Connected and Automated Vehicles (CAVs) have the poten- tial to revolutionize transportation systems, but their integration with human-driven vehicles (HDVs) in mixed traffic environments presents significant challenges, particularly in complex scenarios such as on-ramp merging.
...
Connected and Automated Vehicles (CAVs) have the poten- tial to revolutionize transportation systems, but their integration with human-driven vehicles (HDVs) in mixed traffic environments presents significant challenges, particularly in complex scenarios such as on-ramp merging. This paper addresses the challenge of on-ramp merging for CAVs in mixed traffic environments, proposing a novel approach called QMIX-QLambdaM. We formulate the problem as a Centralized Train- ing with Decentralized Execution (CTDE) Cooperative Multi-Agent Re- inforcement Learning (MARL) task, capable of handling dynamic sce- narios with both CAVs and HDVs. QMIX-QLambdaM enhances the QMIX algorithm by incorporating Q(λ) returns for improved value es- timation and an action masking mechanism for safer action selection. Our comprehensive experiments demonstrate that QMIX-QLambdaM consistently outperforms state-of-the-art algorithms, including QMIX, MAA2C, and COMA, across various performance metrics related to traf- fic efficiency and safety. The proposed method exhibits superior adapt- ability across different traffic densities, maintaining high performance in terms of safety, efficiency, and overall rewards. Furthermore, case stud- ies illustrate QMIX-QLambdaM’s ability to generate effective strate- gic control for both main-lane and merging-lane vehicles, showcasing smoother driving behavior and better collision avoidance compared to baseline methods. The learning curve comparison also reveals QMIX- QLambdaM’s advantage in credit assignment compared to other CTDE baselines for the formulated problem. The code are available at https: //github.com/ayton-zhang/MARL_qmix_merging.
