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arXiv · 2005.01082

Low-complexity Learning of Linear Quadratic Regulators from Noisy Data

Claudio De Persis·Pietro Tesi
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Abstract

This paper considers the Linear Quadratic Regulator problem for linear systems with unknown dynamics, a central problem in data-driven control and reinforcement learning. We propose a method that uses data to directly return a controller without estimating a model of the system. Sufficient conditions are given under which this method returns a stabilizing controller with guaranteed relative error when the data used to design the controller are affected by noise. This method has low complexity as it only requires a finite number of samples of the system response to a sufficiently exciting input, and can be efficiently implemented as a semi-definite program. Further, the method does not require assumptions on the noise statistics, and the relative error nicely scales with the noise magnitude.

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Claudio De Persis, Pietro Tesi. 2020-05-03. Low-complexity Learning of Linear Quadratic Regulators from Noisy Data. https://arxiv.org/abs/2005.01082

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