mirror of
https://github.com/gryf/coach.git
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a1c56edd98
1. Having the standard checkpoint prefix in order for the data store to grab it, and sync it to S3. 2. Removing the reference to Redis so that it won't try to pickle that in. 3. Enable restoring a checkpoint into a single-worker run, which was saved by a single-node-multiple-worker run.
1042 lines
51 KiB
Python
1042 lines
51 KiB
Python
#
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# Copyright (c) 2017 Intel Corporation
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import copy
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import os
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import random
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from collections import OrderedDict
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from typing import Dict, List, Union, Tuple
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import numpy as np
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from pandas import read_pickle
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from six.moves import range
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from rl_coach.agents.agent_interface import AgentInterface
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from rl_coach.architectures.network_wrapper import NetworkWrapper
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from rl_coach.base_parameters import AgentParameters, Device, DeviceType, DistributedTaskParameters, Frameworks
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from rl_coach.core_types import RunPhase, PredictionType, EnvironmentEpisodes, ActionType, Batch, Episode, StateType
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from rl_coach.core_types import Transition, ActionInfo, TrainingSteps, EnvironmentSteps, EnvResponse
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from rl_coach.logger import screen, Logger, EpisodeLogger
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from rl_coach.memories.episodic.episodic_experience_replay import EpisodicExperienceReplay
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from rl_coach.saver import SaverCollection
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from rl_coach.spaces import SpacesDefinition, VectorObservationSpace, GoalsSpace, AttentionActionSpace
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from rl_coach.utils import Signal, force_list
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from rl_coach.utils import dynamic_import_and_instantiate_module_from_params
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from rl_coach.memories.backend.memory_impl import get_memory_backend
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class Agent(AgentInterface):
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def __init__(self, agent_parameters: AgentParameters, parent: Union['LevelManager', 'CompositeAgent']=None):
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"""
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:param agent_parameters: A AgentParameters class instance with all the agent parameters
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"""
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super().__init__()
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self.ap = agent_parameters
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self.task_id = self.ap.task_parameters.task_index
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self.is_chief = self.task_id == 0
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self.shared_memory = type(agent_parameters.task_parameters) == DistributedTaskParameters \
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and self.ap.memory.shared_memory
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if self.shared_memory:
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self.shared_memory_scratchpad = self.ap.task_parameters.shared_memory_scratchpad
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self.name = agent_parameters.name
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self.parent = parent
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self.parent_level_manager = None
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self.full_name_id = agent_parameters.full_name_id = self.name
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if type(agent_parameters.task_parameters) == DistributedTaskParameters:
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screen.log_title("Creating agent - name: {} task id: {} (may take up to 30 seconds due to "
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"tensorflow wake up time)".format(self.full_name_id, self.task_id))
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else:
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screen.log_title("Creating agent - name: {}".format(self.full_name_id))
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self.imitation = False
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self.agent_logger = Logger()
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self.agent_episode_logger = EpisodeLogger()
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# get the memory
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# - distributed training + shared memory:
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# * is chief? -> create the memory and add it to the scratchpad
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# * not chief? -> wait for the chief to create the memory and then fetch it
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# - non distributed training / not shared memory:
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# * create memory
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memory_name = self.ap.memory.path.split(':')[1]
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self.memory_lookup_name = self.full_name_id + '.' + memory_name
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if self.shared_memory and not self.is_chief:
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self.memory = self.shared_memory_scratchpad.get(self.memory_lookup_name)
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else:
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# modules
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self.memory = dynamic_import_and_instantiate_module_from_params(self.ap.memory)
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if hasattr(self.ap.memory, 'memory_backend_params'):
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self.memory_backend = get_memory_backend(self.ap.memory.memory_backend_params)
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if self.ap.memory.memory_backend_params.run_type != 'trainer':
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self.memory.set_memory_backend(self.memory_backend)
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if agent_parameters.memory.load_memory_from_file_path:
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screen.log_title("Loading replay buffer from pickle. Pickle path: {}"
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.format(agent_parameters.memory.load_memory_from_file_path))
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self.memory.load(agent_parameters.memory.load_memory_from_file_path)
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if self.shared_memory and self.is_chief:
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self.shared_memory_scratchpad.add(self.memory_lookup_name, self.memory)
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# set devices
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if type(agent_parameters.task_parameters) == DistributedTaskParameters:
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self.has_global = True
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self.replicated_device = agent_parameters.task_parameters.device
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self.worker_device = "/job:worker/task:{}".format(self.task_id)
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if agent_parameters.task_parameters.use_cpu:
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self.worker_device += "/cpu:0"
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else:
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self.worker_device += "/device:GPU:0"
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else:
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self.has_global = False
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self.replicated_device = None
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if agent_parameters.task_parameters.use_cpu:
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self.worker_device = Device(DeviceType.CPU)
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else:
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self.worker_device = [Device(DeviceType.GPU, i)
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for i in range(agent_parameters.task_parameters.num_gpu)]
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# filters
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self.input_filter = self.ap.input_filter
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self.input_filter.set_name('input_filter')
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self.output_filter = self.ap.output_filter
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self.output_filter.set_name('output_filter')
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self.pre_network_filter = self.ap.pre_network_filter
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self.pre_network_filter.set_name('pre_network_filter')
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device = self.replicated_device if self.replicated_device else self.worker_device
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# TODO-REMOVE This is a temporary flow dividing to 3 modes. To be converged to a single flow once distributed tf
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# is removed, and Redis is used for sharing data between local workers.
