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Reinforcement learning-based resource allocation in cellular telecommunications systems

The work in this thesis concerns the use of reinforcement learning solutions to re-source allocation problems in channelised cellular networks. The methodology of re-inforcement learning techniques was chosen for application to these problems due to its capability of finding efficient policies in a fully on-line, adaptable manner, without requiring specific environment models. All of the presented agent architectures are assumed to simultaneously learn and perform network control functions in a totally on-line and unsupervised manner, and agents are developed with a view to real-world implementability by focussing on techniques that have low resource requirements and make use of only local system information.

Identiferoai:union.ndltd.org:ADTP/201930
Date January 2005
CreatorsLilith, Nimrod
Source SetsAustraliasian Digital Theses Program
LanguageEN-AUS
Detected LanguageEnglish
RightsCopyrith Nimrod Lilith 2005

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