Adaptive Q-Learning for Data-Based Optimal Output Regulation With Experience Replay
文献类型：期刊论文
| 作者 | Luo, Biao1 ; Yang, Yin2; Liu, Derong3
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| 刊名 | IEEE TRANSACTIONS ON CYBERNETICS
 |
| 出版日期 | 2018-12-01 |
| 卷号 | 48期号:12页码:3337-3348 |
| ISSN号 | 2168-2267 |
| 关键词 | Data-based experience replay neural networks (NNs) off-policy optimal control Q-learning (QL) |
| DOI | 10.1109/TCYB.2018.2821369 |
| 通讯作者 | Luo, Biao(biao.luo@hotmail.com) |
| 英文摘要 | In this paper, the data-based optimal output regulation problem of discrete-time systems is investigated. An off-policy adaptive Q-learning (QL) method is developed by using real system data without requiring the knowledge of system dynamics and the mathematical model of utility function. By introducing the Q-function, an off-policy adaptive QI, algorithm is developed to learn the optimal Q-function. An adaptive parameter alpha(i) in the policy evaluation is used to achieve tradeoff between the current and future Q-functions. The convergence of adaptive QI, algorithm is proved and the influence of the adaptive parameter is analyzed. To realize the adaptive QL algorithm with real system data, the actor-critic neural network (NN) structure is developed. The least-squares scheme and the batch gradient descent method are developed to update the critic and actor NN weights, respectively. The experience replay technique is employed in the learning process, which leads to simple and convenient implementation of the adaptive QL method. Finally, the effectiveness of the developed adaptive QL method is verified through numerical simulations. |
| WOS关键词 | DISCRETE-TIME-SYSTEMS ; H-INFINITY CONTROL ; SPATIALLY DISTRIBUTED PROCESSES ; UNCERTAIN NONLINEAR-SYSTEMS ; BARRIER LYAPUNOV FUNCTIONS ; POLICY ITERATION ; CONTROL DESIGN ; CONTROLLER-DESIGN ; UNKNOWN DYNAMICS ; TRACKING CONTROL |
| 资助项目 | National Natural Science Foundation of China[61503377] ; National Natural Science Foundation of China[61533017] ; National Natural Science Foundation of China[U1501251] ; Qatar National Research Fund under National Priority Research Project[NPRP9-466-1-103] |
| WOS研究方向 | Automation & Control Systems ; Computer Science |
| 语种 | 英语 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| WOS记录号 | WOS:000450613100007 |
| 资助机构 | National Natural Science Foundation of China ; Qatar National Research Fund under National Priority Research Project |
| 源URL | [http://ir.ia.ac.cn/handle/173211/22602]  |
| 专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_智能化团队 |
| 通讯作者 | Luo, Biao |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 2.Hamad Bin Khalifa Univ, Coll Sci & Engn, Doha, Qatar 3.Guangdong Univ Technol, Sch Automat, Guangzhou 510006, Guangdong, Peoples R China |
| 推荐引用方式 GB/T 7714 | Luo, Biao,Yang, Yin,Liu, Derong. Adaptive Q-Learning for Data-Based Optimal Output Regulation With Experience Replay[J]. IEEE TRANSACTIONS ON CYBERNETICS,2018,48(12):3337-3348. |
| APA | Luo, Biao,Yang, Yin,&Liu, Derong.(2018).Adaptive Q-Learning for Data-Based Optimal Output Regulation With Experience Replay.IEEE TRANSACTIONS ON CYBERNETICS,48(12),3337-3348. |
| MLA | Luo, Biao,et al."Adaptive Q-Learning for Data-Based Optimal Output Regulation With Experience Replay".IEEE TRANSACTIONS ON CYBERNETICS 48.12(2018):3337-3348. |
入库方式： OAI收割
来源：自动化研究所
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