  This paper develops a decentralized reinforcement learning (RL) scheme for
multi-intersection adaptive traffic signal control (TSC), called "CVLight",
that leverages data collected from connected vehicles (CVs). The state and
reward design facilitates coordination among agents and considers travel delays
collected by CVs. A novel algorithm, Asymmetric Advantage Actor-critic
(Asym-A2C), is proposed where both CV and non-CV information is used to train
the critic network, while only CV information is used to execute optimal signal
timing. Comprehensive experiments show the superiority of CVLight over
state-of-the-art algorithms under a 2-by-2 synthetic road network with various
traffic demand patterns and penetration rates. The learned policy is then
visualized to further demonstrate the advantage of Asym-A2C. A pre-train
technique is applied to improve the scalability of CVLight, which significantly
shortens the training time and shows the advantage in performance under a
5-by-5 road network. A case study is performed on a 2-by-2 road network located
in State College, Pennsylvania, USA, to further demonstrate the effectiveness
of the proposed algorithm under real-world scenarios. Compared to other
baseline models, the trained CVLight agent can efficiently control multiple
intersections solely based on CV data and achieve the best performance,
especially under low CV penetration rates.
