def generate_random_score()

in data/envs/atari/generate_random_score.py [0:0]

• 41 lines of code
• 15 McCabe index (conditional complexity)

def generate_random_score(task_name):
    cfg = parse_atari_args(evaluation=True)
    env_id = task_name.replace("-", "_")
    if env_id == "atari_asteroids":
        env_id = "atari_asteroid"
    if env_id == "atari_montezumarevenge":
        env_id = "atari_montezuma"
    if env_id == "atari_kungfumaster":
        env_id = "atari_kongfumaster"
    if env_id == "atari_privateeye":
        env_id = "atari_privateye"
    cfg.env = env_id
    eval_env_frameskip = cfg.env_frameskip if cfg.eval_env_frameskip is None else cfg.eval_env_frameskip
    cfg.env_frameskip = cfg.eval_env_frameskip = eval_env_frameskip
    cfg.num_envs = 16

    env = make_env_func_batched(cfg, env_config=AttrDict(worker_index=0, vector_index=0, env_id=0))

    ep_rewards = []
    env.reset()

    with torch.no_grad():
        while len(ep_rewards) < TOT_NUM_EPISODES:
            _, _, _, _, infos = env.step(np.array([env.action_space.sample() for _ in range(cfg.num_envs)]))

            for info in infos:
                if "episode" in info:
                    ep_rewards.append(info["episode"]["r"][0])
                    if len(ep_rewards) % 10 == 0:
                        print(f"Task {task_name} - progress {int(len(ep_rewards) / TOT_NUM_EPISODES * 100)}%")

    env.close()

    # Load the scores dictionary
    if not os.path.exists(FILENAME):
        scores_dict = {}
    else:
        with open(FILENAME, "r") as file:
            scores_dict = json.load(file)

    # Add the random scores to the dictionary
    if task_name not in scores_dict:
        scores_dict[task_name] = {}
    scores_dict[task_name]["random"] = {"mean": float(np.mean(ep_rewards)), "std": float(np.std(ep_rewards))}

    # Save the dictionary to a file
    with open(FILENAME, "w") as file:
        scores_dict = {
            task: {agent: scores_dict[task][agent] for agent in sorted(scores_dict[task])}
            for task in sorted(scores_dict)
        }
        json.dump(scores_dict, file, indent=4)