AI RESEARCH
Momentum Based Reward Design for Low Emission Traffic Signal Control
arXiv CS.LG
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ArXi:2605.29693v1 Announce Type: new Urban traffic congestion is a growing global issue contributing significantly to long commute times and environmental pollution. Traditional traffic signal control systems often fail to adapt to dynamic traffic conditions. Adaptive traffic signal control can improve urban traffic without changing road infrastructure. Deep Reinforcement Learning (DRL) has shown strong performance for this task, but existing delay and queue-based rewards often produce short-sighted or unstable policies.