search

ray-project / ray

Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
https://ray.io
Apache License 2.0
33.23k stars 5.62k forks source link

[Rllib, Core, Tune] observation = OrderedDict(sorted(observation.items())) AttributeError: 'NoneType' object has no attribute 'items' #39807

Open fardinabbasi opened 1 year ago

fardinabbasi commented 1 year ago

What happened + What you expected to happen

I want to train a PPO agent in my custom environment called RankingEnv, but I'm encountering several errors and warnings that result in the agent's termination. The majority of these issues are: observation = OrderedDict(sorted(observation.items())) AttributeError: 'NoneType' object has no attribute 'items' 

WARNING deprecation.py:50 -- DeprecationWarning: `ValueNetworkMixin` has been deprecated. This will raise an error in the future!
WARNING deprecation.py:50 -- DeprecationWarning: `LearningRateSchedule` has been deprecated. This will raise an error in the future!
WARNING deprecation.py:50 -- DeprecationWarning: `EntropyCoeffSchedule` has been deprecated. This will raise an error in the future!
WARNING deprecation.py:50 -- DeprecationWarning: `KLCoeffMixin` has been deprecated. This will raise an error in the future!
DeprecationWarning: `DirectStepOptimizer` has been deprecated. This will raise an error in the future!
WARNING algorithm_config.py:672 -- Cannot create PPOConfig from given `config_dict`! Property __stdout_file__ not supported.

Costum Environment

class RankingEnv(gym.Env):
    def __init__(self, config: dict):
      super().__init__()
      self.coins = config['df']['tic'].unique()
      features_col = config['df'].columns.difference(['date','tic','score','close_growth(%)'])

      self.df=config['df'].groupby('date')
      self.dates = list(self.df.groups.keys())

      self.observation_space = gym.spaces.Dict({coin: gym.spaces.Box(low=-np.inf, high=np.inf, shape=(len(features_col),), dtype=np.float64) for coin in self.coins})
      self.action_space = gym.spaces.Dict({coin: gym.spaces.Box(low=np.float64(1.0), high=np.float64(len(self.coins)), shape=(1,), dtype=np.float64) for coin in self.coins})#actions are the scores

    def step(self, action):
      group = self.df.get_group(self.dates[self.time])
      true_scores = group.set_index('tic')['score'].to_dict()
      # The indexes are according to predicted score and the values are true score!(Sorting true score by predicted score)
      scores = [true_scores[coin] for coin in sorted(action, key=action.get, reverse=True)]
      ideal_scores = sorted(true_scores.values(), reverse=True) #This is just the True_score as list

      dcg = self.calculate_dcg(scores)
      idcg = self.calculate_dcg(ideal_scores)
      reward = dcg/idcg # reward = ndcg

      self.time+=1
      terminated = self.time >= len(self.dates)
      info = {}
      return self._get_obs() if not terminated else None, reward, terminated, False, info

    def calculate_dcg(self, scores):
      dcg = 0.0
      for i in range(len(scores)):
        dcg += (2 ** scores[i] - 1) / np.log2(i + 2)
      return dcg

    def reset(self, *, seed: Optional[int] = None, options: Optional[dict] = None):
      super().reset(seed=seed,options=options)
      self.time = 0
      info={}
      return self._get_obs() ,info

    def _get_obs(self):
      group = self.df.get_group(self.dates[self.time])
      obs = group.drop(['date','score','close_growth(%)'], axis=1)

      obs = obs.set_index('tic').agg(lambda x: np.array(x.tolist()), axis=1).to_dict()

      return obs

    def render(self):
        pass

    def close(self):
        pass

Agent

class DRLlibv2:

    def __init__(
        self,
        trainable: str | Any,
        params: dict,
        train_env=None,
        run_name: str = "tune_run",
        local_dir: str = "tune_results",
        search_alg=None,
        concurrent_trials: int = 0,
        num_samples: int = 0,
        scheduler_=None,
        num_cpus: float | int = 2,
        dataframe_save: str = "tune.csv",
        metric: str = "episode_reward_mean",
        mode: str | list[str] = "max",
        max_failures: int = 0,
        training_iterations: int = 100,
        checkpoint_num_to_keep: None | int = None,
        checkpoint_freq: int = 0,
        reuse_actors: bool = True
    ):
        self.params = params

        self.train_env = train_env
        self.run_name = run_name
        self.local_dir = local_dir
        self.search_alg = search_alg
        if concurrent_trials != 0:
            self.search_alg = ConcurrencyLimiter(
                self.search_alg, max_concurrent=concurrent_trials
            )
        self.scheduler_ = scheduler_
        self.num_samples = num_samples
        self.trainable = trainable
        if isinstance(self.trainable, str):
            self.trainable = self.trainable.upper()
        self.num_cpus = num_cpus
        self.dataframe_save = dataframe_save
        self.metric = metric
        self.mode = mode
        self.max_failures = max_failures
        self.training_iterations = training_iterations
        self.checkpoint_freq = checkpoint_freq
        self.checkpoint_num_to_keep = checkpoint_num_to_keep
        self.reuse_actors = reuse_actors

    def train_tune_model(self):

        if ray.is_initialized():
          ray.shutdown()

        ray.init(num_cpus=self.num_cpus, num_gpus=self.params['num_gpus'], ignore_reinit_error=True)

        if self.train_env is not None:
            register_env(self.params['env'], lambda env_config: self.train_env)

        tuner = tune.Tuner(
            self.trainable,
            param_space=self.params,
            tune_config=TuneConfig(
                search_alg=self.search_alg,
                scheduler=self.scheduler_,
                num_samples=self.num_samples,
                # metric=self.metric,
                # mode=self.mode,
                **({'metric': self.metric, 'mode': self.mode} if self.scheduler_ is None else {}),
                reuse_actors=self.reuse_actors,

