AI RESEARCH
CoordLight: Learning Decentralized Coordination for Network-Wide Traffic Signal Control
arXiv CS.LG
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ArXi:2603.24366v1 Announce Type: new 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.