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57 lines
2.4 KiB
Python
57 lines
2.4 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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import tensorflow as tf
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class Bayesian(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.keep_probability = tuning_parameters.exploration.initial_keep_probability
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self.final_keep_probability = tuning_parameters.exploration.final_keep_probability
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self.keep_probability_decay_delta = (
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tuning_parameters.exploration.initial_keep_probability - tuning_parameters.exploration.final_keep_probability) \
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/ float(tuning_parameters.exploration.keep_probability_decay_steps)
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self.action_space_size = tuning_parameters.env.action_space_size
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self.network = tuning_parameters.network
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self.epsilon = 0
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def decay_keep_probability(self):
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if (self.keep_probability > self.final_keep_probability and self.keep_probability_decay_delta > 0) \
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or (self.keep_probability < self.final_keep_probability and self.keep_probability_decay_delta < 0):
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self.keep_probability -= self.keep_probability_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_keep_probability()
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# dropout = self.network.get_layer('variable_dropout_1')
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# with tf.Session() as sess:
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# print(dropout.rate.eval())
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# set_value(dropout.rate, 1-self.keep_probability)
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print(self.keep_probability)
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self.network.curr_keep_prob = self.keep_probability
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return np.argmax(action_values)
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def get_control_param(self):
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return self.keep_probability
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