Source code for rl_coach.exploration_policies.ou_process

# 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
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.

from typing import List

import numpy as np

from rl_coach.core_types import RunPhase, ActionType
from rl_coach.exploration_policies.exploration_policy import ContinuousActionExplorationPolicy, ExplorationParameters
from rl_coach.spaces import ActionSpace, BoxActionSpace, GoalsSpace

# Based on on the description in:

class OUProcessParameters(ExplorationParameters):
    def __init__(self):
        super().__init__() = 0
        self.theta = 0.15
        self.sigma = 0.2
        self.dt = 0.01

    def path(self):
        return 'rl_coach.exploration_policies.ou_process:OUProcess'

# Ornstein-Uhlenbeck process
[docs]class OUProcess(ContinuousActionExplorationPolicy): """ OUProcess exploration policy is intended for continuous action spaces, and selects the action according to an Ornstein-Uhlenbeck process. The Ornstein-Uhlenbeck process implements the action as a Gaussian process, where the samples are correlated between consequent time steps. """ def __init__(self, action_space: ActionSpace, mu: float=0, theta: float=0.15, sigma: float=0.2, dt: float=0.01): """ :param action_space: the action space used by the environment """ super().__init__(action_space) = float(mu) * np.ones(self.action_space.shape) self.theta = float(theta) self.sigma = float(sigma) * np.ones(self.action_space.shape) self.state = np.zeros(self.action_space.shape) self.dt = dt def reset(self): self.state = np.zeros(self.action_space.shape) def noise(self): x = self.state dx = self.theta * ( - x) * self.dt + self.sigma * np.random.randn(len(x)) * np.sqrt(self.dt) self.state = x + dx return self.state def get_action(self, action_values: List[ActionType]) -> ActionType: if self.phase == RunPhase.TRAIN: noise = self.noise() else: noise = np.zeros(self.action_space.shape) action = action_values.squeeze() + noise return action def get_control_param(self): if self.phase == RunPhase.TRAIN: return self.state else: return np.zeros(self.action_space.shape)