class_docstr: An exploration policy takes the predicted actions or action values from the agent, and selects the action to
  actually apply to the environment using some predefined algorithm.
code: def get_control_param(self):
      return 0
  
imports: from typing import List
  from rl_coach.base_parameters import Parameters
  from rl_coach.core_types import RunPhase, ActionType
  from rl_coach.spaces import ActionSpace, DiscreteActionSpace, BoxActionSpace, GoalsSpace
label_desc: Use image node_id 6 for calling the ExplorationPolicy obj's underlying member method code with example usage: obj.get_control_param() and returns: int