A Case Study on the Robustness of Traffic Signal Control from a Data Science Perspective: The RobustLight Diffusion Reinforcement Learning Framework

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Damaged sensor data, often caused by noise interference and data loss, impairs the decision-making accuracy of Deep Reinforcement Learning (DRL) traffic signal control models. This paper takes RobustLight (ICML 2025) as the core case and analyzes it from a data science perspective. We introduce DRL-based traffic signal control and diffusion model principles, then examine RobustLight's dual-process framework, DSI algorithm, Denoise and Repaint modules, and its non-Markov loss design. Experiments on Jinan, Hangzhou and New York datasets under four adversarial attacks and sensor damage show that RobustLight achieves up to 50.43% improvement in state recovery, with ATT reduced by up to 42%. Finally, we discuss real-world challenges including anomaly-detection dependency, inference latency, and cross-domain generalization.