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# Filters MoW will be split between different configurations
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# 1. Distributed coach synchrnization type (=distributed across multiple nodes) - Redis based data sharing + numpy arithmetic backend
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# 2. Distributed TF (=distributed on a single node, using distributed TF) - TF for both data sharing and arithmetic backend
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# 3. Single worker (=both TF and Mxnet) - no data sharing needed + numpy arithmetic backend
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if hasattr(self.ap.memory, 'memory_backend_params') and self.ap.algorithm.distributed_coach_synchronization_type:
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self.input_filter.set_device(device, memory_backend_params=self.ap.memory.memory_backend_params, mode='numpy')
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self.output_filter.set_device(device, memory_backend_params=self.ap.memory.memory_backend_params, mode='numpy')
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self.pre_network_filter.set_device(device, memory_backend_params=self.ap.memory.memory_backend_params, mode='numpy')
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elif (type(agent_parameters.task_parameters) == DistributedTaskParameters and
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agent_parameters.task_parameters.framework_type == Frameworks.tensorflow):
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self.input_filter.set_device(device, mode='tf')
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self.output_filter.set_device(device, mode='tf')
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self.pre_network_filter.set_device(device, mode='tf')
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else:
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self.input_filter.set_device(device, mode='numpy')
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self.output_filter.set_device(device, mode='numpy')
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self.pre_network_filter.set_device(device, mode='numpy')
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# initialize all internal variables
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self._phase = RunPhase.HEATUP
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self.total_shaped_reward_in_current_episode = 0
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self.total_reward_in_current_episode = 0
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self.total_steps_counter = 0
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self.running_reward = None
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self.training_iteration = 0
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self.last_target_network_update_step = 0
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self.last_training_phase_step = 0
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self.current_episode = self.ap.current_episode = 0
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self.curr_state = {}
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self.current_hrl_goal = None
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self.current_episode_steps_counter = 0
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self.episode_running_info = {}
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self.last_episode_evaluation_ran = 0
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self.running_observations = []
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self.agent_logger.set_current_time(self.current_episode)
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self.exploration_policy = None
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self.networks = {}
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self.last_action_info = None
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self.running_observation_stats = None
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self.running_reward_stats = None
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self.accumulated_rewards_across_evaluation_episodes = 0
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self.accumulated_shaped_rewards_across_evaluation_episodes = 0
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self.num_successes_across_evaluation_episodes = 0
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self.num_evaluation_episodes_completed = 0
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self.current_episode_buffer = Episode(discount=self.ap.algorithm.discount, n_step=self.ap.algorithm.n_step)
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# TODO: add agents observation rendering for debugging purposes (not the same as the environment rendering)
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# environment parameters
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self.spaces = None
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self.in_action_space = self.ap.algorithm.in_action_space
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# signals
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self.episode_signals = []
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self.step_signals = []
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self.loss = self.register_signal('Loss')
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self.curr_learning_rate = self.register_signal('Learning Rate')
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self.unclipped_grads = self.register_signal('Grads (unclipped)')
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self.reward = self.register_signal('Reward', dump_one_value_per_episode=False, dump_one_value_per_step=True)
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self.shaped_reward = self.register_signal('Shaped Reward', dump_one_value_per_episode=False, dump_one_value_per_step=True)
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self.discounted_return = self.register_signal('Discounted Return')
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if isinstance(self.in_action_space, GoalsSpace):
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self.distance_from_goal = self.register_signal('Distance From Goal', dump_one_value_per_step=True)
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# use seed
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if self.ap.task_parameters.seed is not None:
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random.seed(self.ap.task_parameters.seed)
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np.random.seed(self.ap.task_parameters.seed)
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else:
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# we need to seed the RNG since the different processes are initialized with the same parent seed
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random.seed()
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np.random.seed()
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@property
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def parent(self) -> 'LevelManager':
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"""
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Get the parent class of the agent
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:return: the current phase
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"""
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return self._parent
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@parent.setter
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def parent(self, val) -> None:
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"""
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Change the parent class of the agent.
