mirror of
https://github.com/gryf/coach.git
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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
97 lines
4.3 KiB
Python
97 lines
4.3 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 numpy as np
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from agents.value_optimization_agent import ValueOptimizationAgent
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from logger import screen
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from utils import RunPhase
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# Neural Episodic Control - https://arxiv.org/pdf/1703.01988.pdf
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class NECAgent(ValueOptimizationAgent):
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def __init__(self, env, tuning_parameters, replicated_device=None, thread_id=0):
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ValueOptimizationAgent.__init__(self, env, tuning_parameters, replicated_device, thread_id,
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create_target_network=False)
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self.current_episode_state_embeddings = []
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self.training_started = False
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def learn_from_batch(self, batch):
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if not self.main_network.online_network.output_heads[0].DND.has_enough_entries(self.tp.agent.number_of_knn):
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return 0
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else:
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if not self.training_started:
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self.training_started = True
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screen.log_title("Finished collecting initial entries in DND. Starting to train network...")
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current_states, next_states, actions, rewards, game_overs, total_return = self.extract_batch(batch)
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TD_targets = self.main_network.online_network.predict(current_states)
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# only update the action that we have actually done in this transition
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for i in range(self.tp.batch_size):
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TD_targets[i, actions[i]] = total_return[i]
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# train the neural network
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result = self.main_network.train_and_sync_networks(current_states, TD_targets)
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total_loss = result[0]
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return total_loss
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def act(self, phase=RunPhase.TRAIN):
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if self.in_heatup:
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# get embedding in heatup (otherwise we get it through choose_action)
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embedding = self.main_network.online_network.predict(
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self.tf_input_state(self.curr_state),
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outputs=self.main_network.online_network.state_embedding)
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self.current_episode_state_embeddings.append(embedding)
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return super().act(phase)
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def get_prediction(self, curr_state):
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# get the actions q values and the state embedding
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embedding, actions_q_values = self.main_network.online_network.predict(
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self.tf_input_state(curr_state),
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outputs=[self.main_network.online_network.state_embedding,
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self.main_network.online_network.output_heads[0].output]
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)
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# store the state embedding for inserting it to the DND later
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self.current_episode_state_embeddings.append(embedding.squeeze())
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actions_q_values = actions_q_values[0][0]
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return actions_q_values
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def reset_game(self, do_not_reset_env=False):
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super().reset_game(do_not_reset_env)
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# get the last full episode that we have collected
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episode = self.memory.get_last_complete_episode()
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if episode is not None:
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# the indexing is only necessary because the heatup can end in the middle of an episode
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# this won't be required after fixing this so that when the heatup is ended, the episode is closed
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returns = episode.get_transitions_attribute('total_return')[:len(self.current_episode_state_embeddings)]
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actions = episode.get_transitions_attribute('action')[:len(self.current_episode_state_embeddings)]
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self.main_network.online_network.output_heads[0].DND.add(self.current_episode_state_embeddings,
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actions, returns)
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self.current_episode_state_embeddings = []
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def save_model(self, model_id):
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self.main_network.save_model(model_id)
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with open(os.path.join(self.tp.save_model_dir, str(model_id) + '.dnd'), 'wb') as f:
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pickle.dump(self.main_network.online_network.output_heads[0].DND, f, pickle.HIGHEST_PROTOCOL)
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