Multi-agent meta-reinforcement learning based on contrastive predictive coding—For rapid adaptive traffic control after emergencies

Abstract

Emergency signal control has to clear emergency vehicles through intersections without creating excessive delay for ordinary traffic. Many existing controllers still depend on manually designed traffic features, and unfamiliar emergency patterns often require additional training before the control policy can be adjusted reliably. For this setting, a multi-agent meta-reinforcement learning framework is developed using Contrastive Predictive Coding (CPC) for traffic state representation. CPC compresses high-dimensional traffic sequences into compact representations that are subsequently used by intersection agents during few-sample policy adaptation. Agent communication is weighted by both distance and transmission delay, while hierarchical emergency priority control adjusts signal decisions according to event severity. Tests on the iTETRIS simulation platform with the CityFlow Emergency dataset produced an emergency response time of 25.3 s, an average regular vehicle delay of 80.5 s per vehicle, and a 98.2% emergency success rate in high-priority scenarios. In the ablation experiments, removing CPC caused the largest single performance loss, whereas the all-module configuration showed a 44.3% contribution to response-time reduction in the contribution analysis. Overall, the framework maintained emergency vehicle priority while keeping the effect on regular traffic within a comparatively limited range under the evaluated emergency conditions.