Abstract
AbstractDistributed Nash equilibrium seeking of aggregative games is investigated and a continuous-time algorithm is proposed. The algorithm is designed by virtue of projected gradient play dynamics and aggregation tracking dynamics, and is applicable to games with constrained strategy sets and weight-balanced communication graphs. The key feature of our method is that the proposed projected dynamics achieves exponential convergence, whereas such convergence results are only obtained for non-projected dynamics in existing works on distributed optimization and equilibrium seeking. Numerical examples illustrate the effectiveness of our methods.
Funder
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Reference26 articles.
1. W. Saad, Z. Han, H.V. Poor, T. Basar, Game-theoretic methods for the smart grid: an overview of microgrid systems, demand-side management, and smart grid communications. IEEE Signal Process. Mag. 29(5), 86–105 (2012)
2. P.R. Wurman, S. Barrett, K. Kawamoto et al., Outracing champion Gran Turismo drivers with deep reinforcement learning. Nature 602, 223–228 (2022)
3. H.K. Khalil, P.V. Kokotovic, Feedback and well-posedness of singularly perturbed Nash games. IEEE Trans. Autom. Control 24(5), 699–708 (1979)
4. J.S. Shamma, G. Arslan, Dynamic fictitious play, dynamic gradient play, and distributed convergence to Nash equilibria. IEEE Trans. Autom. Control 50(3), 312–327 (2005)
5. M. Zinkevich, M. Johanson, M. Bowling, C. Piccione, Regret minimization in games with incomplete information, in Advances in Neural Information Processing Systems, vol. 20 (Curran Associates, Vancouver, 2007), pp. 1729–1736
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