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Additionally, updates the full name of the agent
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:param val: the new parent
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:return: None
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"""
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self._parent = val
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if self._parent is not None:
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if not hasattr(self._parent, 'name'):
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raise ValueError("The parent of an agent must have a name")
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self.full_name_id = self.ap.full_name_id = "{}/{}".format(self._parent.name, self.name)
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def setup_logger(self) -> None:
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"""
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Setup the logger for the agent
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:return: None
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"""
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# dump documentation
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logger_prefix = "{graph_name}.{level_name}.{agent_full_id}".\
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format(graph_name=self.parent_level_manager.parent_graph_manager.name,
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level_name=self.parent_level_manager.name,
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agent_full_id='.'.join(self.full_name_id.split('/')))
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self.agent_logger.set_logger_filenames(self.ap.task_parameters.experiment_path, logger_prefix=logger_prefix,
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add_timestamp=True, task_id=self.task_id)
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if self.ap.visualization.dump_in_episode_signals:
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self.agent_episode_logger.set_logger_filenames(self.ap.task_parameters.experiment_path,
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logger_prefix=logger_prefix,
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add_timestamp=True, task_id=self.task_id)
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def set_session(self, sess) -> None:
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"""
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Set the deep learning framework session for all the agents in the composite agent
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:return: None
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"""
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self.input_filter.set_session(sess)
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self.output_filter.set_session(sess)
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self.pre_network_filter.set_session(sess)
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[network.set_session(sess) for network in self.networks.values()]
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def register_signal(self, signal_name: str, dump_one_value_per_episode: bool=True,
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dump_one_value_per_step: bool=False) -> Signal:
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"""
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Register a signal such that its statistics will be dumped and be viewable through dashboard
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:param signal_name: the name of the signal as it will appear in dashboard
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:param dump_one_value_per_episode: should the signal value be written for each episode?
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:param dump_one_value_per_step: should the signal value be written for each step?
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:return: the created signal
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"""
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signal = Signal(signal_name)
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if dump_one_value_per_episode:
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self.episode_signals.append(signal)
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if dump_one_value_per_step:
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self.step_signals.append(signal)
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return signal
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def set_environment_parameters(self, spaces: SpacesDefinition):
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"""
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Sets the parameters that are environment dependent. As a side effect, initializes all the components that are
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dependent on those values, by calling init_environment_dependent_modules
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:param spaces: the environment spaces definition
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:return: None
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"""
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self.spaces = copy.deepcopy(spaces)
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if self.ap.algorithm.use_accumulated_reward_as_measurement:
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if 'measurements' in self.spaces.state.sub_spaces:
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self.spaces.state['measurements'].shape += 1
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self.spaces.state['measurements'].measurements_names += ['accumulated_reward']
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else:
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self.spaces.state['measurements'] = VectorObservationSpace(1, measurements_names=['accumulated_reward'])
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for observation_name in self.spaces.state.sub_spaces.keys():
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self.spaces.state[observation_name] = \
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self.pre_network_filter.get_filtered_observation_space(observation_name,
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self.input_filter.get_filtered_observation_space(observation_name,
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self.spaces.state[observation_name]))
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self.spaces.reward = self.pre_network_filter.get_filtered_reward_space(
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self.input_filter.get_filtered_reward_space(self.spaces.reward))
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self.spaces.action = self.output_filter.get_unfiltered_action_space(self.spaces.action)
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if isinstance(self.in_action_space, GoalsSpace):
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# TODO: what if the goal type is an embedding / embedding change?
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self.spaces.goal = self.in_action_space
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self.spaces.goal.set_target_space(self.spaces.state[self.spaces.goal.goal_name])
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self.init_environment_dependent_modules()
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def create_networks(self) -> Dict[str, NetworkWrapper]:
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"""
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Create all the networks of the agent.
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The network creation will be done after setting the environment parameters for the agent, since they are needed
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for creating the network.