            ),
            run_config=RunConfig(
                name=self.run_name,
                storage_path=self.local_dir,
                failure_config=FailureConfig(
                    max_failures=self.max_failures, fail_fast=False
                ),
                stop={"training_iteration": self.training_iterations},
                checkpoint_config=CheckpointConfig(
                    num_to_keep=self.checkpoint_num_to_keep,
                    checkpoint_score_attribute=self.metric,
                    checkpoint_score_order=self.mode,
                    checkpoint_frequency=self.checkpoint_freq,
                    checkpoint_at_end=True,
                ),
                verbose=3,#Verbosity mode. 0 = silent, 1 = default, 2 = verbose, 3 = detailed
            ),
        )

        self.results = tuner.fit()
        if self.search_alg is not None:
            self.search_alg.save_to_dir(self.local_dir)
        # ray.shutdown()
        return self.results

    def infer_results(self, to_dataframe: str = None, mode: str = "a"):

        results_df = self.results.get_dataframe()

        if to_dataframe is None:
            to_dataframe = self.dataframe_save

        results_df.to_csv(to_dataframe, mode=mode)

        best_result = self.results.get_best_result()
        # best_result = self.results.get_best_result()
        # best_metric = best_result.metrics
        # best_checkpoint = best_result.checkpoint
        # best_trial_dir = best_result.log_dir
        # results_df = self.results.get_dataframe()

        return results_df, best_result

    def restore_agent(
        self,
        checkpoint_path: str = "",
        restore_search: bool = False,
        resume_unfinished: bool = True,
        resume_errored: bool = False,
        restart_errored: bool = False,
    ):

        # if restore_search:
        # self.search_alg = self.search_alg.restore_from_dir(self.local_dir)
        if checkpoint_path == "":
            checkpoint_path = self.results.get_best_result().checkpoint._local_path

        restored_agent = tune.Tuner.restore(
            checkpoint_path,
            restart_errored=restart_errored,
            resume_unfinished=resume_unfinished,
            resume_errored=resume_errored,
        )
        print(restored_agent)
        self.results = restored_agent.fit()

        if self.search_alg is not None:
            self.search_alg.save_to_dir(self.local_dir)
        return self.results

    def get_test_agent(self, test_env_name: str, test_env=None, checkpoint=None):

        # if test_env is not None:
        #     register_env(test_env_name, lambda config: [test_env])

        if checkpoint is None:
            checkpoint = self.results.get_best_result().checkpoint

        testing_agent = Algorithm.from_checkpoint(checkpoint)
        # testing_agent.config['env'] = test_env_name

        return testing_agent

Versions / Dependencies

Reproduction script

train_env_config = {'df': train_data}
train_config = (PPOConfig()
          .training(lr=tune.loguniform(5e-5, 0.001), entropy_coeff=tune.loguniform(0.00000001, 0.1),sgd_minibatch_size=tune.choice([32, 64, 128, 256, 512]),lambda_=tune.choice([0.1,0.3,0.5,0.7,0.9,1.0]))
          .resources(num_gpus=0)
          .debugging(log_level="DEBUG", seed = 1234)
          .rollouts(num_rollout_workers=1)
          .framework("torch")
          .environment(env=RankingEnv, disable_env_checking=True, env_config=train_env_config)
          )
train_config.model['fcnet_hiddens'] = [256, 256]

search_alg = OptunaSearch(metric="episode_reward_mean",mode="max")#what if metric=step_reward??
scheduler_ = ASHAScheduler(metric="episode_reward_mean",mode="max",max_t=5,grace_period=1,reduction_factor=2)#max_t: The maximum budget for each trial(hyperparameter), in seconds. grace_period: The number of seconds to wait before terminating a trial that has not reported any results.
# wandb_callback = WandbLoggerCallback(project="Ray Tune Trial Run",log_config=True,save_checkpoints=True)

drl_agent = DRLlibv2(
    trainable="PPO",
    # train_env = RankingEnv,
    run_name = "PPO_TRAIN",
    local_dir = "/content/PPO_TRAIN",
    params = train_config.to_dict(),
    num_samples = 1,#Number of samples of hyperparameters config to run
    training_iterations=5,
    checkpoint_freq=5,
    # scheduler_=scheduler_,
    search_alg=search_alg,
    metric = "episode_reward_mean",
    mode = "max"
    # callbacks=[wandb_callback]
)

res = drl_agent.train_tune_model()
results_df, best_result = drl_agent.infer_results()
2023-09-22 12:20:08,561 INFO tune.py:654 -- [output] This will use the new output engine with verbosity 2. To disable the new output and use the legacy output engine, set the environment variable RAY_AIR_NEW_OUTPUT=0. For more information, please see https://github.com/ray-project/ray/issues/36949
2023-09-22 12:20:08,640 WARNING deprecation.py:50 -- DeprecationWarning: `build_tf_policy` has been deprecated. This will raise an error in the future!
2023-09-22 12:20:08,650 WARNING deprecation.py:50 -- DeprecationWarning: `build_policy_class` has been deprecated. This will raise an error in the future!
2023-09-22 12:20:08,737 WARNING algorithm_config.py:2578 -- Setting `exploration_config={}` because you set `_enable_rl_module_api=True`. When RLModule API are enabled, exploration_config can not be set. If you want to implement custom exploration behaviour, please modify the `forward_exploration` method of the RLModule at hand. On configs that have a default exploration config, this must be done with `config.exploration_config={}`.
/usr/local/lib/python3.10/dist-packages/gymnasium/spaces/box.py:130: UserWarning: WARN: Box bound precision lowered by casting to float32
  gym.logger.warn(f"Box bound precision lowered by casting to {self.dtype}")
/usr/local/lib/python3.10/dist-packages/gymnasium/utils/passive_env_checker.py:164: UserWarning: WARN: The obs returned by the `reset()` method was expecting numpy array dtype to be float32, actual type: float64
  logger.warn(
/usr/local/lib/python3.10/dist-packages/gymnasium/utils/passive_env_checker.py:188: UserWarning: WARN: The obs returned by the `reset()` method is not within the observation space.
  logger.warn(f"{pre} is not within the observation space.")
2023-09-22 12:20:08,838 WARNING algorithm_config.py:2578 -- Setting `exploration_config={}` because you set `_enable_rl_module_api=True`. When RLModule API are enabled, exploration_config can not be set. If you want to implement custom exploration behaviour, please modify the `forward_exploration` method of the RLModule at hand. On configs that have a default exploration config, this must be done with `config.exploration_config={}`.
[I 2023-09-22 12:20:08,896] A new study created in memory with name: optuna
2023-09-22 12:20:08,944 WARNING algorithm_config.py:2578 -- Setting `exploration_config={}` because you set `_enable_rl_module_api=True`. When RLModule API are enabled, exploration_config can not be set. If you want to implement custom exploration behaviour, please modify the `forward_exploration` method of the RLModule at hand. On configs that have a default exploration config, this must be done with `config.exploration_config={}`.
+--------------------------------------------------+
| Configuration for experiment     PPO_TRAIN       |
+--------------------------------------------------+
| Search algorithm                 SearchGenerator |
| Scheduler                        FIFOScheduler   |
| Number of trials                 1               |
+--------------------------------------------------+

