Predictive Inverse Optimal Control for Linear-Quadratic-Gaussian Systems

Predictive inverse optimal control is a powerful approach for estimating the control policy of an agent from observed control demonstrations. Its usefulness has been established in a number of large-scale sequential decision settings characterized by complete state observability. However, many real decisions are made in situations where the state is not fully known to the agent making decisions. Though extensions of predictive inverse optimal control to partially observable Markov decision processes have been developed, their applicability has been limited by the complexities of inference in those representations. In this work, we extend predictive inverse optimal control to the linear- quadratic-Gaussian control setting. We establish close connections between optimal control laws for this setting and the probabilistic predictions under our approach. We demonstrate the effectiveness and benefit in estimating control policies that are influenced by partial observability on both synthetic and real datasets.