Robust Optimization for Hybrid MDPs with State-Dependent Noise

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dc.contributor.authorZamani, Zahra
dc.contributor.authorSanner, Scott
dc.contributor.authorDelgado, Karina Valdivia
dc.contributor.authorDe Barros, Leliane Nunes
dc.coverage.spatialBeijing China
dc.date.accessioned2015-12-10T23:20:07Z
dc.date.createdAugust 3-9 2013
dc.date.issued2013
dc.date.updated2015-12-10T10:20:34Z
dc.description.abstractRecent advances in solutions to Hybrid MDPs with discrete and continuous state and action spaces have significantly extended the class of MDPs for which exact solutions can be derived, albeit at the expense of a restricted transition noise model. In this
dc.identifier.isbn9781577356332
dc.identifier.urihttp://hdl.handle.net/1885/66180
dc.publisherAAAI Press
dc.relation.ispartofseries23rd International Joint Conference on Artificial Intelligence, IJCAI 2013
dc.sourceIJCAI International Joint Conference on Artificial Intelligence
dc.source.urihttp://ijcai.org/papers13/contents.php
dc.titleRobust Optimization for Hybrid MDPs with State-Dependent Noise
dc.typeConference paper
local.bibliographicCitation.lastpage2443
local.bibliographicCitation.startpage2437
local.contributor.affiliationZamani, Zahra, College of Engineering and Computer Science, ANU
local.contributor.affiliationSanner, Scott, College of Engineering and Computer Science, ANU
local.contributor.affiliationDelgado, Karina Valdivia, University of Sao Paulo
local.contributor.affiliationDe Barros, Leliane Nunes, University of Sao Paulo
local.contributor.authoruidZamani, Zahra, u4602049
local.contributor.authoruidSanner, Scott, u1817461
local.description.embargo2037-12-31
local.description.notesImported from ARIES
local.description.refereedYes
local.identifier.absfor080110 - Simulation and Modelling
local.identifier.absseo970108 - Expanding Knowledge in the Information and Computing Sciences
local.identifier.ariespublicationu4334215xPUB1236
local.identifier.scopusID2-s2.0-84896063404
local.type.statusPublished Version

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