LLM-Guided Reinforcement Learning with Representative Agents for Traffic Modeling
The framework models traffic flow using single LLM agents representing entire classes of travelers instead of one per person, improving scalability. It uses an LLM to reason about experiences and identify good actions, while explicit rules guide the strategy update to ensure stability and interpretability. This allows the model to capture complex human travel behaviors like the decoy effect.