Publication

Crowd-Averse Robust Mean-Field Games: Approximation via State Space Extension

Bauso, D., Mylvaganam, T. & Astolfi, A., Jul-2016, In : IEEE Transactions on Automatic Control. 61, 7, p. 1882-1894 13 p.

Research output: Contribution to journalArticleAcademicpeer-review

APA

Bauso, D., Mylvaganam, T., & Astolfi, A. (2016). Crowd-Averse Robust Mean-Field Games: Approximation via State Space Extension. IEEE Transactions on Automatic Control, 61(7), 1882-1894. https://doi.org/10.1109/TAC.2015.2479927

Author

Bauso, Dario ; Mylvaganam, Thulasi ; Astolfi, Alessandro. / Crowd-Averse Robust Mean-Field Games : Approximation via State Space Extension. In: IEEE Transactions on Automatic Control. 2016 ; Vol. 61, No. 7. pp. 1882-1894.

Harvard

Bauso, D, Mylvaganam, T & Astolfi, A 2016, 'Crowd-Averse Robust Mean-Field Games: Approximation via State Space Extension', IEEE Transactions on Automatic Control, vol. 61, no. 7, pp. 1882-1894. https://doi.org/10.1109/TAC.2015.2479927

Standard

Crowd-Averse Robust Mean-Field Games : Approximation via State Space Extension. / Bauso, Dario; Mylvaganam, Thulasi; Astolfi, Alessandro.

In: IEEE Transactions on Automatic Control, Vol. 61, No. 7, 07.2016, p. 1882-1894.

Research output: Contribution to journalArticleAcademicpeer-review

Vancouver

Bauso D, Mylvaganam T, Astolfi A. Crowd-Averse Robust Mean-Field Games: Approximation via State Space Extension. IEEE Transactions on Automatic Control. 2016 Jul;61(7):1882-1894. https://doi.org/10.1109/TAC.2015.2479927


BibTeX

@article{04ce45a22c4c43309d548caecda8bcf6,
title = "Crowd-Averse Robust Mean-Field Games: Approximation via State Space Extension",
abstract = "We consider a population of dynamic agents, also referred to as players. The state of each player evolves according to a linear stochastic differential equation driven by a Brownian motion and under the influence of a control and an adversarial disturbance. Every player minimizes a cost functional which involves quadratic terms on state and control plus a cross-coupling mean-field term measuring the congestion resulting from the collective behavior, which motivates the term {"}crowd-averse.{"} Motivations for this model are analyzed and discussed in three main contexts: a stock market application, a production engineering example, and a dynamic demand management problem in power systems. For the problem in its abstract formulation, we illustrate the paradigm of robust mean-field games. Main contributions involve first the formulation of the problem as a robust mean-field game; second, the development of a new approximate solution approach based on the extension of the state space; third, a relaxation method to minimize the approximation error. Further results are provided for the scalar case, for which we establish performance bounds, and analyze stochastic stability of both the microscopic and the macroscopic dynamics.",
keywords = "Closed loop systems, control design, control engineering, optimal control",
author = "Dario Bauso and Thulasi Mylvaganam and Alessandro Astolfi",
year = "2016",
month = "7",
doi = "10.1109/TAC.2015.2479927",
language = "English",
volume = "61",
pages = "1882--1894",
journal = "IEEE-Transactions on Automatic Control",
issn = "0018-9286",
publisher = "IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC",
number = "7",

}

RIS

TY - JOUR

T1 - Crowd-Averse Robust Mean-Field Games

T2 - Approximation via State Space Extension

AU - Bauso, Dario

AU - Mylvaganam, Thulasi

AU - Astolfi, Alessandro

PY - 2016/7

Y1 - 2016/7

N2 - We consider a population of dynamic agents, also referred to as players. The state of each player evolves according to a linear stochastic differential equation driven by a Brownian motion and under the influence of a control and an adversarial disturbance. Every player minimizes a cost functional which involves quadratic terms on state and control plus a cross-coupling mean-field term measuring the congestion resulting from the collective behavior, which motivates the term "crowd-averse." Motivations for this model are analyzed and discussed in three main contexts: a stock market application, a production engineering example, and a dynamic demand management problem in power systems. For the problem in its abstract formulation, we illustrate the paradigm of robust mean-field games. Main contributions involve first the formulation of the problem as a robust mean-field game; second, the development of a new approximate solution approach based on the extension of the state space; third, a relaxation method to minimize the approximation error. Further results are provided for the scalar case, for which we establish performance bounds, and analyze stochastic stability of both the microscopic and the macroscopic dynamics.

AB - We consider a population of dynamic agents, also referred to as players. The state of each player evolves according to a linear stochastic differential equation driven by a Brownian motion and under the influence of a control and an adversarial disturbance. Every player minimizes a cost functional which involves quadratic terms on state and control plus a cross-coupling mean-field term measuring the congestion resulting from the collective behavior, which motivates the term "crowd-averse." Motivations for this model are analyzed and discussed in three main contexts: a stock market application, a production engineering example, and a dynamic demand management problem in power systems. For the problem in its abstract formulation, we illustrate the paradigm of robust mean-field games. Main contributions involve first the formulation of the problem as a robust mean-field game; second, the development of a new approximate solution approach based on the extension of the state space; third, a relaxation method to minimize the approximation error. Further results are provided for the scalar case, for which we establish performance bounds, and analyze stochastic stability of both the microscopic and the macroscopic dynamics.

KW - Closed loop systems

KW - control design

KW - control engineering

KW - optimal control

U2 - 10.1109/TAC.2015.2479927

DO - 10.1109/TAC.2015.2479927

M3 - Article

VL - 61

SP - 1882

EP - 1894

JO - IEEE-Transactions on Automatic Control

JF - IEEE-Transactions on Automatic Control

SN - 0018-9286

IS - 7

ER -

ID: 72166880