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Stackelberg games for model-free continuous-time stochastic systems based on adaptive dynamic programming

Author

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  • Liu, Xikui
  • Ge, Yingying
  • Li, Yan

Abstract

Solving the Stackelberg game problem generally needs full data of the system. In this paper, two online adaptive dynamic programming algorithms are proposed to solve the Stackelberg game problem for model-free linear continuous-time systems subject to multiplicative noise. Stackelberg games are based on two different strategies: Nash-based Stackelberg strategy and Pareto-based Stackelberg strategy. We apply directly the state and input information to iteratively update Stackelberg games online. The effectiveness of the algorithms is verified by two simulation examples.

Suggested Citation

  • Liu, Xikui & Ge, Yingying & Li, Yan, 2019. "Stackelberg games for model-free continuous-time stochastic systems based on adaptive dynamic programming," Applied Mathematics and Computation, Elsevier, vol. 363(C), pages 1-1.
    • Handle: RePEc:eee:apmaco:v:363:y:2019:i:c:2
    DOI: 10.1016/j.amc.2019.124568
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    References listed on IDEAS

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    1. G. Freiling & G. Jank & S. R. Lee, 2001. "Existence and Uniqueness of Open-Loop Stackelberg Equilibria in Linear-Quadratic Differential Games," Journal of Optimization Theory and Applications, Springer, vol. 110(3), pages 515-544, September.
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    Cited by:

    1. Liu, Chong & Zhang, Huaguang & Luo, Yanhong & Zhang, Kun, 2021. "Echo state network-based online optimal control for discrete-time nonlinear systems," Applied Mathematics and Computation, Elsevier, vol. 409(C).
    2. Sun, Zhongshi & Jia, Guangyan, 2023. "Reinforcement learning for exploratory linear-quadratic two-person zero-sum stochastic differential games," Applied Mathematics and Computation, Elsevier, vol. 442(C).
    3. Tian, Xiu-Qin & Liu, Shu-Jun & Yang, Xue, 2024. "Stochastic adaptive linear quadratic nonzero-sum differential games," Applied Mathematics and Computation, Elsevier, vol. 477(C).

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