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82 lines
2.8 KiB
Python
82 lines
2.8 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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from typing import List
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import numpy as np
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from rl_coach.core_types import RunPhase, ActionType
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from rl_coach.exploration_policies.exploration_policy import ExplorationPolicy, ExplorationParameters
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from rl_coach.spaces import ActionSpace, BoxActionSpace, GoalsSpace
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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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class OUProcessParameters(ExplorationParameters):
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def __init__(self):
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super().__init__()
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self.mu = 0
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self.theta = 0.15
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self.sigma = 0.2
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self.dt = 0.01
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@property
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def path(self):
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return 'rl_coach.exploration_policies.ou_process:OUProcess'
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# Ornstein-Uhlenbeck process
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class OUProcess(ExplorationPolicy):
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def __init__(self, action_space: ActionSpace, mu: float=0, theta: float=0.15, sigma: float=0.2, dt: float=0.01):
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"""
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:param action_space: the action space used by the environment
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"""
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super().__init__(action_space)
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self.mu = float(mu) * np.ones(self.action_space.shape)
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self.theta = float(theta)
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self.sigma = float(sigma) * np.ones(self.action_space.shape)
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self.state = np.zeros(self.action_space.shape)
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self.dt = dt
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if not (isinstance(action_space, BoxActionSpace) or isinstance(action_space, GoalsSpace)):
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raise ValueError("OU process exploration works only for continuous controls."
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"The given action space is of type: {}".format(action_space.__class__.__name__))
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def reset(self):
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self.state = np.zeros(self.action_space.shape)
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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
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def get_action(self, action_values: List[ActionType]) -> ActionType:
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if self.phase == RunPhase.TRAIN:
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noise = self.noise()
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else:
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noise = np.zeros(self.action_space.shape)
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action = action_values.squeeze() + noise
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return action
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def get_control_param(self):
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if self.phase == RunPhase.TRAIN:
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return self.state
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else:
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return np.zeros(self.action_space.shape)
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