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110 lines
3.5 KiB
Python
110 lines
3.5 KiB
Python
# pylint: disable=g-bad-file-header
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# Copyright 2019 DeepMind Technologies Limited. All Rights Reserved.
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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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"""Threaded batch environment wrapper."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from concurrent import futures
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from six.moves import range
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from six.moves import zip
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from tvt import nest_utils
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class BatchEnv(object):
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"""Wrapper that steps multiple environments in separate threads.
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The threads are stepped in lock step, so all threads progress by one step
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before any move to the next step.
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"""
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def __init__(self, batch_size, env_builder, **env_kwargs):
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self.batch_size = batch_size
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self._envs = [env_builder(**env_kwargs) for _ in range(batch_size)]
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self._num_actions = self._envs[0].num_actions
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self._observation_shape = self._envs[0].observation_shape
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self._episode_length = self._envs[0].episode_length
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self._executor = futures.ThreadPoolExecutor(max_workers=self.batch_size)
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def reset(self):
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"""Reset the entire batch of environments."""
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def reset_environment(env):
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return env.reset()
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try:
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output_list = []
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for env in self._envs:
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output_list.append(self._executor.submit(reset_environment, env))
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output_list = [env_output.result() for env_output in output_list]
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except KeyboardInterrupt:
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self._executor.shutdown(wait=True)
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raise
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observations, rewards = nest_utils.nest_stack(output_list)
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return observations, rewards
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def step(self, action_list):
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"""Step batch of envs.
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Args:
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action_list: A list of actions, one per environment in the batch. Each one
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should be a scalar int or a numpy scaler int.
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Returns:
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A tuple (observations, rewards):
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observations: A nest of observations, each one a numpy array where the
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first dimension has size equal to the number of environments in the
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batch.
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rewards: An array of rewards with size equal to the number of
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environments in the batch.
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"""
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def step_environment(env, action):
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return env.step(action)
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try:
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output_list = []
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for env, action in zip(self._envs, action_list):
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output_list.append(self._executor.submit(step_environment, env, action))
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output_list = [env_output.result() for env_output in output_list]
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except KeyboardInterrupt:
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self._executor.shutdown(wait=True)
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raise
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observations, rewards = nest_utils.nest_stack(output_list)
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return observations, rewards
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@property
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def observation_shape(self):
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"""Observation shape per environment, i.e. with no batch dimension."""
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return self._observation_shape
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@property
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def num_actions(self):
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return self._num_actions
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@property
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def episode_length(self):
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return self._episode_length
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def last_phase_rewards(self):
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return [env.last_phase_reward() for env in self._envs]
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