code: def evaluate_single_config(
      hparams, sampling_temp, max_num_noops, agent_model_dir,
      eval_fn=_eval_fn_with_learner
  ):
    
    tf.logging.info("Evaluating metric %s", get_metric_name(
        sampling_temp, max_num_noops, clipped=False
    ))
    eval_hparams = trainer_lib.create_hparams(hparams.base_algo_params)
    env = setup_env(
        hparams, batch_size=hparams.eval_batch_size, max_num_noops=max_num_noops,
        rl_env_max_episode_steps=hparams.eval_rl_env_max_episode_steps,
        env_name=hparams.rl_env_name)
    env.start_new_epoch(0)
    eval_fn(env, hparams, eval_hparams, agent_model_dir, sampling_temp)
    rollouts = env.current_epoch_rollouts()
    env.close()
  
    return tuple(
        compute_mean_reward(rollouts, clipped) for clipped in (True, False)
    )
docs: Evaluate the PPO agent in the real environment.