly recommend following the corresponding +`tutorial `_ +in the GitHub repo. -1. Create a new class for your environment, and inherit the Environment class. +1. Create a new class for your environment, and inherit the Environment class. -2. Coach defines a simple API for implementing a new environment, which are defined in environment/environment.py. - There are several functions to implement, but only some of them are mandatory. +2. Coach defines a simple API for implementing a new environment, which are defined in environment/environment.py. + There are several functions to implement, but only some of them are mandatory. - Here are the important ones: + Here are the important ones: + + .. code-block:: python def _take_action(self, action_idx: ActionType) -> None: """ @@ -59,10 +71,12 @@ There are a few simple steps to follow, and we will walk through them one by one :return: numpy array containing the image that will be rendered to the screen """ -3. Create a new parameters class for your environment, which inherits the EnvironmentParameters class. - In the __init__ of your class, define all the parameters you used in your Environment class. - Additionally, fill the path property of the class with the path to your Environment class. - For example, take a look at the EnvironmentParameters class used for Doom: +3. Create a new parameters class for your environment, which inherits the EnvironmentParameters class. + In the __init__ of your class, define all the parameters you used in your Environment class. + Additionally, fill the path property of the class with the path to your Environment class. + For example, take a look at the EnvironmentParameters class used for Doom: + + .. code-block:: python class DoomEnvironmentParameters(EnvironmentParameters): def __init__(self): diff --git a/docs_raw/docs/dashboard.md b/docs/_sources/dashboard.rst.txt similarity index 66% rename from docs_raw/docs/dashboard.md rename to docs/_sources/dashboard.rst.txt index c00a1ac..2d89e3e 100644 --- a/docs_raw/docs/dashboard.md +++ b/docs/_sources/dashboard.rst.txt @@ -1,86 +1,63 @@ -Reinforcement learning algorithms are neat. That is - when they work. But when they don't, RL algorithms are often quite tricky to debug. +Coach Dashboard +=============== + +Reinforcement learning algorithms are neat. That is - when they work. But when they don't, RL algorithms are often quite tricky to debug. Finding the root cause for why things break in RL is rather difficult. Moreover, different RL algorithms shine in some aspects, but then lack on other. Comparing the algorithms faithfully is also a hard task, which requires the right tools. Coach Dashboard is a visualization tool which simplifies the analysis of the training process. Each run of Coach extracts a lot of information from within the algorithm and stores it in the experiment directory. This information is very valuable for debugging, analyzing and comparing different algorithms. But without a good visualization tool, this information can not be utilized. This is where Coach Dashboard takes place. -### Visualizing Signals +Visualizing Signals +------------------- Coach Dashboard exposes a convenient user interface for visualizing the training signals. The signals are dynamically updated - during the agent training. Additionaly, it allows selecting a subset of the available signals, and then overlaying them on top of each other. -

- -Updating Dynamically - -

+.. image:: /_static/img/updating_dynamically.gif + :width: 800px + :align: center * Holding the CTRL key, while selecting signals, will allow visualizing more than one signal. * Signals can be visualized, using either of the Y-axes, in order to visualize signals with different scales. To move a signal to the second Y-axis, select it and press the 'Toggle Second Axis' button. -### Tracking Statistics +Tracking Statistics +------------------- -When running parallel algorithms, such as A3C, it often helps visualizing the learning of all the workers, at the same time. Coach Dashboard allows viewing multiple signals (and even smooth them out, if required) from multiple workers. In addition, it supports viewing the mean and standard deviation of the same signal, across different workers, using Bollinger bands. +When running parallel algorithms, such as A3C, it often helps visualizing the learning of all the workers, at the same time. Coach Dashboard allows viewing multiple signals (and even smooth them out, if required) from multiple workers. In addition, it supports viewing the mean and standard deviation of the same signal, across different workers, using Bollinger bands. -

- - - - - -
- Bollinger Bands - Displaying Bollinger Bands - - Separate Signals - Displaying All The Workers -
+.. figure:: /_static/img/bollinger_bands.png + :width: 800px + :align: center + + **Displaying Bollinger Bands** +.. figure:: /_static/img/separate_signals.png + :width: 800px + :align: center + **Displaying all the Workers** - -

- - - -### Comparing Runs +Comparing Runs +-------------- Reinforcement learning algorithms are notoriously known as unstable, and suffer from high run-to-run variance. This makes benchmarking and comparing different algorithms even harder. To ease this process, it is common to execute several runs of the same algorithm and average over them. This is easy to do with Coach Dashboard, by centralizing all the experiment directories in a single directory, and then loading them as a single group. Loading several groups of different algorithms then allows comparing the averaged signals, such as the total episode reward. In RL, there are several interesting performance metrics to consider, and this is easy to do by controlling the X-axis units in Coach Dashboard. It is possible to switch between several options such as the total number of steps or the total training time. -

+ +.. figure:: /_static/img/compare_by_time.png + :width: 800px + :align: center + + **Comparing Several Algorithms According to the Time Passed** + + +.. figure:: /_static/img/compare_by_num_episodes.png + :width: 800px + :align: center + + **Comparing Several Algorithms According to the Number of Episodes Played** - - - - - - - -
- -Comparing By Time - - -Comparing Several Algorithms According to the Time Passed - - - - -Comparing By Number of Episodes - - -Comparing Several Algorithms According to the Number of Episodes Played - - -
- - - -

- - diff --git a/docs_raw/docs/design/control_flow.md b/docs/_sources/design/control_flow.rst.txt similarity index 73% rename from docs_raw/docs/design/control_flow.md rename to docs/_sources/design/control_flow.rst.txt index b21132f..b41ddfd 100644 --- a/docs_raw/docs/design/control_flow.md +++ b/docs/_sources/design/control_flow.rst.txt @@ -1,35 +1,34 @@ - -# Coach Control Flow +Control Flow +============ Coach is built in a modular way, encouraging modules reuse and reducing the amount of boilerplate code needed for developing new algorithms or integrating a new challenge as an environment. On the other hand, it can be overwhelming for new users to ramp up on the code. To help with that, here's a short overview of the control flow. -## Graph Manager +Graph Manager +------------- -The main entry point for Coach is **coach.py**. +The main entry point for Coach is :code:`coach.py`. The main functionality of this script is to parse the command line arguments and invoke all the sub-processes needed for the given experiment. -**coach.py** executes the given **preset** file which returns a **GraphManager** object. +:code:`coach.py` executes the given **preset** file which returns a :code:`GraphManager` object. A **preset** is a design pattern that is intended for concentrating the entire definition of an experiment in a single file. This helps with experiments reproducibility, improves readability and prevents confusion. -The outcome of a preset is a **GraphManager** which will usually be instantiated in the final lines of the preset. +The outcome of a preset is a :code:`GraphManager` which will usually be instantiated in the final lines of the preset. -A **GraphManager** is an object that holds all the agents and environments of an experiment, and is mostly responsible +A :code:`GraphManager` is an object that holds all the agents and environments of an experiment, and is mostly responsible for scheduling their work. Why is it called a **graph** manager? Because agents and environments are structured into a graph of interactions. For example, in hierarchical reinforcement learning schemes, there will often be a master policy agent, that will control a sub-policy agent, which will interact with the environment. Other schemes can have much more complex graphs of control, such as several hierarchy layers, each with multiple agents. The graph manager's main loop is the improve loop. -

- -Improve loop - -

+.. image:: /_static/img/improve.png + :width: 400px + :align: center The improve loop skips between 3 main phases - heatup, training and evaluation: @@ -48,7 +47,8 @@ The improve loop skips between 3 main phases - heatup, training and evaluation: evaluation will be averaged in order to reduce the stochasticity effects of all the components. -## Level Manager +Level Manager +------------- In each of the 3 phases described above, the graph manager will invoke all the hierarchy levels in the graph in a synchronized manner. In Coach, agents do not interact directly with the environment. Instead, they go through a @@ -63,32 +63,40 @@ level can be seen as an interaction between an agent and an environment, even if a lower hierarchy level. -## Agent +Agent +----- The base agent class has 3 main function that will be used during those phases - observe, act and train. * **Observe** - this function gets the latest response from the environment as input, and updates the internal state of the agent with the new information. The environment response will - be first passed through the agent's **InputFilter** object, which will process the values in the response, according + be first passed through the agent's :code:`InputFilter` object, which will process the values in the response, according to the specific agent definition. The environment response will then be converted into a - **Transition** which will contain the information from a single step - ($ s_{t}, a_{t}, r_{t}, s_{t+1}, terminal signal $), and store it in the memory. + :code:`Transition` which will contain the information from a single step + :math:`(s_{t}, a_{t}, r_{t}, s_{t+1}, \textrm{terminal signal})`, and store it in the memory. + +.. image:: /_static/img/observe.png + :width: 700px + :align: center -Observe * **Act** - this function uses the current internal state of the agent in order to select the next action to take on - the environment. This function will call the per-agent custom function **choose_action** that will use the network + the environment. This function will call the per-agent custom