电子科技2026，Vol.39Issue(9)：67-77,11.DOI:10.16180/j.cnki.issn1007-7820.2026.09.009
基于空间通道注意力的深度强化学习交通信号控制
Traffic Signal Control by Deep Reinforcement Learning Based on Spatial Channel Attention
摘要
Abstract
The existing traffic signal control methods based on reinforcement learning directly input the pre-defined traffic states into the policy network,which easily leads to redundant state information and difficulty in ex-traction,thereby affecting the policy output of the agent.For this problem,a deep reinforcement learning traffic sig-nal control model combining spatial and channel attention mechanisms is proposed.By introducing the convolution-al block attention module into the four-dimensional convolutional neural network,the proposed model can model the spatial state feature map and the interdependence between channels,thereby improving the feature quality and optimizing the signal control effect.For different traffic flow conditions,different state feature dimensions are set and sent to their respective independent experience pools.During the Q-network learning process,samples from both experience pools are considered simultaneously to ensure the output of the optimal strategy under different traf-fic conditions.The simulation results show that on the SUMO(Simulation of Urban Mobility)simulation platform,compared with other benchmark algorithms,the proposed model has better performance in terms of average waiting time,average speed and average queue length of vehicles under off-peak and peak conditions of the Nine-road in-tersection traffic network.关键词
智能交通信号控制/注意力机制/深度强化学习/深度对抗Q网络/奖励函数/经验回放/SUMO/自适应控制/马尔科夫博弈Key words
intelligent traffic signal control/attention mechanism/deep reinforcement learning/deep adver-sarial Q-network/reward function/experience replay/SUMO/adaptive control/Markov game分类
信息技术与安全科学引用本文复制引用
焦明怡,叶宝林,王翔,高慧敏,张一嘉..基于空间通道注意力的深度强化学习交通信号控制[J].电子科技,2026,39(9):67-77,11.基金项目
浙江省自然科学基金(LTGS23F030002) （LTGS23F030002）
嘉兴市应用性基础研究项目(2023AY11034)Natural Science Foundation of Zhejiang(LTGS23F030002) （2023AY11034）
Jiaxing Applied Basic Research Project(2023AY11034) （2023AY11034）
