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.
docstr: Given a list of values corresponding to each action, 
  choose one actions according to the exploration policy
  :param action_values: A list of action values
  :return: The chosen action,
           The probability of the action (if available, otherwise 1 for absolute certainty in the action)
docstr_tok: ["Given","a","list","of","values","corresponding","to","each","action",",","choose","one","actions","according","to","the","exploration","policy",":","param","action_values",":","A","list","of","action","values",":","return",":","The","chosen","action",",","The","probability","of","the","action","(","if","available",",","otherwise","1","for","absolute","certainty","in","the","action",")"]
code: def get_action(self, action_values: List[ActionType]) -> ActionType:
      """
      Given a list of values corresponding to each action,
      choose one actions according to the exploration policy
      :param action_values: A list of action values
      :return: The chosen action,
               The probability of the action (if available, otherwise 1 for absolute certainty in the action)
      """
      raise NotImplementedError()
  
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 3 for calling the ExplorationPolicy obj's underlying member method code with example usage: obj.get_action(action_values) without return types