coach v0.8.0

exploration_policies/ou_process.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.
+#
+
+import numpy as np
+from exploration_policies.exploration_policy import *
+
+# Based on on the description in:
+# https://math.stackexchange.com/questions/1287634/implementing-ornstein-uhlenbeck-in-matlab
+
+# Ornstein-Uhlenbeck process
+class OUProcess(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.action_space_size = tuning_parameters.env.action_space_size
+        self.mu = float(tuning_parameters.exploration.mu) * np.ones(self.action_space_size)
+        self.theta = tuning_parameters.exploration.theta
+        self.sigma = float(tuning_parameters.exploration.sigma) * np.ones(self.action_space_size)
+        self.state = np.zeros(self.action_space_size)
+        self.dt = tuning_parameters.exploration.dt
+
+    def reset(self):
+        self.state = np.zeros(self.action_space_size)
+
+    def noise(self):
+        x = self.state
+        dx = self.theta * (self.mu - x) * self.dt + self.sigma * np.random.randn(len(x)) * np.sqrt(self.dt)
+        self.state = x + dx
+        return self.state[0]
+
+    def get_action(self, action_values):
+        noise = self.noise()
+        return action_values.squeeze() + noise
+
+    def get_control_param(self):
+        return self.state[0]