Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/93913
Title: | Robust mean field linear-quadratic-Gaussian games with unknown L2-disturbance | Authors: | Huang, J Huang, M |
Issue Date: | 2017 | Source: | SIAM journal on control and optimization, 2017, v. 55, no. 5, p. 2811-2840 | Abstract: | This paper considers a class of mean field linear-quadratic-Gaussian games with model uncertainty. The drift term in the dynamics of the agents contains a common unknown function. We take a robust optimization approach where a representative agent in the limiting model views the drift uncertainty as an adversarial player. By including the mean field dynamics in an augmented state space, we solve two optimal control problems sequentially, which combined with consistent mean field approximations provides a solution to the robust game. A set of decentralized control strategies is derived by use of forward-backward stochastic differential equations and is shown to be a robust "-Nash equilibrium. | Keywords: | Decentralized control Mean field game Model uncertainty Nash equilibrium Robust control |
Publisher: | Society for Industrial and Applied Mathematics | Journal: | SIAM journal on control and optimization | ISSN: | 0363-0129 | EISSN: | 1095-7138 | DOI: | 10.1137/15M1014437 | Rights: | © 2017 Society for Industrial and Applied Mathematics The following publication Huang, J., & Huang, M. (2017). Robust mean field linear-quadratic-Gaussian games with unknown L^2-disturbance. SIAM Journal on Control and Optimization, 55(5), 2811-2840 is available at https://doi.org/10.1137/15M1014437 |
Appears in Collections: | Journal/Magazine Article |
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