Multi Armed Bandits In Large Scale Complex Systems
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Multi Armed Bandits In Large Scale Complex Systems
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Author : Xiao Xu
language : en
Publisher:
Release Date : 2020
Multi Armed Bandits In Large Scale Complex Systems written by Xiao Xu and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
This dissertation focuses on the multi-armed bandit problem (MAB) where the objective is a sequential arm selection policy that maximizes the total reward over time. In canonical formulations of MAB, the following assumptions are adopted: the size of the action space is much smaller than the length of the time horizon, computation resources such as memory are unlimited in the learning process, and the generative models of arm rewards are time-invariant. This dissertation aims to relax these assumptions, which are unrealistic in emerging applications involving large-scale complex systems, and develop corresponding techniques to address the resulting new issues. The first part of the dissertation aims to address the issue of a massive number of actions. A stochastic bandit problem with side information on arm similarity and dissimilarity is studied. The main results include a unit interval graph (UIG) representation of the action space that succinctly models the side information and a two-step learning structure that fully exploits the topological structure of the UIG to achieve an optimal scaling of the learning cost with the size of the action space. Specifically, in the UIG representation, each node represents an arm and the presence (absence) of an edge between two nodes indicates similarity (dissimilarity) between their mean rewards. Based on whether the UIG is fully revealed by the side information, two settings with complete and partial side information are considered. For each setting, a two-step learning policy consisting of an offline reduction of the action space and online aggregation of reward observations from similar arms is developed. The computation efficiency and the order optimality of the proposed strategies in terms of the size of the action space and the time length are established. Numerical experiments on both synthetic and real-world datasets are conducted to verify the performance of the proposed policies in practice. In the second part of the dissertation, the issue of limited memory during the learning process is studied in the adversarial bandit setting. Specifically, a learning policy can only store the statistics of a subset of arms summarizing their reward history. A general hierarchical learning structure that trades off the regret order with memory complexity is developed based on multi-level partitions of the arm set into groups and the time horizon into epochs. The proposed learning policy requires only a sublinear order of memory space in terms of the number of arms. Its sublinear regret orders with respect to the time horizon are established for both weak regret and shifting regret in expectation and/or with high probability, when appropriate learning strategies are adopted as subroutines at all levels. By properly choosing the number of levels in the adopted hierarchy, the policy adapts to different sizes of the available memory space. A memory-dependent regret bound is established to characterize the tradeoff between memory complexity and the regret performance of the policy. Numerical examples are provided to verify the performance of the policy. The third part of the dissertation focuses on the issue of time-varying rewards within the contextual bandit framework, which finds applications in various online recommendation systems. The main results include two reward models characterizing the fact that the preferences of users toward different items change asynchronously and distinctly, and a learning algorithm that adapts to the dynamic environment. In particular, the two models assume disjoint and hybrid rewards. In the disjoint setting, the mean reward of playing an arm is determined by an arm-specific preference vector, which is piecewise-stationary with asynchronous change times across arms. In the hybrid setting, the mean reward of an arm also depends on a joint coefficient vector shared by all arms representing the time-invariant component of user interests, in addition to the arm-specific one that is time-varying. Two algorithms based on change detection and restarts are developed in the two settings respectively, of which the performance is verified through simulations on both synthetic and real-world data. Theoretical regret analysis of the algorithm with certain modifications is provided under the disjoint reward model, which shows that a near-optimal regret order in the time length is achieved.
Introduction To Multi Armed Bandits
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Author : Aleksandrs Slivkins
language : en
Publisher:
Release Date : 2019-10-31
Introduction To Multi Armed Bandits written by Aleksandrs Slivkins and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-10-31 with Computers categories.
Multi-armed bandits is a rich, multi-disciplinary area that has been studied since 1933, with a surge of activity in the past 10-15 years. This is the first book to provide a textbook like treatment of the subject.
Bandit Algorithms
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Author : Tor Lattimore
language : en
Publisher: Cambridge University Press
Release Date : 2020-07-16
Bandit Algorithms written by Tor Lattimore and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-16 with Business & Economics categories.
A comprehensive and rigorous introduction for graduate students and researchers, with applications in sequential decision-making problems.
Regret Analysis Of Stochastic And Nonstochastic Multi Armed Bandit Problems
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Author : Sébastien Bubeck
language : en
Publisher: Now Pub
Release Date : 2012
Regret Analysis Of Stochastic And Nonstochastic Multi Armed Bandit Problems written by Sébastien Bubeck and has been published by Now Pub this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with Computers categories.
