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163 lines
6.9 KiB
Python
163 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 collections import OrderedDict
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from enum import Enum
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from typing import Union
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import numpy as np
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from rl_coach.agents.agent import Agent
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from rl_coach.core_types import Batch, ActionInfo
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from rl_coach.logger import screen
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from rl_coach.spaces import DiscreteActionSpace, BoxActionSpace
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from rl_coach.utils import eps
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class PolicyGradientRescaler(Enum):
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TOTAL_RETURN = 0
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FUTURE_RETURN = 1
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FUTURE_RETURN_NORMALIZED_BY_EPISODE = 2
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FUTURE_RETURN_NORMALIZED_BY_TIMESTEP = 3 # baselined
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Q_VALUE = 4
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A_VALUE = 5
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TD_RESIDUAL = 6
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DISCOUNTED_TD_RESIDUAL = 7
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GAE = 8
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## This is an abstract agent - there is no learn_from_batch method ##
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class PolicyOptimizationAgent(Agent):
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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.policy_gradient_rescaler = None
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if hasattr(self.ap.algorithm, 'policy_gradient_rescaler'):
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self.policy_gradient_rescaler = self.ap.algorithm.policy_gradient_rescaler
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# statistics for variance reduction
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self.last_gradient_update_step_idx = 0
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self.max_episode_length = 100000
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self.mean_return_over_multiple_episodes = np.zeros(self.max_episode_length)
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self.num_episodes_where_step_has_been_seen = np.zeros(self.max_episode_length)
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self.entropy = self.register_signal('Entropy')
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def log_to_screen(self):
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# log to screen
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log = OrderedDict()
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log["Name"] = self.full_name_id
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if self.task_id is not None:
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log["Worker"] = self.task_id
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log["Episode"] = self.current_episode
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log["Total reward"] = round(self.total_reward_in_current_episode, 2)
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log["Steps"] = self.total_steps_counter
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log["Training iteration"] = self.training_iteration
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screen.log_dict(log, prefix=self.phase.value)
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def update_episode_statistics(self, episode):
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episode_discounted_returns = []
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for i in range(episode.length()):
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transition = episode.get_transition(i)
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episode_discounted_returns.append(transition.n_step_discounted_rewards)
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self.num_episodes_where_step_has_been_seen[i] += 1
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self.mean_return_over_multiple_episodes[i] -= self.mean_return_over_multiple_episodes[i] / \
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self.num_episodes_where_step_has_been_seen[i]
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self.mean_return_over_multiple_episodes[i] += transition.n_step_discounted_rewards / \
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self.num_episodes_where_step_has_been_seen[i]
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self.mean_discounted_return = np.mean(episode_discounted_returns)
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self.std_discounted_return = np.std(episode_discounted_returns)
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def train(self):
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episode = self.current_episode_buffer
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# check if we should calculate gradients or skip
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num_steps_passed_since_last_update = episode.length() - self.last_gradient_update_step_idx
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is_t_max_steps_passed = num_steps_passed_since_last_update >= self.ap.algorithm.num_steps_between_gradient_updates
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if not (is_t_max_steps_passed or episode.is_complete):
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return 0
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total_loss = 0
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if num_steps_passed_since_last_update > 0:
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for network in self.networks.values():
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network.set_is_training(True)
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# we need to update the returns of the episode until now
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episode.update_transitions_rewards_and_bootstrap_data()
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# get t_max transitions or less if the we got to a terminal state
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# will be used for both actor-critic and vanilla PG.
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# In order to get full episodes, Vanilla PG will set the end_idx to a very big value.
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transitions = episode[self.last_gradient_update_step_idx:]
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batch = Batch(transitions)
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# move the pointer for the last update step
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if episode.is_complete:
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self.last_gradient_update_step_idx = 0
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else:
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self.last_gradient_update_step_idx = episode.length()
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# update the statistics for the variance reduction techniques
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if self.policy_gradient_rescaler in \
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[PolicyGradientRescaler.FUTURE_RETURN_NORMALIZED_BY_EPISODE,
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PolicyGradientRescaler.FUTURE_RETURN_NORMALIZED_BY_TIMESTEP]:
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self.update_episode_statistics(episode)
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# accumulate the gradients
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total_loss, losses, unclipped_grads = self.learn_from_batch(batch)
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# apply the gradients once in every apply_gradients_every_x_episodes episodes
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if self.current_episode % self.ap.algorithm.apply_gradients_every_x_episodes == 0:
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for network in self.networks.values():
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network.apply_gradients_and_sync_networks()
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self.training_iteration += 1
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for network in self.networks.values():
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network.set_is_training(False)
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# run additional commands after the training is done
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self.post_training_commands()
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return total_loss
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def learn_from_batch(self, batch):
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raise NotImplementedError("PolicyOptimizationAgent is an abstract agent. Not to be used directly.")
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def get_prediction(self, states):
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tf_input_state = self.prepare_batch_for_inference(states, "main")
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return self.networks['main'].online_network.predict(tf_input_state)
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def choose_action(self, curr_state):
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# convert to batch so we can run it through the network
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action_values = self.get_prediction(curr_state)
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if isinstance(self.spaces.action, DiscreteActionSpace):
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# DISCRETE
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action_probabilities = np.array(action_values).squeeze()
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action = self.exploration_policy.get_action(action_probabilities)
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action_info = ActionInfo(action=action,
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action_probability=action_probabilities[action])
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self.entropy.add_sample(-np.sum(action_probabilities * np.log(action_probabilities + eps)))
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elif isinstance(self.spaces.action, BoxActionSpace):
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# CONTINUOUS
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action = self.exploration_policy.get_action(action_values)
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action_info = ActionInfo(action=action)
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else:
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raise ValueError("The action space of the environment is not compatible with the algorithm")
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return action_info
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