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
  
file_path: coach/tutorials/Resources/exploration.py
imports: import numpy
  from typing import List
  from rl_coach.core_types import ActionType
  from rl_coach.spaces import ActionSpace
  from rl_coach.exploration_policies.exploration_policy import ExplorationPolicy, ExplorationParameters
label_desc: Use image node_id 3 for calling the MyExplorationPolicy obj's underlying member method code with example usage: obj.get_control_param() and returns: int