CuraLight uses RL-generated trajectories and multi-LLM debate to curate training data for an LLM traffic-signal controller, yielding 5-7% gains in travel time, queue length, and waiting time over baselines in SUMO simulations of real networks.
Multi- agent reinforcement learning for traffic signal control through universal communication method
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CuraLight: Debate-Guided Data Curation for LLM-Centered Traffic Signal Control
CuraLight uses RL-generated trajectories and multi-LLM debate to curate training data for an LLM traffic-signal controller, yielding 5-7% gains in travel time, queue length, and waiting time over baselines in SUMO simulations of real networks.