class_docstr: The Boltzmann exploration policy is intended for discrete action spaces. It assumes that each of the possible
  actions has some value assigned to it (such as the Q value), and uses a softmax function to convert these values
  into a distribution over the actions. It then samples the action for playing out of the calculated distribution.
  An additional temperature schedule can be given by the user, and will control the steepness of the softmax function.
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docstr: :param action_space: the action space used by the environment
  :param temperature_schedule: the schedule for the temperature parameter of the softmax
docstr_tok: [":","param","action_space",":","the","action","space","used","by","the","environment",":","param","temperature_schedule",":","the","schedule","for","the","temperature","parameter","of","the","softmax"]
code: def __init__(
      self, action_space: ActionSpace, temperature_schedule: Schedule
  ):
      """
      :param action_space: the action space used by the environment
      :param temperature_schedule: the schedule for the temperature parameter of the softmax
      """
      super().__init__(action_space)
      self.temperature_schedule = temperature_schedule
  
file_path: coach/rl_coach/exploration_policies/boltzmann.py
imports: from typing import List
  import numpy
  from rl_coach.core_types import RunPhase, ActionType
  from rl_coach.exploration_policies.exploration_policy import DiscreteActionExplorationPolicy, ExplorationParameters
  from rl_coach.schedules import Schedule
  from rl_coach.spaces import ActionSpace
label_desc: Use image node_id 1 to create a new Boltzmann object from inherited base classes: DiscreteActionExplorationPolicy with example: obj = Boltzmann(action_space, temperature_schedule)