In this monograph, the focus is on two extreme cases in which the analysis of regret is particularly simple and elegant: independent and identically distributed payoffs and adversarial payoffs. Besides the basic setting of finitely many actions, it analyzes some of the most important variants and extensions, such as the contextual bandit model.
Transactions On Large Scale Data And Knowledge Centered Systems Xxviii
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Author : Abdelkader Hameurlain
language : en
Publisher: Springer
Release Date : 2016-09-09
Transactions On Large Scale Data And Knowledge Centered Systems Xxviii written by Abdelkader Hameurlain and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-09-09 with Computers categories.
This, the 28th issue of Transactions on Large-Scale Data- and Knowledge-Centered Systems, contains extended and revised versions of six papers presented at the 26th International Conference on Database- and Expert-Systems Applications, DEXA 2015, held in Valencia, Spain, in September 2015. Topics covered include efficient graph processing, machine learning on big data, multistore big data integration, ontology matching, and the optimization of histograms for the Semantic Web.
Bandit Algorithms For Website Optimization
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Author : John Myles White
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2012-12-10
Bandit Algorithms For Website Optimization written by John Myles White and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-10 with Computers categories.
When looking for ways to improve your website, how do you decide which changes to make? And which changes to keep? This concise book shows you how to use Multiarmed Bandit algorithms to measure the real-world value of any modifications you make to your site. Author John Myles White shows you how this powerful class of algorithms can help you boost website traffic, convert visitors to customers, and increase many other measures of success. This is the first developer-focused book on bandit algorithms, which were previously described only in research papers. You’ll quickly learn the benefits of several simple algorithms—including the epsilon-Greedy, Softmax, and Upper Confidence Bound (UCB) algorithms—by working through code examples written in Python, which you can easily adapt for deployment on your own website. Learn the basics of A/B testing—and recognize when it’s better to use bandit algorithms Develop a unit testing framework for debugging bandit algorithms Get additional code examples written in Julia, Ruby, and JavaScript with supplemental online materials
Bandit Problems
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Author : Donald A. Berry
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-04-17
Bandit Problems written by Donald A. Berry and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-04-17 with Science categories.
Our purpose in writing this monograph is to give a comprehensive treatment of the subject. We define bandit problems and give the necessary foundations in Chapter 2. Many of the important results that have appeared in the literature are presented in later chapters; these are interspersed with new results. We give proofs unless they are very easy or the result is not used in the sequel. We have simplified a number of arguments so many of the proofs given tend to be conceptual rather than calculational. All results given have been incorporated into our style and notation. The exposition is aimed at a variety of types of readers. Bandit problems and the associated mathematical and technical issues are developed from first principles. Since we have tried to be comprehens ive the mathematical level is sometimes advanced; for example, we use measure-theoretic notions freely in Chapter 2. But the mathema tically uninitiated reader can easily sidestep such discussion when it occurs in Chapter 2 and elsewhere. We have tried to appeal to graduate students and professionals in engineering, biometry, econ omics, management science, and operations research, as well as those in mathematics and statistics. The monograph could serve as a reference for professionals or as a telA in a semester or year-long graduate level course.
Reinforcement Learning Second Edition
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Author : Richard S. Sutton
language : en
Publisher: MIT Press
Release Date : 2018-11-13
Reinforcement Learning Second Edition written by Richard S. Sutton and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-11-13 with Computers categories.
The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics. Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.
Artificial Intelligence And Machine Learning In The Travel Industry
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Author : Ben Vinod
language : en
Publisher: Springer Nature
Release Date : 2023-05-26
Artificial Intelligence And Machine Learning In The Travel Industry written by Ben Vinod and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-05-26 with Business & Economics categories.
Over the past decade, Artificial Intelligence has proved invaluable in a range of industry verticals such as automotive and assembly, life sciences, retail, oil and gas, and travel. The leading sectors adopting AI rapidly are Financial Services, Automotive and Assembly, High Tech and Telecommunications. Travel has been slow in adoption, but the opportunity for generating incremental value by leveraging AI to augment traditional analytics driven solutions is extremely high. The contributions in this book, originally published as a special issue for the Journal of Revenue and Pricing Management, showcase the breadth and scope of the technological advances that have the potential to transform the travel experience, as well as the individuals who are already putting them into practice.
Bandit Algorithms In Information Retrieval
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Author : Dorota Glowacka
language : en
Publisher: Foundations and Trends(r) in I
Release Date : 2019-05-23
Bandit Algorithms In Information Retrieval written by Dorota Glowacka and has been published by Foundations and Trends(r) in I this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-05-23 with Computers categories.
This monograph provides an overview of bandit algorithms inspired by various aspects of Information Retrieval. It is accessible to anyone who has completed introductory to intermediate level courses in machine learning and/or statistics.