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
• ISSN: 2168-2267; 2168-2275
• DOI: 10.1109/TCYB.2020.3015811

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 modeling the behaviors of other agents for each individual agent. In this article, a new MARL, called cooperative double <inline-formula> <tex-math notation="LaTeX">Q </tex-math></inline-formula>-learning (Co-DQL), is proposed, which has several prominent features. It uses a highly scalable independent double <inline-formula> <tex-math notation="LaTeX">Q </tex-math></inline-formula>-learning method based on double estimators and the upper confidence bound (UCB) policy, which can eliminate the over-estimation problem existing in traditional independent <inline-formula> <tex-math notation="LaTeX">Q </tex-math></inline-formula>-learning while ensuring exploration. It uses mean-field approximation to model the interaction among agents, thereby making agents learn a better cooperative strategy. In order to improve the stability and robustness of the learning process, we introduce a new reward allocation mechanism and a local state sharing method. In addition, we analyze the convergence properties of the proposed algorithm. Co-DQL is applied to TSC and tested on various traffic flow scenarios of TSC simulators. The results show that Co-DQL outperforms the state-of-the-art decentralized MARL algorithms in terms of multiple traffic metrics.