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This paper develops a decentralized reinforcement learning (RL) scheme formulti-intersection adaptive traffic signal control (TSC), called "CVLight",that leverages data collected from connected vehicles (CVs). The state andreward design facilitates coordination among agents and considers travel delayscollected by CVs. A novel algorithm, Asymmetric Advantage Actor-critic(Asym-A2C), is proposed where both CV and non-CV information is used to trainthe critic network, while only CV information is used to execute optimal signaltiming. Comprehensive experiments show the superiority of CVLight overstate-of-the-art algorithms under a 2-by-2 synthetic road network with varioustraffic demand patterns and penetration rates. The learned policy is thenvisualized to further demonstrate the advantage of Asym-A2C. A pre-traintechnique is applied to improve the scalability of CVLight, which significantlyshortens the training time and shows the advantage in performance under a5-by-5 road network. A case study is performed on a 2-by-2 road network locatedin State College, Pennsylvania, USA, to further demonstrate the effectivenessof the proposed algorithm under real-world scenarios. Compared to otherbaseline models, the trained CVLight agent can efficiently control multipleintersections solely based on CV data and achieve the best performance,especially under low CV penetration rates.

CVLight: Decentralized Learning for Adaptive Traffic Signal Control with Connected Vehicles