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Learning convex bounds for linear quadratic control policy synthesis
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.ORCID iD: 0000-0001-5183-234X
2018 (English)In: Neural Information Processing Systems 2018, 2018, Vol. 31Conference paper, Published paper (Refereed)
Abstract [en]

Learning to make decisions from observed data in dynamic environments remains a problem of fundamental importance in a numbers of fields, from artificial intelligence and robotics, to medicine and finance. This paper concerns the problem of learning control policies for unknown linear dynamical systems so as to maximize a quadratic reward function. We present a method to optimize the expected value of the reward over the posterior distribution of the unknown system parameters, given data. The algorithm involves sequential convex programing, and enjoys reliable local convergence and robust stability guarantees. Numerical simulations and stabilization of a real-world inverted pendulum are used to demonstrate the approach, with strong performance and robustness properties observed in both.

Place, publisher, year, edition, pages
2018. Vol. 31
Series
Advances in Neural Information Processing Systems, ISSN 1049-5258
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:uu:diva-368620ISI: 000461852004015OAI: oai:DiVA.org:uu-368620DiVA, id: diva2:1268450
Conference
32nd Conference on Neural Information Processing Systems (NIPS), DEC 02-08, 2018, Montreal, CANADA
Funder
Swedish Foundation for Strategic Research , RIT15-0012Swedish Research Council, 2017-03807Swedish Research Council, 621-2016-06079Available from: 2018-12-05 Created: 2018-12-05 Last updated: 2019-04-24Bibliographically approved

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https://papers.nips.cc/book/advances-in-neural-information-processing-systems-31-2018

Authority records BETA

Umenberger, JackSchön, Thomas B.

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