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_action(self, action_values: List[ActionType]) -> ActionType:
      if np.random.rand() < 0.5:
          chosen_action = self.action_space.sample()
      else:
          chosen_action = np.argmax(action_values)
      probabilities = np.zeros(len(self.action_space.actions))
      probabilities[chosen_action] = 1
      return chosen_action, probabilities
  
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 2 for calling the MyExplorationPolicy obj's underlying member method code with example usage: obj.get_action(action_values) and returns: chosen_action,  probabilities