function :code:`choose_action` that will use the network and the exploration policy in order to select an action. The action will be stored, together with any additional - information (like the action value for example) in an **ActionInfo** object. The ActionInfo object will then be - passed through the agent's **OutputFilter** to allow any processing of the action (like discretization, + information (like the action value for example) in an :code:`ActionInfo` object. The ActionInfo object will then be + passed through the agent's :code:`OutputFilter` to allow any processing of the action (like discretization, or shifting, for example), before passing it to the environment. -Act +.. image:: /_static/img/act.png + :width: 700px + :align: center * **Train** - this function will sample a batch from the memory and train on it. The batch of transitions will be - first wrapped into a **Batch** object to allow efficient querying of the batch values. It will then be passed into - the agent specific **learn_from_batch** function, that will extract network target values from the batch and will + first wrapped into a :code:`Batch` object to allow efficient querying of the batch values. It will then be passed into + the agent specific :code:`learn_from_batch` function, that will extract network target values from the batch and will train the networks accordingly. Lastly, if there's a target network defined for the agent, it will sync the target network weights with the online network. -Train +.. image:: /_static/img/train.png + :width: 700px + :align: center diff --git a/docs_raw/docs/design/horizontal_scaling.md b/docs/_sources/design/horizontal_scaling.rst.txt similarity index 100% rename from docs_raw/docs/design/horizontal_scaling.md rename to docs/_sources/design/horizontal_scaling.rst.txt diff --git a/docs/_sources/design/network.rst.txt b/docs/_sources/design/network.rst.txt new file mode 100644 index 0000000..aa45b76 --- /dev/null +++ b/docs/_sources/design/network.rst.txt @@ -0,0 +1,56 @@ +Network Design +============== + +Each agent has at least one neural network, used as the function approximator, for choosing the actions. +The network is designed in a modular way to allow reusability in different agents. +It is separated into three main parts: + +* **Input Embedders** - This is the first stage of the network, meant to convert the input into a feature vector representation. + It is possible to combine several instances of any of the supported embedders, in order to allow varied combinations of inputs. + + There are two main types of input embedders: + + 1. Image embedder - Convolutional neural network. + 2. Vector embedder - Multi-layer perceptron. + + +* **Middlewares** - The middleware gets the output of the input embedder, and processes it into a different representation domain, + before sending it through the output head. The goal of the middleware is to enable processing the combined outputs of + several input embedders, and pass them through some extra processing. + This, for instance, might include an LSTM or just a plain simple FC layer. + +* **Output Heads** - The output head is used in order to predict the values required from the network. + These might include action-values, state-values or a policy. As with the input embedders, + it is possible to use several output heads in the same network. For example, the *Actor Critic* agent combines two + heads - a policy head and a state-value head. + In addition, the output heads defines the loss function according to the head type. + + ​ +.. image:: /_static/img/network.png + :width: 400px + :align: center + +Keeping Network Copies in Sync +------------------------------ + +Most of the reinforcement learning agents include more than one copy of the neural network. +These copies serve as counterparts of the main network which are updated in different rates, +and are often synchronized either locally or between parallel workers. For easier synchronization of those copies, +a wrapper around these copies exposes a simplified API, which allows hiding these complexities from the agent. +In this wrapper, 3 types of networks can be defined: + +* **online network** - A mandatory network which is the main network the agent will use + +* **global network** - An optional network which is shared between workers in single-node multi-process distributed learning. + It is updated by all the workers directly, and holds the most up-to-date weights. + +* **target network** - An optional network which is local for each worker. It can be used in order to keep a copy of + the weights stable for a long period of time. This is used in different agents, like DQN for example, in order to + have stable targets for the online network while training it. + + +.. image:: /_static/img/distributed.png + :width: 600px + :align: center + + diff --git a/docs/_sources/features/algorithms.rst.txt b/docs/_sources/features/algorithms.rst.txt new file mode 100644 index 0000000..eb5d19f --- /dev/null +++ b/docs/_sources/features/algorithms.rst.txt @@ -0,0 +1,10 @@ +Algorithms +========== + +Coach supports many state-of-the-art reinforcement learning algorithms, which are separated into three main classes - +value optimization, policy optimization and imitation learning. +A detailed description of those algorithms may be found in the `agents <../components/agents/index.html>`_ section. + +.. image:: /_static/img/algorithms.png + :width: 600px + :align: center \ No newline at end of file diff --git a/docs/_sources/features/benchmarks.rst.txt b/docs/_sources/features/benchmarks.rst.txt new file mode 100644 index 0000000..292c849 --- /dev/null +++ b/docs/_sources/features/benchmarks.rst.txt @@ -0,0 +1,22 @@ +Benchmarks +========== + +Reinforcement learning is a developing field, and so far it has been particularly difficult to reproduce some of the +results published in the original papers. Some reasons for this are: + +* Reinforcement learning algorithms are notoriously known as having an unstable learning process. + The data the neural networks trains on is dynamic, and depends on the random seed defined for the environment. + +* Reinforcement learning algorithms have many moving parts. For some environments and agents, there are many + "tricks" which are needed to get the exact behavior the paper authors had seen. Also, there are **a lot** of + hyper-parameters to set. + +In order for a reinforcement learning implementation to be useful for research or for data science, it must be +shown that it achieves the expected behavior. For this reason, we collected a set of benchmark results from most +of the algorithms implemented in Coach. The algorithms were tested on a subset of the same environments that were +used in the original papers, and with multiple seed for each environment. +Additionally, Coach uses some strict testing mechanisms to try and make sure the results we show for these +benchmarks stay intact as Coach continues to develop. + +To see the benchmark results, please visit the +`following GitHub page `_. \ No newline at end of file diff --git a/docs/_sources/features/environments.rst.txt b/docs/_sources/features/environments.rst.txt new file mode 100644 index 0000000..e1d72ac --- /dev/null +++ b/docs/_sources/features/environments.rst.txt @@ -0,0 +1,31 @@ +Environments +============ + +Coach supports a large number of environments which can be solved using reinforcement learning. +To find a detailed documentation of the environments API, see the `environments section <../components/environments/index.html>`_. +The supported environments are: + +* `DeepMind Control Suite `_ - a set of reinforcement learning environments + powered by the MuJoCo physics engine. + +* `Blizzard Starcraft II `_ - a popular strategy game which was wrapped with a + python interface by DeepMind. + +* `ViZDoom `_ - a Doom-based AI research platform for reinforcement learning + from raw visual information. + +* `CARLA `_ - an open-source simulator for autonomous driving research. + +* `OpenAI Gym `_ - a library which consists of a set of environments, from games to robotics. + Additionally, it can be extended using the API defined by the authors. + + In Coach, we support all the native environments in Gym, along with several extensions such as: + + * `Roboschool `_ - a set of environments powered by the PyBullet engine, + that offer a free alternative to MuJoCo. + + * `Gym Extensions `_ - a set of environments that extends Gym for + auxiliary tasks (multitask learning, transfer learning, inverse reinforcement learning, etc.) + + * `PyBullet `_ - a physics engine that + includes a set of robotics environments. diff --git a/docs/_sources/features/index.rst.txt b/docs/_sources/features/index.rst.txt new file mode 100644 index 0000000..3661755 --- /dev/null +++ b/docs/_sources/features/index.rst.txt @@ -0,0 +1,10 @@ +Features +======== + +.. toctree:: + :maxdepth: 1 + :caption: Features + + algorithms + environments + benchmarks \ No newline at end of file diff --git a/docs/_sources/index.rst.txt b/docs/_sources/index.rst.txt new file mode 100644 index 0000000..1543fba --- /dev/null +++ b/docs/_sources/index.rst.txt @@ -0,0 +1,72 @@ +.. Reinforcement Learning Coach documentation master file, created by + sphinx-quickstart on Sun Oct 28 15:35:09 2018. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + + +Reinforcement Learning Coach +============================ + +Coach is a python framework which models the interaction between an agent and an environment in a modular way. +With Coach, it is possible to model an agent by combining various building blocks, and training the agent on multiple environments. +The available environments allow testing the agent in different fields such as robotics, autonomous driving, games and more. +It exposes a set of easy-to-use APIs for experimenting with new RL algorithms, and allows simple integration of +new environments to solve. +Coach collects statistics from the training process and supports advanced visualization techniques for debugging the agent being trained. + +.. image:: _static/img/design.png + :width: 800px + +Blog posts from the Intel® AI website: + +* `Release 0.8.0 `_ (initial release) + +* `Release 0.9.0 `_ + +* `Release 0.10.0 `_ + +* `Release 0.11.0 `_ (current release) + +You can find more details in the `GitHub repository `_. + + +.. toctree:: + :maxdepth: 2 + :caption: Intro + :titlesonly: + + usage + features/index + selecting_an_algorithm + dashboard + + +.. toctree:: + :maxdepth: 1 + :caption: Design + + design/control_flow + design/network + +.. toctree:: + :maxdepth: 1 + :caption: Contributing + + contributing/add_agent + contributing/add_env + +.. toctree:: + :maxdepth: 1 + :caption: Components + + components/agents/index + components/architectures/index + components/environments/index + components/exploration_policies/index + components/filters/index + components/memories/index + components/core_types + components/spaces + components/additional_parameters + + diff --git a/docs/_sources/selecting_an_algorithm.rst.txt b/docs/_sources/selecting_an_algorithm.rst.txt new file mode 100644 index 0000000..c867191 --- /dev/null +++ b/docs/_sources/selecting_an_algorithm.rst.txt @@ -0,0 +1,270 @@ +Selecting an Algorithm +====================== + +As you probably already noticed, Coach has a lot of algorithms implemented into it: + +.. image:: /_static/img/algorithms.png + :width: 800px + :align: center + +**"ok that's prefect, but I am trying to build a solution for my application, how do I select the right algorithm?"** + +We collected some guidelines for how to choose the right algorithm for your application. +Answer the following questions to see what are the best algorithms for your task. +The algorithms are ordered by their release date in descending order. + +.. raw:: html + + + + +
