docstr: Given a list of actions from multiple agents, decide on a single action to take.
  :param actions_info: a dictionary of agent names and their corresponding
                       ActionInfo instances containing information for each agents action
  :return: a single action and the corresponding action info
docstr_tok: ["Given","a","list","of","actions","from","multiple","agents",",","decide","on","a","single","action","to","take",".",":","param","actions_info",":","a","dictionary","of","agent","names","and","their","corresponding","ActionInfo","instances","containing","information","for","each","agents","action",":","return",":","a","single","action","and","the","corresponding","action","info"]
code: def choose_action(
      self, actions_info: Dict[str, ActionInfo]
  ) -> ActionInfo:
      """
      Given a list of actions from multiple agents, decide on a single action to take.
      :param actions_info: a dictionary of agent names and their corresponding
                           ActionInfo instances containing information for each agents action
      :return: a single action and the corresponding action info
      """
      raise NotImplementedError("")
  
imports: import copy
  import itertools
  from enum import Enum
  from typing import Union, List, Dict
  import numpy
  from rl_coach.agents.agent_interface import AgentInterface
  from rl_coach.base_parameters import AgentParameters, VisualizationParameters
  from rl_coach.core_types import ActionInfo, EnvResponse, ActionType, RunPhase
  from rl_coach.filters.observation.observation_crop_filter import ObservationCropFilter
  from rl_coach.saver import SaverCollection
  from rl_coach.spaces import ActionSpace
  from rl_coach.spaces import AgentSelection, AttentionActionSpace, SpacesDefinition
  from rl_coach.utils import short_dynamic_import
label_desc: Use image node_id 1 for calling the DecisionPolicy obj's underlying member method code with example usage: obj.choose_action(actions_info) without return types