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:return: A list containing all the networks
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"""
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networks = {}
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for network_name in sorted(self.ap.network_wrappers.keys()):
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networks[network_name] = NetworkWrapper(name=network_name,
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agent_parameters=self.ap,
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has_target=self.ap.network_wrappers[network_name].create_target_network,
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has_global=self.has_global,
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spaces=self.spaces,
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replicated_device=self.replicated_device,
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worker_device=self.worker_device)
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if self.ap.visualization.print_networks_summary:
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print(networks[network_name])
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return networks
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def init_environment_dependent_modules(self) -> None:
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"""
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Initialize any modules that depend on knowing information about the environment such as the action space or
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the observation space
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:return: None
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"""
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# initialize exploration policy
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if isinstance(self.ap.exploration, dict):
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if self.spaces.action.__class__ in self.ap.exploration.keys():
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self.ap.exploration = self.ap.exploration[self.spaces.action.__class__]
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else:
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raise ValueError("The exploration parameters were defined as a mapping between action space types and "
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"exploration types, but the action space used by the environment ({}) was not part of "
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"the exploration parameters dictionary keys ({})"
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.format(self.spaces.action.__class__, list(self.ap.exploration.keys())))
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self.ap.exploration.action_space = self.spaces.action
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self.exploration_policy = dynamic_import_and_instantiate_module_from_params(self.ap.exploration)
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# create all the networks of the agent
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self.networks = self.create_networks()
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@property
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def phase(self) -> RunPhase:
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"""
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The current running phase of the agent
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:return: RunPhase
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"""
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return self._phase
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@phase.setter
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def phase(self, val: RunPhase) -> None:
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"""
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Change the phase of the run for the agent and all the sub components
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:param val: the new run phase (TRAIN, TEST, etc.)
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:return: None
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"""
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self.reset_evaluation_state(val)
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self._phase = val
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self.exploration_policy.change_phase(val)
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def reset_evaluation_state(self, val: RunPhase) -> None:
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"""
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Perform accumulators initialization when entering an evaluation phase, and signal dumping when exiting an
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evaluation phase. Entering or exiting the evaluation phase is determined according to the new phase given
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by val, and by the current phase set in self.phase.
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:param val: The new phase to change to
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:return: None
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"""
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starting_evaluation = (val == RunPhase.TEST)
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ending_evaluation = (self.phase == RunPhase.TEST)
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if starting_evaluation:
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self.accumulated_rewards_across_evaluation_episodes = 0
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self.accumulated_shaped_rewards_across_evaluation_episodes = 0
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self.num_successes_across_evaluation_episodes = 0
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self.num_evaluation_episodes_completed = 0
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if self.ap.is_a_highest_level_agent or self.ap.task_parameters.verbosity == "high":
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screen.log_title("{}: Starting evaluation phase".format(self.name))
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elif ending_evaluation:
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# we write to the next episode, because it could be that the current episode was already written
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# to disk and then we won't write it again
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self.agent_logger.set_current_time(self.current_episode + 1)
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evaluation_reward = self.accumulated_rewards_across_evaluation_episodes / self.num_evaluation_episodes_completed
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self.agent_logger.create_signal_value(
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'Evaluation Reward', evaluation_reward)
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self.agent_logger.create_signal_value(
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'Shaped Evaluation Reward',
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self.accumulated_shaped_rewards_across_evaluation_episodes / self.num_evaluation_episodes_completed)
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success_rate = self.num_successes_across_evaluation_episodes / self.num_evaluation_episodes_completed
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self.agent_logger.create_signal_value(
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"Success Rate",
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success_rate
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)
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if self.ap.is_a_highest_level_agent or self.ap.task_parameters.verbosity == "high":
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screen.log_title("{}: Finished evaluation phase. Success rate = {}, Avg Total Reward = {}"
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.format(self.name, np.round(success_rate, 2), np.round(evaluation_reward, 2)))
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def call_memory(self, func, args=()):
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"""
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This function is a wrapper to allow having the same calls for shared or unshared memories.
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It should be used instead of calling the memory directly in order to allow different algorithms to work
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both with a shared and a local memory.
