Deep Reinforcement Learning for Energy-efficient Train Operation of Automatic Driving

被引：0
|
作者：
Meng, Xianglin [1 ]
Wang, He [1 ]
Lin, Mu [1 ]
Zhou, Yonghua [1 ]
机构：
[1] Beijing Jiaotong Univ, Sch Elect & Informat Engn, Beijing, Peoples R China
来源：
2020 IEEE 8TH INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND NETWORK TECHNOLOGY (ICCSNT) | 2020年
基金：
北京市自然科学基金; 中国国家自然科学基金;
关键词：
Machine learning; Deep reinforcement learning; Deep Q-Network; Urban rail transit; Energy-efficient optimization;
D O I：
10.1109/iccsnt50940.2020.9305007
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
With the rapid development of urban rail transit and the improvement of machine learning technology, the application of deep reinforcement learning to train operation control has become a research hotspot. In this paper, the train operation control method based on deep reinforcement learning is established for urban rail transit. A subway line is employed to perform simulation, and the developed method is verified. The simulation results revealed the applicability and practicability of the method.
引用
收藏
页码：123 / 126
页数：4
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