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COIN: Collaborative Interaction-Aware Multi-Agent Reinforcement Learning for Self-Driving Systems

Description

Multi-Agent Self-Driving (MASD) systems provide an effective solution for coordinating autonomous vehicles to reduce congestion and enhance both safety and operational efficiency in future intelligent transportation systems. Multi-Agent Reinforcement Learning (MARL) has emerged as a promising approach for developing advanced end-to-end MASD systems. However, achieving efficient and safe collaboration in dynamic MASD systems remains a significant challenge in dense scenarios with complex agent in

Source

http://arxiv.org/abs/2603.24931v1