Social Optima in Robust Mean Field LQG Control: From Finite to Infinite Horizon

TL;DR

This paper develops a robust mean field LQG control framework accounting for uncertain common drift, providing decentralized strategies that are asymptotically optimal for both finite and infinite horizons.

Contribution

It introduces a robust optimization approach for mean field LQG models with common uncertainty, extending analysis to both finite and infinite time horizons.

Findings

Decentralized strategies are asymptotically optimal.

Robust control handles common uncertain drift effectively.

Framework applies to both finite and infinite horizon cases.

Abstract

This paper studies social optimal control of mean field LQG (linear-quadratic-Gaussian) models with uncertainty. Specially, the uncertainty is represented by a uncertain drift which is common for all agents. A robust optimization approach is applied by assuming all agents treat the uncertain drift as an adversarial player. In our model, both dynamics and costs of agents are coupled by mean field terms, and both finite- and infinite-time horizon cases are considered. By examining social functional variation and exploiting person-by-person optimality principle, we construct an auxiliary control problem for the generic agent via a class of forward-backward stochastic differential equation system. By solving the auxiliary problem and constructing consistent mean field approximation, a set of decentralized control strategies is designed and shown to be asymptotically optimal.