Exploration and exploitation in Bayes sequential decision problems

Bayes sequential decision problems are an extensive problem class with wide application. They involve taking actions in sequence in a system which has characteristics which are unknown or only partially known. These characteristics can be learnt over time as a result of our actions. Therefore we are...

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Main Author: Edwards, James
Other Authors: Glazebrook, Kevin ; Fearnhead, Paul ; Leslie, David
Published: Lancaster University 2016
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Online Access:https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.702592
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spelling ndltd-bl.uk-oai-ethos.bl.uk-7025922018-10-03T03:22:40ZExploration and exploitation in Bayes sequential decision problemsEdwards, JamesGlazebrook, Kevin ; Fearnhead, Paul ; Leslie, David2016Bayes sequential decision problems are an extensive problem class with wide application. They involve taking actions in sequence in a system which has characteristics which are unknown or only partially known. These characteristics can be learnt over time as a result of our actions. Therefore we are faced with a trade-off between choosing actions that give desirable short term outcomes (exploitation) and actions that yield useful information about the system which can be used to improve longer term outcomes (exploration). Gittins indices provide an optimal method for a small but important subclass of these problems. Unfortunately the optimality of index methods does not hold generally and Gittins indices can be impractical to calculate for many problems. This has motivated the search for easy-to-calculate heuristics with general application. One such non-index method is the knowledge gradient heuristic. A thorough investigation of the method is made which identifies crucial weaknesses. Index and non-index variants are developed which avoid these weaknesses. The problem of choosing multiple website elements to present to user is an important problem relevant to many major web-based businesses. A Bayesian multi-armed bandit model is developed which captures the interactions between elements and the dual uncertainties of both user preferences and element quality. The problem has many challenging features but solution methods are proposed that are both easy to implement and which can be adapted to particular applications. Finally, easy-to-use software to calculate Gittins indices for Bernoulli and normal rewards has been developed as part of this thesis and has been made publicly available. The methodology used is presented together with a study of accuracy and speed.519.5Lancaster Universityhttps://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.702592http://eprints.lancs.ac.uk/84589/Electronic Thesis or Dissertation
collection NDLTD
sources NDLTD
topic 519.5
spellingShingle 519.5
Edwards, James
Exploration and exploitation in Bayes sequential decision problems
description Bayes sequential decision problems are an extensive problem class with wide application. They involve taking actions in sequence in a system which has characteristics which are unknown or only partially known. These characteristics can be learnt over time as a result of our actions. Therefore we are faced with a trade-off between choosing actions that give desirable short term outcomes (exploitation) and actions that yield useful information about the system which can be used to improve longer term outcomes (exploration). Gittins indices provide an optimal method for a small but important subclass of these problems. Unfortunately the optimality of index methods does not hold generally and Gittins indices can be impractical to calculate for many problems. This has motivated the search for easy-to-calculate heuristics with general application. One such non-index method is the knowledge gradient heuristic. A thorough investigation of the method is made which identifies crucial weaknesses. Index and non-index variants are developed which avoid these weaknesses. The problem of choosing multiple website elements to present to user is an important problem relevant to many major web-based businesses. A Bayesian multi-armed bandit model is developed which captures the interactions between elements and the dual uncertainties of both user preferences and element quality. The problem has many challenging features but solution methods are proposed that are both easy to implement and which can be adapted to particular applications. Finally, easy-to-use software to calculate Gittins indices for Bernoulli and normal rewards has been developed as part of this thesis and has been made publicly available. The methodology used is presented together with a study of accuracy and speed.
author2 Glazebrook, Kevin ; Fearnhead, Paul ; Leslie, David
author_facet Glazebrook, Kevin ; Fearnhead, Paul ; Leslie, David
Edwards, James
author Edwards, James
author_sort Edwards, James
title Exploration and exploitation in Bayes sequential decision problems
title_short Exploration and exploitation in Bayes sequential decision problems
title_full Exploration and exploitation in Bayes sequential decision problems
title_fullStr Exploration and exploitation in Bayes sequential decision problems
title_full_unstemmed Exploration and exploitation in Bayes sequential decision problems
title_sort exploration and exploitation in bayes sequential decision problems
publisher Lancaster University
publishDate 2016
url https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.702592
work_keys_str_mv AT edwardsjames explorationandexploitationinbayessequentialdecisionproblems
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