GeneraLight: Improving Environment Generalization of Traffic Signal Control via Meta Reinforcement Learning
This addresses the lack of generalization ability in traffic signal control models for real-world applications, though it is incremental as it builds on existing meta-RL and generative methods.
The paper tackles the overfitting problem in reinforcement learning-based traffic signal control by proposing GeneraLight, a meta-RL framework that improves generalization to varying traffic flows, achieving superior performance on real-world datasets.
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.