Velocity control in a right-turn across traffic scenario for autonomous vehicles using kernel-based reinforcement learning

被引：0

作者：

Zhang, Yuxiang ^{[1
]}

Gao, Bingzhao ^{[1
]}

Zhou, Jinghua ^{[2
]}

Guo, Lulu ^{[1
,3
]}

Chen, Hong ^{[1
,3
]}

机构：

[1] Jilin Univ, State Key Lab Automot Simulat & Control, Changchun 130025, Peoples R China

[2] Jilin Normal Univ, Coll Comp, Siping 136000, Peoples R China

[3] Jilin Univ, Dept Control Sci & Engn, Changchun 130025, Peoples R China

来源：

2017 CHINESE AUTOMATION CONGRESS (CAC) | 2017年

基金：

中国国家自然科学基金;

关键词：

autonomous vehicle; reinforcement learning (RL); kernel-based least squares policy iteration (KLSPI);

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Recently, advanced control methods like machine leaning are increasingly applied to autonomous vehicle. This paper focuses on velocity control in a right-turn traffic scenario. A Markov Decision Processes(MDPs) is modeled and the actor-critic reinforcement learning architecture is employed. Then the kernel-based least squares policy iteration algorithm(KLSPI) is applied. Simulation results show that the proposed method can perform different policy in different cases, which preliminarily verify the rationality.

引用

页码：6211 / 6216

页数：6

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