Data-driven adaptive dynamic programming for continuous-time fully cooperative games with partially constrained inputs
文献类型：期刊论文
| 作者 | Zhang, Qichao1,2 ; Zhao, Dongbin1,2; Zhu, Yuanheng1,2
|
| 刊名 | NEUROCOMPUTING
 |
| 出版日期 | 2017-05-17 |
| 卷号 | 238期号:*页码:377-386 |
| 关键词 | Adaptive Dynamic Programming Optimal Control Neural Network Fully Cooperative Games Data-driven Constrained Input |
| DOI | 10.1016/j.neucom.2017.01.076 |
| 文献子类 | Article |
| 英文摘要 | In this paper, the fully cooperative game with partially constrained inputs in the continuous-time Markov decision process environment is investigated using a novel data-driven adaptive dynamic programming method. First, the model-based policy iteration algorithm with one iteration loop is proposed, where the knowledge of system dynamics is required. Then, it is proved that the iteration sequences of value functions and control policies can converge to the optimal ones. In order to relax the exact knowledge of the system dynamics, a model-free iterative equation is derived based on the model-based algorithm and the integral reinforcement learning. Furthermore, a data-driven adaptive dynamic programming is developed to solve the model-free equation using generated system data. From the theoretical analysis, we prove that this model-free iterative equation is equivalent to the model-based iterative equations, which means that the data-driven algorithm can approach the optimal value function and control policies. For the implementation purpose, three neural networks are constructed to approximate the solution of the model-free iteration equation using the off-policy learning scheme after the available system data is collected in the online measurement phase. Finally, two examples are provided to demonstrate the effectiveness of the proposed scheme. (C) 2017 Published by Elsevier B.V. |
| WOS关键词 | ZERO-SUM GAMES ; H-INFINITY CONTROL ; DIFFERENTIAL GRAPHICAL GAMES ; NONLINEAR-SYSTEMS ; LEARNING SOLUTION ; UNKNOWN DYNAMICS ; MULTIAGENT SYSTEMS ; EXPERIENCE REPLAY ; CONTROL DESIGN ; ALGORITHM |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000397372100033 |
| 资助机构 | National Natural Science Foundation of China (NSFC)(61273136 ; National Key Research and Development Plan(2016YFB0101000) ; 61573353 ; 61533017 ; 61603382) |
| 源URL | [http://ir.ia.ac.cn/handle/173211/14336]  |
| 专题 | 复杂系统管理与控制国家重点实验室_深度强化学习 |
| 通讯作者 | Zhao, Dongbin |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhang, Qichao,Zhao, Dongbin,Zhu, Yuanheng. Data-driven adaptive dynamic programming for continuous-time fully cooperative games with partially constrained inputs[J]. NEUROCOMPUTING,2017,238(*):377-386. |
| APA | Zhang, Qichao,Zhao, Dongbin,&Zhu, Yuanheng.(2017).Data-driven adaptive dynamic programming for continuous-time fully cooperative games with partially constrained inputs.NEUROCOMPUTING,238(*),377-386. |
| MLA | Zhang, Qichao,et al."Data-driven adaptive dynamic programming for continuous-time fully cooperative games with partially constrained inputs".NEUROCOMPUTING 238.*(2017):377-386. |
入库方式： OAI收割
来源：自动化研究所
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