rlmesh.numpy.EnvFactory

classfrom rlmesh.numpy import EnvFactory[source]

NumPy-backed EnvFactory -- the default, named for symmetry.

Equivalent to subclassing rlmesh.EnvFactory; the served env’s obs/action seam stays on the default numpy/Auto path. Provided so rlmesh.numpy carries the env-author class next to Model, matching rlmesh.torch / rlmesh.jax.

Methods

tags_for()Return the EnvTags for one discriminant binding.
prepare()Optional: one-time setup before make().
describe()Return this factory's full metadata envelope (see rlmesh.describe()).
make()Construct and return the environment.
close()Optional: release resources.
serve()Host this env on address (blocking): prepare() + make(**make_kwargs), publish tags.

params

attribute[source]

params: ParamSpec | None

tag_params

attribute[source]

tag_params: tuple[str, ...]

reset_options

attribute[source]

reset_options: tuple[str, ...]

workflow_edition

attribute[source]

workflow_edition: str | None

tags_for

function[source]

tags_for(**params: Any) -> EnvTags | None

Return the EnvTags for one discriminant binding.

Override it alongside tag_params when the declared discriminants pick between different contracts (e.g. an action_type that switches end-effector deltas for absolute targets); params carries every declared discriminant, defaults applied. The default returns tags, so a tag_params that only moves the spaces needs no override.

The binding whose values are make’s own signature defaults is the default branch, and its result must be tags – that is the contract a reader with no branch information sees.

prepare

function[source]

prepare() -> None

Optional: one-time setup before make().

describe

function[source]

describe() -> dict[str, Any]

Return this factory’s full metadata envelope (see rlmesh.describe()).

make

function[source]

make(**kwargs: Any) -> EnvLike[Any, Any]

Construct and return the environment.

Your override returns a plain env; the returned env is automatically stamped with this factory’s tags (in env.metadata), so the tag rides the environment – a spec’d model can resolve its adapter from the env alone, whether it is served or driven locally via rlmesh.session().

close

function[source]

close() -> None

Optional: release resources.

serve

function[source]

serve(
    address: str,
    *,
    num_envs: int = 1,
    vectorization_mode: str | None = None,
    framework: str | None = None,
    device: object | None = None,
    workflow_edition: str | None = None,
    **make_kwargs: Any,
) -> None

Host this env on address (blocking): prepare() + make(**make_kwargs), publish tags.

The named keywords are serving options, forwarded to rlmesh.serve.serve_env() (num_envs > 1 fans make out into a vector env; framework/device type and place the served obs/action seam; workflow_edition overrides this class’s workflow_edition declaration for this endpoint); every other keyword goes to make. Naming them here keeps a make kwarg from silently binding to a serving option – a make parameter that shares a serving option’s name cannot ride through serve.