Online Policy Gradient for Model Free Learning of Linear Quadratic Regulators with $\sqrt$T Regret
Proceedings of the 38th International Conference on Machine Learning, PMLR 139:1304-1313, 2021.
We consider the task of learning to control a linear dynamical system under fixed quadratic costs, known as the Linear Quadratic Regulator (LQR) problem. While model-free approaches are often favorable in practice, thus far only model-based methods, which rely on costly system identification, have been shown to achieve regret that scales with the optimal dependence on the time horizon T. We present the first model-free algorithm that achieves similar regret guarantees. Our method relies on an efficient policy gradient scheme, and a novel and tighter analysis of the cost of exploration in policy space in this setting.