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Bayesian reinforcement learning with exploration

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dc.contributor.authorLattimore, Tor
dc.contributor.authorHutter, Marcus
dc.coverage.spatialBled Slovenia
dc.date.accessioned2015-12-10T22:43:28Z
dc.date.createdOctober 8-10 2014
dc.date.issued2014
dc.date.updated2016-02-24T10:33:49Z
dc.description.abstractWe consider a general reinforcement learning problem and show that carefully combining the Bayesian optimal policy and an exploring policy leads to minimax sample-complexity bounds in a very general class of (history-based) environments. We also prove lower bounds and show that the new algorithm displays adaptive behaviour when the environment is easier than worst-case.
dc.identifier.isbn9783319116617
dc.identifier.urihttp://hdl.handle.net/1885/58180
dc.publisherSpringer
dc.relation.ispartofseries25th International Conference on Algorithmic Learning Theory, ALT 2014
dc.rightsCopyright Information: © Springer International Publishing Switzerland 2014. http://www.sherpa.ac.uk/romeo/issn/0302-9743/..."Author's post-print on any open access repository after 12 months after publication" from SHERPA/RoMEO site (as at 13/08/15)
dc.rightsAuthor/s retain copyrighten_AU
dc.sourceLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Volume 8776
dc.titleBayesian reinforcement learning with exploration
dc.typeConference paper
dcterms.accessRightsOpen Accessen_AU
local.bibliographicCitation.lastpage184
local.bibliographicCitation.startpage170
local.contributor.affiliationLattimore, Tor, University of Alberta
local.contributor.affiliationHutter, Marcus, College of Engineering and Computer Science, ANU
local.contributor.authoruidHutter, Marcus, u4350841
local.description.notesImported from ARIES
local.description.refereedYes
local.identifier.absfor080100 - ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING
local.identifier.absseo970108 - Expanding Knowledge in the Information and Computing Sciences
local.identifier.ariespublicationu4056230xPUB431
local.identifier.doi10.1007/978-3-319-11662-4_13
local.identifier.scopusID2-s2.0-84910077608
local.type.statusPublished Version

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