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178 lines
8.0 KiB
Python
178 lines
8.0 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 os
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import pickle
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from typing import Union
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import numpy as np
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from rl_coach.agents.value_optimization_agent import ValueOptimizationAgent
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from rl_coach.architectures.tensorflow_components.heads.dnd_q_head import DNDQHeadParameters
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from rl_coach.architectures.tensorflow_components.middlewares.fc_middleware import FCMiddlewareParameters
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from rl_coach.base_parameters import AlgorithmParameters, NetworkParameters, AgentParameters
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from rl_coach.architectures.tensorflow_components.embedders.embedder import InputEmbedderParameters
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from rl_coach.core_types import RunPhase, EnvironmentSteps, Episode, StateType
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from rl_coach.exploration_policies.e_greedy import EGreedyParameters
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from rl_coach.logger import screen
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from rl_coach.memories.episodic.episodic_experience_replay import EpisodicExperienceReplayParameters, MemoryGranularity
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from rl_coach.schedules import ConstantSchedule
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class NECNetworkParameters(NetworkParameters):
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def __init__(self):
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super().__init__()
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self.input_embedders_parameters = {'observation': InputEmbedderParameters()}
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self.middleware_parameters = FCMiddlewareParameters()
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self.heads_parameters = [DNDQHeadParameters()]
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self.loss_weights = [1.0]
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self.rescale_gradient_from_head_by_factor = [1]
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self.optimizer_type = 'Adam'
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class NECAlgorithmParameters(AlgorithmParameters):
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def __init__(self):
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super().__init__()
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self.dnd_size = 500000
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self.l2_norm_added_delta = 0.001
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self.new_value_shift_coefficient = 0.1
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self.number_of_knn = 50
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self.DND_key_error_threshold = 0
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self.num_consecutive_playing_steps = EnvironmentSteps(4)
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self.propagate_updates_to_DND = False
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self.n_step = 100
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self.bootstrap_total_return_from_old_policy = True
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class NECMemoryParameters(EpisodicExperienceReplayParameters):
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def __init__(self):
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super().__init__()
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self.max_size = (MemoryGranularity.Transitions, 100000)
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class NECAgentParameters(AgentParameters):
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def __init__(self):
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super().__init__(algorithm=NECAlgorithmParameters(),
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exploration=EGreedyParameters(),
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memory=NECMemoryParameters(),
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networks={"main": NECNetworkParameters()})
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self.exploration.epsilon_schedule = ConstantSchedule(0.1)
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self.exploration.evaluation_epsilon = 0.01
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@property
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def path(self):
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return 'rl_coach.agents.nec_agent:NECAgent'
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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, agent_parameters, parent: Union['LevelManager', 'CompositeAgent']=None):
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super().__init__(agent_parameters, parent)
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self.current_episode_state_embeddings = []
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self.training_started = False
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self.current_episode_buffer = \
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Episode(discount=self.ap.algorithm.discount,
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n_step=self.ap.algorithm.n_step,
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bootstrap_total_return_from_old_policy=self.ap.algorithm.bootstrap_total_return_from_old_policy)
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def learn_from_batch(self, batch):
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if not self.networks['main'].online_network.output_heads[0].DND.has_enough_entries(self.ap.algorithm.number_of_knn):
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return 0, [], 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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network_keys = self.ap.network_wrappers['main'].input_embedders_parameters.keys()
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TD_targets = self.networks['main'].online_network.predict(batch.states(network_keys))
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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.ap.network_wrappers['main'].batch_size):
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TD_targets[i, batch.actions()[i]] = batch.total_returns()[i]
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# set the gradients to fetch for the DND update
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fetches = []
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head = self.networks['main'].online_network.output_heads[0]
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if self.ap.algorithm.propagate_updates_to_DND:
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fetches = [head.dnd_embeddings_grad, head.dnd_values_grad, head.dnd_indices]
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# train the neural network
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result = self.networks['main'].train_and_sync_networks(batch.states(network_keys), TD_targets, fetches)
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total_loss, losses, unclipped_grads = result[:3]
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# update the DND keys and values using the extracted gradients
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if self.ap.algorithm.propagate_updates_to_DND:
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embedding_gradients = np.swapaxes(result[-1][0], 0, 1)
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value_gradients = np.swapaxes(result[-1][1], 0, 1)
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indices = np.swapaxes(result[-1][2], 0, 1)
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head.DND.update_keys_and_values(batch.actions(), embedding_gradients, value_gradients, indices)
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return total_loss, losses, unclipped_grads
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def act(self):
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if self.phase == RunPhase.HEATUP:
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# get embedding in heatup (otherwise we get it through get_prediction)
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embedding = self.networks['main'].online_network.predict(
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self.prepare_batch_for_inference(self.curr_state, 'main'),
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outputs=self.networks['main'].online_network.state_embedding)
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self.current_episode_state_embeddings.append(embedding)
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return super().act()
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def get_all_q_values_for_states(self, states: StateType):
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# we need to store the state embeddings regardless if the action is random or not
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return self.get_prediction(states)
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def get_prediction(self, states):
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# get the actions q values and the state embedding
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embedding, actions_q_values = self.networks['main'].online_network.predict(
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self.prepare_batch_for_inference(states, 'main'),
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outputs=[self.networks['main'].online_network.state_embedding,
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self.networks['main'].online_network.output_heads[0].output]
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)
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if self.phase != RunPhase.TEST:
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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_internal_state(self):
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super().reset_internal_state()
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self.current_episode_state_embeddings = []
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self.current_episode_buffer = \
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Episode(discount=self.ap.algorithm.discount,
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n_step=self.ap.algorithm.n_step,
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bootstrap_total_return_from_old_policy=self.ap.algorithm.bootstrap_total_return_from_old_policy)
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def handle_episode_ended(self):
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super().handle_episode_ended()
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# get the last full episode that we have collected
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episode = self.call_memory('get_last_complete_episode')
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if episode is not None and self.phase != RunPhase.TEST:
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assert len(self.current_episode_state_embeddings) == episode.length()
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returns = episode.get_transitions_attribute('total_return')
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actions = episode.get_transitions_attribute('action')
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self.networks['main'].online_network.output_heads[0].DND.add(self.current_episode_state_embeddings,
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actions, returns)
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def save_checkpoint(self, checkpoint_id):
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with open(os.path.join(self.ap.task_parameters.save_checkpoint_dir, str(checkpoint_id) + '.dnd'), 'wb') as f:
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pickle.dump(self.networks['main'].online_network.output_heads[0].DND, f, pickle.HIGHEST_PROTOCOL)
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