google-deepmind / mujoco

**MuJoCo** stands for **Mu**lti-**Jo**int dynamics with **Co**ntact. It is a
general purpose physics engine that aims to facilitate research and development
in robotics, biomechanics, graphics and animation, machine learning, and other
areas which demand fast and accurate simulation of articulated structures
interacting with their environment.
This repository is maintained by Google DeepMind.
MuJoCo has a C API and is intended for researchers and developers. The runtime
simulation module is tuned to maximize performance and operates on low-level
data structures that are preallocated by the built-in XML compiler. The library
includes interactive visualization with a native GUI, rendered in OpenGL. MuJoCo
further exposes a large number of utility functions for computing
physics-related quantities.
We also provide [Python bindings] and a plug-in for the [Unity] game engine.
## Documentation
MuJoCo's documentation can be found at [mujoco.readthedocs.io]. Upcoming
features due for the next release can be found in the [changelog] in the
"latest" branch.
## Getting Started
There are two easy ways to get started with MuJoCo:
1. **Run `simulate` on your machine.**
This video shows a screen capture
of `simulate`, MuJoCo's native interactive viewer. Follow the steps described in
the [Getting Started] section of the documentation to get `simulate` running on
your machine.
2. **Explore our online IPython notebooks.**
If you are a Python user, you might want to start with our tutorial notebooks
running on Google Colab:
 - The **introductory** tutorial teaches MuJoCo basics:
 - The **Model Editing** tutorial shows how to create and edit models procedurally:
 - The **rollout** tutorial shows how to use the multithreaded `rollout` module:
 - The **LQR** tutorial synthesizes a linear-quadratic controller, balancing a
 humanoid on one leg:
 - The **least-squares** tutorial explains how to use the Python-based nonlinear
 least-squares solver:
 - The **MJX** tutorial provides usage examples of
 MuJoCo XLA, a branch of MuJoCo written in JAX:
 - The **differentiable physics** tutorial trains locomotion policies with
 analytical gradients automatically derived from MuJoCo's physics step:
## Installation
### Prebuilt binaries
Versioned releases are available as precompiled binaries from the GitHub
[releases page], built for Linux (x86-64 and AArch64), Windows (x86-64 only),
and macOS (universal). This is the recommended way to use the software.
### Building from source
Users who wish to build MuJoCo from source should consult the [build from
source] section of the documentation. However, note that the commit at
the tip of the `main` branch may be unstable.
### Python (>= 3.10)
The native Python bindings, which come pre-packaged with a copy of MuJoCo, can
be installed from [PyPI] via:
 [code] 
Note that Pre-built Linux wheels target `manylinux2014`, see
here for compatible distributions. For more
information such as building the bindings from source, see the [Python bindi