View detailed results here: /content/PPO_TRAIN/PPO_TRAIN
To visualize your results with TensorBoard, run: `tensorboard --logdir /root/ray_results/PPO_TRAIN`

Trial status: 1 PENDING
Current time: 2023-09-22 12:20:09. Total running time: 0s
Logical resource usage: 0/2 CPUs, 0/0 GPUs
+------------------------------------------------------------------------------------------------------+
| Trial name                status              lr     sgd_minibatch_size     entropy_coeff     lambda |
+------------------------------------------------------------------------------------------------------+
| PPO_RankingEnv_8689c516   PENDING    0.000166759                    256       4.82692e-07        0.1 |
+------------------------------------------------------------------------------------------------------+
(pid=2755) /usr/local/lib/python3.10/dist-packages/tensorflow_probability/python/__init__.py:57: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
(pid=2755)   if (distutils.version.LooseVersion(tf.__version__) <
(pid=2755) DeprecationWarning: `DirectStepOptimizer` has been deprecated. This will raise an error in the future!
(pid=2755) /usr/local/lib/python3.10/dist-packages/google/rpc/__init__.py:20: DeprecationWarning: Deprecated call to `pkg_resources.declare_namespace('google.rpc')`.
(pid=2755) Implementing implicit namespace packages (as specified in PEP 420) is preferred to `pkg_resources.declare_namespace`. See https://setuptools.pypa.io/en/latest/references/keywords.html#keyword-namespace-packages
(pid=2755)   pkg_resources.declare_namespace(__name__)
(pid=2755) /usr/local/lib/python3.10/dist-packages/pkg_resources/__init__.py:2349: DeprecationWarning: Deprecated call to `pkg_resources.declare_namespace('google')`.
(pid=2755) Implementing implicit namespace packages (as specified in PEP 420) is preferred to `pkg_resources.declare_namespace`. See https://setuptools.pypa.io/en/latest/references/keywords.html#keyword-namespace-packages
(pid=2755)   declare_namespace(parent)
(PPO pid=2755) 2023-09-22 12:20:18,560  WARNING algorithm_config.py:2578 -- Setting `exploration_config={}` because you set `_enable_rl_module_api=True`. When RLModule API are enabled, exploration_config can not be set. If you want to implement custom exploration behaviour, please modify the `forward_exploration` method of the RLModule at hand. On configs that have a default exploration config, this must be done with `config.exploration_config={}`.
(PPO pid=2755) 2023-09-22 12:20:18,561  WARNING algorithm_config.py:672 -- Cannot create PPOConfig from given `config_dict`! Property __stdout_file__ not supported.
(pid=2819) /usr/local/lib/python3.10/dist-packages/tensorflow_probability/python/__init__.py:57: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
(pid=2819)   if (distutils.version.LooseVersion(tf.__version__) <
(pid=2819) DeprecationWarning: `DirectStepOptimizer` has been deprecated. This will raise an error in the future!
(pid=2819) /usr/local/lib/python3.10/dist-packages/google/rpc/__init__.py:20: DeprecationWarning: Deprecated call to `pkg_resources.declare_namespace('google.rpc')`.
(pid=2819) Implementing implicit namespace packages (as specified in PEP 420) is preferred to `pkg_resources.declare_namespace`. See https://setuptools.pypa.io/en/latest/references/keywords.html#keyword-namespace-packages
(pid=2819)   pkg_resources.declare_namespace(__name__)
(pid=2819) /usr/local/lib/python3.10/dist-packages/pkg_resources/__init__.py:2349: DeprecationWarning: Deprecated call to `pkg_resources.declare_namespace('google')`.
(pid=2819) Implementing implicit namespace packages (as specified in PEP 420) is preferred to `pkg_resources.declare_namespace`. See https://setuptools.pypa.io/en/latest/references/keywords.html#keyword-namespace-packages
(pid=2819)   declare_namespace(parent)
(RolloutWorker pid=2819) 2023-09-22 12:20:29,813    DEBUG rollout_worker.py:1761 -- Creating policy for default_policy
(RolloutWorker pid=2819) 2023-09-22 12:20:29,813    DEBUG preprocessors.py:304 -- Creating sub-preprocessor for Box(-inf, inf, (25,), float64)
(RolloutWorker pid=2819) 2023-09-22 12:20:29,814    DEBUG preprocessors.py:304 -- Creating sub-preprocessor for Box(-inf, inf, (25,), float64)
(RolloutWorker pid=2819) 2023-09-22 12:20:29,814    DEBUG preprocessors.py:304 -- Creating sub-preprocessor for Box(-inf, inf, (25,), float64)
(RolloutWorker pid=2819) 2023-09-22 12:20:29,815    DEBUG preprocessors.py:304 -- Creating sub-preprocessor for Box(-inf, inf, (25,), float64)
(RolloutWorker pid=2819) 2023-09-22 12:20:29,815    DEBUG preprocessors.py:304 -- Creating sub-preprocessor for Box(-inf, inf, (25,), float64)
(RolloutWorker pid=2819) 2023-09-22 12:20:29,815    DEBUG preprocessors.py:304 -- Creating sub-preprocessor for Box(-inf, inf, (25,), float64)
(RolloutWorker pid=2819) 2023-09-22 12:20:29,816    DEBUG preprocessors.py:304 -- Creating sub-preprocessor for Box(-inf, inf, (25,), float64)
(RolloutWorker pid=2819) 2023-09-22 12:20:29,816    DEBUG preprocessors.py:304 -- Creating sub-preprocessor for Box(-inf, inf, (25,), float64)
(RolloutWorker pid=2819) 2023-09-22 12:20:29,816    DEBUG preprocessors.py:304 -- Creating sub-preprocessor for Box(-inf, inf, (25,), float64)
(RolloutWorker pid=2819) 2023-09-22 12:20:29,816    DEBUG preprocessors.py:304 -- Creating sub-preprocessor for Box(-inf, inf, (25,), float64)
(RolloutWorker pid=2819) 2023-09-22 12:20:29,817    DEBUG catalog.py:789 -- Created preprocessor <ray.rllib.models.preprocessors.DictFlatteningPreprocessor object at 0x7e19968ff280>: Dict('AAVEUSDT': Box(-inf, inf, (25,), float64), 'AVAXUSDT': Box(-inf, inf, (25,), float64), 'BTCUSDT': Box(-inf, inf, (25,), float64), 'ETHUSDT': Box(-inf, inf, (25,), float64), 'LINKUSDT': Box(-inf, inf, (25,), float64), 'LTCUSDT': Box(-inf, inf, (25,), float64), 'MATICUSDT': Box(-inf, inf, (25,), float64), 'NEARUSDT': Box(-inf, inf, (25,), float64), 'SOLUSDT': Box(-inf, inf, (25,), float64), 'UNIUSDT': Box(-inf, inf, (25,), float64)) -> (250,)
