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Neuro-optimal control for discrete stochastic processes via a novel policy iteration algorithm | |
Liang, Mingming1![]() ![]() | |
发表期刊 | IEEE Transactions on Systems, Man, and Cybernetics: Systems
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ISSN | 2168-2216 |
2020 | |
卷号 | 50期号:11页码:3972-3985 |
通讯作者 | Wang, Ding(dingwang@bjut.edu.cn) |
摘要 | In this paper, a novel policy iteration adaptive dynamic programming (ADP) algorithm is presented which is called “local policy iteration ADP algorithm” to obtain the optimal control for discrete stochastic processes. In the proposed local policy iteration ADP algorithm, the iterative decision rules are updated in a local space of the whole state space. Hence, we can significantly reduce the computational burden for the CPU in comparison with the conventional policy iteration algorithm. By analyzing the convergence properties of the proposed algorithm, it is shown that the iterative value functions are monotonically nonincreasing. Besides, the iterative value functions can converge to the optimum in a local policy space. In addition, this local policy space will be described in detail for the first time. Under a few weak constraints, it is also shown that the iterative value function will converge to the optimal performance index function of the global policy space. Finally, a simulation example is presented to validate the effectiveness of the developed method. |
关键词 | Adaptive critic designs adaptive dynamic programming (ADP) local policy iteration neuro-dynamic programming optimal control stochastic processes |
DOI | 10.1109/TSMC.2019.2907991 |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Natural Science Foundation of China[61773373] ; National Natural Science Foundation of China[U1501251] ; National Natural Science Foundation of China[61533017] ; Young Elite Scientists Sponsorship Program by the China Association for Science and Technology ; Youth Innovation Promotion Association of the Chinese Academy of Sciences |
项目资助者 | National Natural Science Foundation of China ; Young Elite Scientists Sponsorship Program by the China Association for Science and Technology ; Youth Innovation Promotion Association of the Chinese Academy of Sciences |
WOS研究方向 | Automation & Control Systems ; Computer Science |
WOS类目 | Automation & Control Systems ; Computer Science, Cybernetics |
WOS记录号 | WOS:000578826300004 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
七大方向——子方向分类 | 智能控制 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/40672 |
专题 | 多模态人工智能系统全国重点实验室_复杂系统智能机理与平行控制团队 |
通讯作者 | Wang, Ding |
作者单位 | 1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 2.Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China 3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 4.Guangdong Univ Technol, Sch Automat, Guangzhou 510006, Peoples R China |
第一作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Liang, Mingming,Wang, Ding,Liu, Derong. Neuro-optimal control for discrete stochastic processes via a novel policy iteration algorithm[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems,2020,50(11):3972-3985. |
APA | Liang, Mingming,Wang, Ding,&Liu, Derong.(2020).Neuro-optimal control for discrete stochastic processes via a novel policy iteration algorithm.IEEE Transactions on Systems, Man, and Cybernetics: Systems,50(11),3972-3985. |
MLA | Liang, Mingming,et al."Neuro-optimal control for discrete stochastic processes via a novel policy iteration algorithm".IEEE Transactions on Systems, Man, and Cybernetics: Systems 50.11(2020):3972-3985. |
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