Traffic signal timing via deep reinforcement learning

被引：353
|
作者：
Li L. [1 ,2 ]
Lv Y. [3 ]
Wang F.-Y. [3 ]
机构：
[1] Department of Automation, Tsinghua University, Beijing
[2] Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Nanjing
[3] State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing
来源：
Li, Li (li-li@tsinghua.edu.cn) | 1600年 / Institute of Electrical and Electronics Engineers Inc.卷 / 03期
关键词：
deep learning; deep reinforcement learning; reinforcement learning; Traffic control;
D O I：
10.1109/JAS.2016.7508798
中图分类号：
学科分类号：
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. © 2014 Chinese Association of Automation.
引用
收藏
页码：247 / 254
页数：7
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