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: Allows exploration policies to define if they require the action values for the current step.
  This can save up a lot of computation. For example in e-greedy, if the random value generated is smaller
  than epsilon, the action is completely random, and the action values don't need to be calculated
  :return: True if the action values are required. False otherwise
docstr_tok: ["Allows","exploration","policies","to","define","if","they","require","the","action","values","for","the","current","step",".","This","can","save","up","a","lot","of","computation",".","For","example","in","e-greedy",",","if","the","random","value","generated","is","smaller","than","epsilon",",","the","action","is","completely","random",",","and","the","action","values","do","n't","need","to","be","calculated",":","return",":","True","if","the","action","values","are","required",".","False","otherwise"]
code: def requires_action_values(self) -> bool:
      """
      Allows exploration policies to define if they require the action values for the current step.
      This can save up a lot of computation. For example in e-greedy, if the random value generated is smaller
      than epsilon, the action is completely random, and the action values don't need to be calculated
      :return: True if the action values are required. False otherwise
      """
      return True
  
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 5 for calling the ExplorationPolicy obj's underlying member method code with example usage: obj.requires_action_values() and returns: True