# MuJoCo XLA (MJX) [![PyPI Python Version][pypi-versions-badge]][pypi] [![PyPI version][pypi-badge]][pypi] [pypi-versions-badge]: https://img.shields.io/pypi/pyversions/mujoco-mjx [pypi-badge]: https://badge.fury.io/py/mujoco-mjx.svg [pypi]: https://pypi.org/project/mujoco-mjx/ This package is a re-implementation of the [MuJoCo physics engine](https://github.com/google-deepmind/mujoco) in [JAX](https://github.com/jax-ml/jax). This library is developed and maintained by Google DeepMind, and is kept up-to-date with the latest developments in MuJoCo itself. The `mujoco-mjx` package is API-compatible with MuJoCo, but is missing some features found in MuJoCo. See our [documentation](https://mujoco.readthedocs.io/en/stable/mjx.html) for more details concerning feature parity. ## Installation The recommended way to install this package is via [PyPI](https://pypi.org/project/mujoco-mjx/): ```sh pip install mujoco-mjx ``` ## Usage Once installed, the package can be imported via `from mujoco import mjx`. Please consult our [documentation](https://mujoco.readthedocs.io/en/stable/mjx.html) for further detail on the package's API. We recommend going through the tutorial notebook which introduces the MJX API and trains a reinforcement learning policy in a few minutes: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/google-deepmind/mujoco/blob/main/mjx/tutorial.ipynb) ## Versioning The `major.minor.micro` portion of the version number matches the version of MuJoCo that this library provides. Optionally, if we release updates to MJX that target the same version of MuJoCo, a `.postN` suffix is added, for example `3.0.1.post2` represents the second update to MJX for MuJoCo 3.0.1. ## License and Disclaimer Copyright 2023 DeepMind Technologies Limited MuJoCo and its libraries are licensed under the Apache License, Version 2.0. You may obtain a copy of the License at https://www.apache.org/licenses/LICENSE-2.0. This is not an officially supported Google product.