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71 lines
3.1 KiB
Python
71 lines
3.1 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 exploration_policies.exploration_policy import *
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class EGreedy(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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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 == RunPhase.TRAIN:
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self.decay_exploration()
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epsilon = self.evaluation_epsilon if self.phase == 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.epsilon
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