[2603.24366] CoordLight: Learning Decentralized Coordination for Network-Wide Traffic Signal Control

Computer Science > Machine Learning arXiv:2603.24366 (cs) [Submitted on 25 Mar 2026] Title:CoordLight: Learning Decentralized Coordination for Network-Wide Traffic Signal Control Authors:Yifeng Zhang, Harsh Goel, Peizhuo Li, Mehul Damani, Sandeep Chinchali, Guillaume Sartoretti View a PDF of the paper titled CoordLight: Learning Decentralized Coordination for Network-Wide Traffic Signal Control, by Yifeng Zhang and 5 other authors View PDF HTML (experimental) Abstract:Adaptive traffic signal control (ATSC) is crucial in alleviating congestion, maximizing throughput and promoting sustainable mobility in ever-expanding cities. Multi-Agent Reinforcement Learning (MARL) has recently shown significant potential in addressing complex traffic dynamics, but the intricacies of partial observability and coordination in decentralized environments still remain key challenges in formulating scalable and efficient control strategies. To address these challenges, we present CoordLight, a MARL-based framework designed to improve intra-neighborhood traffic by enhancing decision-making at individual junctions (agents), as well as coordination with neighboring agents, thereby scaling up to network-level traffic optimization. Specifically, we introduce the Queue Dynamic State Encoding (QDSE), a novel state representation based on vehicle queuing models, which strengthens the agents' capability to analyze, predict, and respond to local traffic dynamics. We further propose an advanced MARL algo...