+
+ What are the type of actions your task requires? +
+ Discrete actions
+ Continuous actions
+
+ Do you have expert demonstrations for your task?
+ Can you collect new data for your task dynamically?
+ Do you have a simulator for your task?
+
+ +
+
+
+ + DQN +
+ Learns action values for discrete actions, and allows learning from a replay buffer with old experiences + +
+
+ + Rainbow +
+ Combines multiple recent innovations on top of DQN for discrete controls, and achieves + much better results on known benchmarks + +
+
+ + HAC +
+ Works only for continuous actions, and uses hierarchy of agents to make the learning + more simple + +
+
+ + DDQN +
+ An improvement over DQN, which learns more accurate action values, and therefore achieves better results + on known benchmarks + +
+
+ + DFP +
+ Works only for discrete actions, by learning to predict the future values of a set of + measurements from the environment, and then using a goal vector to weight the importance of each of the + measurements + +
+
+ + MMC +
+ A simple modification to DQN, which instead of learning action values only by bootstrapping the current + action value prediction, it mixes in the total discounted return as well. This helps learn the correct + action values faster, and is particularly useful for environments with delayed rewards. + +
+
+ + PAL +
+ An improvement over DQN, that tries to deal with the approximation errors present in reinforcement + learning by increasing the gap between the value of the best action and the second best action. + +
+
+ + NAF +
+ A variant of Q learning for continuous control. + +
+
+ + NEC +
+ Uses a memory to "memorize" its experience and learn much faster by querying the memory on newly + seen states. + +
+
+ + QR DQN +
+ Uses quantile regression to learn a distribution over the action values instead of only their mean. + This boosts performance on known benchmarks. + +
+
+ + Bootstrapped DQN +
+ Uses an ensemble of DQN networks, where each network learns from a different subset of the experience + in order to improve exploration. + +
+
+ + N-Step Q Learning +
+ A variant of Q learning that uses bootstrapping of N steps ahead, instead of 1 step. Doing this + makes the algorithm on-policy and therefore requires having multiple workers training in parallel in + order for it to work well. + +
+
+ + Categorical DQN +
+ Learns a distribution over the action values instead of only their mean. This boosts performance on + known algorithms but requires knowing the range of possible values for the accumulated rewards before hand. + +
+
+ + Policy Gradient +
+ Based on the REINFORCE algorithm, this algorithm learn a probability distribution over the actions. + This is the most simple algorithm available in Coach, but also has the worse results. + +
+
+ + Actor Critic (A3C / A2C) +
+ Combines REINFORCE with a learned baseline (Critic) to improve stability of learning. It also + introduced the parallel learning of multiple workers to speed up data collection and improve the + learning stability and speed, both for discrete and continuous action spaces. + +
+
+ + DDPG +
+ An actor critic scheme for continuous action spaces which assumes that the policy is deterministic, + and therefore it is able to use a replay buffer in order to improve sample efficiency. + +
+
+ + PPO +
+ An actor critic scheme which uses bounded updates to the policy in order to make the learning process + very stable. + +
+
+ + Clipped PPO +
+ A simplification of PPO, that reduces the code complexity while achieving similar results. + +
+
+ + BC +
+ The simplest form of imitation learning. Uses supervised learning on a dataset of expert demonstrations + in order to imitate the expert behavior. + +
+
+ + CIL +
+ A variant of behavioral cloning, where the learned policy is disassembled to several skills + (such as turning left or right in an intersection), and each skill is learned separately from the + human demonstrations. + +
+
+
+ + +1. Does your environment have a discrete or continuous action space? +-------------------------------------------------------------------- + +Some reinforcement learning algorithms work only for discrete action spaces, where the agent needs to select +one out of several possible actions. Other algorithms work only for continuous action spaces, where there are +infinite possible actions, but there is some spatial relationship between the actions. And there are some algorithms +that can be applied in both cases. The available algorithms highly depend on the task at hand. + + +2. Is collecting more samples from your environment painful? +------------------------------------------------------------ + +Reinforcement learning algorithm are notoriously known for the amount of samples they need for training. +Typically, on-policy algorithms are much less sample efficient compared to off-policy algorithms. But there are +other algorithmic features that allow improving the sample efficiency even more, like using a DND in NEC, or using +Hindsight Experience Replay. It is hard to say which algorithm is the most sample efficient, but we can at least say +which ones are not sample efficient. + + +3. Do you have a simulator that can be parallelized across multiple processes or nodes? +--------------------------------------------------------------------------------------- + +Parallelizing training across multiple workers which are located on the same node or on different nodes is a technique +that has been introduced in recent years and achieved a lot of success in improving the results of multiple algorithms. +As part of this, there are some algorithms that don't work well without being parallelized with multiple workers +working in parallel, which requires having a simulator for each worker. + + +4. Do you have human demonstrations for solving the task? +--------------------------------------------------------- + +If human demonstrations are available for a task, most of the time it would be better to use those instead of training +using regular reinforcement learning from scratch. To use human demonstrations we have implemented several tools and +algorithms for imitation learning in Coach. diff --git a/docs/_sources/test.rst.txt b/docs/_sources/test.rst.txt new file mode 100644 index 0000000..51c4298 --- /dev/null +++ b/docs/_sources/test.rst.txt @@ -0,0 +1,8 @@ +test +---- + +.. important:: Its a note! in markdown! + +.. autoclass:: rl_coach.agents.dqn_agent.DQNAgent + :members: + :inherited-members: \ No newline at end of file diff --git a/docs/_sources/usage.rst.txt b/docs/_sources/usage.rst.txt new file mode 100644 index 0000000..d9eeba9 --- /dev/null +++ b/docs/_sources/usage.rst.txt @@ -0,0 +1,158 @@ +Usage +===== + +One of the mechanism Coach uses for running experiments is the **Preset** mechanism. +As its name implies, a preset defines a set of predefined experiment parameters. +This allows defining a *complex* agent-environment interaction, with multiple parameters, and later running it through +a very *simple* command line. + +The preset includes all the components that are used in the experiment, such as the agent internal components and +the environment to use. +It additionally defines general parameters for the experiment itself, such as the training schedule, +visualization parameters, and testing parameters. + +Training an Agent +----------------- + +Single-threaded Algorithms +++++++++++++++++++++++++++ + +This is the most common case. Just choose a preset using the `-p` flag and press enter. +To list the available presets, use the `-l` flag. + +*Example:* + +.. code-block:: python + + coach -p CartPole_DQN + +Multi-threaded Algorithms ++++++++++++++++++++++++++ + +Multi-threaded algorithms are very common this days. +They typically achieve the best results, and scale gracefully with the number of threads. +In Coach, running such algorithms is done by selecting a suitable preset, and choosing the number of threads to run using the :code:`-n` flag. + +*Example:* + +.. code-block:: python + + coach -p CartPole_A3C -n 8 + +Evaluating an Agent +------------------- + +There are several options for evaluating an agent during the training: + +* For multi-threaded runs, an evaluation agent will constantly run in the background and evaluate the model during the training. + +* For single-threaded runs, it is possible to define an evaluation period through the preset. This will run several episodes of evaluation once in a while. + +Additionally, it is possible to save checkpoints of the agents networks and then run only in evaluation mode. +Saving checkpoints can be done by specifying the number of seconds between storing checkpoints using the :code:`-s` flag. +The checkpoints will be saved into the experiment directory. +Loading a model for evaluation can be done by specifying the :code:`-crd` flag with the experiment directory, and the :code:`--evaluate` flag to disable training. + +*Example:* + +.. code-block:: python + + coach -p CartPole_DQN -s 60 + coach -p CartPole_DQN --evaluate -crd CHECKPOINT_RESTORE_DIR + +Playing with the Environment as a Human +--------------------------------------- + +Interacting with the environment as a human can be useful for understanding its difficulties and for collecting data for imitation learning. +In Coach, this can be easily done by selecting a preset that defines the environment to use, and specifying the :code:`--play` flag. +When the environment is loaded, the available keyboard buttons will be printed to the screen. +Pressing the escape key when finished will end the simulation and store the replay buffer in the experiment dir. + +*Example:* + +.. code-block:: python + + coach -et rl_coach.environments.gym_environment:Atari -lvl BreakoutDeterministic-v4 --play + +Learning Through Imitation Learning +----------------------------------- + +Learning through imitation of human behavior is a nice way to speedup the learning. +In Coach, this can be done in two steps - + +1. Create a dataset of demonstrations by playing with