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Logarithmic Regret Bound in Partially Observable Linear Dynamical Systems

Lale, Sahin and Azizzadenesheli, Kamyar and Hassibi, Babak and Anandkumar, Anima (2020) Logarithmic Regret Bound in Partially Observable Linear Dynamical Systems. In: Advances in neural information processing systems 33 pre-proceedings (NeurIPS 2020). Neural Information Processing Foundation , La Jolla, CA, pp. 1-13. ISBN 9781713829546.

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We study the problem of adaptive control in partially observable linear dynamical systems. We propose a novel algorithm, adaptive control online learning algorithm (AdaptOn), which efficiently explores the environment, estimates the system dynamics episodically and exploits these estimates to design effective controllers to minimize the cumulative costs. Through interaction with the environment, AdaptOn deploys online convex optimization to optimize the controller while simultaneously learning the system dynamics to improve the accuracy of controller updates. We show that when the cost functions are strongly convex, after T times step of agent-environment interaction, AdaptOn achieves regret upper bound of polylog(T). To the best of our knowledge, AdaptOn is the first algorithm which achieves polylog(T) regret in adaptive control of unknown partially observable linear dynamical systems which includes linear quadratic Gaussian (LQG) control.

Item Type:Book Section
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URLURL TypeDescription ItemDiscussion Paper
Lale, Sahin0000-0002-7191-346X
Azizzadenesheli, Kamyar0000-0001-8507-1868
Hassibi, Babak0000-0002-1375-5838
Anandkumar, Anima0000-0002-6974-6797
Additional Information:S. Lale is supported in part by DARPA PAI. K. Azizzadenesheli gratefully acknowledge the financial support of Raytheon and Amazon Web Services. B. Hassibi is supported in part by the National Science Foundation under grants CNS-0932428, CCF-1018927, CCF-1423663 and CCF-1409204, by a grant from Qualcomm Inc., by NASA’s Jet Propulsion Laboratory through the President and Director’s Fund, and by King Abdullah University of Science and Technology. A. Anandkumar is supported in part by Bren endowed chair, DARPA PAIHR00111890035 and LwLL grants, Raytheon, Microsoft, Google, and Adobe faculty fellowships.
Funding AgencyGrant Number
Defense Advanced Research Projects Agency (DARPA)PAIHR00111890035
Raytheon CompanyUNSPECIFIED
Amazon Web ServicesUNSPECIFIED
JPL President and Director's FundUNSPECIFIED
King Abdullah University of Science and Technology (KAUST)UNSPECIFIED
Bren Professor of Computing and Mathematical SciencesUNSPECIFIED
Learning with Less Labels (LwLL)UNSPECIFIED
Microsoft Faculty FellowshipUNSPECIFIED
Google Faculty Research AwardUNSPECIFIED
Record Number:CaltechAUTHORS:20221222-222544264
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:118598
Deposited By: George Porter
Deposited On:23 Dec 2022 00:25
Last Modified:23 Dec 2022 00:25

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