Knowledge Commons of Institute of Automation,CAS
Traffic Signal Timing via Deep Reinforcement Learning | |
Li Li; Lv YS(吕宜生)![]() ![]() | |
发表期刊 | IEEE/CAA Journal of Automatica Sinica
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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. |
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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 |
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