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Non-Stochastic Control with Bandit Feedback

Author(s): Gradu, Paula; Hallman, John; Hazan, Elad

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dc.contributor.authorGradu, Paula-
dc.contributor.authorHallman, John-
dc.contributor.authorHazan, Elad-
dc.date.accessioned2021-10-08T19:50:50Z-
dc.date.available2021-10-08T19:50:50Z-
dc.date.issued2020en_US
dc.identifier.citationGradu, Paula, John Hallman, and Elad Hazan. "Non-Stochastic Control with Bandit Feedback." Advances in Neural Information Processing Systems 33 (2020).en_US
dc.identifier.issn1049-5258-
dc.identifier.urihttps://proceedings.neurips.cc/paper/2020/file/7a1d9028a78f418cb8f01909a348d9b2-Paper.pdf-
dc.identifier.urihttp://arks.princeton.edu/ark:/88435/pr1wg37-
dc.description.abstractWe study the problem of controlling a linear dynamical system with adversarial perturbations where the only feedback available to the controller is the scalar loss, and the loss function itself is unknown. For this problem, with either a known or unknown system, we give an efficient sublinear regret algorithm. The main algorithmic difficulty is the dependence of the loss on past controls. To overcome this issue, we propose an efficient algorithm for the general setting of bandit convex optimization for loss functions with memory, which may be of independent interest.en_US
dc.language.isoen_USen_US
dc.relation.ispartofAdvances in Neural Information Processing Systemsen_US
dc.rightsFinal published version. Article is made available in OAR by the publisher's permission or policy.en_US
dc.titleNon-Stochastic Control with Bandit Feedbacken_US
dc.typeConference Articleen_US
pu.type.symplectichttp://www.symplectic.co.uk/publications/atom-terms/1.0/conference-proceedingen_US

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