Human-compatible driving partners through data-regularized self-play reinforcement learning

arXiv:2403.19648v1 Announce Type: cross
Abstract: A central challenge for autonomous vehicles is coordinating with humans. Therefore, incorporating realistic human agents is essential for scalable training and evaluation of autonomous driving systems in simulation. Simulation agents are typically developed by imitating large-scale, high-quality datasets of human driving. However, pure imitation learning agents empirically have high collision rates when executed in a multi-agent closed-loop setting. To build agents that are realistic and effective in closed-loop settings, we propose Human-Regularized PPO (HR-PPO), a …

arxiv cs.ai cs.lg cs.ma cs.ro data driving human partners reinforcement reinforcement learning self-play through type