{"paper":{"title":"Beyond Safety Filtering: Control Barrier Function-Informed Reinforcement Learning for Connected and Automated Vehicles","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"Converting Control Barrier Function constraints into rewards guides multi-agent reinforcement learning to higher performance with reduced hyperparameter sensitivity in connected vehicle intersections.","cross_cats":["cs.SY","eess.SY"],"primary_cat":"cs.RO","authors_text":"Bassam Alrifaee, Jianye Xu","submitted_at":"2026-05-16T09:12:59Z","abstract_excerpt":"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 hyperpara"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"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.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That converting CBF constraint values under joint MARL actions into a reward signal will reliably guide safe learning without introducing new instabilities or performance trade-offs in the multi-agent intersection setting.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"CBF-informed rewards for multi-agent RL achieve higher task performance and lower sensitivity to hyperparameters 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