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IEEE/CAA Journal of Automatica Sinica

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Y. Li, Y. Zhang, X. Li, and C. Sun, “Regional multi-agent cooperative reinforcement learning for city-level traffic grid signal control,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 9, pp. 1–12, Sept. 2024.
Citation: Y. Li, Y. Zhang, X. Li, and C. Sun, “Regional multi-agent cooperative reinforcement learning for city-level traffic grid signal control,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 9, pp. 1–12, Sept. 2024.

Regional Multi-Agent Cooperative Reinforcement Learning for City-Level Traffic Grid Signal Control

Funds:  This work was supported by the National Science and Technology Major Project (2021ZD0112702), the National Natural Science Foundation (NNSF) of China (62373100, 62233003), and the Natural Science Foundation of Jiangsu Province of China (BK20202006)
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  • This article studies the effective traffic signal control problem of multiple intersections in a city-level traffic system. A novel regional multi-agent cooperative reinforcement learning algorithm called RegionSTLight is proposed to improve the traffic efficiency. Firstly a regional multi-agent Q-learning framework is proposed, which can equivalently decompose the global Q value of the traffic system into the local values of several regions. Based on the framework and the idea of human-machine cooperation, a dynamic zoning method is designed to divide the traffic network into several strong-coupled regions according to real-time traffic flow densities. In order to achieve better cooperation inside each region, a lightweight spatio-temporal fusion feature extraction network is designed. The experiments in synthetic, real-world and city-level scenarios show that the proposed RegionSTLight converges more quickly, is more stable, and obtains better asymptotic performance compared to state-of-the-art models.

     

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