mirror of
https://github.com/gryf/coach.git
synced 2025-12-17 11:10:20 +01:00
a7206ed702
* Multiple improvements and bug fixes:
* Using lazy stacking to save on memory when using a replay buffer
* Remove step counting for evaluation episodes
* Reset game between heatup and training
* Major bug fixes in NEC (is reproducing the paper results for pong now)
* Image input rescaling to 0-1 is now optional
* Change the terminal title to be the experiment name
* Observation cropping for atari is now optional
* Added random number of noop actions for gym to match the dqn paper
* Fixed a bug where the evaluation episodes won't start with the max possible ale lives
* Added a script for plotting the results of an experiment over all the atari games
177 lines
6.9 KiB
Python
177 lines
6.9 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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from memories.memory import *
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import threading
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from typing import Union
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class EpisodicExperienceReplay(Memory):
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def __init__(self, tuning_parameters):
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"""
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:param tuning_parameters: A Preset class instance with all the running paramaters
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:type tuning_parameters: Preset
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"""
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Memory.__init__(self, tuning_parameters)
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self.tp = tuning_parameters
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self.max_size_in_episodes = tuning_parameters.agent.num_episodes_in_experience_replay
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self.max_size_in_transitions = tuning_parameters.agent.num_transitions_in_experience_replay
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self.discount = tuning_parameters.agent.discount
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self.buffer = [Episode()] # list of episodes
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self.transitions = []
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self._length = 1
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self._num_transitions = 0
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self._num_transitions_in_complete_episodes = 0
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self.return_is_bootstrapped = tuning_parameters.agent.bootstrap_total_return_from_old_policy
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def length(self):
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""" Get the number of episodes in the ER (even if they are not complete) """
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if self._length is not 0 and self.buffer[-1].is_empty():
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return self._length - 1
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return self._length
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def num_complete_episodes(self):
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""" Get the number of complete episodes in ER """
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return self._length - 1
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def num_transitions(self):
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return self._num_transitions
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def num_transitions_in_complete_episodes(self):
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return self._num_transitions_in_complete_episodes
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def sample_episode(self):
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episode_idx = np.random.randint(self.num_complete_episodes())
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return self.buffer[episode_idx]
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def sample_n_episodes(self, n):
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num_n_episodes = (self.num_complete_episodes()) / n
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assert num_n_episodes > 0, \
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'Tried sampling {} episodes when only {} completed episodes are available in the memory' \
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.format(n, self.num_complete_episodes())
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start_episode_idx = np.random.randint(num_n_episodes) * n
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return self.buffer[start_episode_idx:(start_episode_idx + n)]
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def sample_last_n_episodes(self, n):
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num_n_episodes = (self.num_complete_episodes()) / n
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assert num_n_episodes > 0, \
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'Tried sampling {} episodes when only {} completed episodes are available in the memory' \
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.format(n, self.num_complete_episodes())
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start_episode_idx = -n
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return self.buffer[start_episode_idx:(start_episode_idx + n)]
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def sample(self, size):
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assert self.num_transitions_in_complete_episodes() > size, \
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'There are not enough transitions in the replay buffer. ' \
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'Available transitions: {}. Requested transitions: {}.'\
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.format(self.num_transitions_in_complete_episodes(), size)
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batch = []
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transitions_idx = np.random.randint(self.num_transitions_in_complete_episodes(), size=size)
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for i in transitions_idx:
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batch.append(self.transitions[i])
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return batch
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def enforce_length(self):
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# clean up if necessary
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if self.max_size_in_transitions is not None:
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while self.max_size_in_transitions != 0 and self.num_transitions() > self.max_size_in_transitions:
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self.remove_episode(0)
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else:
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while self.length() > self.max_size_in_episodes:
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self.remove_episode(0)
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def store(self, transition):
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if len(self.buffer) == 0:
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self.buffer.append(Episode())
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last_episode = self.buffer[-1]
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last_episode.insert(transition)
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self.transitions.append(transition)
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self._num_transitions += 1
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if transition.game_over:
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self._num_transitions_in_complete_episodes += last_episode.length()
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self._length += 1
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self.buffer[-1].update_returns(self.discount,
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is_bootstrapped=self.tp.agent.bootstrap_total_return_from_old_policy,
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n_step_return=self.tp.agent.n_step)
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self.buffer[-1].update_measurements_targets(self.tp.agent.num_predicted_steps_ahead)
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# self.buffer[-1].update_actions_probabilities() # used for off-policy policy optimization
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self.buffer.append(Episode())
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self.enforce_length()
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def insert_full_episode(self, episode):
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# Do not add a new episode if the last one is not closed yet
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if self.buffer[-1].get_last_transition().done != True:
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return False
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episode.update_returns(self.discount)
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episode.update_measurements_targets(self.tp.agent.num_predicted_steps_ahead)
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self.buffer.append(episode)
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self.transitions += episode.transitions
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self._length += 1
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self._num_transitions += episode.length()
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self.enforce_length()
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return True
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def get_episode(self, episode_index):
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if self.length() == 0:
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return None
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episode = self.buffer[episode_index]
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return episode
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def remove_episode(self, episode_index):
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if len(self.buffer) > episode_index:
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episode_length = self.buffer[episode_index].length()
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self._length -= 1
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self._num_transitions -= episode_length
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self._num_transitions_in_complete_episodes -= episode_length
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del self.transitions[:episode_length]
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del self.buffer[episode_index]
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# for API compatibility
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def get(self, index):
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return self.get_episode(index)
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def get_last_complete_episode(self) -> Union[None, Episode]:
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"""
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Returns the last complete episode in the memory or None if there are no complete episodes
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:return: None or the last complete episode
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"""
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last_complete_episode_index = self.num_complete_episodes()-1
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if last_complete_episode_index >= 0:
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return self.get(last_complete_episode_index)
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else:
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return None
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def update_last_transition_info(self, info):
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episode = self.buffer[-1]
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if episode.length() == 0:
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if len(self.buffer) < 2:
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return
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episode = self.buffer[-2]
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for key, val in info.items():
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episode.transitions[-1].info[key] = val
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def clean(self):
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self.transitions = []
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self.buffer = [Episode()]
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self._length = 1
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self._num_transitions = 0
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self._num_transitions_in_complete_episodes = 0
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