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Reinforcement learning for routing in communication networks

Thesis (MSc)--Stellenbosch University, 2003.

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Bibliographic Details
Main Author: Andrag, Walter H.
Other Authors: Omlin, Christian W.
Format: Thesis
Language:en_ZA
Published: Stellenbosch : Stellenbosch University 2012
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access_status_str Open Access
author Andrag, Walter H.
author2 Omlin, Christian W.
author_browse Andrag, Walter H.
Omlin, Christian W.
author_facet Omlin, Christian W.
Andrag, Walter H.
author_sort Andrag, Walter H.
collection Thesis
dc_rights_str_mv Stellenbosch University
description Thesis (MSc)--Stellenbosch University, 2003.
format Thesis
id oai:scholar.sun.ac.za:10019.1/53570
institution Stellenbosch University (South Africa)
language en_ZA
last_indexed 2026-06-10T12:44:50.018Z
license_str Other — see source repository
provenance_str_mv Harvested via OAI-PMH from SUNScholar — Stellenbosch University Repository
publishDate 2012
publishDateRange 2012
publishDateSort 2012
publisher Stellenbosch : Stellenbosch University
publisherStr Stellenbosch : Stellenbosch University
record_format dspace
source_str SUNScholar — Stellenbosch University Repository
spelling oai:scholar.sun.ac.za:10019.1/53570 Reinforcement learning for routing in communication networks Andrag, Walter H. Omlin, Christian W. Stellenbosch University. Faculty of Science. Dept. of Mathematical Sciences. Computer Science. Computer networks Computer algorithms Telecommunication Routing policies Pocket-switched communication networks Dissertations -- Computer science Theses -- Computer science Dissertations -- Mathematical sciences Theses -- Mathematical sciences Thesis (MSc)--Stellenbosch University, 2003. ENGLISH 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. AFRIKAANSE 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. 2012-08-27T11:35:32Z 2012-08-27T11:35:32Z 2003-04 Thesis http://hdl.handle.net/10019.1/53570 en_ZA Stellenbosch University 67 p : ill. application/pdf Stellenbosch : Stellenbosch University
spellingShingle Computer networks
Computer algorithms
Telecommunication
Routing policies
Pocket-switched communication networks
Dissertations -- Computer science
Theses -- Computer science
Dissertations -- Mathematical sciences
Theses -- Mathematical sciences
Andrag, Walter H.
Reinforcement learning for routing in communication networks
title Reinforcement learning for routing in communication networks
title_full Reinforcement learning for routing in communication networks
title_fullStr Reinforcement learning for routing in communication networks
title_full_unstemmed Reinforcement learning for routing in communication networks
title_short Reinforcement learning for routing in communication networks
title_sort reinforcement learning for routing in communication networks
topic Computer networks
Computer algorithms
Telecommunication
Routing policies
Pocket-switched communication networks
Dissertations -- Computer science
Theses -- Computer science
Dissertations -- Mathematical sciences
Theses -- Mathematical sciences
url http://hdl.handle.net/10019.1/53570
work_keys_str_mv AT andragwalterh reinforcementlearningforroutingincommunicationnetworks