Meta-Reinforcement Learning for Multiple Traffic Signals Control

Abstract

Despite the success of recent reinforcement learning (RL) in traffic signal control which has shown to outperform the conventional control methods, current RL-based methods require large amounts of samples to learn and lack the generalization ability to a new environment. In order to solve these problems, we propose a new context-based meta-RL model that disentangles task inference and control, which improves the meta-training efficiency and accelerates the learning process in a new environment. Moreover, the Graph Attention Network is employed to achieve effective cooperation between intersections. The experiments show that our method not only improves the traffic control efficiency but also converges faster and performs more stably, compared with traditional, RL-based, and meta-RL-based traffic control methods.