(RolloutWorker pid=2819) 2023-09-22 12:20:29,820    WARNING algorithm_config.py:2578 -- Setting `exploration_config={}` because you set `_enable_rl_module_api=True`. When RLModule API are enabled, exploration_config can not be set. If you want to implement custom exploration behaviour, please modify the `forward_exploration` method of the RLModule at hand. On configs that have a default exploration config, this must be done with `config.exploration_config={}`.
(RolloutWorker pid=2819) 2023-09-22 12:20:29,885    INFO policy.py:1294 -- Policy (worker=1) running on CPU.
(RolloutWorker pid=2819) 2023-09-22 12:20:29,886    INFO torch_policy_v2.py:113 -- Found 0 visible cuda devices.
(RolloutWorker pid=2819) 2023-09-22 12:20:29,886    WARNING deprecation.py:50 -- DeprecationWarning: `ValueNetworkMixin` has been deprecated. This will raise an error in the future!
(RolloutWorker pid=2819) 2023-09-22 12:20:29,886    WARNING deprecation.py:50 -- DeprecationWarning: `LearningRateSchedule` has been deprecated. This will raise an error in the future!
(RolloutWorker pid=2819) 2023-09-22 12:20:29,886    WARNING deprecation.py:50 -- DeprecationWarning: `EntropyCoeffSchedule` has been deprecated. This will raise an error in the future!
(RolloutWorker pid=2819) 2023-09-22 12:20:29,886    WARNING deprecation.py:50 -- DeprecationWarning: `KLCoeffMixin` has been deprecated. This will raise an error in the future!
(RolloutWorker pid=2819) 2023-09-22 12:20:30,074    DEBUG preprocessors.py:304 -- Creating sub-preprocessor for Box(-inf, inf, (25,), float64)
(RolloutWorker pid=2819) 2023-09-22 12:20:30,075    DEBUG preprocessors.py:304 -- Creating sub-preprocessor for Box(-inf, inf, (25,), float64)
(RolloutWorker pid=2819) 2023-09-22 12:20:30,075    DEBUG preprocessors.py:304 -- Creating sub-preprocessor for Box(-inf, inf, (25,), float64)
(RolloutWorker pid=2819) 2023-09-22 12:20:30,075    DEBUG preprocessors.py:304 -- Creating sub-preprocessor for Box(-inf, inf, (25,), float64)
(RolloutWorker pid=2819) 2023-09-22 12:20:30,075    DEBUG preprocessors.py:304 -- Creating sub-preprocessor for Box(-inf, inf, (25,), float64)
(RolloutWorker pid=2819) 2023-09-22 12:20:30,075    DEBUG preprocessors.py:304 -- Creating sub-preprocessor for Box(-inf, inf, (25,), float64)
(RolloutWorker pid=2819) 2023-09-22 12:20:30,076    DEBUG preprocessors.py:304 -- Creating sub-preprocessor for Box(-inf, inf, (25,), float64)
(RolloutWorker pid=2819) 2023-09-22 12:20:30,076    DEBUG preprocessors.py:304 -- Creating sub-preprocessor for Box(-inf, inf, (25,), float64)
(RolloutWorker pid=2819) 2023-09-22 12:20:30,076    DEBUG preprocessors.py:304 -- Creating sub-preprocessor for Box(-inf, inf, (25,), float64)
(RolloutWorker pid=2819) 2023-09-22 12:20:30,076    DEBUG preprocessors.py:304 -- Creating sub-preprocessor for Box(-inf, inf, (25,), float64)
(RolloutWorker pid=2819) 2023-09-22 12:20:30,077    INFO util.py:118 -- Using connectors:
(RolloutWorker pid=2819) 2023-09-22 12:20:30,077    INFO util.py:119 --     AgentConnectorPipeline
(RolloutWorker pid=2819)         ObsPreprocessorConnector
(RolloutWorker pid=2819)         StateBufferConnector
(RolloutWorker pid=2819)         ViewRequirementAgentConnector
(RolloutWorker pid=2819) 2023-09-22 12:20:30,078    INFO util.py:120 --     ActionConnectorPipeline
(RolloutWorker pid=2819)         ConvertToNumpyConnector
(RolloutWorker pid=2819)         NormalizeActionsConnector
(RolloutWorker pid=2819)         ImmutableActionsConnector
(RolloutWorker pid=2819) 2023-09-22 12:20:30,078    DEBUG rollout_worker.py:645 -- Created rollout worker with env <ray.rllib.env.vector_env.VectorEnvWrapper object at 0x7e19969901f0> (<RankingEnv instance>), policies <PolicyMap lru-caching-capacity=100 policy-IDs=['default_policy']>
(PPO pid=2755) 2023-09-22 12:20:30,116  INFO worker_set.py:297 -- Inferred observation/action spaces from remote worker (local worker has no env): {'default_policy': (Dict('AAVEUSDT': Box(-inf, inf, (25,), float64), 'AVAXUSDT': Box(-inf, inf, (25,), float64), 'BTCUSDT': Box(-inf, inf, (25,), float64), 'ETHUSDT': Box(-inf, inf, (25,), float64), 'LINKUSDT': Box(-inf, inf, (25,), float64), 'LTCUSDT': Box(-inf, inf, (25,), float64), 'MATICUSDT': Box(-inf, inf, (25,), float64), 'NEARUSDT': Box(-inf, inf, (25,), float64), 'SOLUSDT': Box(-inf, inf, (25,), float64), 'UNIUSDT': Box(-inf, inf, (25,), float64)), Dict('AAVEUSDT': Box(1.0, 10.0, (1,), float64), 'AVAXUSDT': Box(1.0, 10.0, (1,), float64), 'BTCUSDT': Box(1.0, 10.0, (1,), float64), 'ETHUSDT': Box(1.0, 10.0, (1,), float64), 'LINKUSDT': Box(1.0, 10.0, (1,), float64), 'LTCUSDT': Box(1.0, 10.0, (1,), float64), 'MATICUSDT': Box(1.0, 10.0, (1,), float64), 'NEARUSDT': Box(1.0, 10.0, (1,), float64), 'SOLUSDT': Box(1.0, 10.0, (1,), float64), 'UNIUSDT': Box(1.0, 10.0, (1,), float64))), '__env__': (Dict('AAVEUSDT': Box(-inf, inf, (25,), float64), 'AVAXUSDT': Box(-inf, inf, (25,), float64), 'BTCUSDT': Box(-inf, inf, (25,), float64), 'ETHUSDT': Box(-inf, inf, (25,), float64), 'LINKUSDT': Box(-inf, inf, (25,), float64), 'LTCUSDT': Box(-inf, inf, (25,), float64), 'MATICUSDT': Box(-inf, inf, (25,), float64), 'NEARUSDT': Box(-inf, inf, (25,), float64), 'SOLUSDT': Box(-inf, inf, (25,), float64), 'UNIUSDT': Box(-inf, inf, (25,), float64)), Dict('AAVEUSDT': Box(1.0, 10.0, (1,), float64), 'AVAXUSDT': Box(1.0, 10.0, (1,), float64), 'BTCUSDT': Box(1.0, 10.0, (1,), float64), 'ETHUSDT': Box(1.0, 10.0, (1,), float64), 'LINKUSDT': Box(1.0, 10.0, (1,), float64), 'LTCUSDT': Box(1.0, 10.0, (1,), float64), 'MATICUSDT': Box(1.0, 10.0, (1,), float64), 'NEARUSDT': Box(1.0, 10.0, (1,), float64), 'SOLUSDT': Box(1.0, 10.0, (1,), float64), 'UNIUSDT': Box(1.0, 10.0, (1,), float64)))}
(PPO pid=2755) 2023-09-22 12:20:30,151  INFO policy.py:1294 -- Policy (worker=local) running on CPU.
(PPO pid=2755) 2023-09-22 12:20:30,173  INFO rollout_worker.py:1742 -- Built policy map: <PolicyMap lru-caching-capacity=100 policy-IDs=['default_policy']>
(PPO pid=2755) 2023-09-22 12:20:30,173  INFO rollout_worker.py:1743 -- Built preprocessor map: {'default_policy': None}
(PPO pid=2755) 2023-09-22 12:20:30,173  INFO rollout_worker.py:550 -- Built filter map: defaultdict(<class 'ray.rllib.utils.filter.NoFilter'>, {})
(PPO pid=2755) 2023-09-22 12:20:30,173  DEBUG rollout_worker.py:645 -- Created rollout worker with env None (None), policies <PolicyMap lru-caching-capacity=100 policy-IDs=['default_policy']>

