Optimal exploration strategies for finite horizon regret minimization in some adaptive control problems

Bombois, Xavier (author): Laboratoire Ampère, UMR CNRS 5005, Ecole Centrale de Lyon, Université de Lyon, France; Centre National de la Recherche Scientifique (CNRS), France

(creator_code:org_t)

Elsevier BV, 2023
2023
English.
In: 22nd IFAC World Congress Yokohama, Japan, July 9-14, 2023. - : Elsevier BV. ; , s. 2564-2569

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Abstract Subject headings

In this work, we consider the problem of regret minimization in adaptive minimum variance and linear quadratic control problems. Regret minimization has been extensively studied in the literature for both types of adaptive control problems. Most of these works give results of the optimal rate of the regret in the asymptotic regime. In the minimum variance case, the optimal asymptotic rate for the regret is log(T) which can be reached without any additional external excitation. On the contrary, for most adaptive linear quadratic problems, it is necessary to add an external excitation in order to get the optimal asymptotic rate of √T. In this paper, we will actually show from a theoretical study, as well as, in simulations that when the control horizon is pre-specified a lower regret can be obtained with either no external excitation or a new exploration type termed immediate.

About the subject

ENGINEERING AND TECHNOLOGY: ENGINEERING AND ...; and Electrical Engin ...; and Control Engineer ...

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