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1b095aeeca
Till now, most of the modules were importing all of the module objects (variables, classes, functions, other imports) into module namespace, which potentially could (and was) cause of unintentional use of class or methods, which was indirect imported. With this patch, all the star imports were substituted with top-level module, which provides desired class or function. Besides, all imports where sorted (where possible) in a way pep8[1] suggests - first are imports from standard library, than goes third party imports (like numpy, tensorflow etc) and finally coach modules. All of those sections are separated by one empty line. [1] https://www.python.org/dev/peps/pep-0008/#imports
87 lines
4.0 KiB
Python
87 lines
4.0 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 numpy as np
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from agents import policy_optimization_agent as poa
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import logger
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import utils
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class PolicyGradientsAgent(poa.PolicyOptimizationAgent):
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def __init__(self, env, tuning_parameters, replicated_device=None, thread_id=0):
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poa.PolicyOptimizationAgent.__init__(self, env, tuning_parameters, replicated_device, thread_id)
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self.returns_mean = utils.Signal('Returns Mean')
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self.returns_variance = utils.Signal('Returns Variance')
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self.signals.append(self.returns_mean)
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self.signals.append(self.returns_variance)
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self.last_gradient_update_step_idx = 0
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def learn_from_batch(self, batch):
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# batch contains a list of episodes to learn from
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current_states, next_states, actions, rewards, game_overs, total_returns = self.extract_batch(batch)
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for i in reversed(range(len(total_returns))):
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if self.policy_gradient_rescaler == poa.PolicyGradientRescaler.TOTAL_RETURN:
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total_returns[i] = total_returns[0]
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elif self.policy_gradient_rescaler == poa.PolicyGradientRescaler.FUTURE_RETURN:
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# just take the total return as it is
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pass
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elif self.policy_gradient_rescaler == poa.PolicyGradientRescaler.FUTURE_RETURN_NORMALIZED_BY_EPISODE:
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# we can get a single transition episode while playing Doom Basic, causing the std to be 0
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if self.std_discounted_return != 0:
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total_returns[i] = (total_returns[i] - self.mean_discounted_return) / self.std_discounted_return
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else:
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total_returns[i] = 0
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elif self.policy_gradient_rescaler == poa.PolicyGradientRescaler.FUTURE_RETURN_NORMALIZED_BY_TIMESTEP:
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total_returns[i] -= self.mean_return_over_multiple_episodes[i]
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else:
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logger.screen.warning("WARNING: The requested policy gradient rescaler is not available")
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targets = total_returns
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if not self.env.discrete_controls and len(actions.shape) < 2:
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actions = np.expand_dims(actions, -1)
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self.returns_mean.add_sample(np.mean(total_returns))
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self.returns_variance.add_sample(np.std(total_returns))
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result = self.main_network.online_network.accumulate_gradients({**current_states, 'output_0_0': actions}, targets)
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total_loss = result[0]
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return total_loss
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def choose_action(self, curr_state, phase=utils.RunPhase.TRAIN):
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# convert to batch so we can run it through the network
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if self.env.discrete_controls:
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# DISCRETE
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action_values = self.main_network.online_network.predict(self.tf_input_state(curr_state)).squeeze()
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if phase == utils.RunPhase.TRAIN:
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action = self.exploration_policy.get_action(action_values)
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else:
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action = np.argmax(action_values)
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action_value = {"action_probability": action_values[action]}
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self.entropy.add_sample(-np.sum(action_values * np.log(action_values + eps)))
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else:
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# CONTINUOUS
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result = self.main_network.online_network.predict(self.tf_input_state(curr_state))
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action_values = result[0].squeeze()
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if phase == utils.RunPhase.TRAIN:
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action = self.exploration_policy.get_action(action_values)
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else:
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action = action_values
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action_value = {}
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return action, action_value
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