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Traffic congestion is a persistent problem in urban areas, which calls forthe development of effective traffic signal control (TSC) systems. Whileexisting Reinforcement Learning (RL)-based methods have shown promisingperformance in optimizing TSC, it is challenging to generalize these methodsacross intersections of different structures. In this work, a universalRL-based TSC framework is proposed for Vehicle-to-Everything (V2X)environments. The proposed framework introduces a novel agent design thatincorporates a junction matrix to characterize intersection states, making theproposed model applicable to diverse intersections. To equip the proposedRL-based framework with enhanced capability of handling various intersectionstructures, novel traffic state augmentation methods are tailor-made for signallight control systems. Finally, extensive experimental results derived frommultiple intersection configurations confirm the effectiveness of the proposedframework. The source code in this work is available athttps://github.com/wmn7/Universal_Light

UniTSA: A Universal Reinforcement Learning Framework for V2X Traffic Signal Control