The effectiveness of traffic light control has been significantly improved by current reinforcement learning-based approaches via better cooperation among multiple traffic lights. However, a persisting issue remains: how to obtain a multi-agent traffic signal control algorithm with remarkable transferability across diverse cities? In this paper, we propose a Transformer on Transformer (TonT) model for cross-city meta multi-agent traffic signal control, named as X-Light: We input the full Markov Decision Process trajectories, and the Lower Transformer aggregates the states, actions, rewards among the target intersection and its neighbors within a city, and the Upper Transformer learns the general decision trajectories across different cities. This dual-level approach bolsters the model's robust generalization and transferability. Notably, when directly transferring to unseen scenarios, ours surpasses all baseline methods with +7.91% on average, and even +16.3% in some cases, yielding the best results.

本文提出了一种名为X-Light的基于Transformer模型的交通信号控制算法，通过在不同城市之间学习一般的决策轨迹，实现了跨城市的跨场景迁移，其性能在未知场景中超过所有基准方法平均7.91％，在某些情况下甚至达到16.3％，结果最佳。

X-Light：利用Transformer on Transformer作为元多智能体强化学习器进行跨城交通信号控制