Traffic Signal Timing via Deep Reinforcement Learning

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 平行智能技术与系统团队

	Traffic Signal Timing via Deep Reinforcement Learning
	Li Li; Lv YS(吕宜生); Fei-Yue Wang
发表期刊	IEEE/CAA Journal of Automatica Sinica
	2016
期号	3 页码:247-254
摘要	In this paper, we propose a set of algorithms to design signal timing plans via deep reinforcement learning. The core idea of this approach is to set up a deep neural network (DNN) to learn the Q-function of reinforcement learning from the sampled traffic state/control inputs and the corresponding traffic system performance output. Based on the obtained DNN, we can find the appropriate signal timing policies by implicitly modeling the control actions and the change of system states. We explain the possible benefits and implementation tricks of this new approach. The relationships between this new approach and some existing approaches are also carefully discussed.
关键词	Traffic control , reinforcement learning , deep learning , deep reinforcement learning
语种	英语
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/47495
专题	多模态人工智能系统全国重点实验室_平行智能技术与系统团队
通讯作者	Li Li
推荐引用方式 GB/T 7714	Li Li,Lv YS,Fei-Yue Wang. Traffic Signal Timing via Deep Reinforcement Learning[J]. IEEE/CAA Journal of Automatica Sinica,2016(3):247-254.
APA	Li Li,Lv YS,&Fei-Yue Wang.(2016).Traffic Signal Timing via Deep Reinforcement Learning.IEEE/CAA Journal of Automatica Sinica(3),247-254.
MLA	Li Li,et al."Traffic Signal Timing via Deep Reinforcement Learning".IEEE/CAA Journal of Automatica Sinica .3(2016):247-254.

条目包含的文件		下载所有文件
文件名称/大小	文献类型	版本类型	开放类型	使用许可
Traffic signal timin（509KB）	期刊论文	作者接受稿	开放获取	CC BY-NC-SA	浏览下载