Traffic Signal Timing via Deep Reinforcement Learning
Li Li; Yisheng Lv; Fei-Yue Wang
Source PublicationIEEE/CAA Journal of Automatica Sinica
2016-06
Volume3Issue:3Pages:247-254
Abstract_; 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.
KeywordTraffic Control Reinforcement Learning Deep Learning Deep Reinforcement Learning.
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/12369
Collection复杂系统管理与控制国家重点实验室_先进控制与自动化
Corresponding AuthorLi Li
Recommended Citation
GB/T 7714
Li Li,Yisheng Lv,Fei-Yue Wang. Traffic Signal Timing via Deep Reinforcement Learning[J]. IEEE/CAA Journal of Automatica Sinica,2016,3(3):247-254.
APA Li Li,Yisheng Lv,&Fei-Yue Wang.(2016).Traffic Signal Timing via Deep Reinforcement Learning.IEEE/CAA Journal of Automatica Sinica,3(3),247-254.
MLA Li Li,et al."Traffic Signal Timing via Deep Reinforcement Learning".IEEE/CAA Journal of Automatica Sinica 3.3(2016):247-254.
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