Optimizing Traffic Signal Control Using LLM-Driven Reward Weight Adjustment in Reinforcement Learning


Sujeong Choi, Yujin Lim, Journal of Information Processing Systems Vol. 21, No. 1, pp. 43-51, Mar. 2025  

https://doi.org/10.3745/JIPS.04.0334
Keywords: Large Language Model, Reinforcement Learning, Traffic signal control
Fulltext:

Abstract

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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Cite this article
[APA Style]
Choi, S. & Lim, Y. (2025). Optimizing Traffic Signal Control Using LLM-Driven Reward Weight Adjustment in Reinforcement Learning. Journal of Information Processing Systems, 21(1), 43-51. DOI: 10.3745/JIPS.04.0334.

[IEEE Style]
S. Choi and Y. Lim, "Optimizing Traffic Signal Control Using LLM-Driven Reward Weight Adjustment in Reinforcement Learning," Journal of Information Processing Systems, vol. 21, no. 1, pp. 43-51, 2025. DOI: 10.3745/JIPS.04.0334.

[ACM Style]
Sujeong Choi and Yujin Lim. 2025. Optimizing Traffic Signal Control Using LLM-Driven Reward Weight Adjustment in Reinforcement Learning. Journal of Information Processing Systems, 21, 1, (2025), 43-51. DOI: 10.3745/JIPS.04.0334.