Trial PPO_RankingEnv_8689c516 started with configuration:
+---------------------------------------------------------------------------+
| Trial PPO_RankingEnv_8689c516 config                                      |
+---------------------------------------------------------------------------+
| _AlgorithmConfig__prior_exploration_config/type        StochasticSampling |
| _disable_action_flattening                                          False |
| _disable_execution_plan_api                                          True |
| _disable_initialize_loss_from_dummy_batch                           False |
| _disable_preprocessor_api                                           False |
| _enable_learner_api                                                  True |
| _enable_rl_module_api                                                True |
| _fake_gpus                                                          False |
| _is_atari                                                                 |
| _learner_class                                                            |
| _tf_policy_handles_more_than_one_loss                               False |
| action_mask_key                                               action_mask |
| action_space                                                              |
| actions_in_input_normalized                                         False |
| always_attach_evaluation_results                                    False |
| auto_wrap_old_gym_envs                                               True |
| batch_mode                                              truncate_episodes |
| callbacks                                            ...efaultCallbacks'> |
| checkpoint_trainable_policies_only                                  False |
| clip_actions                                                        False |
| clip_param                                                            0.3 |
| clip_rewards                                                              |
| compress_observations                                               False |
| count_steps_by                                                  env_steps |
| create_env_on_driver                                                False |
| custom_eval_function                                                      |
| delay_between_worker_restarts_s                                       60. |
| disable_env_checking                                                 True |
| eager_max_retraces                                                     20 |
| eager_tracing                                                        True |
| enable_async_evaluation                                             False |
| enable_connectors                                                    True |
| enable_tf1_exec_eagerly                                             False |
| entropy_coeff                                                          0. |
| entropy_coeff_schedule                                                    |
| env                                                  ...in__.RankingEnv'> |
| env_config/df                                        ...ows x 29 columns] |
| env_runner_cls                                                            |
| env_task_fn                                                               |
| evaluation_config                                                         |
| evaluation_duration                                                    10 |
| evaluation_duration_unit                                         episodes |
| evaluation_interval                                                       |
| evaluation_num_workers                                                  0 |
| evaluation_parallel_to_training                                     False |
| evaluation_sample_timeout_s                                          180. |
| explore                                                              True |
| export_native_model_files                                           False |
| fake_sampler                                                        False |
| framework                                                           torch |
| gamma                                                                0.99 |
| grad_clip                                                                 |
| grad_clip_by                                                  global_norm |
| ignore_worker_failures                                              False |
| in_evaluation                                                       False |
| input                                                             sampler |
| keep_per_episode_custom_metrics                                     False |
| kl_coeff                                                              0.2 |
| kl_target                                                            0.01 |
| lambda                                                                0.1 |
| local_gpu_idx                                                           0 |
| local_tf_session_args/inter_op_parallelism_threads                      8 |
| local_tf_session_args/intra_op_parallelism_threads                      8 |
| log_level                                                           DEBUG |
| log_sys_usage                                                        True |
| logger_config                                                             |
| logger_creator                                                            |
| lr                                                                0.00017 |
| lr_schedule                                                               |
| max_num_worker_restarts                                              1000 |
| max_requests_in_flight_per_sampler_worker                               2 |
| metrics_episode_collection_timeout_s                                  60. |
| metrics_num_episodes_for_smoothing                                    100 |
| min_sample_timesteps_per_iteration                                      0 |
| min_time_s_per_iteration                                                  |
| min_train_timesteps_per_iteration                                       0 |
| model/_disable_action_flattening                                    False |
| model/_disable_preprocessor_api                                     False |
| model/_time_major                                                   False |
| model/_use_default_native_models                                       -1 |
| model/always_check_shapes                                           False |
| model/attention_dim                                                    64 |
| model/attention_head_dim                                               32 |
| model/attention_init_gru_gate_bias                                    2.0 |
| model/attention_memory_inference                                       50 |
| model/attention_memory_training                                        50 |
| model/attention_num_heads                                               1 |
| model/attention_num_transformer_units                                   1 |
| model/attention_position_wise_mlp_dim                                  32 |
| model/attention_use_n_prev_actions                                      0 |
| model/attention_use_n_prev_rewards                                      0 |
| model/conv_activation                                                relu |
| model/conv_filters                                                        |
| model/custom_action_dist                                                  |
| model/custom_model                                                        |
| model/custom_preprocessor                                                 |
| model/dim                                                              84 |
