class_docstr: 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.
docstr: :param action_space: the action space used by the environment
docstr_tok: [":","param","action_space",":","the","action","space","used","by","the","environment"]
code: 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)
      self.mu = 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
  
file_path: coach/rl_coach/exploration_policies/ou_process.py
imports: from typing import List
  import numpy
  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
label_desc: Use image node_id 1 to create a new OUProcess object from inherited base classes: ContinuousActionExplorationPolicy with example: obj = OUProcess(action_space, mu, theta, sigma, dt)