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49 lines
2.1 KiB
Python
49 lines
2.1 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 Boltzmann(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.temperature = tuning_parameters.exploration.initial_temperature
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self.final_temperature = tuning_parameters.exploration.final_temperature
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self.temperature_decay_delta = (
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tuning_parameters.exploration.initial_temperature - tuning_parameters.exploration.final_temperature) \
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/ float(tuning_parameters.exploration.temperature_decay_steps)
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def decay_temperature(self):
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if self.temperature > self.final_temperature:
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self.temperature -= self.temperature_decay_delta
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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_temperature()
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# softmax calculation
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exp_probabilities = np.exp(action_values / self.temperature)
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probabilities = exp_probabilities / np.sum(exp_probabilities)
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probabilities[-1] = 1 - np.sum(probabilities[:-1]) # make sure probs sum to 1
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# choose actions according to the probabilities
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return np.random.choice(range(self.action_space_size), p=probabilities)
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def get_control_param(self):
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return self.temperature
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