Robust exploration in linear quadratic reinforcement learning

Jack Umenberger; Mina Ferizbegovic; Thomas B. Schön; Håkan Hjalmarsson

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arXiv · 1906.01584

Robust exploration in linear quadratic reinforcement learning

Jack Umenberger·Mina Ferizbegovic·Thomas B. Schön·Håkan Hjalmarsson

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Abstract

This paper concerns the problem of learning control policies for an unknown linear dynamical system to minimize a quadratic cost function. We present a method, based on convex optimization, that accomplishes this task robustly: i.e., we minimize the worst-case cost, accounting for system uncertainty given the observed data. The method balances exploitation and exploration, exciting the system in such a way so as to reduce uncertainty in the model parameters to which the worst-case cost is most sensitive. Numerical simulations and application to a hardware-in-the-loop servo-mechanism demonstrate the approach, with appreciable performance and robustness gains over alternative methods observed in both.

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BibTeX RIS

Jack Umenberger, Mina Ferizbegovic, Thomas B. Schön, Håkan Hjalmarsson. 2019-06-04. Robust exploration in linear quadratic reinforcement learning. https://arxiv.org/abs/1906.01584

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