# Buggy code
def __init__(
    self,
    *,
    env_creator: Optional[Callable[[EnvContext], EnvType]] = None,
    validate_env: Optional[Callable[[EnvType], None]] = None,
    policy_class: Optional[Type[Policy]] = None,
    trainer_config: Optional[TrainerConfigDict] = None,
    num_workers: int = 0,
    logdir: Optional[str] = None,
    _setup: bool = True,
):
    """Create a new WorkerSet and initialize its workers.

    Args:
        env_creator (Optional[Callable[[EnvContext], EnvType]]): Function
            that returns env given env config.
        validate_env (Optional[Callable[[EnvType], None]]): Optional
            callable to validate the generated environment (only on
            worker=0).
        policy (Optional[Type[Policy]]): A rllib.policy.Policy class.
        trainer_config (Optional[TrainerConfigDict]): Optional dict that
            extends the common config of the Trainer class.
        num_workers (int): Number of remote rollout workers to create.
        logdir (Optional[str]): Optional logging directory for workers.
        _setup (bool): Whether to setup workers. This is only for testing.
    """

    if not trainer_config:
        from ray.rllib.agents.trainer import COMMON_CONFIG

        trainer_config = COMMON_CONFIG

    self._env_creator = env_creator
    self._policy_class = policy_class
    self._remote_config = trainer_config
    self._logdir = logdir

    if _setup:
        self._local_config = merge_dicts(
            trainer_config, {"tf_session_args": trainer_config["local_tf_session_args"]}
        )

        # Create a number of remote workers.
        self._remote_workers = []
        self.add_workers(num_workers)

        # If num_workers > 0, get the action_spaces and observation_spaces
        # to not be forced to create an Env on the driver.
        if self._remote_workers:
            remote_spaces = ray.get(
                self.remote_workers()[0].foreach_policy.remote(
                    lambda p, pid: (pid, p.observation_space, p.action_space)
                )
            )
            spaces = {e[0]: (e[1], e[2]) for e in remote_spaces}
        else:
            spaces = None

        # Always create a local worker.
        self._local_worker = self._make_worker(
            cls=RolloutWorker,
            env_creator=env_creator,
            validate_env=validate_env,
            policy_cls=self._policy_class,
            worker_index=0,
            num_workers=num_workers,
            config=self._local_config,
            spaces=spaces,
        )


# Traceback (AttributeError)
Traceback (most recent call last):
File "/data2/huangcq/miniconda3/envs/majenv/lib/python3.8/site-packages/ray/rllib/models/preprocessors.py", line 60, in check_shape
if not self._obs_space.contains(observation):
File "/data2/huangcq/miniconda3/envs/majenv/lib/python3.8/site-packages/gym/spaces/box.py", line 128, in contains
return x.shape == self.shape and np.all(x >= self.low) and np.all(x <= self.high)
AttributeError: 'dict' object has no attribute 'shape'

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File "/notebooks/projects/hanyu/ReferProject/MahjongFastPK/test.py", line 96, in <module>
actions = np.array([trainer.compute_action(state) for i in range(10)])
File "/notebooks/projects/hanyu/ReferProject/MahjongFastPK/test.py", line 96, in <listcomp>
actions = np.array([trainer.compute_action(state) for i in range(10)])
File "/data2/huangcq/miniconda3/envs/majenv/lib/python3.8/site-packages/ray/rllib/agents/trainer.py", line 819, in compute_action
preprocessed = self.workers.local_worker().preprocessors[
File "/data2/huangcq/miniconda3/envs/majenv/lib/python3.8/site-packages/ray/rllib/models/preprocessors.py", line 166, in transform
self.check_shape(observation)
File "/data2/huangcq/miniconda3/envs/majenv/lib/python3.8/site-packages/ray/rllib/models/preprocessors.py", line 66, in check_shape
raise ValueError(
ValueError: ('Observation for a Box/MultiBinary/MultiDiscrete space should be an np.array, not a Python list.', {'action_mask': array([0, 1, 1, 1, 1]), 'avail_actions': array([1., 1., 1., 1., 1.]), 'state': array([ 1, 12,  2,  1,  4,  2,  4,  2,  1, 10,  0])})

# Fixed code
def __init__(
    self,
    *,
    env_creator: Optional[Callable[[EnvContext], EnvType]] = None,
    validate_env: Optional[Callable[[EnvType], None]] = None,
    policy_class: Optional[Type[Policy]] = None,
    trainer_config: Optional[TrainerConfigDict] = None,
    num_workers: int = 0,
    logdir: Optional[str] = None,
    _setup: bool = True,
):
    """Create a new WorkerSet and initialize its workers.

    Args:
        env_creator (Optional[Callable[[EnvContext], EnvType]]): Function
            that returns env given env config.
        validate_env (Optional[Callable[[EnvType], None]]): Optional
            callable to validate the generated environment (only on
            worker=0).
        policy (Optional[Type[Policy]]): A rllib.policy.Policy class.
        trainer_config (Optional[TrainerConfigDict]): Optional dict that
            extends the common config of the Trainer class.
        num_workers (int): Number of remote rollout workers to create.
        logdir (Optional[str]): Optional logging directory for workers.
        _setup (bool): Whether to setup workers. This is only for testing.
    """

    if not trainer_config:
        from ray.rllib.agents.trainer import COMMON_CONFIG

        trainer_config = COMMON_CONFIG

    self._env_creator = env_creator
    self._policy_class = policy_class
    self._remote_config = trainer_config
    self._logdir = logdir

    if _setup:
        self._local_config = merge_dicts(
            trainer_config, {"tf_session_args": trainer_config["local_tf_session_args"]}
        )

        # Create a number of remote workers.
        self._remote_workers = []
        self.add_workers(num_workers)

        # If num_workers > 0, get the action_spaces and observation_spaces
        # to not be forced to create an Env on the driver.
        if self._remote_workers:
            remote_spaces = ray.get(
                self.remote_workers()[0].foreach_policy.remote(
                    lambda p, pid: (pid, p.observation_space, p.action_space)
                )
            )
            spaces = {
                e[0]: (getattr(e[1], "original_space", e[1]), e[2])
                for e in remote_spaces
            }
        else:
            spaces = None

        # Always create a local worker.
        self._local_worker = self._make_worker(
            cls=RolloutWorker,
            env_creator=env_creator,
            validate_env=validate_env,
            policy_cls=self._policy_class,
            worker_index=0,
            num_workers=num_workers,
            config=self._local_config,
            spaces=spaces,
        )