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:param func: the name of the memory function to call
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:param args: the arguments to supply to the function
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:return: the return value of the function
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"""
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if self.shared_memory:
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result = self.shared_memory_scratchpad.internal_call(self.memory_lookup_name, func, args)
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else:
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|
if type(args) != tuple:
|
|
args = (args,)
|
|
result = getattr(self.memory, func)(*args)
|
|
return result
|
|
|
|
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)
|
|
|
|
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()
|
|
|
|
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()
|
|
|
|
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()
|
|
|
|
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()
|
|
|
|
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):
|
|
"""
|
|
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()
|
|
|
|
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 = self.ap.algorithm.act_for_full_episodes
|
|
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
|
|
|
|
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
|
|
|
|
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
|
|
|
|
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
|
|
|
|
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
|
|
|
|
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
|
|
|
|
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
|
|
|
|
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
|
|
|
|
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
|
|
|
|
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
|
|
|
|
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]
|
|
|
|
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])
|
|
|
|
def save_checkpoint(self, checkpoint_prefix: str) -> None:
|
|
"""
|
|
Allows agents to store additional information when saving checkpoints.
|
|
|
|
:param checkpoint_prefix: The prefix of the checkpoint file to save
|
|
:return: None
|
|
"""
|
|
checkpoint_dir = self.ap.task_parameters.checkpoint_save_dir
|
|
|
|
checkpoint_prefix = '.'.join([checkpoint_prefix] + self.full_name_id.split('/')) # adds both level name and agent name
|
|
|
|
self.input_filter.save_state_to_checkpoint(checkpoint_dir, checkpoint_prefix)
|
|
self.output_filter.save_state_to_checkpoint(checkpoint_dir, checkpoint_prefix)
|
|
self.pre_network_filter.save_state_to_checkpoint(checkpoint_dir, checkpoint_prefix)
|
|
|
|
def restore_checkpoint(self, checkpoint_dir: str) -> None:
|
|
"""
|
|
Allows agents to store additional information when saving checkpoints.
|
|
|
|
:param checkpoint_dir: The checkpoint dir to restore from
|
|
:return: None
|
|
"""
|
|
checkpoint_prefix = '.'.join(self.full_name_id.split('/')) # adds both level name and agent name
|
|
self.input_filter.restore_state_from_checkpoint(checkpoint_dir, checkpoint_prefix)
|
|
self.pre_network_filter.restore_state_from_checkpoint(checkpoint_dir, checkpoint_prefix)
|
|
|
|
# no output filters currently have an internal state to restore
|
|
# self.output_filter.restore_state_from_checkpoint(checkpoint_dir)
|
|
|
|
def sync(self) -> None:
|
|
"""
|
|
Sync the global network parameters to local networks
|
|
|
|
:return: None
|
|
"""
|
|
for network in self.networks.values():
|
|
network.sync()
|
|
|
|
# TODO-remove - this is a temporary flow, used by the trainer worker, duplicated from observe() - need to create
|
|
# an external trainer flow reusing the existing flow and methods [e.g. observe(), step(), act()]
|
|
def emulate_observe_on_trainer(self, transition: Transition) -> bool:
|
|
"""
|
|
This emulates the observe using the transition obtained from the rollout worker on the training worker
|
|
in case of distributed training.
|
|
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.
|
|
:return:
|
|
"""
|
|
|
|
# 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
|
|
return transition.game_over
|
|
else:
|
|
# sum up the total shaped reward
|
|
self.total_shaped_reward_in_current_episode += transition.reward
|
|
self.total_reward_in_current_episode += transition.reward
|
|
self.shaped_reward.add_sample(transition.reward)
|
|
self.reward.add_sample(transition.reward)
|
|
|
|
# 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
|
|
|
|
# TODO-remove - this is a temporary flow, used by the trainer worker, duplicated from observe() - need to create
|
|
# an external trainer flow reusing the existing flow and methods [e.g. observe(), step(), act()]
|
|
def emulate_act_on_trainer(self, transition: Transition) -> ActionInfo:
|
|
"""
|
|
This emulates the act using the transition obtained from the rollout worker on the training worker
|
|
in case of distributed training.
|
|
Given the agents current knowledge, decide on the next action to apply to the environment
|
|
:return: an action and a dictionary containing 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
|
|
|
|
self.last_action_info = transition.action
|
|
|
|
return self.last_action_info
|
|
|
|
def get_success_rate(self) -> float:
|
|
return self.num_successes_across_evaluation_episodes / self.num_evaluation_episodes_completed
|
|
|
|
def collect_savers(self, parent_path_suffix: str) -> SaverCollection:
|
|
"""
|
|
Collect all of agent's network savers
|
|
:param parent_path_suffix: path suffix of the parent of the agent
|
|
(could be name of level manager or composite agent)
|
|
:return: collection of all agent savers
|
|
"""
|
|
parent_path_suffix = "{}.{}".format(parent_path_suffix, self.name)
|
|
savers = SaverCollection()
|
|
for network in self.networks.values():
|
|
savers.update(network.collect_savers(parent_path_suffix))
|
|
return savers
|