Core Exports

Note

This is the autodoc API reference. For authoring guides see Environments and Models.

The top-level rlmesh package re-exports the common entry points: environment serving and clients, model running, sandboxing, and the spaces, types, and adapters subpackages.

The top-level client and model classes are dependency-free wrappers around RLMesh-native values. Reach for them when you want native values and no framework dependency; reach for a backend module (Framework Backends) when you want tensor leaves decoded to NumPy arrays or Torch tensors.

Import	Description
rlmesh.EnvServer	Serve a Gymnasium-compatible environment endpoint (scalar or vector).
rlmesh.RemoteEnv	Connect to one environment and preserve RLMesh-native values.
rlmesh.RemoteVectorEnv	Connect to a vector endpoint and preserve RLMesh-native values.
rlmesh.SandboxEnv	Build an env image and own the container behind a single client.
rlmesh.SandboxVectorEnv	Build an env image and own the container behind a vector client.
rlmesh.Model	Wrap a Python prediction function as a native-value model worker.
rlmesh.RemoteModel	Connect to an already-served model and drive it against an env.
rlmesh.SandboxModel	Run a model policy in its own container (experimental).
rlmesh.ServeOptions	Native serve lifecycle options.
rlmesh.Tensor	Native tensor value used by dependency-free clients.
rlmesh.adapters	Observation/action adapters and contract-based resolution.
rlmesh.spaces	Space wrappers and Gymnasium conversion helpers.
rlmesh.types	Structural protocols and value aliases.

The detailed pages below describe the shared behavior:

• Serving and Clients

• Serving and Clients

• Models

• Contracts and Specs

Types

The rlmesh.types module defines the structural protocols that EnvServer accepts and the shared value aliases used by dependency-free clients. The protocols are structural, so any object with the right methods satisfies them; you do not subclass anything. Reach for them to type-annotate an environment or a value, or to check what EnvServer expects. For authoring an environment against these protocols see Environments.

Public structural protocols and shared value aliases.

class rlmesh.types.EnvLike

Bases: Protocol[EnvObsT, EnvActT]

Structural protocol for a single environment.

property observation_space: SpaceLike[EnvObsT]

Space describing reset and step observations.

property action_space: SpaceLike[EnvActT]

Space describing accepted actions.

reset(*, seed=None, options=None)

Reset the environment and return the initial observation.

Parameters:

• seed (int | None)

• options (dict[str, object] | None)

Return type:

tuple[EnvObsT, dict[str, object]]

step(action)

Apply one action and return the Gymnasium-style step tuple.

Parameters:

action (EnvActT)

Return type:

tuple[EnvObsT, SupportsFloat, bool, bool, dict[str, object]]

close()

Release environment resources.

Return type:

None

__init__(*args, **kwargs)

class rlmesh.types.SpaceLike

Bases: Protocol[SpaceT]

Structural protocol for RLMesh-compatible spaces.

sample()

Return one valid sample from the space.

Return type:

SpaceT

contains(value, /)

Return whether value belongs to the space.

Parameters:

value (Any)

Return type:

bool

seed(seed=None)

Seed the space sampler.

Parameters:

seed (int | None)

Return type:

int | list[int] | dict[str, int] | None

__init__(*args, **kwargs)

class rlmesh.types.VectorEnvLike

Bases: Protocol[BatchActionT, VectorObsT, VectorActT]

Structural protocol for vectorized environments.

property num_envs: int

Number of environment instances in the vector.

property single_observation_space: SpaceLike[VectorObsT]

Observation space for one environment in the vector.

property single_action_space: SpaceLike[VectorActT]

Action space for one environment in the vector.

reset(*, seed=None, options=None)

Reset all environments and return batched observations.

Parameters:

• seed (int | None)

• options (dict[str, object] | None)

Return type:

tuple[object, dict[str, object]]

step(actions)

Apply a batch of actions and return batched step values.

Parameters:

actions (BatchActionT)

Return type:

tuple[object, object, object, object, dict[str, object]]

close()

Release vector environment resources.

Return type:

None

__init__(*args, **kwargs)