mirror of
https://github.com/gryf/coach.git
synced 2025-12-17 11:10:20 +01:00
1b095aeeca
Till now, most of the modules were importing all of the module objects (variables, classes, functions, other imports) into module namespace, which potentially could (and was) cause of unintentional use of class or methods, which was indirect imported. With this patch, all the star imports were substituted with top-level module, which provides desired class or function. Besides, all imports where sorted (where possible) in a way pep8[1] suggests - first are imports from standard library, than goes third party imports (like numpy, tensorflow etc) and finally coach modules. All of those sections are separated by one empty line. [1] https://www.python.org/dev/peps/pep-0008/#imports
94 lines
4.3 KiB
Python
94 lines
4.3 KiB
Python
#
|
|
# 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 agents import value_optimization_agent as voa
|
|
from logger import screen
|
|
import utils
|
|
|
|
|
|
# Neural Episodic Control - https://arxiv.org/pdf/1703.01988.pdf
|
|
class NECAgent(voa.ValueOptimizationAgent):
|
|
def __init__(self, env, tuning_parameters, replicated_device=None, thread_id=0):
|
|
voa.ValueOptimizationAgent.__init__(self, env, tuning_parameters, replicated_device, thread_id,
|
|
create_target_network=False)
|
|
self.current_episode_state_embeddings = []
|
|
self.training_started = False
|
|
|
|
def learn_from_batch(self, batch):
|
|
if not self.main_network.online_network.output_heads[0].DND.has_enough_entries(self.tp.agent.number_of_knn):
|
|
return 0
|
|
else:
|
|
if not self.training_started:
|
|
self.training_started = True
|
|
screen.log_title("Finished collecting initial entries in DND. Starting to train network...")
|
|
|
|
current_states, next_states, actions, rewards, game_overs, total_return = self.extract_batch(batch)
|
|
|
|
TD_targets = self.main_network.online_network.predict(current_states)
|
|
|
|
# only update the action that we have actually done in this transition
|
|
for i in range(self.tp.batch_size):
|
|
TD_targets[i, actions[i]] = total_return[i]
|
|
|
|
# train the neural network
|
|
result = self.main_network.train_and_sync_networks(current_states, TD_targets)
|
|
|
|
total_loss = result[0]
|
|
|
|
return total_loss
|
|
|
|
def act(self, phase=utils.RunPhase.TRAIN):
|
|
if self.in_heatup:
|
|
# get embedding in heatup (otherwise we get it through choose_action)
|
|
embedding = self.main_network.online_network.predict(
|
|
self.tf_input_state(self.curr_state),
|
|
outputs=self.main_network.online_network.state_embedding)
|
|
self.current_episode_state_embeddings.append(embedding)
|
|
|
|
return super().act(phase)
|
|
|
|
def get_prediction(self, curr_state):
|
|
# get the actions q values and the state embedding
|
|
embedding, actions_q_values = self.main_network.online_network.predict(
|
|
self.tf_input_state(curr_state),
|
|
outputs=[self.main_network.online_network.state_embedding,
|
|
self.main_network.online_network.output_heads[0].output]
|
|
)
|
|
|
|
# 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_game(self, do_not_reset_env=False):
|
|
super().reset_game(do_not_reset_env)
|
|
|
|
# get the last full episode that we have collected
|
|
episode = self.memory.get_last_complete_episode()
|
|
if episode is not None:
|
|
# the indexing is only necessary because the heatup can end in the middle of an episode
|
|
# this won't be required after fixing this so that when the heatup is ended, the episode is closed
|
|
returns = episode.get_transitions_attribute('total_return')[:len(self.current_episode_state_embeddings)]
|
|
actions = episode.get_transitions_attribute('action')[:len(self.current_episode_state_embeddings)]
|
|
self.main_network.online_network.output_heads[0].DND.add(self.current_episode_state_embeddings,
|
|
actions, returns)
|
|
|
|
self.current_episode_state_embeddings = []
|
|
|
|
def save_model(self, model_id):
|
|
self.main_network.save_model(model_id)
|
|
with open(os.path.join(self.tp.save_model_dir, str(model_id) + '.dnd'), 'wb') as f:
|
|
pickle.dump(self.main_network.online_network.output_heads[0].DND, f, pickle.HIGHEST_PROTOCOL)
|