| model/encoder_latent_dim                                                  |
| model/fcnet_activation                                               tanh |
| model/fcnet_hiddens                                            [256, 256] |
| model/framestack                                                     True |
| model/free_log_std                                                  False |
| model/grayscale                                                     False |
| model/lstm_cell_size                                                  256 |
| model/lstm_use_prev_action                                          False |
| model/lstm_use_prev_action_reward                                      -1 |
| model/lstm_use_prev_reward                                          False |
| model/max_seq_len                                                      20 |
| model/no_final_linear                                               False |
| model/post_fcnet_activation                                          relu |
| model/post_fcnet_hiddens                                               [] |
| model/use_attention                                                 False |
| model/use_lstm                                                      False |
| model/vf_share_layers                                               False |
| model/zero_mean                                                      True |
| normalize_actions                                                    True |
| num_consecutive_worker_failures_tolerance                             100 |
| num_cpus_for_driver                                                     1 |
| num_cpus_per_learner_worker                                             1 |
| num_cpus_per_worker                                                     1 |
| num_envs_per_worker                                                     1 |
| num_gpus                                                                0 |
| num_gpus_per_learner_worker                                             0 |
| num_gpus_per_worker                                                     0 |
| num_learner_workers                                                     0 |
| num_sgd_iter                                                           30 |
| num_workers                                                             1 |
| observation_filter                                               NoFilter |
| observation_fn                                                            |
| observation_space                                                         |
| offline_sampling                                                    False |
| ope_split_batch_by_episode                                           True |
| output                                                                    |
| output_compress_columns                                ['obs', 'new_obs'] |
| output_max_file_size                                             67108864 |
| placement_strategy                                                   PACK |
| policies/default_policy                              ...None, None, None) |
| policies_to_train                                                         |
| policy_map_cache                                                       -1 |
| policy_map_capacity                                                   100 |
| policy_mapping_fn                                    ...t 0x7ad1d496b910> |
| policy_states_are_swappable                                         False |
| postprocess_inputs                                                  False |
| preprocessor_pref                                                deepmind |
| recreate_failed_workers                                             False |
| remote_env_batch_wait_ms                                                0 |
| remote_worker_envs                                                  False |
| render_env                                                          False |
| replay_sequence_length                                                    |
| restart_failed_sub_environments                                     False |
| rl_module_spec                                                            |
| rollout_fragment_length                                              auto |
| sample_async                                                        False |
| sample_collector                                     ...leListCollector'> |
| sampler_perf_stats_ema_coef                                               |
| seed                                                                 1234 |
| sgd_minibatch_size                                                    256 |
| shuffle_buffer_size                                                     0 |
| shuffle_sequences                                                    True |
| simple_optimizer                                                       -1 |
| sync_filters_on_rollout_workers_timeout_s                             60. |
| synchronize_filters                                                    -1 |
| tf_session_args/allow_soft_placement                                 True |
| tf_session_args/device_count/CPU                                        1 |
| tf_session_args/gpu_options/allow_growth                             True |
| tf_session_args/inter_op_parallelism_threads                            2 |
| tf_session_args/intra_op_parallelism_threads                            2 |
| tf_session_args/log_device_placement                                False |
| torch_compile_learner                                               False |
| torch_compile_learner_dynamo_backend                             inductor |
| torch_compile_learner_dynamo_mode                                         |
| torch_compile_learner_what_to_compile                ...ile.FORWARD_TRAIN |
| torch_compile_worker                                                False |
| torch_compile_worker_dynamo_backend                                onnxrt |
| torch_compile_worker_dynamo_mode                                          |
| train_batch_size                                                     4000 |
| update_worker_filter_stats                                           True |
| use_critic                                                           True |
| use_gae                                                              True |
| use_kl_loss                                                          True |
| use_worker_filter_stats                                              True |
| validate_workers_after_construction                                  True |
| vf_clip_param                                                         10. |
| vf_loss_coeff                                                          1. |
| vf_share_layers                                                        -1 |
| worker_cls                                                             -1 |
| worker_health_probe_timeout_s                                          60 |
| worker_restore_timeout_s                                             1800 |
+---------------------------------------------------------------------------+
(PPO pid=2755) Trainable.setup took 11.358 seconds. If your trainable is slow to initialize, consider setting reuse_actors=True to reduce actor creation overheads.
(PPO pid=2755) Install gputil for GPU system monitoring.
(RolloutWorker pid=2819) 2023-09-22 12:20:30,361    INFO rollout_worker.py:690 -- Generating sample batch of size 4000
2023-09-22 12:20:30,496 ERROR tune_controller.py:1502 -- Trial task failed for trial PPO_RankingEnv_8689c516
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/ray/air/execution/_internal/event_manager.py", line 110, in resolve_future
    result = ray.get(future)
  File "/usr/local/lib/python3.10/dist-packages/ray/_private/auto_init_hook.py", line 24, in auto_init_wrapper
