Thompson sampling-based online decision making in network routing

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dc.contributor.authorHuang, Zhiming
dc.contributor.supervisorPan, Jianping
dc.date.accessioned2020-09-03T04:41:14Z
dc.date.available2020-09-03T04:41:14Z
dc.date.copyright2020en_US
dc.date.issued2020-09-02
dc.degree.departmentDepartment of Computer Science
dc.degree.levelMaster of Science M.Sc.en_US
dc.description.abstractOnline decision making is a kind of machine learning problems where decisions are made in a sequential manner so as to accumulate as many rewards as possible. Typical examples include multi-armed bandit (MAB) problems where an agent needs to decide which arm to pull in each round, and network routing problems where each router needs to decide the next hop for each packet. Thompson sampling (TS) is an efficient and effective algorithm for online decision making problems. Although TS has been proposed for a long time, it was not until recent years that the theoretical guarantees for TS in the standard MAB were given. In this thesis, we first analyze the performance of TS both theoretically and practically in a special MAB called combinatorial MAB with sleeping arms and long-term fairness constraints (CSMAB-F). Then, we apply TS to a novel reactive network routing problem, called \emph{opportunistic routing without link metrics known a priori}, and use the proof techniques we developed for CSMAB-F to analyze the performance.en_US
dc.description.scholarlevelGraduateen_US
dc.identifier.urihttp://hdl.handle.net/1828/12095
dc.languageEnglisheng
dc.language.isoenen_US
dc.rightsAvailable to the World Wide Weben_US
dc.subjectOnline Decision Makingen_US
dc.subjectMulti-armed Banditsen_US
dc.subjectThompson Samplingen_US
dc.subjectNetwork Routingen_US
dc.titleThompson sampling-based online decision making in network routingen_US
dc.typeThesisen_US

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