MuJoCo's documentation can be found at https://mujoco.readthedocs.io[mujoco.readthedocs.io]. Upcoming
features due for the next release can be found in the https://mujoco.readthedocs.io/en/latest/changelog.html[changelog] in the
"latest" branch.

There are two easy ways to get started with MuJoCo:

. *Run `simulate` on your machine.*
https://www.youtube.com/watch?v=P83tKA1iz2Y[This video] shows a screen capture
of `simulate`, MuJoCo's native interactive viewer. Follow the steps described in
the https://mujoco.readthedocs.io/en/latest/programming#getting-started[Getting Started] section of the documentation to get `simulate` running on
your machine.
. *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:
image:https://colab.research.google.com/assets/colab-badge.svg[Open In Colab,link=https://colab.research.google.com/github/google-deepmind/mujoco/blob/main/python/tutorial.ipynb]
* The *Model Editing* tutorial shows how to create and edit models procedurally:
image:https://colab.research.google.com/assets/colab-badge.svg[Open In Colab,link=https://colab.research.google.com/github/google-deepmind/mujoco/blob/main/python/mjspec.ipynb]
* The *rollout* tutorial shows how to use the multithreaded `rollout` module:
image:https://colab.research.google.com/assets/colab-badge.svg[Open In Colab,link=https://colab.research.google.com/github/google-deepmind/mujoco/blob/main/python/rollout.ipynb]
* The *LQR* tutorial synthesizes a linear-quadratic controller, balancing a
humanoid on one leg:
image:https://colab.research.google.com/assets/colab-badge.svg[Open In Colab,link=https://colab.research.google.com/github/google-deepmind/mujoco/blob/main/python/LQR.ipynb]
* The *least-squares* tutorial explains how to use the Python-based nonlinear
least-squares solver:
image:https://colab.research.google.com/assets/colab-badge.svg[Open In Colab,link=https://colab.research.google.com/github/google-deepmind/mujoco/blob/main/python/least_squares.ipynb]
* The *MJX* tutorial provides usage examples of
https://mujoco.readthedocs.io/en/stable/mjx.html[MuJoCo XLA], a branch of MuJoCo written in JAX:
image:https://colab.research.google.com/assets/colab-badge.svg[Open In Colab,link=https://colab.research.google.com/github/google-deepmind/mujoco/blob/main/mjx/tutorial.ipynb]
* The *differentiable physics* tutorial trains locomotion policies with
analytical gradients automatically derived from MuJoCo's physics step:
image:https://colab.research.google.com/assets/colab-badge.svg[Open In Colab,link=https://colab.research.google.com/github/google-deepmind/mujoco/blob/main/mjx/training_apg.ipynb]

We aim to release MuJoCo in the first week of each month. Our versioning
standards changed to modified Semantic Versioning in 3.5.0,
see xref:VERSIONING.adoc[versioning] for details.

We welcome community engagement: questions, requests for help, bug reports and
feature requests. To read more about bug reports, feature requests and more
ambitious contributions, please see our xref:CONTRIBUTING.adoc[contributors guide]
and xref:STYLEGUIDE.adoc[style guide].

Questions and requests for help are welcome as a GitHub
https://github.com/google-deepmind/mujoco/discussions/categories/asking-for-help["Asking for Help" Discussion]
and should focus on a specific problem or question.

GitHub https://github.com/google-deepmind/mujoco/issues[Issues] are reserved
for bug reports, feature requests and other development-related subjects.

MuJoCo is the backbone for numerous environment packages. Below we list several
bindings and converters.

If you use MuJoCo for published research, please cite:

----
@inproceedings{todorov2012mujoco,
title={MuJoCo: A physics engine for model-based control},
author={Todorov, Emanuel and Erez, Tom and Tassa, Yuval},
booktitle={2012 IEEE/RSJ International Conference on Intelligent Robots and Systems},
pages={5026--5033},
year={2012},
organization={IEEE},
doi={10.1109/IROS.2012.6386109}
}
----

Copyright 2021 DeepMind Technologies Limited.

Box collision code (https://github.com/google-deepmind/mujoco/blob/main/src/engine/engine_collision_box.c[`engine_collision_box.c`])
is Copyright 2016 Svetoslav Kolev.

ReStructuredText documents, images, and videos in the `doc` directory are made
available under the terms of the Creative Commons Attribution 4.0 (CC BY 4.0)
license. You may obtain a copy of the License at
https://creativecommons.org/licenses/by/4.0/legalcode.

Source code is 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.