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On suitability of the reinforcement learning methodology in dynamic, heterogeneous, self-optimizing networks

dc.contributor.authorRovcanin, Milos
dc.contributor.authorDe Poorter, Eli
dc.contributor.authorMoerman, Ingrid
dc.contributor.authorDemeester, Piet
dc.date.accessioned2021-10-21T11:34:52Z
dc.date.available2021-10-21T11:34:52Z
dc.date.issued2013
dc.identifier.urihttps://imec-publications.be/handle/20.500.12860/23019
dc.sourceIIOimport
dc.titleOn suitability of the reinforcement learning methodology in dynamic, heterogeneous, self-optimizing networks
dc.typeProceedings paper
dc.contributor.imecauthorDe Poorter, Eli
dc.contributor.imecauthorMoerman, Ingrid
dc.contributor.imecauthorDemeester, Piet
dc.contributor.orcidimecDe Poorter, Eli::0000-0002-0214-5751
dc.contributor.orcidimecMoerman, Ingrid::0000-0003-2377-3674
dc.contributor.orcidimecDemeester, Piet::0000-0003-2810-3899
dc.date.embargo9999-12-31
dc.source.peerreviewyes
dc.source.beginpage162
dc.source.endpage175
dc.source.conference13th International Conference on Next Generation Wired/Wireless Advanced Networking - NEW2AN
dc.source.conferencedate28/08/2013
dc.source.conferencelocationSint-Petersburg Russia
dc.identifier.urlhttp://rd.springer.com/chapter/10.1007%2F978-3-642-40316-3_15#
imec.availabilityPublished - open access
imec.internalnotesLecture Notes in Computer Science; Vol. 8121


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