Title: RobustLight++: A Meta-Diffusion Framework for Robust Traffic Signal
Primary area: other topics in machine learning (i.e., none of the above)
Keywords: diffusion model, meta learning

Abstract: While numerous Reinforcement Learning (RL) methods have been proposed for Traffic Signal Control (TSC), most lack the essential robustness and inference timeliness capabilities required for real-world deployment. The recent state-of-the-art (SoTA) method, RobustLight, represents a significant step towards practical application, yet it still exhibits critical limitations. These include poor generalization to new city environments, high inference latency that hinders real-time adaptation, and insufficient robustness for industrial-grade deployment, particularly against noisy or damaged sensor data. To overcome these obstacles, we propose RobustLight++, a novel meta-diffusion framework. We establish a theoretical connection between Denoising Diffusion Implicit Models (DDIM) and Reptile meta-learning, creating a powerful approach for robust and inference timeliness TSC. Extensive experiments on real-world TSC datasets demonstrate that RobustLight++ sets a new SoTA. It reduces inference latency by over 91.9\% compared to its predecessor and improves performance by 7.41\% to 52.13\% even under severe sensor damage, proving its industrial-grade reliability. By bridging the gap between research and practical deployment, RobustLight++ paves the way for scalable and efficient real-time traffic optimization across diverse urban landscapes.  Our code is available at https://anonymous.4open.science/r/RobustLightPlus-E14F.