  We present Brax, an open source library for rigid body simulation with a
focus on performance and parallelism on accelerators, written in JAX. We
present results on a suite of tasks inspired by the existing reinforcement
learning literature, but remade in our engine. Additionally, we provide
reimplementations of PPO, SAC, ES, and direct policy optimization in JAX that
compile alongside our environments, allowing the learning algorithm and the
environment processing to occur on the same device, and to scale seamlessly on
accelerators. Finally, we include notebooks that facilitate training of
performant policies on common OpenAI Gym MuJoCo-like tasks in minutes.
