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124 lines
5.3 KiB
Python
124 lines
5.3 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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from agents.agent import *
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from memories.memory import Episode
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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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class PolicyOptimizationAgent(Agent):
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def __init__(self, env, tuning_parameters, replicated_device=None, thread_id=0, create_target_network=False):
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Agent.__init__(self, env, tuning_parameters, replicated_device, thread_id)
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self.main_network = NetworkWrapper(tuning_parameters, create_target_network, self.has_global, 'main',
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self.replicated_device, self.worker_device)
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self.networks.append(self.main_network)
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self.policy_gradient_rescaler = PolicyGradientRescaler().get(self.tp.agent.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 = Signal('Entropy')
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self.signals.append(self.entropy)
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self.reset_game(do_not_reset_env=True)
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def log_to_screen(self, phase):
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# log to screen
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if self.current_episode > 0:
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screen.log_dict(
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OrderedDict([
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("Worker", self.task_id),
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("Episode", self.current_episode),
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("total reward", self.total_reward_in_current_episode),
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("steps", self.total_steps_counter),
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("training iteration", self.training_iteration)
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]),
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prefix=phase
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)
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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.total_return)
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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.total_return / \
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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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if self.memory.length() == 0:
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return 0
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episode = self.memory.get_episode(0)
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# check if we should calculate gradients or skip
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episode_ended = self.memory.num_complete_episodes() >= 1
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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.tp.agent.num_steps_between_gradient_updates
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if not (is_t_max_steps_passed or episode_ended):
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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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# we need to update the returns of the episode until now
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episode.update_returns(self.tp.agent.discount)
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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 = []
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start_idx = self.last_gradient_update_step_idx
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end_idx = episode.length()
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for idx in range(start_idx, end_idx):
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transitions.append(episode.get_transition(idx))
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self.last_gradient_update_step_idx = end_idx
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# update the statistics for the variance reduction techniques
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if self.tp.agent.type == 'PolicyGradientsAgent':
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self.update_episode_statistics(episode)
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# accumulate the gradients and apply them once in every apply_gradients_every_x_episodes episodes
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total_loss = self.learn_from_batch(transitions)
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if self.current_episode % self.tp.agent.apply_gradients_every_x_episodes == 0:
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self.main_network.apply_gradients_and_sync_networks()
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# move the pointer to the next episode start and discard the episode. we use it only once
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if episode_ended:
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self.memory.remove_episode(0)
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self.last_gradient_update_step_idx = 0
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return total_loss
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