CRAX: Fast Safe Reinforcement Learning Benchmarking

Abstract:Safety is a core concern for deploying reinforcement learning (RL) agents in real-world domains such as robotics and autonomous driving. While benchmarks have been central to progress in RL, existing 3D physics-based safety benchmarks remain computationally slow, limiting large-scale experimentation and rapid prototyping. To address this gap, we propose CRAX (Constrained RL Accelerated with JAX). Built on top of the MuJoCo XLA (MJX) physics engine, CRAX leverages vectorized operations and hardware acceleration, yielding up to 200x faster training over comparable CPU-based safety benchmarks. The benchmark features eight tasks spanning three difficulty levels and multiple agent morphologies. Evaluating seven popular safe RL methods, we find that none dominates across tasks, and that learning safe policies from pixels remains largely unsolved.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2606.20376 [cs.LG]

(or arXiv:2606.20376v3 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2606.20376 Focus to learn more arXiv-issued DOI via DataCite

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