A Control Strategy of Autonomous Vehicles based on Deep Reinforcement Learning

被引：0

作者：

Xia, Wei ^{[1
]}

Li, Huiyun ^{[1
]}

Li, Baopu ^{[1
,2
]}

机构：

[1] Chinese Acad Sci, Shenzhen Inst Adv Technol, Beijing, Peoples R China

[2] Shenzhen Univ, Dept Biomed Engn, Shenzhen, Peoples R China

来源：

PROCEEDINGS OF 2016 9TH INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE AND DESIGN (ISCID), VOL 2 | 2016年

关键词：

Autonomous vehicles; neural network; deep reinforcement learning;

D O I：

10.1109/ISCID.2016.159

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Deep reinforcement learning has received considerable attention after the outstanding performance of AlphaGo. In this paper, we propose a new control strategy of self-driving vehicles using the deep reinforcement learning model, in which learning with an experience of professional driver and a Q-learning algorithm with filtered experience replay are proposed. Experimental results demonstrate that the proposed model can reduce the time consumption of learning by 71.2%,and the stability increases by about 32%, compared with the existing neural fitted Q-iteration algorithm.

引用

页码：198 / 201

页数：4

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