ScholarWorks@숙명여자대학교

초록

With advancements in information technology, traffic signal control has become a crucial component of smart transportation systems, and research based on reinforcement learning (RL) for this purpose is being actively conducted. However, tuning the weights of a multi-objective reward function remains a challenging task. This paper proposes an algorithm that leverages a large language model (LLM) to dynamically adjust the weights of the RL reward function in real time, enabling efficient traffic signal control at intersections. We compare the performance of dynamic weight adjustment via LLM and evaluate the signal control efficiency of the proposed model under various weather conditions.

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