Inverse Linear-Quadratic Gaussian Differential Games
For researchers in multi-agent systems and control, this provides a method to infer player objectives and system noise from data, addressing a known bottleneck in inverse game theory.
This paper solves the inverse stochastic differential game problem in finite-horizon LQG games, recovering cost and noise parameters from observed trajectories. Simulations show the method accurately recovers parameters, producing trajectories that closely match observations.
This paper presents a method for solving the Inverse Stochastic Differential Game (ISDG) problem in finite-horizon linear-quadratic Gaussian (LQG) differential games. The objective is to recover cost function parameters of all players, as well as noise scaling parameters of the stochastic system, consistent with observed trajectories. The proposed framework combines (i) estimation of the feedback strategies, (ii) identification of the cost function parameters via a novel reformulation of the coupled Riccati differential equations, and (iii) maximum likelihood estimation of the noise scaling parameters. Simulation results demonstrate that the approach recovers parameters, yielding trajectories that closely match the observed trajectories.