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Brax is a fast and fully differentiable physics engine used for research and development of robotics, human perception, materials science, reinforcement learning, and other simulation-heavy applications.
Brax is written in JAX and is designed for use on acceleration hardware. It is both efficient for single-device simulation, and scalable to massively parallel simulation on multiple devices, without the need for pesky datacenters.
Brax simulates environments at millions of physics steps per second on TPU, and includes a suite of learning algorithms that train agents in seconds to minutes:
Brax offers four distinct physics pipelines that are easy to swap:
These pipelines share the same API and can run side-by-side within the same simulation. This makes Brax well suited for experiments in transfer learning and closing the gap between simulation and the real world.
Explore Brax easily and quickly through a series of colab notebooks:
MJX
physics simulator.To install Brax from pypi, install it with:
python3 -m venv env
source env/bin/activate
pip install --upgrade pip
pip install brax
You may also install from Conda or Mamba:
conda install -c conda-forge brax # s/conda/mamba for mamba
Alternatively, to install Brax from source, clone this repo, cd
to it, and then:
python3 -m venv env
source env/bin/activate
pip install --upgrade pip
pip install -e .
To train a model:
learn
Training on NVidia GPU is supported, but you must first install CUDA, CuDNN, and JAX with GPU support.
For a deep dive into Brax's design and performance characteristics, please see our paper, Brax -- A Differentiable Physics Engine for Large Scale Rigid Body Simulation , which appeared in the Datasets and Benchmarks Track at NeurIPS 2021.
If you would like to reference Brax in a publication, please use:
@software{brax2021github,
author = {C. Daniel Freeman and Erik Frey and Anton Raichuk and Sertan Girgin and Igor Mordatch and Olivier Bachem},
title = {Brax - A Differentiable Physics Engine for Large Scale Rigid Body Simulation},
url = {http://github.com/google/brax},
version = {0.12.4},
year = {2021},
}
Brax has come a long way since its original publication. We offer gratitude and effusive praise to the following people:
FAQs
A differentiable physics engine written in JAX.
We found that brax demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 3 open source maintainers collaborating on the project.
Did you know?
Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.
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