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Cooperation among the traffic signals enables vehicles to move throughintersections more quickly. Conventional transportation approaches implementcooperation by pre-calculating the offsets between two intersections. Suchpre-calculated offsets are not suitable for dynamic traffic environments. Toenable cooperation of traffic signals, in this paper, we propose a model,CoLight, which uses graph attentional networks to facilitate communication.Specifically, for a target intersection in a network, CoLight can not onlyincorporate the temporal and spatial influences of neighboring intersections tothe target intersection, but also build up index-free modeling of neighboringintersections. To the best of our knowledge, we are the first to use graphattentional networks in the setting of reinforcement learning for trafficsignal control and to conduct experiments on the large-scale road network withhundreds of traffic signals. In experiments, we demonstrate that by learningthe communication, the proposed model can achieve superior performance againstthe state-of-the-art methods.

CoLight: Learning Network-level Cooperation for Traffic Signal Control