Brax is a differentiable physics engine that simulates environments made up of rigid bodies, joints, and actuators. It's also a suite of learning algorithms to train agents to operate in these environments (PPO, SAC, evolutionary strategy, and direct trajectory optimization are implemented).
Brax is written in JAX and is designed for use on acceleration hardware. It is both efficient for single-core training, and scalable to massively parallel simulation, without the need for pesky datacenters.
Some policies trained via Brax. Brax simulates these environments at millions of physics steps per second on TPU.
Explore Brax easily and quickly through a series of colab notebooks:
- Brax Basics introduces the Brax API, and shows how to simulate basic physics primitives.
- Brax Training introduces Brax environments and training algorithms, and lets you train your own policies directly within the colab.
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.
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 = {0.1.0},
year = {2021},
}