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Robust exploration in linear quadratic reinforcement learning
Jack Umenberger · Mina Ferizbegovic · Thomas Schön · Håkan Hjalmarsson

Tue Dec 10 05:10 PM -- 05:15 PM (PST) @ West Ballroom A + B
in Track 3 Session 2 »

Learning to make decisions in an uncertain and dynamic environment is a task of fundamental performance in a number of domains. This paper concerns the problem of learning control policies for an unknown linear dynamical system so as to minimize a quadratic cost function. We present a method, based on convex optimization, that accomplishes this task ‘robustly’, i.e., the worst-case cost, accounting for system uncertainty given the observed data, is minimized. 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 are used to demonstrate the approach, with appreciable performance and robustness gains over alternative methods observed in both.

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Author Information

Jack Umenberger (Uppsala University)
Mina Ferizbegovic (KTH Royal Institute of Technology)
Thomas Schön (Uppsala University)
Håkan Hjalmarsson (KTH)

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