    return fn(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/ray/_private/client_mode_hook.py", line 103, in wrapper
    return func(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/ray/_private/worker.py", line 2547, in get
    raise value.as_instanceof_cause()
ray.exceptions.RayTaskError(AttributeError): ray::PPO.train() (pid=2755, ip=172.28.0.12, actor_id=334c1673a75a460bc3f3ad2101000000, repr=PPO)
  File "/usr/local/lib/python3.10/dist-packages/ray/tune/trainable/trainable.py", line 400, in train
    raise skipped from exception_cause(skipped)
  File "/usr/local/lib/python3.10/dist-packages/ray/tune/trainable/trainable.py", line 397, in train
    result = self.step()
  File "/usr/local/lib/python3.10/dist-packages/ray/rllib/algorithms/algorithm.py", line 853, in step
    results, train_iter_ctx = self._run_one_training_iteration()
  File "/usr/local/lib/python3.10/dist-packages/ray/rllib/algorithms/algorithm.py", line 2838, in _run_one_training_iteration
    results = self.training_step()
  File "/usr/local/lib/python3.10/dist-packages/ray/rllib/algorithms/ppo/ppo.py", line 429, in training_step
    train_batch = synchronous_parallel_sample(
  File "/usr/local/lib/python3.10/dist-packages/ray/rllib/execution/rollout_ops.py", line 85, in synchronous_parallel_sample
    sample_batches = worker_set.foreach_worker(
  File "/usr/local/lib/python3.10/dist-packages/ray/rllib/evaluation/worker_set.py", line 680, in foreach_worker
    handle_remote_call_result_errors(remote_results, self._ignore_worker_failures)
  File "/usr/local/lib/python3.10/dist-packages/ray/rllib/evaluation/worker_set.py", line 76, in handle_remote_call_result_errors
    raise r.get()
ray.exceptions.RayTaskError(AttributeError): ray::RolloutWorker.apply() (pid=2819, ip=172.28.0.12, actor_id=fc63482e3750d98323a830f401000000, repr=<ray.rllib.evaluation.rollout_worker.RolloutWorker object at 0x7e19975be8c0>)
  File "/usr/local/lib/python3.10/dist-packages/ray/rllib/utils/actor_manager.py", line 185, in apply
    raise e
  File "/usr/local/lib/python3.10/dist-packages/ray/rllib/utils/actor_manager.py", line 176, in apply
    return func(self, *args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/ray/rllib/execution/rollout_ops.py", line 86, in <lambda>
    lambda w: w.sample(), local_worker=False, healthy_only=True
  File "/usr/local/lib/python3.10/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 696, in sample
    batches = [self.input_reader.next()]
  File "/usr/local/lib/python3.10/dist-packages/ray/rllib/evaluation/sampler.py", line 92, in next
    batches = [self.get_data()]
  File "/usr/local/lib/python3.10/dist-packages/ray/rllib/evaluation/sampler.py", line 277, in get_data
    item = next(self._env_runner)
  File "/usr/local/lib/python3.10/dist-packages/ray/rllib/evaluation/env_runner_v2.py", line 344, in run
    outputs = self.step()
  File "/usr/local/lib/python3.10/dist-packages/ray/rllib/evaluation/env_runner_v2.py", line 370, in step
    active_envs, to_eval, outputs = self._process_observations(
  File "/usr/local/lib/python3.10/dist-packages/ray/rllib/evaluation/env_runner_v2.py", line 637, in _process_observations
    processed = policy.agent_connectors(acd_list)
  File "/usr/local/lib/python3.10/dist-packages/ray/rllib/connectors/agent/pipeline.py", line 41, in __call__
    ret = c(ret)
  File "/usr/local/lib/python3.10/dist-packages/ray/rllib/connectors/connector.py", line 265, in __call__
    return [self.transform(d) for d in acd_list]
  File "/usr/local/lib/python3.10/dist-packages/ray/rllib/connectors/connector.py", line 265, in <listcomp>
    return [self.transform(d) for d in acd_list]
  File "/usr/local/lib/python3.10/dist-packages/ray/rllib/connectors/agent/obs_preproc.py", line 58, in transform
    d[SampleBatch.NEXT_OBS] = self._preprocessor.transform(
  File "/usr/local/lib/python3.10/dist-packages/ray/rllib/models/preprocessors.py", line 319, in transform
    self.write(observation, array, 0)
  File "/usr/local/lib/python3.10/dist-packages/ray/rllib/models/preprocessors.py", line 325, in write
    observation = OrderedDict(sorted(observation.items()))
AttributeError: 'NoneType' object has no attribute 'items'
(PPO pid=2755) 2023-09-22 12:20:30,490  ERROR actor_manager.py:500 -- Ray error, taking actor 1 out of service. ray::RolloutWorker.apply() (pid=2819, ip=172.28.0.12, actor_id=fc63482e3750d98323a830f401000000, repr=<ray.rllib.evaluation.rollout_worker.RolloutWorker object at 0x7e19975be8c0>)
(PPO pid=2755)   File "/usr/local/lib/python3.10/dist-packages/ray/rllib/utils/actor_manager.py", line 185, in apply
(PPO pid=2755)     raise e
(PPO pid=2755)   File "/usr/local/lib/python3.10/dist-packages/ray/rllib/utils/actor_manager.py", line 176, in apply
(PPO pid=2755)     return func(self, *args, **kwargs)
(PPO pid=2755)   File "/usr/local/lib/python3.10/dist-packages/ray/rllib/execution/rollout_ops.py", line 86, in <lambda>
(PPO pid=2755)     lambda w: w.sample(), local_worker=False, healthy_only=True
(PPO pid=2755)   File "/usr/local/lib/python3.10/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 696, in sample
(PPO pid=2755)     batches = [self.input_reader.next()]
(PPO pid=2755)   File "/usr/local/lib/python3.10/dist-packages/ray/rllib/evaluation/sampler.py", line 92, in next
(PPO pid=2755)     batches = [self.get_data()]
(PPO pid=2755)   File "/usr/local/lib/python3.10/dist-packages/ray/rllib/evaluation/sampler.py", line 277, in get_data
(PPO pid=2755)     item = next(self._env_runner)
(PPO pid=2755)   File "/usr/local/lib/python3.10/dist-packages/ray/rllib/evaluation/env_runner_v2.py", line 344, in run
(PPO pid=2755)     outputs = self.step()
(PPO pid=2755)   File "/usr/local/lib/python3.10/dist-packages/ray/rllib/evaluation/env_runner_v2.py", line 370, in step
(PPO pid=2755)     active_envs, to_eval, outputs = self._process_observations(
(PPO pid=2755)   File "/usr/local/lib/python3.10/dist-packages/ray/rllib/evaluation/env_runner_v2.py", line 637, in _process_observations
(PPO pid=2755)     processed = policy.agent_connectors(acd_list)
(PPO pid=2755)   File "/usr/local/lib/python3.10/dist-packages/ray/rllib/connectors/agent/pipeline.py", line 41, in __call__
(PPO pid=2755)     ret = c(ret)
(PPO pid=2755)   File "/usr/local/lib/python3.10/dist-packages/ray/rllib/connectors/connector.py", line 265, in __call__
(PPO pid=2755)     return [self.transform(d) for d in acd_list]
(PPO pid=2755)   File "/usr/local/lib/python3.10/dist-packages/ray/rllib/connectors/connector.py", line 265, in <listcomp>
(PPO pid=2755)     return [self.transform(d) for d in acd_list]
(PPO pid=2755)   File "/usr/local/lib/python3.10/dist-packages/ray/rllib/connectors/agent/obs_preproc.py", line 58, in transform
(PPO pid=2755)     d[SampleBatch.NEXT_OBS] = self._preprocessor.transform(
(PPO pid=2755)   File "/usr/local/lib/python3.10/dist-packages/ray/rllib/models/preprocessors.py", line 319, in transform
(PPO pid=2755)     self.write(observation, array, 0)
(PPO pid=2755)   File "/usr/local/lib/python3.10/dist-packages/ray/rllib/models/preprocessors.py", line 325, in write
(PPO pid=2755)     observation = OrderedDict(sorted(observation.items()))
(PPO pid=2755) AttributeError: 'NoneType' object has no attribute 'items'
2023-09-22 12:20:30,568 WARNING experiment_state.py:371 -- Experiment checkpoint syncing has been triggered multiple times in the last 30.0 seconds. A sync will be triggered whenever a trial has checkpointed more than `num_to_keep` times since last sync or if 300 seconds have passed since last sync. If you have set `num_to_keep` in your `CheckpointConfig`, consider increasing the checkpoint frequency or keeping more checkpoints. You can supress this warning by changing the `TUNE_WARN_EXCESSIVE_EXPERIMENT_CHECKPOINT_SYNC_THRESHOLD_S` environment variable.