the environment as a human. + After this step, a pickle of the replay buffer containing your game play will be stored in the experiment directory. + The path to this replay buffer will be printed to the screen. + To do so, you should select an environment type and level through the command line, and specify the :code:`--play` flag. + + *Example:* + +.. code-block:: python + + coach -et rl_coach.environments.doom_environment:DoomEnvironmentParameters -lvl Basic --play + + +2. Next, use an imitation learning preset and set the replay buffer path accordingly. + The path can be set either from the command line or from the preset itself. + + *Example:* + +.. code-block:: python + + coach -p Doom_Basic_BC -cp='agent.load_memory_from_file_path=\"/replay_buffer.p\"' + + +Visualizations +-------------- + +Rendering the Environment ++++++++++++++++++++++++++ + +Rendering the environment can be done by using the :code:`-r` flag. +When working with multi-threaded algorithms, the rendered image will be representing the game play of the evaluation worker. +When working with single-threaded algorithms, the rendered image will be representing the single worker which can be either training or evaluating. +Keep in mind that rendering the environment in single-threaded algorithms may slow the training to some extent. +When playing with the environment using the :code:`--play` flag, the environment will be rendered automatically without the need for specifying the :code:`-r` flag. + +*Example:* + +.. code-block:: python + + coach -p Breakout_DQN -r + +Dumping GIFs +++++++++++++ + +Coach allows storing GIFs of the agent game play. +To dump GIF files, use the :code:`-dg` flag. +The files are dumped after every evaluation episode, and are saved into the experiment directory, under a gifs sub-directory. + +*Example:* + +.. code-block:: python + + coach -p Breakout_A3C -n 4 -dg + +Switching Between Deep Learning Frameworks +------------------------------------------ + +Coach uses TensorFlow as its main backend framework, but it also supports MXNet. +MXNet is optional, and by default, TensorFlow will be used. +If MXNet was installed, it is possible to switch to MXNet using the :code:`-f` flag. + +*Example:* + +.. code-block:: python + + coach -p Doom_Basic_DQN -f mxnet + +Additional Flags +---------------- + +There are several convenient flags which are important to know about. +The most up to date description can be found by using the :code:`-h` flag. + +.. 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rv:([\w.]+)|)/.exec(ua) || + []; + + return { + browser: match[ 1 ] || "", + version: match[ 2 ] || "0" + }; + }; + jQuery.browser = {}; + jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true; +} + +/** + * Small JavaScript module for the documentation. + */ +var Documentation = { + + init : function() { + this.fixFirefoxAnchorBug(); + this.highlightSearchWords(); + this.initIndexTable(); + if (DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) { + this.initOnKeyListeners(); + } + }, + + /** + * i18n support + */ + TRANSLATIONS : {}, + PLURAL_EXPR : function(n) { return n === 1 ? 0 : 1; }, + LOCALE : 'unknown', + + // gettext and ngettext don't access this so that the functions + // can safely bound to a different name (_ = Documentation.gettext) + gettext : function(string) { + var translated = Documentation.TRANSLATIONS[string]; + if (typeof translated === 'undefined') + return string; + return (typeof translated === 'string') ? translated : translated[0]; + }, + + ngettext : function(singular, plural, n) { + var translated = Documentation.TRANSLATIONS[singular]; + if (typeof translated === 'undefined') + return (n == 1) ? singular : plural; + return translated[Documentation.PLURALEXPR(n)]; + }, + + addTranslations : function(catalog) { + for (var key in catalog.messages) + this.TRANSLATIONS[key] = catalog.messages[key]; + this.PLURAL_EXPR = new Function('n', 'return +(' + catalog.plural_expr + ')'); + this.LOCALE = catalog.locale; + }, + + /** + * add context elements like header anchor links + */ + addContextElements : function() { + $('div[id] > :header:first').each(function() { + $('\u00B6'). + attr('href', '#' + this.id). + attr('title', _('Permalink to this headline')). + appendTo(this); + }); + $('dt[id]').each(function() { + $('\u00B6'). + attr('href', '#' + this.id). + attr('title', _('Permalink to this definition')). + appendTo(this); + }); + }, + + /** + * workaround a firefox stupidity + * see: https://bugzilla.mozilla.org/show_bug.cgi?id=645075 + */ + fixFirefoxAnchorBug : function() { + if (document.location.hash && $.browser.mozilla) + window.setTimeout(function() { + document.location.href += ''; + }, 10); + }, + + /** + * highlight the search words provided in the url in the text + */ + highlightSearchWords : function() { + var params = $.getQueryParameters(); + var terms = (params.highlight) ? params.highlight[0].split(/\s+/) : []; + if (terms.length) { + var body = $('div.body'); + if (!body.length) { + body = $('body'); + } + window.setTimeout(function() { + $.each(terms, function() { + body.highlightText(this.toLowerCase(), 'highlighted'); + }); + }, 10); + $('

' + _('Hide Search Matches') + '

') + .appendTo($('#searchbox')); + } + }, + + /** + * init the domain index toggle buttons + */ + initIndexTable : function() { + var togglers = $('img.toggler').click(function() { + var src = $(this).attr('src'); + var idnum = $(this).attr('id').substr(7); + $('tr.cg-' + idnum).toggle(); + if (src.substr(-9) === 'minus.png') + $(this).attr('src', src.substr(0, src.length-9) + 'plus.png'); + else + $(this).attr('src', src.substr(0, src.length-8) + 'minus.png'); + }).css('display', ''); + if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) { + togglers.click(); + } + }, + + /** + * helper function to hide the search marks again + */ + hideSearchWords : function() { + $('#searchbox .highlight-link').fadeOut(300); + $('span.highlighted').removeClass('highlighted'); + }, + + /** + * make the url absolute + */ + makeURL : function(relativeURL) { + return DOCUMENTATION_OPTIONS.URL_ROOT + '/' + relativeURL; + }, + + /** + * get the current relative url + */ + getCurrentURL : function() { + var path = document.location.pathname; + var parts = path.split(/\//); + $.each(DOCUMENTATION_OPTIONS.URL_ROOT.split(/\//), function() { + if (this === '..') + parts.pop(); + }); + var url = parts.join('/'); + return path.substring(url.lastIndexOf('/') + 1, path.length - 1); + }, + + initOnKeyListeners: function() { + $(document).keyup(function(event) { + var activeElementType = document.activeElement.tagName; + // don't navigate when in search box or textarea + if (activeElementType !== 'TEXTAREA' && activeElementType !== 'INPUT' && activeElementType !== 'SELECT') { + switch (event.keyCode) { + case 37: // left + var prevHref = $('link[rel="prev"]').prop('href'); + if (prevHref) { + window.location.href = prevHref; + return false; + } + case 39: // right + var nextHref = $('link[rel="next"]').prop('href'); + if (nextHref) { + window.location.href = nextHref; + return false; + } + } + } + }); + } +}; + +// quick alias for translations +_ = Documentation.gettext; + +$(document).ready(function() { + Documentation.init(); +}); diff --git a/docs/_static/documentation_options.js b/docs/_static/documentation_options.js new file mode 100644 index 0000000..4e5aeec --- /dev/null +++ b/docs/_static/documentation_options.js @@ -0,0 +1,296 @@ +var DOCUMENTATION_OPTIONS = { + URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'), + VERSION: '0.11.0', + LANGUAGE: 'None', + COLLAPSE_INDEX: false, + FILE_SUFFIX: '.html', + HAS_SOURCE: true, + SOURCELINK_SUFFIX: '.txt', + NAVIGATION_WITH_KEYS: false, + SEARCH_LANGUAGE_STOP_WORDS: ["a","and","are","as","at","be","but","by","for","if","in","into","is","it","near","no","not","of","on","or","such","that","the","their","then","there","these","they","this","to","was","will","with"] +}; + + + +/* Non-minified version JS is _stemmer.js if file is provided */ +/** + * Porter Stemmer + */ +var Stemmer = function() { + + var step2list = { + ational: 'ate', + tional: 'tion', + enci: 'ence', + anci: 'ance', + izer: 'ize', + bli: 'ble', + alli: 'al', + entli: 'ent', + eli: 'e', + ousli: 'ous', + ization: 'ize', + ation: 'ate', + ator: 'ate', + alism: 'al', + iveness: 'ive', + fulness: 'ful', + ousness: 'ous', + aliti: 'al', + iviti: 'ive', + biliti: 'ble', + logi: 'log' + }; + + var step3list = { + icate: 'ic', + ative: '', + alize: 'al', + iciti: 'ic', + ical: 'ic', + ful: '', + ness: '' + }; + + var c = "[^aeiou]"; // consonant + var v = "[aeiouy]"; // vowel + var C = c + "[^aeiouy]*"; // consonant sequence + var V = v + "[aeiou]*"; // vowel sequence + + var mgr0 = "^(" + C + ")?" + V + C; // [C]VC... is m>0 + var meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$"; // [C]VC[V] is m=1 + var mgr1 = "^(" + C + ")?" + V + C + V + C; // [C]VCVC... is m>1 + var s_v = "^(" + C + ")?" + v; // vowel in stem + + this.stemWord = function (w) { + var stem; + var suffix; + var firstch; + var origword = w; + + if (w.length < 3) + return w; + + var re; + var re2; + var re3; + var re4; + + firstch = w.substr(0,1); + if (firstch == "y") + w = firstch.toUpperCase() + w.substr(1); + + // Step 1a + re = /^(.+?)(ss|i)es$/; + re2 = /^(.+?)([^s])s$/; + + if (re.test(w)) + w = w.replace(re,"$1$2"); + else if (re2.test(w)) + w = w.replace(re2,"$1$2"); + + // Step 1b + re = /^(.+?)eed$/; + re2 = /^(.+?)(ed|ing)$/; + if (re.test(w)) { + var fp = re.exec(w); + re = new RegExp(mgr0); + if (re.test(fp[1])) { + re = /.$/; + w = w.replace(re,""); + } + } + else if (re2.test(w)) { + var fp = re2.exec(w); + stem = fp[1]; + re2 = new RegExp(s_v); + if (re2.test(stem)) { + w = stem; + re2 = /(at|bl|iz)$/; + re3 = new RegExp("([^aeiouylsz])\\1$"); + re4 = new RegExp("^" + C + v + "[^aeiouwxy]$"); + if (re2.test(w)) + w = w + "e"; + else if (re3.test(w)) { + re = /.$/; + w = w.replace(re,""); + } + else if (re4.test(w)) + w = w + "e"; + } + } + + // Step 1c + re = /^(.+?)y$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + re = new RegExp(s_v); + if (re.test(stem)) + w = stem + "i"; + } + + // Step 2 + re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + suffix = fp[2]; + re = new RegExp(mgr0); + if (re.test(stem)) + w = stem + step2list[suffix]; + } + + // Step 3 + re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + suffix = fp[2]; + re = new RegExp(mgr0); + if (re.test(stem)) + w = stem + step3list[suffix]; + } + + // Step 4 + re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/; + re2 = /^(.+?)(s|t)(ion)$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + re = new RegExp(mgr1); + if (re.test(stem)) + w = stem; + } + else if (re2.test(w)) { + var fp = re2.exec(w); + stem = fp[1] + fp[2]; + re2 = new RegExp(mgr1); + if (re2.test(stem)) + w = stem; + } + + // Step 5 + re = /^(.