{"ID":2893912,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.12110","arxiv_id":"2507.12110","title":"Topology Enhanced MARL for Multi-Vehicle Cooperative Decision-Making of CAVs","abstract":"The exploration-exploitation trade-off constitutes one of the fundamental challenges in reinforcement learning (RL), which is exacerbated in multi-agent reinforcement learning (MARL) due to the exponential growth of joint state-action spaces. This paper proposes a topology-enhanced MARL (TPE-MARL) method for optimizing cooperative decision-making of connected and autonomous vehicles (CAVs) in mixed traffic. This work presents two primary contributions: First, we construct a game topology tensor for dynamic traffic flow, effectively compressing high-dimensional traffic state information and decrease the search space for MARL algorithms. Second, building upon the designed game topology tensor and using QMIX as the backbone RL algorithm, we establish a topology-enhanced MARL framework incorporating visit counts and agent mutual information. Extensive simulations across varying traffic densities and CAV penetration rates demonstrate the effectiveness of TPE-MARL. Evaluations encompassing training dynamics, exploration patterns, macroscopic traffic performance metrics, and microscopic vehicle behaviors reveal that TPE-MARL successfully balances exploration and exploitation. Consequently, it exhibits superior performance in terms of traffic efficiency, safety, decision smoothness, and task completion. Furthermore, the algorithm demonstrates decision-making rationality comparable to or exceeding that of human drivers in both mixed-autonomy and fully autonomous traffic scenarios. Code of our work is available at \\href{https://github.com/leoPub/tpemarl}{https://github.com/leoPub/tpemarl}.","short_abstract":"The exploration-exploitation trade-off constitutes one of the fundamental challenges in reinforcement learning (RL), which is exacerbated in multi-agent reinforcement learning (MARL) due to the exponential growth of joint state-action spaces. This paper proposes a topology-enhanced MARL (TPE-MARL) method for optimizing...","url_abs":"https://arxiv.org/abs/2507.12110","url_pdf":"https://arxiv.org/pdf/2507.12110v1","authors":"[\"Ye Han\",\"Lijun Zhang\",\"Dejian Meng\",\"Zhuang Zhang\"]","published":"2025-07-16T10:27:36Z","proceeding":"cs.AI","tasks":"[\"cs.AI\"]","methods":"[\"Reinforcement Learning\",\"LoRA\"]","has_code":false,"code_links":[{"ID":612079,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2893912,"paper_url":"https://arxiv.org/abs/2507.12110","paper_title":"Topology Enhanced MARL for Multi-Vehicle Cooperative Decision-Making of CAVs","repo_url":"https://github.com/leoPub/tpemarl","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}