Logarithmic Regret Bound in Partially Observable Linear Dynamical Systems

Abstract

We study the problem of adaptive control in partially observable linear dynamical systems. We propose a novel algorithm, adaptive control online learning algorithm (AdaptOn), which efficiently explores the environment, estimates the system dynamics episodically and exploits these estimates to design effective controllers to minimize the cumulative costs. Through interaction with the environment, AdaptOn deploys online convex optimization to optimize the controller while simultaneously learning the system dynamics to improve the accuracy of controller updates. We show that when the cost functions are strongly convex, after $T$ times step of agent-environment interaction, AdaptOn achieves regret upper bound of $\text{polylog}\left(T\right)$. To the best of our knowledge, AdaptOn is the first algorithm which achieves $\text{polylog}\left(T\right)$ regret in adaptive control of unknown partially observable linear dynamical systems which includes linear quadratic Gaussian (LQG) control.