wehub-resource-sync
127 lines
5.1 KiB
Python
127 lines
5.1 KiB
Python
from ray.rllib.utils.annotations import PublicAPI
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@PublicAPI
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class UnsupportedSpaceException(Exception):
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"""Error for an unsupported action or observation space."""
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pass
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@PublicAPI
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class EnvError(Exception):
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"""Error if we encounter an error during RL environment validation."""
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pass
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@PublicAPI
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class MultiAgentEnvError(Exception):
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"""Error if we encounter an error during MultiAgentEnv stepping/validation."""
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pass
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@PublicAPI
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class NotSerializable(Exception):
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"""Error if we encounter objects that can't be serialized by ray."""
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pass
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# -------
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# Error messages
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# -------
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# Message explaining there are no GPUs available for the
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# num_gpus=n or num_gpus_per_env_runner=m settings.
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ERR_MSG_NO_GPUS = """Found {} GPUs on your machine (GPU devices found: {})! If your
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machine does not have any GPUs, you should set the config keys
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`num_gpus_per_learner` and `num_gpus_per_env_runner` to 0. They may be set to
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1 by default for your particular RL algorithm."""
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ERR_MSG_INVALID_ENV_DESCRIPTOR = """The env string you provided ('{}') is:
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a) Not a supported or an installed environment.
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b) Not a tune-registered environment creator.
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c) Not a valid env class string.
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Try one of the following:
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a) For Atari support: `pip install gymnasium[atari]` and prefix the environment name with `ale_py:`, for example, `"ale_py:ALE/Pong-v5"`.
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b) To register your custom env, do `from ray import tune; tune.register_env('[name]', lambda cfg: [return env obj from here using cfg])`.
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Then in your config, do `config.environment(env='[name]').
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c) Make sure you provide a fully qualified classpath, e.g.:
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`ray.rllib.examples.envs.classes.repeat_after_me_env.RepeatAfterMeEnv`
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"""
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ERR_MSG_OLD_GYM_API = """Your environment ({}) does not abide to the new gymnasium-style API!
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From Ray 2.3 on, RLlib only supports the new (gym>=0.26 or gymnasium) Env APIs.
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{}
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Learn more about the most important changes here:
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https://github.com/openai/gym and here: https://github.com/Farama-Foundation/Gymnasium
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In order to fix this problem, do the following:
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1) Run `pip install gymnasium` on your command line.
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2) Change all your import statements in your code from
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`import gym` -> `import gymnasium as gym` OR
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`from gym.spaces import Discrete` -> `from gymnasium.spaces import Discrete`
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For your custom (single agent) gym.Env classes:
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3.1) Either wrap your old Env class via the provided `from gymnasium.wrappers import
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EnvCompatibility` wrapper class.
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3.2) Alternatively to 3.1:
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- Change your `reset()` method to have the call signature 'def reset(self, *,
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seed=None, options=None)'
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- Return an additional info dict (empty dict should be fine) from your `reset()`
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method.
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- Return an additional `truncated` flag from your `step()` method (between `done` and
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`info`). This flag should indicate, whether the episode was terminated prematurely
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due to some time constraint or other kind of horizon setting.
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For your custom RLlib `MultiAgentEnv` classes:
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4.1) Either wrap your old MultiAgentEnv via the provided
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`from ray.rllib.env.wrappers.multi_agent_env_compatibility import
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MultiAgentEnvCompatibility` wrapper class.
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4.2) Alternatively to 4.1:
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- Change your `reset()` method to have the call signature
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'def reset(self, *, seed=None, options=None)'
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- Return an additional per-agent info dict (empty dict should be fine) from your
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`reset()` method.
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- Rename `dones` into `terminateds` and only set this to True, if the episode is really
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done (as opposed to has been terminated prematurely due to some horizon/time-limit
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setting).
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- Return an additional `truncateds` per-agent dictionary flag from your `step()`
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method, including the `__all__` key (100% analogous to your `dones/terminateds`
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per-agent dict).
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Return this new `truncateds` dict between `dones/terminateds` and `infos`. This
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flag should indicate, whether the episode (for some agent or all agents) was
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terminated prematurely due to some time constraint or other kind of horizon setting.
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""" # noqa
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ERR_MSG_TF_POLICY_CANNOT_SAVE_KERAS_MODEL = """Could not save keras model under self[TfPolicy].model.base_model!
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This is either due to ..
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a) .. this Policy's ModelV2 not having any `base_model` (tf.keras.Model) property
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b) .. the ModelV2's `base_model` not being used by the Algorithm and thus its
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variables not being properly initialized.
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""" # noqa
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ERR_MSG_TORCH_POLICY_CANNOT_SAVE_MODEL = """Could not save torch model under self[TorchPolicy].model!
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This is most likely due to the fact that you are using an Algorithm that
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uses a Catalog-generated TorchModelV2 subclass, which is torch.save() cannot pickle.
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""" # noqa
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# -------
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# HOWTO_ strings can be added to any error/warning/into message
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# to eplain to the user, how to actually fix the encountered problem.
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# -------
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# HOWTO change the RLlib config, depending on how user runs the job.
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HOWTO_CHANGE_CONFIG = """
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To change the config for `tune.Tuner().fit()` in a script: Modify the python dict
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passed to `tune.Tuner(param_space=[...]).fit()`.
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To change the config for an RLlib Algorithm instance: Modify the python dict
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passed to the Algorithm's constructor, e.g. `PPO(config=[...])`.
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"""
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