+?)e$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + re = new RegExp(mgr1); + re2 = new RegExp(meq1); + re3 = new RegExp("^" + C + v + "[^aeiouwxy]$"); + if (re.test(stem) || (re2.test(stem) && !(re3.test(stem)))) + w = stem; + } + re = /ll$/; + re2 = new RegExp(mgr1); + if (re.test(w) && re2.test(w)) { + re = /.$/; + w = w.replace(re,""); + } + + // and turn initial Y back to y + if (firstch == "y") + w = firstch.toLowerCase() + w.substr(1); + return w; + } +} + + + + + +var splitChars = (function() { + var result = {}; + var singles = [96, 180, 187, 191, 215, 247, 749, 885, 903, 907, 909, 930, 1014, 1648, + 1748, 1809, 2416, 2473, 2481, 2526, 2601, 2609, 2612, 2615, 2653, 2702, + 2706, 2729, 2737, 2740, 2857, 2865, 2868, 2910, 2928, 2948, 2961, 2971, + 2973, 3085, 3089, 3113, 3124, 3213, 3217, 3241, 3252, 3295, 3341, 3345, + 3369, 3506, 3516, 3633, 3715, 3721, 3736, 3744, 3748, 3750, 3756, 3761, + 3781, 3912, 4239, 4347, 4681, 4695, 4697, 4745, 4785, 4799, 4801, 4823, + 4881, 5760, 5901, 5997, 6313, 7405, 8024, 8026, 8028, 8030, 8117, 8125, + 8133, 8181, 8468, 8485, 8487, 8489, 8494, 8527, 11311, 11359, 11687, 11695, + 11703, 11711, 11719, 11727, 11735, 12448, 12539, 43010, 43014, 43019, 43587, + 43696, 43713, 64286, 64297, 64311, 64317, 64319, 64322, 64325, 65141]; + var i, j, start, end; + for (i = 0; i < singles.length; i++) { + result[singles[i]] = true; + } + var ranges = [[0, 47], [58, 64], [91, 94], [123, 169], [171, 177], [182, 184], [706, 709], + [722, 735], [741, 747], [751, 879], [888, 889], [894, 901], [1154, 1161], + [1318, 1328], [1367, 1368], [1370, 1376], [1416, 1487], [1515, 1519], [1523, 1568], + [1611, 1631], [1642, 1645], [1750, 1764], [1767, 1773], [1789, 1790], [1792, 1807], + [1840, 1868], [1958, 1968], [1970, 1983], [2027, 2035], [2038, 2041], [2043, 2047], + [2070, 2073], [2075, 2083], [2085, 2087], [2089, 2307], [2362, 2364], [2366, 2383], + [2385, 2391], [2402, 2405], [2419, 2424], [2432, 2436], [2445, 2446], [2449, 2450], + [2483, 2485], [2490, 2492], [2494, 2509], [2511, 2523], [2530, 2533], [2546, 2547], + [2554, 2564], [2571, 2574], [2577, 2578], [2618, 2648], [2655, 2661], [2672, 2673], + [2677, 2692], [2746, 2748], [2750, 2767], [2769, 2783], [2786, 2789], [2800, 2820], + [2829, 2830], [2833, 2834], [2874, 2876], [2878, 2907], [2914, 2917], [2930, 2946], + [2955, 2957], [2966, 2968], [2976, 2978], [2981, 2983], [2987, 2989], [3002, 3023], + [3025, 3045], [3059, 3076], [3130, 3132], [3134, 3159], [3162, 3167], [3170, 3173], + [3184, 3191], [3199, 3204], [3258, 3260], [3262, 3293], [3298, 3301], [3312, 3332], + [3386, 3388], [3390, 3423], [3426, 3429], [3446, 3449], [3456, 3460], [3479, 3481], + [3518, 3519], [3527, 3584], [3636, 3647], [3655, 3663], [3674, 3712], [3717, 3718], + [3723, 3724], [3726, 3731], [3752, 3753], [3764, 3772], [3774, 3775], [3783, 3791], + [3802, 3803], [3806, 3839], [3841, 3871], [3892, 3903], [3949, 3975], [3980, 4095], + [4139, 4158], [4170, 4175], [4182, 4185], [4190, 4192], [4194, 4196], [4199, 4205], + [4209, 4212], [4226, 4237], [4250, 4255], [4294, 4303], [4349, 4351], [4686, 4687], + [4702, 4703], [4750, 4751], [4790, 4791], [4806, 4807], [4886, 4887], [4955, 4968], + [4989, 4991], [5008, 5023], [5109, 5120], [5741, 5742], [5787, 5791], [5867, 5869], + [5873, 5887], [5906, 5919], [5938, 5951], [5970, 5983], [6001, 6015], [6068, 6102], + [6104, 6107], [6109, 6111], [6122, 6127], [6138, 6159], [6170, 6175], [6264, 6271], + [6315, 6319], [6390, 6399], [6429, 6469], [6510, 6511], [6517, 6527], [6572, 6592], + [6600, 6607], [6619, 6655], [6679, 6687], [6741, 6783], [6794, 6799], [6810, 6822], + [6824, 6916], [6964, 6980], [6988, 6991], [7002, 7042], [7073, 7085], [7098, 7167], + [7204, 7231], [7242, 7244], [7294, 7400], [7410, 7423], [7616, 7679], [7958, 7959], + [7966, 7967], [8006, 8007], [8014, 8015], [8062, 8063], [8127, 8129], [8141, 8143], + [8148, 8149], [8156, 8159], [8173, 8177], [8189, 8303], [8306, 8307], [8314, 8318], + [8330, 8335], [8341, 8449], [8451, 8454], [8456, 8457], [8470, 8472], [8478, 8483], + [8506, 8507], [8512, 8516], [8522, 8525], [8586, 9311], [9372, 9449], [9472, 10101], + [10132, 11263], [11493, 11498], [11503, 11516], [11518, 11519], [11558, 11567], + [11622, 11630], [11632, 11647], [11671, 11679], [11743, 11822], [11824, 12292], + [12296, 12320], [12330, 12336], [12342, 12343], [12349, 12352], [12439, 12444], + [12544, 12548], [12590, 12592], [12687, 12689], [12694, 12703], [12728, 12783], + [12800, 12831], [12842, 12880], [12896, 12927], [12938, 12976], [12992, 13311], + [19894, 19967], [40908, 40959], [42125, 42191], [42238, 42239], [42509, 42511], + [42540, 42559], [42592, 42593], [42607, 42622], [42648, 42655], [42736, 42774], + [42784, 42785], [42889, 42890], [42893, 43002], [43043, 43055], [43062, 43071], + [43124, 43137], [43188, 43215], [43226, 43249], [43256, 43258], [43260, 43263], + [43302, 43311], [43335, 43359], [43389, 43395], [43443, 43470], [43482, 43519], + [43561, 43583], [43596, 43599], [43610, 43615], [43639, 43641], [43643, 43647], + [43698, 43700], [43703, 43704], [43710, 43711], [43715, 43738], [43742, 43967], + [44003, 44015], [44026, 44031], [55204, 55215], [55239, 55242], [55292, 55295], + [57344, 63743], [64046, 64047], [64110, 64111], [64218, 64255], [64263, 64274], + [64280, 64284], [64434, 64466], [64830, 64847], [64912, 64913], [64968, 65007], + [65020, 65135], [65277, 65295], [65306, 65312], [65339, 65344], [65371, 65381], + [65471, 65473], [65480, 65481], [65488, 65489], [65496, 65497]]; + for (i = 0; i < ranges.length; i++) { + start = ranges[i][0]; + end = ranges[i][1]; + for (j = start; j <= end; j++) { + result[j] = true; + } + } + return result; +})(); + +function splitQuery(query) { + var result = []; + var start = -1; + for (var i = 0; i < query.length; i++) { + if (splitChars[query.charCodeAt(i)]) { + if (start !== -1) { + result.push(query.slice(start, i)); + start = -1; + } + } else if (start === -1) { + start = i; + } + } + if (start !== -1) { + 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ConjugateTranspose Conjunction Connect ConnectedComponents ConnectedGraphQ ConnesWindow ConoverTest ConsoleMessage ConsoleMessagePacket ConsolePrint Constant ConstantArray Constants ConstrainedMax ConstrainedMin ContentPadding ContentsBoundingBox ContentSelectable ContentSize Context ContextMenu Contexts ContextToFilename ContextToFileName Continuation Continue ContinuedFraction ContinuedFractionK ContinuousAction ContinuousMarkovProcess ContinuousTimeModelQ ContinuousWaveletData ContinuousWaveletTransform ContourDetect ContourGraphics ContourIntegral ContourLabels ContourLines ContourPlot ContourPlot3D Contours ContourShading ContourSmoothing ContourStyle ContraharmonicMean Control ControlActive ControlAlignment ControllabilityGramian ControllabilityMatrix ControllableDecomposition ControllableModelQ ControllerDuration ControllerInformation ControllerInformationData ControllerLinking ControllerManipulate ControllerMethod ControllerPath ControllerState ControlPlacement ControlsRendering ControlType Convergents ConversionOptions ConversionRules ConvertToBitmapPacket ConvertToPostScript ConvertToPostScriptPacket Convolve ConwayGroupCo1 ConwayGroupCo2 ConwayGroupCo3 CoordinateChartData CoordinatesToolOptions CoordinateTransform CoordinateTransformData CoprimeQ Coproduct CopulaDistribution Copyable CopyDirectory CopyFile CopyTag CopyToClipboard CornerFilter CornerNeighbors Correlation CorrelationDistance CorrelationFunction CorrelationTest Cos Cosh CoshIntegral CosineDistance CosineWindow CosIntegral Cot Coth Count CounterAssignments CounterBox CounterBoxOptions CounterClockwiseContourIntegral CounterEvaluator CounterFunction CounterIncrements CounterStyle CounterStyleMenuListing CountRoots CountryData Covariance CovarianceEstimatorFunction CovarianceFunction CoxianDistribution CoxIngersollRossProcess CoxModel CoxModelFit CramerVonMisesTest CreateArchive CreateDialog CreateDirectory CreateDocument CreateIntermediateDirectories CreatePalette CreatePalettePacket CreateScheduledTask CreateTemporary CreateWindow CriticalityFailureImportance CriticalitySuccessImportance CriticalSection Cross CrossingDetect CrossMatrix Csc Csch CubeRoot Cubics Cuboid CuboidBox Cumulant CumulantGeneratingFunction Cup CupCap Curl CurlyDoubleQuote CurlyQuote CurrentImage CurrentlySpeakingPacket CurrentValue CurvatureFlowFilter CurveClosed Cyan CycleGraph CycleIndexPolynomial Cycles CyclicGroup Cyclotomic Cylinder CylinderBox CylindricalDecomposition D DagumDistribution DamerauLevenshteinDistance DampingFactor Darker Dashed Dashing DataCompression DataDistribution DataRange DataReversed Date DateDelimiters DateDifference DateFunction DateList DateListLogPlot DateListPlot DatePattern DatePlus DateRange DateString DateTicksFormat DaubechiesWavelet DavisDistribution DawsonF DayCount DayCountConvention DayMatchQ DayName DayPlus DayRange DayRound DeBruijnGraph Debug DebugTag Decimal DeclareKnownSymbols DeclarePackage Decompose Decrement DedekindEta Default DefaultAxesStyle DefaultBaseStyle DefaultBoxStyle DefaultButton DefaultColor DefaultControlPlacement DefaultDuplicateCellStyle DefaultDuration DefaultElement DefaultFaceGridsStyle DefaultFieldHintStyle DefaultFont DefaultFontProperties DefaultFormatType DefaultFormatTypeForStyle DefaultFrameStyle DefaultFrameTicksStyle DefaultGridLinesStyle DefaultInlineFormatType DefaultInputFormatType DefaultLabelStyle DefaultMenuStyle DefaultNaturalLanguage DefaultNewCellStyle DefaultNewInlineCellStyle DefaultNotebook DefaultOptions DefaultOutputFormatType DefaultStyle DefaultStyleDefinitions DefaultTextFormatType DefaultTextInlineFormatType DefaultTicksStyle DefaultTooltipStyle DefaultValues Defer DefineExternal DefineInputStreamMethod DefineOutputStreamMethod Definition Degree DegreeCentrality DegreeGraphDistribution DegreeLexicographic DegreeReverseLexicographic Deinitialization Del Deletable Delete DeleteBorderComponents DeleteCases DeleteContents DeleteDirectory DeleteDuplicates DeleteFile DeleteSmallComponents DeleteWithContents DeletionWarning Delimiter DelimiterFlashTime DelimiterMatching Delimiters Denominator DensityGraphics DensityHistogram DensityPlot DependentVariables Deploy Deployed Depth DepthFirstScan Derivative DerivativeFilter DescriptorStateSpace DesignMatrix Det DGaussianWavelet DiacriticalPositioning Diagonal DiagonalMatrix Dialog DialogIndent DialogInput DialogLevel DialogNotebook DialogProlog DialogReturn DialogSymbols Diamond DiamondMatrix DiceDissimilarity DictionaryLookup DifferenceDelta DifferenceOrder DifferenceRoot DifferenceRootReduce Differences DifferentialD DifferentialRoot DifferentialRootReduce DifferentiatorFilter DigitBlock DigitBlockMinimum DigitCharacter DigitCount DigitQ DihedralGroup Dilation Dimensions DiracComb DiracDelta DirectedEdge DirectedEdges DirectedGraph DirectedGraphQ DirectedInfinity Direction Directive Directory DirectoryName DirectoryQ DirectoryStack DirichletCharacter DirichletConvolve DirichletDistribution