UniTSA: A Universal Reinforcement Learning Framework for V2X Traffic Signal Control
This work addresses traffic congestion in urban areas by improving traffic signal control systems, but it appears incremental as it builds on existing RL methods with specific enhancements for generalization.
The authors tackled the challenge of generalizing reinforcement learning-based traffic signal control across diverse intersection structures by proposing a universal framework with a novel agent design and traffic state augmentation methods, achieving effective performance in multiple intersection configurations.
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