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Robotic Tracking Control with Kernel Trick-based Reinforcement Learning
Hu YZ(胡亚洲)1,2; Wang WX(王文学)1; Liu, Hao3; Liu LQ(刘连庆)1
Department机器人学研究室
Conference Name2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2019
Conference DateNovember 3-8, 2019
Conference PlaceMacau, China
Source Publication2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2019
PublisherIEEE
Publication PlaceNew York
2019
Pages997-1002
Indexed ByEI ; CPCI(ISTP)
EI Accession number20201108294719
WOS IDWOS:000544658400105
Contribution Rank1
ISSN2153-0858
ISBN978-1-7281-4004-9
AbstractIn recent years, reinforcement learning has been developed dramatically and is widely used to solve control problems, e.g., playing games. However, there are still some problems for reinforcement learning to perform robotic control tasks. Fortunately, the kernel trick-based methods provide a chance to deal with those challenges. This work aims at developing a kernel trick-based learning control method to carry out robotic tracking control tasks. A reward system, in this work, is presented in order to speed up the learning processes. And then, a kernel trick-based reinforcement learning tracking controller is presented to perform tracking control tasks on a robotic manipulator system. To evaluate the policy and assist the reward system to accelerate the speed of finding the optimal control policy, a critic system is introduced. Finally, from the comparison with the benchmark, the simulation results illustrate that our algorithm has faster convergence rate and can execute tracking control tasks effectively, the reward function and the critic system proposed in this work is efficient.
Language英语
Citation statistics
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/26426
Collection机器人学研究室
Corresponding AuthorWang WX(王文学)
Affiliation1.State Key Laboratory of Robotics, Shenyang Institute of Automation, Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, 110016 China
2.University of Chinese Academy of Sciences, Beijing, 100049, China
3.Georgia Institute of Technology, Department of Mathematics, Atlanta, GA 30332, United States
Recommended Citation
GB/T 7714
Hu YZ,Wang WX,Liu, Hao,et al. Robotic Tracking Control with Kernel Trick-based Reinforcement Learning[C]. New York:IEEE,2019:997-1002.
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