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https://github.com/vale981/ray
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60 lines
2.1 KiB
Python
60 lines
2.1 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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# -------
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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_worker=m settings.
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ERR_MSG_NO_GPUS = """Found {} GPUs on your machine (GPU devices found: {})! If your machine
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does not have any GPUs, you should set the config keys `num_gpus` and
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`num_gpus_per_worker` to 0 (they may be set to 1 by default for your
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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/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 gym[atari] autorom[accept-rom-license]`.
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For VizDoom support: Install VizDoom
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(https://github.com/mwydmuch/ViZDoom/blob/master/doc/Building.md) and
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`pip install vizdoomgym`.
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For PyBullet support: `pip install pybullet`.
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b) To register your custom env, do `from ray import tune;
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tune.register('[name]', lambda cfg: [return env obj from here using cfg])`.
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Then in your config, do `config['env'] = [name]`.
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c) Make sure you provide a fully qualified classpath, e.g.:
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`ray.rllib.examples.env.repeat_after_me_env.RepeatAfterMeEnv`
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"""
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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 the `rllib train|rollout` command, use
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`--config={'[key]': '[value]'}` on the command line.
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To change the config for `tune.run()` in a script: Modify the python dict
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passed to `tune.run(config=[...])`.
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To change the config for an RLlib Trainer instance: Modify the python dict
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passed to the Trainer's constructor, e.g. `PPO(config=[...])`.
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"""
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