Beyond Safety Filtering: Control Barrier Function-Informed Reinforcement Learning for Connected and Automated Vehicles

arXiv (Cornell University)

Abstract

Reinforcement Learning (RL) uses rewards to guide learning, yet reward design is typically hand-crafted using heuristics that can be difficult to tune. We propose a Control Barrier Function (CBF)-informed reward design for Multi-Agent RL (MARL) that converts CBF constraint values under joint MARL actions into a reward signal that explicitly guides safe learning. We compare against two heuristic reward baselines in a four-way multi-lane intersection with connected and automated vehicles. Results show that our method achieves the highest task performance and is less sensitive to reward hyperparameters, yielding consistently strong performance across the tested hyperparameter range. Code for reproducing the experimental results and a video demonstration are available at https://github.com/bassamlab/SigmaRL.

Authors 2

1. Jianye Xu Aachen Department of Computer Science

   RWTH Aachen University

   Affiliation as printed

   Department of Computer Science , RWTH Aachen University , Germany,

2. Bassam Alrifaee

   Universität der Bundeswehr München

   Affiliation as printed

   Department of Aerospace Engineering , University of the Bundeswehr Munich , Germany,