coach v0.8.0

exploration_policies/boltzmann.py

@@ -0,0 +1,48 @@
+#
+# 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 *
+
+
+class Boltzmann(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.temperature = tuning_parameters.exploration.initial_temperature
+        self.final_temperature = tuning_parameters.exploration.final_temperature
+        self.temperature_decay_delta = (
+                                       tuning_parameters.exploration.initial_temperature - tuning_parameters.exploration.final_temperature) \
+                                       / float(tuning_parameters.exploration.temperature_decay_steps)
+
+    def decay_temperature(self):
+        if self.temperature > self.final_temperature:
+            self.temperature -= self.temperature_decay_delta
+
+    def get_action(self, action_values):
+        if self.phase == RunPhase.TRAIN:
+            self.decay_temperature()
+        # softmax calculation
+        exp_probabilities = np.exp(action_values / self.temperature)
+        probabilities = exp_probabilities / np.sum(exp_probabilities)
+        probabilities[-1] = 1 - np.sum(probabilities[:-1])  # make sure probs sum to 1
+        # choose actions according to the probabilities
+        return np.random.choice(range(self.action_space_size), p=probabilities)
+
+    def get_control_param(self):
+        return self.temperature