Large-Scale Traffic Signal Control Using a Novel Multiagent Reinforcement Learning

• Published in: IEEE transactions on cybernetics Vol. 51; no. 1; pp. 174 - 187
• Main Authors: Wang, Xiaoqiang, Ke, Liangjun, Qiao, Zhimin, Chai, Xinghua
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.01.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Convergence; Double estimators; Games; Learning; Learning (artificial intelligence); Markov processes; mean-field approximation; multiagent reinforcement learning (MARL); Multiagent systems; Nash equilibrium; Simulators; Traffic control; Traffic flow; traffic signal control (TSC); Traffic signals

Finding the optimal signal timing strategy is a difficult task for the problem of large-scale traffic signal control (TSC). Multiagent reinforcement learning (MARL) is a promising method to solve this problem. However, there is still room for improvement in extending to large-scale problems and mode...