C2T learns an LLM-derived common-sense reward function to improve cooperative multi-intersection traffic control policies, outperforming standard MARL baselines on efficiency, safety, and energy proxies while allowing prompt-based policy tuning.
Efficient-CoLight: Learning efficient pressure rep- resentation for network-level traffic signal control
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C$^2$T: Captioning-Structure and LLM-Aligned Common-Sense Reward Learning for Traffic--Vehicle Coordination
C2T learns an LLM-derived common-sense reward function to improve cooperative multi-intersection traffic control policies, outperforming standard MARL baselines on efficiency, safety, and energy proxies while allowing prompt-based policy tuning.