Abstract:In this paper, we study the statistical difficulty of learning to control linear systems. We focus on two standard benchmarks, the sample complexity of stabilization, and the regret of the online learning of the Linear Quadratic Regulator (LQR). Prior results state that the statistical difficulty for both benchmarks scales polynomially with the system state dimension up to systemtheoretic quantities. However, this does not reveal the whole picture. By utilizing minimax lower bounds for both benchmarks, we prov… Show more
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