class_docstr: The Greedy exploration policy is intended for both discrete and continuous action spaces.
  For discrete action spaces, it always selects the action with the maximum value, as given by the agent.
  For continuous action spaces, it always return the exact action, as it was given by the agent.
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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/greedy.py
imports: from typing import List
  import numpy
  from rl_coach.core_types import ActionType
  from rl_coach.exploration_policies.exploration_policy import ExplorationParameters, ExplorationPolicy
  from rl_coach.spaces import ActionSpace, DiscreteActionSpace, BoxActionSpace
label_desc: Use image node_id 1 to create a new Greedy object from inherited base classes: ExplorationPolicy with example: obj = Greedy(action_space)