class_docstr: Object that is responsible for evaluating asynchronous multiple agents.
  
  Args:
      n_steps (int): Number of timesteps used in each evaluation.
      n_episodes (int): Number of episodes used in each evaluation.
      eval_interval (int): Interval of evaluations in steps.
      outdir (str): Path to a directory to save things.
      max_episode_len (int): Maximum length of episodes used in evaluations.
      step_offset (int): Offset of steps used to schedule evaluations.
      evaluation_hooks (Sequence): Sequence of
          pfrl.experiments.evaluation_hooks.EvaluationHook objects. They are
          called after each evaluation.
      save_best_so_far_agent (bool): If set to True, after each evaluation,
          if the score (= mean return of evaluation episodes) exceeds
          the best-so-far score, the current agent is saved.
code: def max_score(self):
      with self._max_score.get_lock():
          v = self._max_score.value
      return v
  
imports: import logging
  import multiprocessing
  import os
  import statistics
  import time
  import numpy
  import pfrl
label_desc: Use image node_id 2 for calling the AsyncEvaluator obj's underlying member method code with example usage: obj.max_score() and returns: v