| 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
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| Department | 机器人学研究室 |
| Conference Name | 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2019 |
| Conference Date | November 3-8, 2019 |
| Conference Place | Macau, China |
| Source Publication | 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2019 |
| Publisher | IEEE |
| Publication Place | New York |
| 2019 | |
| Pages | 997-1002 |
| Indexed By | EI ; CPCI（ISTP） |
| EI Accession number | 20201108294719 |
| WOS ID | WOS:000544658400105 |
| Contribution Rank | 1 |
| ISSN | 2153-0858 |
| ISBN | 978-1-7281-4004-9 |
| Abstract | In 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 |
| Identifier | http://ir.sia.cn/handle/173321/26426 |
| Collection | 机器人学研究室 |
| Corresponding Author | Wang WX(王文学) |
| Affiliation | 1.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. |
| Files in This Item: | ||||||
| File Name/Size | DocType | Version | Access | License | ||
| Robotic Tracking Con（369KB） | 会议论文 | 开放获取 | CC BY-NC-SA | View Application Full Text | ||
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