3 papers
cs.LG2026
CRAX: Fast Safe Reinforcement Learning Benchmarking
Tristan Tomilin, Mourad Boustani, Mickey Beurskens +1
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 progr…
cs.AI2025
HASARD: A Benchmark for Vision-Based Safe Reinforcement Learning in Embodied Agents
Tristan Tomilin, Meng Fang, Mykola Pechenizkiy
Advancing safe autonomous systems through reinforcement learning (RL) requires robust benchmarks to evaluate performance, analyze methods, and assess agent competencies. Humans pri…
cs.LG2025
SocialJax: An Evaluation Suite for Multi-agent Reinforcement Learning in Sequential Social Dilemmas
Zihao Guo, Shuqing Shi, Richard Willis +3
Sequential social dilemmas pose a significant challenge in the field of multi-agent reinforcement learning (MARL), requiring environments that accurately reflect the tension betwee…