  The heavy traffic congestion problem has always been a concern for modern
cities. To alleviate traffic congestion, researchers use reinforcement learning
(RL) to develop better traffic signal control (TSC) algorithms in recent years.
However, most RL models are trained and tested in the same traffic flow
environment, which results in a serious overfitting problem. Since the traffic
flow environment in the real world keeps varying, these models can hardly be
applied due to the lack of generalization ability. Besides, the limited number
of accessible traffic flow data brings extra difficulty in testing the
generalization ability of the models. In this paper, we design a novel traffic
flow generator based on Wasserstein generative adversarial network to generate
sufficient diverse and quality traffic flows and use them to build proper
training and testing environments. Then we propose a meta-RL TSC framework
GeneraLight to improve the generalization ability of TSC models. GeneraLight
boosts the generalization performance by combining the idea of flow clustering
and model-agnostic meta-learning. We conduct extensive experiments on multiple
real-world datasets to show the superior performance of GeneraLight on
generalizing to different traffic flows.
