DGLight: DQN-Guided GRPO Fine-Tuning of Large Language Models for Traffic Signal Control
Traffic signal control (TSC) plays a central role in reducing congestion and maintaining urban mobility. This dissertation introduces DGLight, a critic-guided reinforcement-learning framework for adapting a pretrained large language model to TSC. DGLight first trains a CoLight-based Deep Q-Network critic to estimate traffic-aware action values from structured intersection states, then uses the frozen critic to score candidate language-model actions and optimize the policy with Group Relative Policy Optimization (GRPO). The resulting controller maps traffic states to interpretable reasoning traces and signal decisions while learning from dense per-state supervision rather than raw cumulati...
| Authors | Chenbo Yu |
|---|---|
| Primary category | cs.LG |
| Published | 28 April 2026 |
| Last updated at source | 28 April 2026 |
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