Learning Controllers for Unstable Linear Quadratic Regulators from a Single Trajectory

Abstract

We present the first approach for learning–from a single trajectory–a linear quadratic regulator (LQR), even for unstable systems, without knowledge of the system dynamics and without requiring an initial stabilizing controller. Our central contribution is an efficient algorithm–emph eXploration–that quickly identifies a stabilizing controller. Our approach utilizes robust System Level Synthesis (SLS), and we prove that it succeeds in a constant number of iterations. Our approach can be used to initialize existing algorithms that require a stabilizing controller as input. When used in this way, it yields a method for learning LQRs from a single trajectory and even for unstable systems, while suffering at most regret.

Publication
Learning for Dynamics & Controll (L4DC) 2021