CASIA OpenIR
Adaptive cruise control via adaptive dynamic programming with experience replay
Wang, Bin1,2; Zhao, Dongbin2; Cheng, Jin1
Source PublicationSOFT COMPUTING
ISSN1432-7643
2019-06-01
Volume23Issue:12Pages:4131-4144
Corresponding AuthorWang, Bin(cse_wangb@ujn.edu.cn)
AbstractThe adaptive cruise control (ACC) problem can be transformed to an optimal tracking control problem for complex nonlinear systems. In this paper, a novel highly efficient model-free adaptive dynamic programming (ADP) approach with experience replay technology is proposed to design the ACC controller. Experience replay increases the data efficiency by recording the available driving data and repeatedly presenting them to the learning procedure of the acceleration controller in the ACC system. The learning framework that combines ADP with experience replay is described in detail. The distinguishing feature of the algorithm is that when estimating parameters of the critic network and the actor network with gradient rules, the gradients of historical data and current data are used to update parameters concurrently. It is proved with Lyapunov theory that the weight estimation errors of the actor network and the critic network are uniformly ultimately bounded under the novel weight update rules. The learning performance of the ACC controller implemented by this ADP algorithm is clearly demonstrated that experience replay can increase data efficiency significantly, and the approximate optimality and adaptability of the learned control policy are tested with typical driving scenarios.
KeywordAdaptive cruise control Adaptive dynamic programming Experience replay Reinforcement learning Neural networks
DOI10.1007/s00500-018-3063-7
WOS KeywordSYSTEMS ; DESIGN
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[61603150] ; National Natural Science Foundation of China[61273136] ; National Natural Science Foundation of China[61573353] ; National Natural Science Foundation of China[61533017] ; National Key Research and Development Plan[2016YFB0101000] ; Doctoral Foundation of University of Jinan[XBS1605]
Funding OrganizationNational Natural Science Foundation of China ; National Key Research and Development Plan ; Doctoral Foundation of University of Jinan
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Interdisciplinary Applications
WOS IDWOS:000466948100018
PublisherSPRINGER
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/24397
Collection中国科学院自动化研究所
Corresponding AuthorWang, Bin
Affiliation1.Univ Jinan, Sch Elect Engn, Jinan 250022, Shandong, Peoples R China
2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
First Author AffilicationChinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Wang, Bin,Zhao, Dongbin,Cheng, Jin. Adaptive cruise control via adaptive dynamic programming with experience replay[J]. SOFT COMPUTING,2019,23(12):4131-4144.
APA Wang, Bin,Zhao, Dongbin,&Cheng, Jin.(2019).Adaptive cruise control via adaptive dynamic programming with experience replay.SOFT COMPUTING,23(12),4131-4144.
MLA Wang, Bin,et al."Adaptive cruise control via adaptive dynamic programming with experience replay".SOFT COMPUTING 23.12(2019):4131-4144.
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