This release is a pre-release and may not be stable for production use.
RLMesh
RLMesh is a Python SDK for model-environment evaluation. It serves Gymnasium-style environments, connects clients over local or remote transports, and adapts values for plain Python, NumPy, Torch, and JAX users.
Pre-1.0 (
0.x): the stable API may change in a minor release, with a migration note, so pin a minor range for active projects.
Installation
Install from PyPI:
pip install rlmesh
Install optional adapters as needed:
pip install "rlmesh[numpy]"
pip install "rlmesh[gymnasium]"
pip install "rlmesh[torch]"
pip install "rlmesh[jax]"
Quickstart
Install RLMesh with Gymnasium support and the NumPy client adapter:
pip install "rlmesh[gymnasium,numpy]"
In one process, serve any Gymnasium-compatible environment:
import gymnasium as gym
from rlmesh import EnvServer
env = gym.make("CartPole-v1")
EnvServer(env, "127.0.0.1:5555").serve()
In another process, connect to it as a remote environment:
from rlmesh.numpy import RemoteEnv
env = RemoteEnv("127.0.0.1:5555")
observation, info = env.reset(seed=0)
terminated = truncated = False
while not (terminated or truncated):
action = env.action_space.sample()
observation, reward, terminated, truncated, info = env.step(action)
env.close()
Links
- Homepage: https://rlmesh.dev
- Documentation: https://docs.rlmesh.dev
- Repository: https://github.com/ArenaX-Labs/rlmesh
- Examples: https://github.com/ArenaX-Labs/rlmesh/tree/main/examples/python
- Issues: https://github.com/ArenaX-Labs/rlmesh/issues
- Default contact: research@competesai.com
License
Licensed under either of Apache License, Version 2.0 or the MIT license, at your option. See LICENSE-APACHE and LICENSE-MIT.
Python wheels also include third-party notices in THIRD_PARTY_NOTICES.md.
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