def evaluate_world_model(
    real_env, hparams, world_model_dir, debug_video_path,
    split=tf.estimator.ModeKeys.EVAL,
):
  """Evaluate the world model (reward accuracy)."""
  frame_stack_size = hparams.frame_stack_size
  rollout_subsequences = []
  def initial_frame_chooser(batch_size):
    assert batch_size == len(rollout_subsequences)
    return np.stack([
        [frame.observation.decode() for frame in subsequence[:frame_stack_size]]    # pylint: disable=g-complex-comprehension
        for subsequence in rollout_subsequences
    ])

  env_fn = rl.make_simulated_env_fn_from_hparams(
      real_env, hparams, batch_size=hparams.wm_eval_batch_size,
      initial_frame_chooser=initial_frame_chooser, model_dir=world_model_dir
  )
  sim_env = env_fn(in_graph=False)
  subsequence_length = int(
      max(hparams.wm_eval_rollout_ratios) * hparams.simulated_rollout_length
  )
  rollouts = real_env.current_epoch_rollouts(
      split=split,
      minimal_rollout_frames=(subsequence_length + frame_stack_size)
  )

  video_writer = common_video.WholeVideoWriter(
      fps=10, output_path=debug_video_path, file_format="avi"
  )

  reward_accuracies_by_length = {
      int(ratio * hparams.simulated_rollout_length): []
      for ratio in hparams.wm_eval_rollout_ratios
  }
  for _ in range(hparams.wm_eval_num_batches):
    rollout_subsequences[:] = random_rollout_subsequences(
        rollouts, hparams.wm_eval_batch_size,
        subsequence_length + frame_stack_size
    )

    eval_subsequences = [
        subsequence[(frame_stack_size - 1):]
        for subsequence in rollout_subsequences
    ]

    # Check that the initial observation is the same in the real and simulated
    # rollout.
    sim_init_obs = sim_env.reset()
    def decode_real_obs(index):
      return np.stack([
          subsequence[index].observation.decode()
          for subsequence in eval_subsequences  # pylint: disable=cell-var-from-loop
      ])
    real_init_obs = decode_real_obs(0)
    assert np.all(sim_init_obs == real_init_obs)

    debug_frame_batches = []
    def append_debug_frame_batch(sim_obs, real_obs, sim_cum_rews,
                                 real_cum_rews, sim_rews, real_rews):
      """Add a debug frame."""
      rews = [[sim_cum_rews, sim_rews], [real_cum_rews, real_rews]]
      headers = []
      for j in range(len(sim_obs)):
        local_nps = []
        for i in range(2):
          img = PIL_Image().new("RGB", (sim_obs.shape[-2], 11),)
          draw = PIL_ImageDraw().Draw(img)
          draw.text((0, 0), "c:{:3}, r:{:3}".format(int(rews[i][0][j]),
                                                    int(rews[i][1][j])),
                    fill=(255, 0, 0))
          local_nps.append(np.asarray(img))
        local_nps.append(np.zeros_like(local_nps[0]))
        headers.append(np.concatenate(local_nps, axis=1))
      errs = absolute_hinge_difference(sim_obs, real_obs)
      headers = np.stack(headers)
      debug_frame_batches.append(  # pylint: disable=cell-var-from-loop
          np.concatenate([headers,
                          np.concatenate([sim_obs, real_obs, errs], axis=2)],
                         axis=1)
      )
    append_debug_frame_batch(sim_init_obs, real_init_obs,
                             np.zeros(hparams.wm_eval_batch_size),
                             np.zeros(hparams.wm_eval_batch_size),
                             np.zeros(hparams.wm_eval_batch_size),
                             np.zeros(hparams.wm_eval_batch_size))

    (sim_cum_rewards, real_cum_rewards) = (
        np.zeros(hparams.wm_eval_batch_size) for _ in range(2)
    )
    for i in range(subsequence_length):
      actions = [subsequence[i].action for subsequence in eval_subsequences]
      (sim_obs, sim_rewards, _) = sim_env.step(actions)
      sim_cum_rewards += sim_rewards

      real_rewards = np.array([
          subsequence[i + 1].reward for subsequence in eval_subsequences
      ])
      real_cum_rewards += real_rewards
      for (length, reward_accuracies) in six.iteritems(
          reward_accuracies_by_length
      ):
        if i + 1 == length:
          reward_accuracies.append(
              np.sum(sim_cum_rewards == real_cum_rewards) /
              len(real_cum_rewards)
          )

      real_obs = decode_real_obs(i + 1)
      append_debug_frame_batch(sim_obs, real_obs, sim_cum_rewards,
                               real_cum_rewards, sim_rewards, real_rewards)

    for debug_frames in np.stack(debug_frame_batches, axis=1):
      debug_frame = None
      for debug_frame in debug_frames:
        video_writer.write(debug_frame)

      if debug_frame is not None:
        # Append two black frames for aesthetics.
        for _ in range(2):
          video_writer.write(np.zeros_like(debug_frame))

  video_writer.finish_to_disk()

  return {
      "reward_accuracy/at_{}".format(length): np.mean(reward_accuracies)
      for (length, reward_accuracies) in six.iteritems(
          reward_accuracies_by_length
      )
  }
Evaluate the world model (reward accuracy).