DirichletL DirichletTransform DirichletWindow DisableConsolePrintPacket DiscreteChirpZTransform DiscreteConvolve DiscreteDelta DiscreteHadamardTransform DiscreteIndicator DiscreteLQEstimatorGains DiscreteLQRegulatorGains DiscreteLyapunovSolve DiscreteMarkovProcess DiscretePlot DiscretePlot3D DiscreteRatio DiscreteRiccatiSolve DiscreteShift DiscreteTimeModelQ DiscreteUniformDistribution DiscreteVariables DiscreteWaveletData DiscreteWaveletPacketTransform DiscreteWaveletTransform Discriminant Disjunction Disk DiskBox DiskMatrix Dispatch DispersionEstimatorFunction Display DisplayAllSteps DisplayEndPacket DisplayFlushImagePacket DisplayForm DisplayFunction DisplayPacket DisplayRules DisplaySetSizePacket DisplayString DisplayTemporary DisplayWith DisplayWithRef DisplayWithVariable DistanceFunction DistanceTransform Distribute Distributed DistributedContexts DistributeDefinitions DistributionChart DistributionDomain DistributionFitTest DistributionParameterAssumptions DistributionParameterQ Dithering Div Divergence Divide DivideBy Dividers Divisible Divisors DivisorSigma DivisorSum DMSList DMSString Do DockedCells DocumentNotebook DominantColors DOSTextFormat Dot DotDashed DotEqual Dotted DoubleBracketingBar DoubleContourIntegral DoubleDownArrow DoubleLeftArrow DoubleLeftRightArrow DoubleLeftTee DoubleLongLeftArrow DoubleLongLeftRightArrow DoubleLongRightArrow DoubleRightArrow DoubleRightTee DoubleUpArrow DoubleUpDownArrow DoubleVerticalBar DoublyInfinite Down DownArrow DownArrowBar DownArrowUpArrow DownLeftRightVector DownLeftTeeVector DownLeftVector DownLeftVectorBar DownRightTeeVector DownRightVector DownRightVectorBar Downsample DownTee DownTeeArrow DownValues DragAndDrop DrawEdges DrawFrontFaces DrawHighlighted Drop DSolve Dt DualLinearProgramming DualSystemsModel DumpGet DumpSave DuplicateFreeQ Dynamic DynamicBox DynamicBoxOptions DynamicEvaluationTimeout DynamicLocation DynamicModule DynamicModuleBox DynamicModuleBoxOptions DynamicModuleParent DynamicModuleValues DynamicName DynamicNamespace DynamicReference DynamicSetting DynamicUpdating DynamicWrapper DynamicWrapperBox DynamicWrapperBoxOptions E EccentricityCentrality EdgeAdd EdgeBetweennessCentrality EdgeCapacity EdgeCapForm EdgeColor EdgeConnectivity EdgeCost EdgeCount EdgeCoverQ EdgeDashing EdgeDelete EdgeDetect EdgeForm EdgeIndex EdgeJoinForm EdgeLabeling EdgeLabels EdgeLabelStyle EdgeList EdgeOpacity EdgeQ EdgeRenderingFunction EdgeRules EdgeShapeFunction EdgeStyle EdgeThickness EdgeWeight Editable EditButtonSettings EditCellTagsSettings EditDistance EffectiveInterest Eigensystem Eigenvalues EigenvectorCentrality Eigenvectors Element ElementData Eliminate EliminationOrder EllipticE EllipticExp EllipticExpPrime EllipticF EllipticFilterModel EllipticK EllipticLog EllipticNomeQ EllipticPi EllipticReducedHalfPeriods EllipticTheta EllipticThetaPrime EmitSound EmphasizeSyntaxErrors EmpiricalDistribution Empty EmptyGraphQ EnableConsolePrintPacket Enabled Encode End EndAdd EndDialogPacket EndFrontEndInteractionPacket EndOfFile EndOfLine EndOfString EndPackage EngineeringForm Enter EnterExpressionPacket EnterTextPacket Entropy EntropyFilter Environment Epilog Equal EqualColumns EqualRows EqualTilde EquatedTo Equilibrium EquirippleFilterKernel Equivalent Erf Erfc Erfi ErlangB ErlangC ErlangDistribution Erosion ErrorBox ErrorBoxOptions ErrorNorm ErrorPacket ErrorsDialogSettings EstimatedDistribution EstimatedProcess EstimatorGains EstimatorRegulator EuclideanDistance EulerE EulerGamma EulerianGraphQ EulerPhi Evaluatable Evaluate Evaluated EvaluatePacket EvaluationCell EvaluationCompletionAction EvaluationElements EvaluationMode EvaluationMonitor EvaluationNotebook EvaluationObject EvaluationOrder Evaluator EvaluatorNames EvenQ EventData EventEvaluator EventHandler EventHandlerTag EventLabels ExactBlackmanWindow ExactNumberQ ExactRootIsolation ExampleData Except ExcludedForms ExcludePods Exclusions ExclusionsStyle Exists Exit ExitDialog Exp Expand ExpandAll ExpandDenominator ExpandFileName ExpandNumerator Expectation ExpectationE ExpectedValue ExpGammaDistribution ExpIntegralE ExpIntegralEi Exponent ExponentFunction ExponentialDistribution ExponentialFamily ExponentialGeneratingFunction ExponentialMovingAverage ExponentialPowerDistribution ExponentPosition ExponentStep Export ExportAutoReplacements ExportPacket ExportString Expression ExpressionCell ExpressionPacket ExpToTrig ExtendedGCD Extension ExtentElementFunction ExtentMarkers ExtentSize ExternalCall ExternalDataCharacterEncoding Extract ExtractArchive ExtremeValueDistribution FaceForm FaceGrids FaceGridsStyle Factor FactorComplete Factorial Factorial2 FactorialMoment FactorialMomentGeneratingFunction FactorialPower FactorInteger FactorList FactorSquareFree FactorSquareFreeList FactorTerms FactorTermsList Fail FailureDistribution False FARIMAProcess FEDisableConsolePrintPacket FeedbackSector FeedbackSectorStyle FeedbackType FEEnableConsolePrintPacket Fibonacci FieldHint FieldHintStyle FieldMasked FieldSize File FileBaseName FileByteCount FileDate FileExistsQ FileExtension FileFormat FileHash FileInformation FileName FileNameDepth FileNameDialogSettings FileNameDrop FileNameJoin FileNames FileNameSetter FileNameSplit FileNameTake FilePrint FileType FilledCurve FilledCurveBox Filling FillingStyle FillingTransform FilterRules FinancialBond FinancialData FinancialDerivative FinancialIndicator Find FindArgMax FindArgMin FindClique FindClusters FindCurvePath FindDistributionParameters FindDivisions FindEdgeCover FindEdgeCut FindEulerianCycle FindFaces FindFile FindFit FindGeneratingFunction FindGeoLocation FindGeometricTransform FindGraphCommunities FindGraphIsomorphism FindGraphPartition FindHamiltonianCycle FindIndependentEdgeSet FindIndependentVertexSet FindInstance FindIntegerNullVector FindKClan FindKClique FindKClub FindKPlex FindLibrary FindLinearRecurrence FindList FindMaximum FindMaximumFlow FindMaxValue FindMinimum FindMinimumCostFlow FindMinimumCut FindMinValue FindPermutation FindPostmanTour FindProcessParameters FindRoot FindSequenceFunction FindSettings FindShortestPath FindShortestTour FindThreshold FindVertexCover FindVertexCut Fine FinishDynamic FiniteAbelianGroupCount FiniteGroupCount FiniteGroupData First FirstPassageTimeDistribution FischerGroupFi22 FischerGroupFi23 FischerGroupFi24Prime FisherHypergeometricDistribution FisherRatioTest FisherZDistribution Fit FitAll FittedModel FixedPoint FixedPointList FlashSelection Flat Flatten FlattenAt FlatTopWindow FlipView Floor FlushPrintOutputPacket Fold FoldList Font FontColor FontFamily FontForm FontName FontOpacity FontPostScriptName FontProperties FontReencoding FontSize FontSlant FontSubstitutions FontTracking FontVariations FontWeight For ForAll Format FormatRules FormatType FormatTypeAutoConvert FormatValues FormBox FormBoxOptions FortranForm Forward ForwardBackward Fourier FourierCoefficient FourierCosCoefficient FourierCosSeries FourierCosTransform FourierDCT FourierDCTFilter FourierDCTMatrix FourierDST FourierDSTMatrix FourierMatrix FourierParameters FourierSequenceTransform FourierSeries FourierSinCoefficient FourierSinSeries FourierSinTransform FourierTransform FourierTrigSeries FractionalBrownianMotionProcess FractionalPart FractionBox FractionBoxOptions FractionLine Frame FrameBox FrameBoxOptions Framed FrameInset FrameLabel Frameless FrameMargins FrameStyle FrameTicks FrameTicksStyle FRatioDistribution FrechetDistribution FreeQ FrequencySamplingFilterKernel FresnelC FresnelS Friday FrobeniusNumber FrobeniusSolve FromCharacterCode FromCoefficientRules FromContinuedFraction FromDate FromDigits FromDMS Front FrontEndDynamicExpression FrontEndEventActions FrontEndExecute FrontEndObject FrontEndResource FrontEndResourceString FrontEndStackSize FrontEndToken FrontEndTokenExecute FrontEndValueCache FrontEndVersion FrontFaceColor FrontFaceOpacity Full FullAxes FullDefinition FullForm FullGraphics FullOptions FullSimplify Function FunctionExpand FunctionInterpolation FunctionSpace FussellVeselyImportance GaborFilter GaborMatrix GaborWavelet GainMargins GainPhaseMargins Gamma GammaDistribution GammaRegularized GapPenalty Gather GatherBy GaugeFaceElementFunction GaugeFaceStyle GaugeFrameElementFunction GaugeFrameSize GaugeFrameStyle GaugeLabels GaugeMarkers GaugeStyle GaussianFilter GaussianIntegers GaussianMatrix GaussianWindow GCD GegenbauerC General GeneralizedLinearModelFit GenerateConditions GeneratedCell GeneratedParameters GeneratingFunction Generic GenericCylindricalDecomposition GenomeData GenomeLookup GeodesicClosing GeodesicDilation GeodesicErosion GeodesicOpening GeoDestination GeodesyData GeoDirection GeoDistance GeoGridPosition GeometricBrownianMotionProcess GeometricDistribution GeometricMean GeometricMeanFilter GeometricTransformation GeometricTransformation3DBox GeometricTransformation3DBoxOptions GeometricTransformationBox GeometricTransformationBoxOptions GeoPosition GeoPositionENU GeoPositionXYZ GeoProjectionData GestureHandler GestureHandlerTag Get GetBoundingBoxSizePacket GetContext GetEnvironment GetFileName GetFrontEndOptionsDataPacket GetLinebreakInformationPacket GetMenusPacket GetPageBreakInformationPacket Glaisher GlobalClusteringCoefficient GlobalPreferences GlobalSession Glow GoldenRatio GompertzMakehamDistribution GoodmanKruskalGamma GoodmanKruskalGammaTest Goto Grad Gradient GradientFilter GradientOrientationFilter Graph GraphAssortativity GraphCenter GraphComplement GraphData GraphDensity GraphDiameter GraphDifference GraphDisjointUnion GraphDistance GraphDistanceMatrix GraphElementData GraphEmbedding GraphHighlight GraphHighlightStyle GraphHub Graphics Graphics3D Graphics3DBox Graphics3DBoxOptions GraphicsArray GraphicsBaseline GraphicsBox GraphicsBoxOptions GraphicsColor GraphicsColumn GraphicsComplex GraphicsComplex3DBox GraphicsComplex3DBoxOptions GraphicsComplexBox GraphicsComplexBoxOptions GraphicsContents GraphicsData GraphicsGrid GraphicsGridBox GraphicsGroup GraphicsGroup3DBox GraphicsGroup3DBoxOptions GraphicsGroupBox GraphicsGroupBoxOptions GraphicsGrouping GraphicsHighlightColor GraphicsRow GraphicsSpacing GraphicsStyle GraphIntersection GraphLayout GraphLinkEfficiency GraphPeriphery GraphPlot GraphPlot3D GraphPower GraphPropertyDistribution GraphQ GraphRadius GraphReciprocity GraphRoot GraphStyle GraphUnion Gray GrayLevel GreatCircleDistance Greater GreaterEqual GreaterEqualLess GreaterFullEqual GreaterGreater GreaterLess GreaterSlantEqual GreaterTilde Green Grid GridBaseline GridBox GridBoxAlignment GridBoxBackground GridBoxDividers GridBoxFrame GridBoxItemSize GridBoxItemStyle GridBoxOptions GridBoxSpacings GridCreationSettings GridDefaultElement GridElementStyleOptions GridFrame GridFrameMargins GridGraph GridLines GridLinesStyle GroebnerBasis GroupActionBase GroupCentralizer GroupElementFromWord GroupElementPosition GroupElementQ GroupElements GroupElementToWord GroupGenerators GroupMultiplicationTable GroupOrbits GroupOrder