Google DeepMind
521d152ec5
This change modifies the Warp JAX FFI to register callbacks for both CUDA and CPU platforms, allowing Warp kernels to be launched on CPU devices. MJX's io.py is updated to allow Warp to use CPU devices if no CUDA GPU is available. Tests are adjusted to no longer skip when CUDA GPUs are absent. A minor fix for sorting precision in test_util.py is also included. Kristian Hartikainen - https://github.com/google-deepmind/mujoco/pull/2948 Github issue - https://github.com/google-deepmind/mujoco/issues/2947 PiperOrigin-RevId: 906018844 Change-Id: I9c026b4a4e2a0276d5d3af3954fd027cf75b6d7a
MuJoCo XLA (MJX)
This package is a re-implementation of the MuJoCo physics engine in 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 for more
details concerning feature parity.
Installation
The recommended way to install this package is via PyPI:
pip install mujoco-mjx
Usage
Once installed, the package can be imported via from mujoco import mjx. Please
consult our documentation
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: 
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.