福州大学学报（自然科学版）2026，Vol.54Issue(3)：292-299,8.DOI:10.7631/issn.1000-2243.25126
结合评价模型与多Q值SAC的交叉口信号控制方法
Adaptive traffic signal control method for intersection based on evaluation model and multi-Q SAC algorithm
摘要
Abstract
To address the challenges of prolonged training time and limited adaptability to dynamic traffic conditions in deep reinforcement learning-based intersection signal control,this study proposes a multi-Q soft actor-critic(Multi-Q SAC)approach integrated with an evaluation model.The evaluation model,constructed from extensive VISSIM simulation data,enables the decoupling of reinforcement learning from microscopic simulations and significantly enhances training efficiency.The model’s evaluation indicators are employed as the state variables of the Multi-Q SAC algorithm,while three representative indicators are weighted to define the reward function.Experimental results demonstrate that,compared with the traditional Webster timing method and other policy-based reinforcement learn-ing algorithms,the proposed method substantially improves intersection performance and maintains fast convergence under complex and varying traffic flows.关键词
智能交通/交通信号控制/深度强化学习/评价模型/软演员评论家算法Key words
intelligent transportation/traffic signal control/deep reinforcement learning/critic model/soft actor-critic分类
交通工程引用本文复制引用
林宇舜,李杰,陈之焕,何龙,廖飞宇..结合评价模型与多Q值SAC的交叉口信号控制方法[J].福州大学学报（自然科学版）,2026,54(3):292-299,8.基金项目
福建省自然科学基金资助项目(2020J05029) （2020J05029）
中央引导地方科技发展专项基金资助项目(2022L3007) （2022L3007）
