coach v0.8.0

exploration_policies/bayesian.py

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+#
+# Copyright (c) 2017 Intel Corporation 
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from exploration_policies.exploration_policy import *
+import tensorflow as tf
+
+
+class Bayesian(ExplorationPolicy):
+    def __init__(self, tuning_parameters):
+        """
+        :param tuning_parameters: A Preset class instance with all the running paramaters
+        :type tuning_parameters: Preset
+        """
+        ExplorationPolicy.__init__(self, tuning_parameters)
+        self.keep_probability = tuning_parameters.exploration.initial_keep_probability
+        self.final_keep_probability = tuning_parameters.exploration.final_keep_probability
+        self.keep_probability_decay_delta = (
+                                            tuning_parameters.exploration.initial_keep_probability - tuning_parameters.exploration.final_keep_probability) \
+                                            / float(tuning_parameters.exploration.keep_probability_decay_steps)
+        self.action_space_size = tuning_parameters.env.action_space_size
+        self.network = tuning_parameters.network
+        self.epsilon = 0
+
+    def decay_keep_probability(self):
+        if (self.keep_probability > self.final_keep_probability and self.keep_probability_decay_delta > 0) \
+                or (self.keep_probability < self.final_keep_probability and self.keep_probability_decay_delta < 0):
+            self.keep_probability -= self.keep_probability_decay_delta
+
+    def get_action(self, action_values):
+        if self.phase == RunPhase.TRAIN:
+            self.decay_keep_probability()
+        # dropout = self.network.get_layer('variable_dropout_1')
+        # with tf.Session() as sess:
+        #     print(dropout.rate.eval())
+        # set_value(dropout.rate, 1-self.keep_probability)
+
+        print(self.keep_probability)
+        self.network.curr_keep_prob = self.keep_probability
+
+        return np.argmax(action_values)
+
+    def get_control_param(self):
+        return self.keep_probability