coach v0.8.0

exploration_policies/e_greedy.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 *
+
+
+class EGreedy(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.epsilon = tuning_parameters.exploration.initial_epsilon
+        self.final_epsilon = tuning_parameters.exploration.final_epsilon
+        self.epsilon_decay_delta = (
+                                   tuning_parameters.exploration.initial_epsilon - tuning_parameters.exploration.final_epsilon) \
+                                   / float(tuning_parameters.exploration.epsilon_decay_steps)
+        self.evaluation_epsilon = tuning_parameters.exploration.evaluation_epsilon
+
+        # for continuous e-greedy (see http://www.cs.ubc.ca/~van/papers/2017-TOG-deepLoco/2017-TOG-deepLoco.pdf)
+        self.variance = tuning_parameters.exploration.initial_noise_variance_percentage
+        self.final_variance = tuning_parameters.exploration.final_noise_variance_percentage
+        self.decay_steps = tuning_parameters.exploration.noise_variance_decay_steps
+        self.variance_decay_delta = (self.variance - self.final_variance) / float(self.decay_steps)
+
+    def decay_exploration(self):
+        # decay epsilon
+        if self.epsilon > self.final_epsilon:
+            self.epsilon -= self.epsilon_decay_delta
+        elif self.epsilon < self.final_epsilon:
+            self.epsilon = self.final_epsilon
+
+        # decay noise variance
+        if not self.discrete_controls:
+            if self.variance > self.final_variance:
+                self.variance -= self.variance_decay_delta
+            elif self.variance < self.final_variance:
+                self.variance = self.final_variance
+
+    def get_action(self, action_values):
+        if self.phase == RunPhase.TRAIN:
+            self.decay_exploration()
+        epsilon = self.evaluation_epsilon if self.phase == RunPhase.TEST else self.epsilon
+
+        if self.discrete_controls:
+            top_action = np.argmax(action_values)
+            if np.random.rand() < epsilon:
+                return np.random.randint(self.action_space_size)
+            else:
+                return top_action
+        else:
+            noise = np.random.randn(1, self.action_space_size) * self.variance * self.action_abs_range
+            return np.squeeze(action_values + (np.random.rand() < epsilon) * noise)
+
+    def get_control_param(self):
+        return self.epsilon