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BRAX
GPU-accelerated physics simulation for RL and robotics
Apache-2.0
ABOUT
Reinforcement learning research requires fast physics simulation for training, but traditional physics engines run on CPU and cannot keep up with the throughput needs of modern GPU-accelerated RL training loops. This forces researchers to choose between simulation fidelity and training speed, slowing down iteration cycles. Brax solves this by implementing fully differentiable rigid-body physics entirely on GPU/TPU using JAX, enabling millions of environment steps per second — orders of magnitude faster than CPU-based physics engines — and allowing gradients to flow through the simulator for differentiable RL and system identification.
INSTALL
pip install braxINTEGRATION GUIDE
1. Train reinforcement learning agents at millions of environment steps per second on a single GPU
2. Run large-scale RL hyperparameter sweeps and population-based training in hours instead of days
3. Implement differentiable physics for system identification, trajectory optimization, and learning-based control
4. Benchmark and compare RL algorithms across standardized environments with hardware-accelerated simulation
5. Prototype and evaluate robot locomotion, manipulation, and control policies in simulation before deploying to hardware
TAGS
pythonjaxphysics-simulationreinforcement-learningroboticsgpuml-research