class_docstr: Categorical exploration policy is intended for discrete action spaces. It expects the action values to
  represent a probability distribution over the action, from which a single action will be sampled.
  In evaluation, the action that has the highest probability will be selected. This is particularly useful for
  actor-critic schemes, where the actors output is a probability distribution over the actions.
class_docstr_tok: ["Categorical","exploration","policy","is","intended","for","discrete","action","spaces",".","It","expects","the","action","values","to","represent","a","probability","distribution","over","the","action",",","from","which","a","single","action","will","be","sampled",".","In","evaluation",",","the","action","that","has","the","highest","probability","will","be","selected",".","This","is","particularly","useful","for","actor-critic","schemes",",","where","the","actors","output","is","a","probability","distribution","over","the","actions","."]
docstr: :param action_space: the action space used by the environment
docstr_tok: [":","param","action_space",":","the","action","space","used","by","the","environment"]
code: def __init__(self, action_space: ActionSpace):
      """
      :param action_space: the action space used by the environment
      """
      super().__init__(action_space)
  
file_path: coach/rl_coach/exploration_policies/categorical.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.spaces import ActionSpace
label_desc: Use image node_id 1 to create a new Categorical object from inherited base classes: DiscreteActionExplorationPolicy with example: obj = Categorical(action_space)