GroupPageBreakWithin GroupSetwiseStabilizer GroupStabilizer GroupStabilizerChain Gudermannian GumbelDistribution HaarWavelet HadamardMatrix HalfNormalDistribution HamiltonianGraphQ HammingDistance HammingWindow HankelH1 HankelH2 HankelMatrix HannPoissonWindow HannWindow HaradaNortonGroupHN HararyGraph HarmonicMean HarmonicMeanFilter HarmonicNumber Hash HashTable Haversine HazardFunction Head HeadCompose Heads HeavisideLambda HeavisidePi HeavisideTheta HeldGroupHe HeldPart HelpBrowserLookup HelpBrowserNotebook HelpBrowserSettings HermiteDecomposition HermiteH HermitianMatrixQ HessenbergDecomposition Hessian HexadecimalCharacter Hexahedron HexahedronBox HexahedronBoxOptions HiddenSurface HighlightGraph HighlightImage HighpassFilter HigmanSimsGroupHS HilbertFilter HilbertMatrix Histogram Histogram3D HistogramDistribution HistogramList HistogramTransform HistogramTransformInterpolation HitMissTransform HITSCentrality HodgeDual HoeffdingD HoeffdingDTest Hold HoldAll HoldAllComplete HoldComplete HoldFirst HoldForm HoldPattern HoldRest HolidayCalendar HomeDirectory HomePage Horizontal HorizontalForm HorizontalGauge HorizontalScrollPosition HornerForm HotellingTSquareDistribution HoytDistribution HTMLSave Hue HumpDownHump HumpEqual HurwitzLerchPhi HurwitzZeta HyperbolicDistribution HypercubeGraph HyperexponentialDistribution Hyperfactorial Hypergeometric0F1 Hypergeometric0F1Regularized Hypergeometric1F1 Hypergeometric1F1Regularized Hypergeometric2F1 Hypergeometric2F1Regularized HypergeometricDistribution HypergeometricPFQ HypergeometricPFQRegularized HypergeometricU Hyperlink HyperlinkCreationSettings Hyphenation HyphenationOptions HypoexponentialDistribution HypothesisTestData I Identity IdentityMatrix If IgnoreCase Im Image Image3D Image3DSlices ImageAccumulate ImageAdd ImageAdjust ImageAlign ImageApply ImageAspectRatio ImageAssemble ImageCache ImageCacheValid ImageCapture ImageChannels ImageClip ImageColorSpace ImageCompose ImageConvolve ImageCooccurrence ImageCorners ImageCorrelate ImageCorrespondingPoints ImageCrop ImageData ImageDataPacket ImageDeconvolve ImageDemosaic ImageDifference ImageDimensions ImageDistance ImageEffect ImageFeatureTrack ImageFileApply ImageFileFilter ImageFileScan ImageFilter ImageForestingComponents ImageForwardTransformation ImageHistogram ImageKeypoints ImageLevels ImageLines ImageMargins ImageMarkers ImageMeasurements ImageMultiply ImageOffset ImagePad ImagePadding ImagePartition ImagePeriodogram ImagePerspectiveTransformation ImageQ ImageRangeCache ImageReflect ImageRegion ImageResize ImageResolution ImageRotate ImageRotated ImageScaled ImageScan ImageSize ImageSizeAction ImageSizeCache ImageSizeMultipliers ImageSizeRaw ImageSubtract ImageTake ImageTransformation ImageTrim ImageType ImageValue ImageValuePositions Implies Import ImportAutoReplacements ImportString ImprovementImportance In IncidenceGraph IncidenceList IncidenceMatrix IncludeConstantBasis IncludeFileExtension IncludePods IncludeSingularTerm Increment Indent IndentingNewlineSpacings IndentMaxFraction IndependenceTest IndependentEdgeSetQ IndependentUnit IndependentVertexSetQ Indeterminate IndexCreationOptions Indexed IndexGraph IndexTag Inequality InexactNumberQ InexactNumbers Infinity Infix Information Inherited InheritScope Initialization InitializationCell InitializationCellEvaluation InitializationCellWarning InlineCounterAssignments InlineCounterIncrements InlineRules Inner Inpaint Input InputAliases InputAssumptions InputAutoReplacements InputField InputFieldBox InputFieldBoxOptions InputForm InputGrouping InputNamePacket InputNotebook InputPacket InputSettings InputStream InputString InputStringPacket InputToBoxFormPacket Insert InsertionPointObject InsertResults Inset Inset3DBox Inset3DBoxOptions InsetBox InsetBoxOptions Install InstallService InString Integer IntegerDigits IntegerExponent IntegerLength IntegerPart IntegerPartitions IntegerQ Integers IntegerString Integral Integrate Interactive InteractiveTradingChart Interlaced Interleaving InternallyBalancedDecomposition InterpolatingFunction InterpolatingPolynomial Interpolation InterpolationOrder InterpolationPoints InterpolationPrecision Interpretation InterpretationBox InterpretationBoxOptions InterpretationFunction InterpretTemplate InterquartileRange Interrupt InterruptSettings Intersection Interval IntervalIntersection IntervalMemberQ IntervalUnion Inverse InverseBetaRegularized InverseCDF InverseChiSquareDistribution InverseContinuousWaveletTransform InverseDistanceTransform InverseEllipticNomeQ InverseErf InverseErfc InverseFourier InverseFourierCosTransform InverseFourierSequenceTransform InverseFourierSinTransform InverseFourierTransform InverseFunction InverseFunctions InverseGammaDistribution InverseGammaRegularized InverseGaussianDistribution InverseGudermannian InverseHaversine InverseJacobiCD InverseJacobiCN InverseJacobiCS InverseJacobiDC InverseJacobiDN InverseJacobiDS InverseJacobiNC InverseJacobiND InverseJacobiNS InverseJacobiSC InverseJacobiSD InverseJacobiSN InverseLaplaceTransform InversePermutation InverseRadon InverseSeries InverseSurvivalFunction InverseWaveletTransform InverseWeierstrassP InverseZTransform Invisible InvisibleApplication InvisibleTimes IrreduciblePolynomialQ IsolatingInterval IsomorphicGraphQ IsotopeData Italic Item ItemBox ItemBoxOptions ItemSize ItemStyle ItoProcess JaccardDissimilarity JacobiAmplitude Jacobian JacobiCD JacobiCN JacobiCS JacobiDC JacobiDN JacobiDS JacobiNC JacobiND JacobiNS JacobiP JacobiSC JacobiSD JacobiSN JacobiSymbol JacobiZeta JankoGroupJ1 JankoGroupJ2 JankoGroupJ3 JankoGroupJ4 JarqueBeraALMTest JohnsonDistribution Join Joined JoinedCurve JoinedCurveBox JoinForm JordanDecomposition JordanModelDecomposition K KagiChart KaiserBesselWindow KaiserWindow KalmanEstimator KalmanFilter KarhunenLoeveDecomposition KaryTree KatzCentrality KCoreComponents KDistribution KelvinBei KelvinBer KelvinKei KelvinKer KendallTau KendallTauTest KernelExecute KernelMixtureDistribution KernelObject Kernels Ket Khinchin KirchhoffGraph KirchhoffMatrix KleinInvariantJ KnightTourGraph KnotData KnownUnitQ KolmogorovSmirnovTest KroneckerDelta KroneckerModelDecomposition KroneckerProduct KroneckerSymbol KuiperTest KumaraswamyDistribution Kurtosis KuwaharaFilter Label Labeled LabeledSlider LabelingFunction LabelStyle LaguerreL LambdaComponents LambertW LanczosWindow LandauDistribution Language LanguageCategory LaplaceDistribution LaplaceTransform Laplacian LaplacianFilter LaplacianGaussianFilter Large Larger Last Latitude LatitudeLongitude LatticeData LatticeReduce Launch LaunchKernels LayeredGraphPlot LayerSizeFunction LayoutInformation LCM LeafCount LeapYearQ LeastSquares LeastSquaresFilterKernel Left LeftArrow LeftArrowBar LeftArrowRightArrow LeftDownTeeVector LeftDownVector LeftDownVectorBar LeftRightArrow LeftRightVector LeftTee LeftTeeArrow LeftTeeVector LeftTriangle LeftTriangleBar LeftTriangleEqual LeftUpDownVector LeftUpTeeVector LeftUpVector LeftUpVectorBar LeftVector LeftVectorBar LegendAppearance Legended LegendFunction LegendLabel LegendLayout LegendMargins LegendMarkers LegendMarkerSize LegendreP LegendreQ LegendreType Length LengthWhile LerchPhi Less LessEqual LessEqualGreater LessFullEqual LessGreater LessLess LessSlantEqual LessTilde LetterCharacter LetterQ Level LeveneTest LeviCivitaTensor LevyDistribution Lexicographic LibraryFunction LibraryFunctionError LibraryFunctionInformation LibraryFunctionLoad LibraryFunctionUnload LibraryLoad LibraryUnload LicenseID LiftingFilterData LiftingWaveletTransform LightBlue LightBrown LightCyan Lighter LightGray LightGreen Lighting LightingAngle LightMagenta LightOrange LightPink LightPurple LightRed LightSources LightYellow Likelihood Limit LimitsPositioning LimitsPositioningTokens LindleyDistribution Line Line3DBox LinearFilter LinearFractionalTransform LinearModelFit LinearOffsetFunction LinearProgramming LinearRecurrence LinearSolve LinearSolveFunction LineBox LineBreak LinebreakAdjustments LineBreakChart LineBreakWithin LineColor LineForm LineGraph LineIndent LineIndentMaxFraction LineIntegralConvolutionPlot LineIntegralConvolutionScale LineLegend LineOpacity LineSpacing LineWrapParts LinkActivate LinkClose LinkConnect LinkConnectedQ LinkCreate LinkError LinkFlush LinkFunction LinkHost LinkInterrupt LinkLaunch LinkMode LinkObject LinkOpen LinkOptions LinkPatterns LinkProtocol LinkRead LinkReadHeld LinkReadyQ Links LinkWrite LinkWriteHeld LiouvilleLambda List Listable ListAnimate ListContourPlot ListContourPlot3D ListConvolve ListCorrelate ListCurvePathPlot ListDeconvolve ListDensityPlot Listen ListFourierSequenceTransform ListInterpolation ListLineIntegralConvolutionPlot ListLinePlot ListLogLinearPlot ListLogLogPlot ListLogPlot ListPicker ListPickerBox ListPickerBoxBackground ListPickerBoxOptions ListPlay ListPlot ListPlot3D ListPointPlot3D ListPolarPlot ListQ ListStreamDensityPlot ListStreamPlot ListSurfacePlot3D ListVectorDensityPlot ListVectorPlot ListVectorPlot3D ListZTransform Literal LiteralSearch LocalClusteringCoefficient LocalizeVariables LocationEquivalenceTest LocationTest Locator LocatorAutoCreate LocatorBox LocatorBoxOptions LocatorCentering LocatorPane LocatorPaneBox LocatorPaneBoxOptions LocatorRegion Locked Log Log10 Log2 LogBarnesG LogGamma LogGammaDistribution LogicalExpand LogIntegral LogisticDistribution LogitModelFit LogLikelihood LogLinearPlot LogLogisticDistribution LogLogPlot LogMultinormalDistribution LogNormalDistribution LogPlot LogRankTest LogSeriesDistribution LongEqual Longest LongestAscendingSequence LongestCommonSequence LongestCommonSequencePositions LongestCommonSubsequence LongestCommonSubsequencePositions LongestMatch LongForm Longitude LongLeftArrow LongLeftRightArrow LongRightArrow Loopback LoopFreeGraphQ LowerCaseQ LowerLeftArrow LowerRightArrow LowerTriangularize LowpassFilter LQEstimatorGains LQGRegulator LQOutputRegulatorGains LQRegulatorGains LUBackSubstitution LucasL LuccioSamiComponents LUDecomposition LyapunovSolve LyonsGroupLy MachineID MachineName MachineNumberQ MachinePrecision MacintoshSystemPageSetup Magenta Magnification Magnify MainSolve MaintainDynamicCaches Majority MakeBoxes MakeExpression MakeRules MangoldtLambda ManhattanDistance Manipulate Manipulator MannWhitneyTest MantissaExponent Manual Map MapAll MapAt MapIndexed MAProcess MapThread MarcumQ MardiaCombinedTest MardiaKurtosisTest MardiaSkewnessTest MarginalDistribution MarkovProcessProperties Masking MatchingDissimilarity MatchLocalNameQ MatchLocalNames MatchQ Material MathematicaNotation MathieuC MathieuCharacteristicA MathieuCharacteristicB MathieuCharacteristicExponent MathieuCPrime MathieuGroupM11 MathieuGroupM12 MathieuGroupM22 MathieuGroupM23 MathieuGroupM24 MathieuS MathieuSPrime MathMLForm MathMLText Matrices MatrixExp MatrixForm MatrixFunction MatrixLog MatrixPlot MatrixPower MatrixQ MatrixRank Max MaxBend MaxDetect MaxExtraBandwidths MaxExtraConditions MaxFeatures MaxFilter Maximize MaxIterations