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dc.contributor.authorLale, Sahin
dc.contributor.authorAzizzadenesheli, Kamyar
dc.contributor.authorHassibi, Babak
dc.contributor.authorAnandkumar, Anima
dc.date.accessioned2020-02-26T08:23:54Z
dc.date.available2020-02-26T08:23:54Z
dc.date.issued2020-01-31
dc.identifier.urihttp://hdl.handle.net/10754/661707
dc.description.abstractWe 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.sponsorshipK. 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.publisherarXiv
dc.relation.urlhttps://arxiv.org/pdf/2002.00082
dc.rightsArchived with thanks to arXiv
dc.titleRegret Minimization in Partially Observable Linear Quadratic Control
dc.typePreprint
dc.eprint.versionPre-print
dc.contributor.institutionDepartment of Electrical Engineering
dc.contributor.institutionDepartment of Computing and Mathematical Sciences California Institute of Technology, Pasadena
dc.identifier.arxivid2002.00082


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