parameters: (self, game, agents: Dict[int, rl_agent.AbstractAgent],
                 use_observation: bool)
docstring: Initializes the joint RL agent policy.
  
      Args:
        game: The game.
        agents: Dictionary of agents keyed by the player IDs.
        use_observation: If true then observation tensor will be used as the
          `info_state` in the step() calls; otherwise, information state tensor
          will be used. See `use_observation` property of
          rl_environment.Environment.
docstring_summary: Initializes the joint RL agent policy.
docstring_tokens: Initializes the joint RL agent policy .
function: def __init__(self, game, agents: Dict[int, rl_agent.AbstractAgent],
                 use_observation: bool):
      """Initializes the joint RL agent policy.
  
      Args:
        game: The game.
        agents: Dictionary of agents keyed by the player IDs.
        use_observation: If true then observation tensor will be used as the
          `info_state` in the step() calls; otherwise, information state tensor
          will be used. See `use_observation` property of
          rl_environment.Environment.
      """
      player_ids = list(sorted(agents.keys()))
      super().__init__(game, player_ids)
      self._agents = agents
      self._obs = {
          "info_state": [None] * game.num_players(),
          "legal_actions": [None] * game.num_players()
      }
      self._use_observation = use_observation
function_tokens: def __init__ ( self , game , agents : Dict [ int , rl_agent . AbstractAgent ] , use_observation : bool ) : player_ids = list ( sorted ( agents . keys ( ) ) ) super ( ) . __init__ ( game , player_ids ) self . _agents = agents self . _obs = { "info_state" : [ None ] * game . num_players ( ) , "legal_actions" : [ None ] * game . num_players ( ) } self . _use_observation = use_observation