MaxMemoryUsed MaxMixtureKernels MaxPlotPoints MaxPoints MaxRecursion MaxStableDistribution MaxStepFraction MaxSteps MaxStepSize MaxValue MaxwellDistribution McLaughlinGroupMcL Mean MeanClusteringCoefficient MeanDegreeConnectivity MeanDeviation MeanFilter MeanGraphDistance MeanNeighborDegree MeanShift MeanShiftFilter Median MedianDeviation MedianFilter Medium MeijerG MeixnerDistribution MemberQ MemoryConstrained MemoryInUse Menu MenuAppearance MenuCommandKey MenuEvaluator MenuItem MenuPacket MenuSortingValue MenuStyle MenuView MergeDifferences Mesh MeshFunctions MeshRange MeshShading MeshStyle Message MessageDialog MessageList MessageName MessageOptions MessagePacket Messages MessagesNotebook MetaCharacters MetaInformation Method MethodOptions MexicanHatWavelet MeyerWavelet Min MinDetect MinFilter MinimalPolynomial MinimalStateSpaceModel Minimize Minors MinRecursion MinSize MinStableDistribution Minus MinusPlus MinValue Missing MissingDataMethod MittagLefflerE MixedRadix MixedRadixQuantity MixtureDistribution Mod Modal Mode Modular ModularLambda Module Modulus MoebiusMu Moment Momentary MomentConvert MomentEvaluate MomentGeneratingFunction Monday Monitor MonomialList MonomialOrder MonsterGroupM MorletWavelet MorphologicalBinarize MorphologicalBranchPoints MorphologicalComponents MorphologicalEulerNumber MorphologicalGraph MorphologicalPerimeter MorphologicalTransform Most MouseAnnotation MouseAppearance MouseAppearanceTag MouseButtons Mouseover MousePointerNote MousePosition MovingAverage MovingMedian MoyalDistribution MultiedgeStyle MultilaunchWarning MultiLetterItalics MultiLetterStyle MultilineFunction Multinomial MultinomialDistribution MultinormalDistribution MultiplicativeOrder Multiplicity Multiselection MultivariateHypergeometricDistribution MultivariatePoissonDistribution MultivariateTDistribution N NakagamiDistribution NameQ Names NamespaceBox Nand NArgMax NArgMin NBernoulliB NCache NDSolve NDSolveValue Nearest NearestFunction NeedCurrentFrontEndPackagePacket NeedCurrentFrontEndSymbolsPacket NeedlemanWunschSimilarity Needs Negative NegativeBinomialDistribution NegativeMultinomialDistribution NeighborhoodGraph Nest NestedGreaterGreater NestedLessLess NestedScriptRules NestList NestWhile NestWhileList NevilleThetaC NevilleThetaD NevilleThetaN NevilleThetaS NewPrimitiveStyle NExpectation Next NextPrime NHoldAll NHoldFirst NHoldRest NicholsGridLines NicholsPlot NIntegrate NMaximize NMaxValue NMinimize NMinValue NominalVariables NonAssociative NoncentralBetaDistribution NoncentralChiSquareDistribution NoncentralFRatioDistribution NoncentralStudentTDistribution NonCommutativeMultiply NonConstants None NonlinearModelFit NonlocalMeansFilter NonNegative NonPositive Nor NorlundB Norm Normal NormalDistribution NormalGrouping Normalize NormalizedSquaredEuclideanDistance NormalsFunction NormFunction Not NotCongruent NotCupCap NotDoubleVerticalBar Notebook NotebookApply NotebookAutoSave NotebookClose NotebookConvertSettings NotebookCreate NotebookCreateReturnObject NotebookDefault NotebookDelete NotebookDirectory NotebookDynamicExpression NotebookEvaluate NotebookEventActions NotebookFileName NotebookFind NotebookFindReturnObject NotebookGet NotebookGetLayoutInformationPacket NotebookGetMisspellingsPacket NotebookInformation NotebookInterfaceObject NotebookLocate NotebookObject NotebookOpen NotebookOpenReturnObject NotebookPath NotebookPrint NotebookPut NotebookPutReturnObject NotebookRead NotebookResetGeneratedCells Notebooks NotebookSave NotebookSaveAs NotebookSelection NotebookSetupLayoutInformationPacket NotebooksMenu NotebookWrite NotElement NotEqualTilde NotExists NotGreater NotGreaterEqual NotGreaterFullEqual NotGreaterGreater NotGreaterLess NotGreaterSlantEqual NotGreaterTilde NotHumpDownHump NotHumpEqual NotLeftTriangle NotLeftTriangleBar NotLeftTriangleEqual NotLess NotLessEqual NotLessFullEqual NotLessGreater NotLessLess NotLessSlantEqual NotLessTilde NotNestedGreaterGreater NotNestedLessLess NotPrecedes NotPrecedesEqual NotPrecedesSlantEqual NotPrecedesTilde NotReverseElement NotRightTriangle NotRightTriangleBar NotRightTriangleEqual NotSquareSubset NotSquareSubsetEqual NotSquareSuperset NotSquareSupersetEqual NotSubset NotSubsetEqual NotSucceeds NotSucceedsEqual NotSucceedsSlantEqual NotSucceedsTilde NotSuperset NotSupersetEqual NotTilde NotTildeEqual NotTildeFullEqual NotTildeTilde NotVerticalBar NProbability NProduct NProductFactors NRoots NSolve NSum NSumTerms Null NullRecords NullSpace NullWords Number NumberFieldClassNumber NumberFieldDiscriminant NumberFieldFundamentalUnits NumberFieldIntegralBasis NumberFieldNormRepresentatives NumberFieldRegulator NumberFieldRootsOfUnity NumberFieldSignature NumberForm NumberFormat NumberMarks NumberMultiplier NumberPadding NumberPoint NumberQ NumberSeparator NumberSigns NumberString Numerator NumericFunction NumericQ NuttallWindow NValues NyquistGridLines NyquistPlot O ObservabilityGramian ObservabilityMatrix ObservableDecomposition ObservableModelQ OddQ Off Offset OLEData On ONanGroupON OneIdentity Opacity Open OpenAppend Opener OpenerBox OpenerBoxOptions OpenerView OpenFunctionInspectorPacket Opening OpenRead OpenSpecialOptions OpenTemporary OpenWrite Operate OperatingSystem OptimumFlowData Optional OptionInspectorSettings OptionQ Options OptionsPacket OptionsPattern OptionValue OptionValueBox OptionValueBoxOptions Or Orange Order OrderDistribution OrderedQ Ordering Orderless OrnsteinUhlenbeckProcess Orthogonalize Out Outer OutputAutoOverwrite OutputControllabilityMatrix OutputControllableModelQ OutputForm OutputFormData OutputGrouping OutputMathEditExpression OutputNamePacket OutputResponse OutputSizeLimit OutputStream Over OverBar OverDot Overflow OverHat Overlaps Overlay OverlayBox OverlayBoxOptions Overscript OverscriptBox OverscriptBoxOptions OverTilde OverVector OwenT OwnValues PackingMethod PaddedForm Padding PadeApproximant PadLeft PadRight PageBreakAbove PageBreakBelow PageBreakWithin PageFooterLines PageFooters PageHeaderLines PageHeaders PageHeight PageRankCentrality PageWidth PairedBarChart PairedHistogram PairedSmoothHistogram PairedTTest PairedZTest PaletteNotebook PalettePath Pane PaneBox PaneBoxOptions Panel PanelBox PanelBoxOptions Paneled PaneSelector PaneSelectorBox PaneSelectorBoxOptions PaperWidth ParabolicCylinderD ParagraphIndent ParagraphSpacing ParallelArray ParallelCombine ParallelDo ParallelEvaluate Parallelization Parallelize ParallelMap ParallelNeeds ParallelProduct ParallelSubmit ParallelSum ParallelTable ParallelTry Parameter ParameterEstimator ParameterMixtureDistribution ParameterVariables ParametricFunction ParametricNDSolve ParametricNDSolveValue ParametricPlot ParametricPlot3D ParentConnect ParentDirectory ParentForm Parenthesize ParentList ParetoDistribution Part PartialCorrelationFunction PartialD ParticleData Partition PartitionsP PartitionsQ ParzenWindow PascalDistribution PassEventsDown PassEventsUp Paste PasteBoxFormInlineCells PasteButton Path PathGraph PathGraphQ Pattern PatternSequence PatternTest PauliMatrix PaulWavelet Pause PausedTime PDF PearsonChiSquareTest PearsonCorrelationTest PearsonDistribution PerformanceGoal PeriodicInterpolation Periodogram PeriodogramArray PermutationCycles PermutationCyclesQ PermutationGroup PermutationLength PermutationList PermutationListQ PermutationMax PermutationMin PermutationOrder PermutationPower PermutationProduct PermutationReplace Permutations PermutationSupport Permute PeronaMalikFilter Perpendicular PERTDistribution PetersenGraph PhaseMargins Pi Pick PIDData PIDDerivativeFilter PIDFeedforward PIDTune Piecewise PiecewiseExpand PieChart PieChart3D PillaiTrace PillaiTraceTest Pink Pivoting PixelConstrained PixelValue PixelValuePositions Placed Placeholder PlaceholderReplace Plain PlanarGraphQ Play PlayRange Plot Plot3D Plot3Matrix PlotDivision PlotJoined PlotLabel PlotLayout PlotLegends PlotMarkers PlotPoints PlotRange PlotRangeClipping PlotRangePadding PlotRegion PlotStyle Plus PlusMinus Pochhammer PodStates PodWidth Point Point3DBox PointBox PointFigureChart PointForm PointLegend PointSize PoissonConsulDistribution PoissonDistribution PoissonProcess PoissonWindow PolarAxes PolarAxesOrigin PolarGridLines PolarPlot PolarTicks PoleZeroMarkers PolyaAeppliDistribution PolyGamma Polygon Polygon3DBox Polygon3DBoxOptions PolygonBox PolygonBoxOptions PolygonHoleScale PolygonIntersections PolygonScale PolyhedronData PolyLog PolynomialExtendedGCD PolynomialForm PolynomialGCD PolynomialLCM PolynomialMod PolynomialQ PolynomialQuotient PolynomialQuotientRemainder PolynomialReduce PolynomialRemainder Polynomials PopupMenu PopupMenuBox PopupMenuBoxOptions PopupView PopupWindow Position Positive PositiveDefiniteMatrixQ PossibleZeroQ Postfix PostScript Power PowerDistribution PowerExpand PowerMod PowerModList PowerSpectralDensity PowersRepresentations PowerSymmetricPolynomial Precedence PrecedenceForm Precedes PrecedesEqual PrecedesSlantEqual PrecedesTilde Precision PrecisionGoal PreDecrement PredictionRoot PreemptProtect PreferencesPath Prefix PreIncrement Prepend PrependTo PreserveImageOptions Previous PriceGraphDistribution PrimaryPlaceholder Prime PrimeNu PrimeOmega PrimePi PrimePowerQ PrimeQ Primes PrimeZetaP PrimitiveRoot PrincipalComponents PrincipalValue Print PrintAction PrintForm PrintingCopies PrintingOptions PrintingPageRange PrintingStartingPageNumber PrintingStyleEnvironment PrintPrecision PrintTemporary Prism PrismBox PrismBoxOptions PrivateCellOptions PrivateEvaluationOptions PrivateFontOptions PrivateFrontEndOptions PrivateNotebookOptions PrivatePaths Probability ProbabilityDistribution ProbabilityPlot ProbabilityPr ProbabilityScalePlot ProbitModelFit ProcessEstimator ProcessParameterAssumptions ProcessParameterQ ProcessStateDomain ProcessTimeDomain Product ProductDistribution ProductLog ProgressIndicator ProgressIndicatorBox 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RogersTanimotoDissimilarity Root RootApproximant RootIntervals RootLocusPlot RootMeanSquare RootOfUnityQ RootReduce Roots RootSum Rotate RotateLabel RotateLeft RotateRight RotationAction RotationBox RotationBoxOptions RotationMatrix RotationTransform Round RoundImplies RoundingRadius Row RowAlignments RowBackgrounds RowBox RowHeights RowLines RowMinHeight RowReduce RowsEqual RowSpacings RSolve RudvalisGroupRu Rule RuleCondition RuleDelayed RuleForm RulerUnits Run RunScheduledTask RunThrough RuntimeAttributes RuntimeOptions RussellRaoDissimilarity SameQ SameTest SampleDepth SampledSoundFunction SampledSoundList SampleRate SamplingPeriod SARIMAProcess SARMAProcess SatisfiabilityCount SatisfiabilityInstances SatisfiableQ Saturday Save Saveable SaveAutoDelete SaveDefinitions SawtoothWave Scale Scaled ScaleDivisions ScaledMousePosition ScaleOrigin ScalePadding ScaleRanges ScaleRangeStyle ScalingFunctions ScalingMatrix ScalingTransform Scan ScheduledTaskActiveQ ScheduledTaskData 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