dc.contributor.author Lale, Sahin dc.contributor.author Azizzadenesheli, Kamyar dc.contributor.author Hassibi, Babak dc.contributor.author Anandkumar, Anima dc.date.accessioned 2020-02-26T08:23:54Z dc.date.available 2020-02-26T08:23:54Z dc.date.issued 2020-01-31 dc.identifier.uri http://hdl.handle.net/10754/661707 dc.description.abstract We study the problem of regret minimization in partially observable linear quadratic control systems when the model dynamics are unknown a priori. We propose ExpCommit, an explore-then-commit algorithm that learns the model Markov parameters and then follows the principle of optimism in the face of uncertainty to design a controller. We propose a novel way to decompose the regret and provide an end-to-end sublinear regret upper bound for partially observable linear quadratic control. Finally, we provide stability guarantees and establish a regret upper bound of $\tilde{\mathcal{O}}(T^{2/3})$ for ExpCommit, where $T$ is the time horizon of the problem. dc.description.sponsorship K. Azizzadenesheli is supported in part by Raytheon. 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. dc.publisher arXiv dc.relation.url https://arxiv.org/pdf/2002.00082 dc.rights Archived with thanks to arXiv dc.title Regret Minimization in Partially Observable Linear Quadratic Control dc.type Preprint dc.eprint.version Pre-print dc.contributor.institution Department of Electrical Engineering dc.contributor.institution Department of Computing and Mathematical Sciences California Institute of Technology, Pasadena dc.identifier.arxivid 2002.00082
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