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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
73 lines
3.2 KiB
Python
73 lines
3.2 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 exploration_policies import exploration_policy
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import utils
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class EGreedy(exploration_policy.ExplorationPolicy):
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def __init__(self, tuning_parameters):
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"""
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:param tuning_parameters: A Preset class instance with all the running paramaters
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:type tuning_parameters: Preset
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"""
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exploration_policy.ExplorationPolicy.__init__(self, tuning_parameters)
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self.epsilon = tuning_parameters.exploration.initial_epsilon
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self.final_epsilon = tuning_parameters.exploration.final_epsilon
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self.epsilon_decay_delta = (
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tuning_parameters.exploration.initial_epsilon - tuning_parameters.exploration.final_epsilon) \
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/ float(tuning_parameters.exploration.epsilon_decay_steps)
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self.evaluation_epsilon = tuning_parameters.exploration.evaluation_epsilon
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# for continuous e-greedy (see http://www.cs.ubc.ca/~van/papers/2017-TOG-deepLoco/2017-TOG-deepLoco.pdf)
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self.variance = tuning_parameters.exploration.initial_noise_variance_percentage
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self.final_variance = tuning_parameters.exploration.final_noise_variance_percentage
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self.decay_steps = tuning_parameters.exploration.noise_variance_decay_steps
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self.variance_decay_delta = (self.variance - self.final_variance) / float(self.decay_steps)
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def decay_exploration(self):
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# decay epsilon
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if self.epsilon > self.final_epsilon:
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self.epsilon -= self.epsilon_decay_delta
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elif self.epsilon < self.final_epsilon:
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self.epsilon = self.final_epsilon
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# decay noise variance
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if not self.discrete_controls:
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if self.variance > self.final_variance:
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self.variance -= self.variance_decay_delta
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elif self.variance < self.final_variance:
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self.variance = self.final_variance
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def get_action(self, action_values):
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if self.phase == utils.RunPhase.TRAIN:
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self.decay_exploration()
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epsilon = self.evaluation_epsilon if self.phase == utils.RunPhase.TEST else self.epsilon
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if self.discrete_controls:
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top_action = np.argmax(action_values)
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if np.random.rand() < epsilon:
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return np.random.randint(self.action_space_size)
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else:
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return top_action
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else:
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noise = np.random.randn(1, self.action_space_size) * self.variance * self.action_abs_range
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return np.squeeze(action_values + (np.random.rand() < epsilon) * noise)
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def get_control_param(self):
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return self.evaluation_epsilon if self.phase == utils.RunPhase.TEST else self.epsilon
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