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Author:

Wang, Jiangyu (Wang, Jiangyu.) | Wang, Ding (Wang, Ding.) (Scholars:王鼎) | Li, Xin (Li, Xin.) | Qiao, Junfei (Qiao, Junfei.)

Indexed by:

EI Scopus SCIE

Abstract:

In this paper, a novel parallel learning framework is developed to solve zero-sum games for discrete -time nonlinear systems. Briefly, the purpose of this study is to determine a tentative function according to the prior knowledge of the value iteration (VI) algorithm. The learning process of the parallel controllers can be guided by the tentative function. That is to say, the neighborhood of the optimal cost function can be compressed within a small range via two typical exploration policies. Based on the parallel learning framework, a novel dichotomy VI algorithm is established to accelerate the learning speed. It is shown that the parallel controllers will converge to the optimal policy from contrary initial policies. Finally, two typical systems are used to demonstrate the learning performance of the constructed dichotomy VI algorithm.(c) 2023 Elsevier Ltd. All rights reserved.

Keyword:

Zero -sum games Value iteration Artificial neural networks Nonlinear systems Parallel learning Adaptive critic

Author Community:

  • [ 1 ] [Wang, Jiangyu]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 2 ] [Wang, Ding]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 3 ] [Li, Xin]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 4 ] [Qiao, Junfei]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 5 ] [Wang, Jiangyu]Beijing Univ Technol, Key Lab Computat Intelligence & Intelligent Syst, Beijing 100124, Peoples R China
  • [ 6 ] [Li, Xin]Beijing Univ Technol, Key Lab Computat Intelligence & Intelligent Syst, Beijing 100124, Peoples R China
  • [ 7 ] [Qiao, Junfei]Beijing Univ Technol, Key Lab Computat Intelligence & Intelligent Syst, Beijing 100124, Peoples R China
  • [ 8 ] [Wang, Jiangyu]Beijing Univ Technol, Beijing Inst Artificial Intelligence, Beijing 100124, Peoples R China
  • [ 9 ] [Li, Xin]Beijing Univ Technol, Beijing Inst Artificial Intelligence, Beijing 100124, Peoples R China
  • [ 10 ] [Qiao, Junfei]Beijing Univ Technol, Beijing Inst Artificial Intelligence, Beijing 100124, Peoples R China
  • [ 11 ] [Wang, Jiangyu]Beijing Univ Technol, Beijing Lab Smart Environm Protect, Beijing 100124, Peoples R China
  • [ 12 ] [Li, Xin]Beijing Univ Technol, Beijing Lab Smart Environm Protect, Beijing 100124, Peoples R China
  • [ 13 ] [Qiao, Junfei]Beijing Univ Technol, Beijing Lab Smart Environm Protect, Beijing 100124, Peoples R China

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Source :

NEURAL NETWORKS

ISSN: 0893-6080

Year: 2023

Volume: 167

Page: 751-762

7 . 8 0 0

JCR@2022

Cited Count:

WoS CC Cited Count: 4

SCOPUS Cited Count: 5

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 1

Affiliated Colleges:

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