Reinforcement learning for routing in communication networks

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dc.contributor.advisorOmlin, Christian W.
dc.contributor.authorAndrag, Walter H.
dc.contributor.otherStellenbosch University. Faculty of Science. Dept. of Mathematical Sciences. Computer Science.
dc.date.accessioned2012-08-27T11:35:32Z
dc.date.available2012-08-27T11:35:32Z
dc.date.issued2003-04
dc.descriptionThesis (MSc)--Stellenbosch University, 2003.
dc.description.abstractENGLISH ABSTRACT: Routing policies for packet-switched communication networks must be able to adapt to changing traffic patterns and topologies. We study the feasibility of implementing an adaptive routing policy using the Q-Learning algorithm which learns sequences of actions from delayed rewards. The Q-Routing algorithm adapts a network's routing policy based on local information alone and converges toward an optimal solution. We demonstrate that Q-Routing is a viable alternative to other adaptive routing methods such as Bellman-Ford. We also study variations of Q-Routing designed to better explore possible routes and to take into consideration limited buffer size and optimize multiple objectives.en_ZA
dc.description.abstractAFRIKAANSE OPSOMMING:Die roetering in kommunikasienetwerke moet kan aanpas by veranderings in netwerktopologie en verkeersverspreidings. Ons bestudeer die bruikbaarheid van 'n aanpasbare roeteringsalgoritme gebaseer op die "Q-Learning"-algoritme wat dit moontlik maak om 'n reeks besluite te kan neem gebaseer op vertraagde vergoedings. Die roeteringsalgoritme gebruik slegs nabygelee inligting om roeteringsbesluite te maak en konvergeer na 'n optimale oplossing. Ons demonstreer dat die roeteringsalgoritme 'n goeie alternatief vir aanpasbare roetering is, aangesien dit in baie opsigte beter vaar as die Bellman-Ford algoritme. Ons bestudeer ook variasies van die roeteringsalgoritme wat beter paaie kan ontdek, minder geheue gebruik by netwerkelemente, en wat meer as een doelfunksie kan optimeer.af_ZA
dc.format.extent67 p : ill.
dc.identifier.urihttp://hdl.handle.net/10019.1/53570
dc.language.isoen_ZA
dc.publisherStellenbosch : Stellenbosch University
dc.rights.holderStellenbosch University
dc.subjectComputer networksen_ZA
dc.subjectComputer algorithmsen_ZA
dc.subjectTelecommunicationen_ZA
dc.subjectRouting policiesen_ZA
dc.subjectPocket-switched communication networksen_ZA
dc.subjectDissertations -- Computer scienceen_ZA
dc.subjectTheses -- Computer scienceen_ZA
dc.subjectDissertations -- Mathematical sciencesen_ZA
dc.subjectTheses -- Mathematical sciencesen_ZA
dc.titleReinforcement learning for routing in communication networksen_ZA
dc.typeThesis
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