UniTSA: A Universal Reinforcement Learning Framework for V2X Traffic
  Signal Control
Maonan Wang Xi Xiong Yuheng Kan Chengcheng Xu Man-On Pun
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