基于神经网络Q- learning 算法的智能车路径规划
卫玉梁; 靳伍银
2019
Source Publication火力与指挥控制
ISSN1002-0640
Volume44Issue:2Pages:46-49
Abstract针对智能小车行走过程中的全局路径规划和路障规避问题,提出了一种基于神经网络Q- learning强化学习算法,采用RBF(Radial Basis Function)网络对Q学习算法的动作值函数进行逼近,基于MATLAB环境开发了智能小车全局路径规划和路障规避仿真系统。与传统的以及基于势场的Q学习算法相比,所采用的算法能更加有效地完成智能小车在行驶环境中的路径规划和路障规避。仿真结果表明:算法具有更好的收敛速度,可增强智能小车的自导航能力。
Keyword路径规划 智能小车 神经网络 仿真
Indexed ByCSCD
Language中文
WOS Research AreaAutomation & Control Systems
WOS SubjectAUTOMATION CONTROL SYSTEMS
CSCD IDCSCD:6439169
Document Type期刊论文
Identifierhttp://ir.lut.edu.cn/handle/2XXMBERH/74550
Collection机电工程学院
Affiliation兰州理工大学机电工程学院, 兰州, 甘肃 730050, 中国
First Author AffilicationColl Mechanoelect Engn
First Signature AffilicationColl Mechanoelect Engn
Recommended Citation
GB/T 7714
卫玉梁,靳伍银. 基于神经网络Q- learning 算法的智能车路径规划[J]. 火力与指挥控制,2019,44(2):46-49.
APA 卫玉梁,&靳伍银.(2019).基于神经网络Q- learning 算法的智能车路径规划.火力与指挥控制,44(2),46-49.
MLA 卫玉梁,et al."基于神经网络Q- learning 算法的智能车路径规划".火力与指挥控制 44.2(2019):46-49.
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