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