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The development of intelligent traffic light control systems is essential forsmart transportation management. While some efforts have been made to optimizethe use of individual traffic lights in an isolated way, related studies havelargely ignored the fact that the use of multi-intersection traffic lights isspatially influenced and there is a temporal dependency of historical trafficstatus for current traffic light control. To that end, in this paper, wepropose a novel SpatioTemporal Multi-Agent Reinforcement Learning (STMARL)framework for effectively capturing the spatio-temporal dependency of multiplerelated traffic lights and control these traffic lights in a coordinating way.Specifically, we first construct the traffic light adjacency graph based on thespatial structure among traffic lights. Then, historical traffic records willbe integrated with current traffic status via Recurrent Neural Networkstructure. Moreover, based on the temporally-dependent traffic information, wedesign a Graph Neural Network based model to represent relationships amongmultiple traffic lights, and the decision for each traffic light will be madein a distributed way by the deep Q-learning method. Finally, the experimentalresults on both synthetic and real-world data have demonstrated theeffectiveness of our STMARL framework, which also provides an insightfulunderstanding of the influence mechanism among multi-intersection trafficlights.

STMARL: A Spatio-Temporal Multi-Agent Reinforcement Learning Approach for Cooperative Traffic Light Control