AI RESEARCH
Convergence of Multiagent Learning Systems for Traffic control
arXiv CS.LG
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ArXi:2511.11654v2 Announce Type: replace Rapid urbanization in cities like Bangalore has led to severe traffic congestion, making efficient Traffic Signal Control (TSC) essential. Multi-Agent Reinforcement Learning (MARL), often modeling each traffic signal as an independent agent using Q-learning, has emerged as a promising strategy to reduce average commuter delays. While prior work Prashant L A et.