摘要
为了提高交通控制算法的适应性和鲁棒性,缓解城市交通拥堵,提出了一种改进的D3QN(dueling double deep Q-network, D3QN)自适应信号控制方法。首先对几种强化学习自适应控制模式进行分析,然后在固定步长动作模式的基础上提出了不定步长动作模式,并构造了一种基于空间占有率的奖励函数;最后使用Sumo软件,对中山市东区街道某交叉口分别在稳定流和随机流场景下进行仿真。仿真结果表明:该方法具有良好的收敛性,有效地降低了延误时间和排队长度。
In order to improve the robustness and adaptability of traffic control algorithms and ease urban traffic congestion,an adaptive traffic signal control method based on improved D3QN(dueling double deep Q-network,D3QN)was proposed.Firstly,several adaptive traffic control modes based on reinforcement learning were analyzed.Subsequently,a variable step-size action mode was proposed based on the fixed step-size action mode and a reward function based on space occupancy was constructed.Finally,an intersection in East Street of Zhongshan was simulated by software Sumo in steady flow and stochastic flow.The simulation results show that the proposed method exhibits excellent convergence and effectively reduces the delay time and the queue length.
作者
徐建闽
周湘鹏
首艳芳
XU Jianmin;ZHOU Xiangpeng;SHOU Yanfang(School of Civil and Transportation Engineering,South China University of Technology,Guangzhou 510640,Guangdong,China;Guangzhou Institute of Modern Industrial Technology,South China University of Technology,Guangzhou 510640,Guangdong,China)
出处
《重庆交通大学学报（自然科学版）》
CAS
CSCD
北大核心
2022年第8期24-29,共6页
Journal of Chongqing Jiaotong University(Natural Science)
基金
国家自然科学基金面上项目(61873098)
广东省自然科学基金项目(2018A030313250)
广东省科技计划项目(2016A030305001)。
关键词
交通工程
交通仿真
自适应控制
交通流
深度强化学习
traffic engineering
traffic simulation
adaptive control
traffic flow
deep reinforcement learning
作者简介
第一作者:徐建闽(1960-),男,山东招远人,教授,博士,主要从事智能交通控制方面的研究。E⁃mail:aujmxu@scut.edu.cn。
