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.
code: 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
  
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 3 for calling the OUProcess obj's underlying member method code with example usage: obj.noise() and returns: self