Traffic congestion is a persistent problem in urban areas, which calls for the development of effective traffic signal control (TSC) systems. While existing Reinforcement Learning (RL)-based methods have shown promising performance in optimizing TSC, it is challenging to generalize these methods across intersections of different structures. In this work, a universal RL-based TSC framework is proposed for Vehicle-to-Everything (V2X) environments. The proposed framework introduces a novel agent design that incorporates a junction matrix to characterize intersection states, making the proposed model applicable to diverse intersections. To equip the proposed RL-based framework with enhanced capability of handling various intersection structures, novel traffic state augmentation methods are tailor-made for signal light control systems. Finally, extensive experimental results derived from multiple intersection configurations confirm the effectiveness of the proposed framework. The source code in this work is available at https://github.com/wmn7/Universal_Light

翻译：交通拥堵是城市区域的持续性问题，亟需开发有效的交通信号控制系统。虽然现有基于强化学习的方法在优化交通信号控制方面展现出良好性能，但这些方法难以推广至不同结构的交叉路口。本文针对车联网环境提出了一种通用的基于强化学习的交通信号控制框架。该框架引入了一种新颖的智能体设计方案，通过交叉口矩阵表征路口状态，使模型能够适用于多样化交叉路口。为赋予该基于强化学习的框架更强的异质路口结构处理能力，本文针对信号灯控制系统定制了创新性的交通状态增强方法。最后，基于多种交叉路口配置的广泛实验结果验证了所提框架的有效性。本文源代码开源于https://github.com/wmn7/Universal_Light