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.
| Identifer | oai:union.ndltd.org:ADTP/201930 |
| Date | January 2005 |
| Creators | Lilith, Nimrod |
| Source Sets | Australiasian Digital Theses Program |
| Language | EN-AUS |
| Detected Language | English |
| Rights | Copyrith Nimrod Lilith 2005 |
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