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.
class_docstr_tok: ["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","."]
code: def get_action(self, action_values: List[ActionType]):
      if type(self.action_space) == DiscreteActionSpace:
          action = np.argmax(action_values)
          one_hot_action_probabilities = np.zeros(
              len(self.action_space.actions)
          )
          one_hot_action_probabilities[action] = 1
          return action, one_hot_action_probabilities
      if type(self.action_space) == BoxActionSpace:
          return action_values
  
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 2 for calling the Greedy obj's underlying member method code with example usage: obj.get_action(action_values) and returns: action,  one_hot_action_probabilities, action_values