  This paper considers a class of mean field linear-quadratic-Gaussian (LQG)
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 (FBSDE) and shown to be a
robust epsilon-Nash equilibrium.
