mirror of
https://github.com/vale981/ray
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111 lines
4.2 KiB
Python
111 lines
4.2 KiB
Python
import copy
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from typing import Optional
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from ray.rllib.utils.annotations import PublicAPI
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from ray.rllib.utils.typing import EnvConfigDict
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@PublicAPI
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class EnvContext(dict):
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"""Wraps env configurations to include extra rllib metadata.
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These attributes can be used to parameterize environments per process.
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For example, one might use `worker_index` to control which data file an
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environment reads in on initialization.
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RLlib auto-sets these attributes when constructing registered envs.
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"""
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def __init__(
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self,
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env_config: EnvConfigDict,
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worker_index: int,
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vector_index: int = 0,
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remote: bool = False,
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num_workers: Optional[int] = None,
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):
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"""Initializes an EnvContext instance.
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Args:
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env_config: The env's configuration defined under the
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"env_config" key in the Trainer's config.
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worker_index: When there are multiple workers created, this
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uniquely identifies the worker the env is created in.
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0 for local worker, >0 for remote workers.
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num_workers: The total number of (remote) workers in the set.
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0 if only a local worker exists.
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vector_index: When there are multiple envs per worker, this
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uniquely identifies the env index within the worker.
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Starts from 0.
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remote: Whether individual sub-environments (in a vectorized
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env) should be @ray.remote actors or not.
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"""
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# Store the env_config in the (super) dict.
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dict.__init__(self, env_config)
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# Set some metadata attributes.
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self.worker_index = worker_index
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self.vector_index = vector_index
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self.remote = remote
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self.num_workers = num_workers
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def copy_with_overrides(
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self,
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env_config: Optional[EnvConfigDict] = None,
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worker_index: Optional[int] = None,
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vector_index: Optional[int] = None,
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remote: Optional[bool] = None,
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num_workers: Optional[int] = None,
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) -> "EnvContext":
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"""Returns a copy of this EnvContext with some attributes overridden.
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Args:
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env_config: Optional env config to use. None for not overriding
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the one from the source (self).
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worker_index: Optional worker index to use. None for not
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overriding the one from the source (self).
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vector_index: Optional vector index to use. None for not
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overriding the one from the source (self).
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remote: Optional remote setting to use. None for not overriding
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the one from the source (self).
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num_workers: Optional num_workers to use. None for not overriding
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the one from the source (self).
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Returns:
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A new EnvContext object as a copy of self plus the provided
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overrides.
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"""
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return EnvContext(
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copy.deepcopy(env_config) if env_config is not None else self,
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worker_index if worker_index is not None else self.worker_index,
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vector_index if vector_index is not None else self.vector_index,
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remote if remote is not None else self.remote,
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num_workers if num_workers is not None else self.num_workers,
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)
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def set_defaults(self, defaults: dict) -> None:
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"""Sets missing keys of self to the values given in `defaults`.
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If `defaults` contains keys that already exist in self, don't override
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the values with these defaults.
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Args:
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defaults: The key/value pairs to add to self, but only for those
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keys in `defaults` that don't exist yet in self.
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Examples:
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>>> env_ctx = EnvContext({"a": 1, "b": 2}, worker_index=0)
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>>> env_ctx.set_defaults({"a": -42, "c": 3})
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>>> print(env_ctx)
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... {"a": 1, "b": 2, "c": 3}
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"""
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for key, value in defaults.items():
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if key not in self:
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self[key] = value
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def __str__(self):
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return (
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super().__str__()[:-1]
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+ f", worker={self.worker_index}/{self.num_workers}, "
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f"vector_idx={self.vector_index}, remote={self.remote}" + "}"
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)
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