Trial PPO_RankingEnv_8689c516 errored after 0 iterations at 2023-09-22 12:20:30. Total running time: 21s
Error file: /root/ray_results/PPO_TRAIN/PPO_RankingEnv_8689c516_1_type=StochasticSampling,disable_action_flattening=False,disable_execution_plan_api=True,disable_initiali_2023-09-22_12-20-08/error.txt

Trial status: 1 ERROR
Current time: 2023-09-22 12:20:30. Total running time: 21s
Logical resource usage: 2.0/2 CPUs, 0/0 GPUs
+------------------------------------------------------------------------------------------------------+
| Trial name                status              lr     sgd_minibatch_size     entropy_coeff     lambda |
+------------------------------------------------------------------------------------------------------+
| PPO_RankingEnv_8689c516   ERROR      0.000166759                    256       4.82692e-07        0.1 |
+------------------------------------------------------------------------------------------------------+

Number of errored trials: 1
+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| Trial name                  # failures   error file                                                                                                                                                                                   |
+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| PPO_RankingEnv_8689c516              1   /root/ray_results/PPO_TRAIN/PPO_RankingEnv_8689c516_1_type=StochasticSampling,disable_action_flattening=False,disable_execution_plan_api=True,disable_initiali_2023-09-22_12-20-08/error.txt |
+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
2023-09-22 12:20:30,939 ERROR tune.py:1139 -- Trials did not complete: [PPO_RankingEnv_8689c516]

Issue Severity

High: It blocks me from completing my task.

fardinabbasi commented 1 year ago

I would appreciate it if you could refrain from closing this issue, as there is no option to reopen it. I will close it myself once I have resolved the problem.

fardinabbasi commented 1 year ago

The AttributeError: 'NoneType' object has no attribute 'items' was from return self._get_obs() if not terminated else None. I’ve made the change to self._get_obs() if not terminated else self.observation_space.sample(). However, I’m still encountering the error message: Cannot create PPOConfig from the given config_dict! Property stdout_file is not supported. @ArturNiederfahrenhorst I would be thankful if you have time to take a look at it.

lyzyn commented 11 months ago

I have also encountered this problem. Have you resolved it? _(PPO pid=2755) 2023-09-22 12:20:18,560 WARNING algorithm_config.py:2578 -- Setting exploration_config={} because you set _enable_rl_module_api=True. When RLModule API are enabled, exploration_config can not be set. If you want to implement custom exploration behaviour, please modify the forward_exploration method of the RLModule at hand. On configs that have a default exploration config, this must be done with config.exploration_config={}._

sven1977 commented 11 months ago

Hey @fardinabbasi , the repro script you provided is not sufficient. There are several (non trivial) imports missing. Could you provide a short, self-sufficient, and single reproduction script that I can just run and debug locally and that will just run without me having to add additional code to it? Thanks!

fardinabbasi commented 11 months ago

Hey @sven1977 , The issue observation = OrderedDict(sorted(observation.items())) AttributeError: 'NoneType' object has no attribute 'items' has been addressed in this post. It primarily occurred because I returned self._get_obs() if not terminated else None in the step function.

Nonetheless, I am still encountering the warning: WARNING algorithm_config.py:672 -- Cannot create PPOConfig from the provided config_dict! Property __stdout_file__ is not supported.

lyzyn commented 11 months ago

嘿@sven1977， 问题observation = OrderedDict(sorted(observation.items())) AttributeError: 'NoneType'对象没有属性“items”已在本文中解决。这主要是因为我self._get_obs() if not terminated else None在步骤函数中返回了。

尽管如此，我仍然遇到警告：WARNING algorithm_config.py:672 -- Cannot create PPOConfig from the provided config_dict! Property __stdout_file__ is not supported。

Yes, so am I.