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作者:

Xin, Peng (Xin, Peng.) | Wang, Ding (Wang, Ding.) | Zhao, Mingming (Zhao, Mingming.) | Ha, Mingming (Ha, Mingming.) | Ren, Jin (Ren, Jin.)

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摘要:

This paper introduces n-step heuristic dynamic programming (NSHDP), which combines regular temporal difference (TD) learning with TD(λ) learning, in order to solve optimal control problems. First, the implementation process of the basic value iteration algorithm is proposed. Then, based on the traditional HDP algorithm, the architecture of the NSHDP(λ) algorithm is described. At the same time, the most important thing is that the stability condition of the NSHDP(λ) algorithm is developed. Furthermore, the one-step critic network, the n-step critic network, and the action network are designed, respectively. Finally, the effectiveness of the proposed algorithm is verified by simulation experiment. © 2022 Technical Committee on Control Theory, Chinese Association of Automation.

关键词:

Dynamic programming Optimal control systems Iterative methods Neural networks Heuristic programming

作者机构:

  • [ 1 ] [Xin, Peng]Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 2 ] [Xin, Peng]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing; 100124, China
  • [ 3 ] [Xin, Peng]Beijing Laboratory of Smart Environmental Protection, Beijing University of Technology, Beijing; 100124, China
  • [ 4 ] [Xin, Peng]Beijing Institute of Artificial Intelligence, Beijing University of Technology, Beijing; 100124, China
  • [ 5 ] [Wang, Ding]Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 6 ] [Wang, Ding]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing; 100124, China
  • [ 7 ] [Wang, Ding]Beijing Laboratory of Smart Environmental Protection, Beijing University of Technology, Beijing; 100124, China
  • [ 8 ] [Wang, Ding]Beijing Institute of Artificial Intelligence, Beijing University of Technology, Beijing; 100124, China
  • [ 9 ] [Zhao, Mingming]Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 10 ] [Zhao, Mingming]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing; 100124, China
  • [ 11 ] [Zhao, Mingming]Beijing Laboratory of Smart Environmental Protection, Beijing University of Technology, Beijing; 100124, China
  • [ 12 ] [Zhao, Mingming]Beijing Institute of Artificial Intelligence, Beijing University of Technology, Beijing; 100124, China
  • [ 13 ] [Ha, Mingming]School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing; 100083, China
  • [ 14 ] [Ren, Jin]Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 15 ] [Ren, Jin]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing; 100124, China
  • [ 16 ] [Ren, Jin]Beijing Laboratory of Smart Environmental Protection, Beijing University of Technology, Beijing; 100124, China
  • [ 17 ] [Ren, Jin]Beijing Institute of Artificial Intelligence, Beijing University of Technology, Beijing; 100124, China

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ISSN: 1934-1768

年份: 2022

卷: 2022-July

页码: 2242-2247

语种: 英文

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