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.
@article{arxiv.2106.13281,
title = {Brax -- A Differentiable Physics Engine for Large Scale Rigid Body Simulation},
author = {C. Daniel Freeman and Erik Frey and Anton Raichuk and Sertan Girgin and Igor Mordatch and Olivier Bachem},
journal= {arXiv preprint arXiv:2106.13281},
year = {2021}
}
Comments
9 pages + 12 pages of appendices and references. In submission at NeurIPS 2021 Datasets and Benchmarks Track