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Nonzero-sum games using actor-critic neural networks: A dynamic event-triggered adaptive dynamic programming.
- Source :
-
Information Sciences . Mar2024, Vol. 662, pN.PAG-N.PAG. 1p. - Publication Year :
- 2024
-
Abstract
- This paper mainly investigates the nonzero-sum games of nonlinear systems with unmatched uncertainty by using actor-critic neural networks. To handle the unmatched components, an auxiliary system with a modified value function is constructed, which transforms the robust stabilization issue into the optimal control issue. Then, a novel dynamic event-triggering condition is designed to further save bandwidth via introducing a dynamic variable. In addition, the actor-critic algorithm is employed in adaptive dynamic programming to achieve Nash equilibrium, which is tuned together with the control policy. By constructing appropriate Lyapunov functions, a criterion is established to ensure that the considered system is uniformly ultimately bounded. Finally, the effectiveness of the developed strategy is demonstrated by an example. [ABSTRACT FROM AUTHOR]
- Subjects :
- *DYNAMIC programming
*NASH equilibrium
*LYAPUNOV functions
*NONLINEAR systems
*GAMES
Subjects
Details
- Language :
- English
- ISSN :
- 00200255
- Volume :
- 662
- Database :
- Academic Search Index
- Journal :
- Information Sciences
- Publication Type :
- Periodical
- Accession number :
- 175456713
- Full Text :
- https://doi.org/10.1016/j.ins.2024.120236