Optimizing Traffic Signal Control Using LLM-Driven Reward Weight Adjustment in Reinforcement Learning
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With advancements in information technology, traffic signal control has become a crucial component of smarttransportation systems, and research based on reinforcement learning (RL) for this purpose is being activelyconducted. However, tuning the weights of a multi-objective reward function remains a challenging task. Thispaper proposes an algorithm that leverages a large language model (LLM) to dynamically adjust the weightsof the RL reward function in real time, enabling efficient traffic signal control at intersections. We compare theperformance of dynamic weight adjustment via LLM and evaluate the signal control efficiency of the proposedmodel under various weather conditions.
Abstract
With advancements in information technology, traffic signal control has become a crucial component of smarttransportation systems, and research based on reinforcement learning (RL) for this purpose is being activelyconducted. However, tuning the weights of a multi-objective reward function remains a challenging task. Thispaper proposes an algorithm that leverages a large language model (LLM) to dynamically adjust the weightsof the RL reward function in real time, enabling efficient traffic signal control at intersections. We compare theperformance of dynamic weight adjustment via LLM and evaluate the signal control efficiency of the proposedmodel under various weather conditions.