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 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
  
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 4 for calling the OUProcess obj's underlying member method code with example usage: obj.get_action(action_values) and returns: action