Bandit Algorithms Download Ebook PDF Epub Online

Author : Tor Lattimore
Csaba Szepesvári
Publisher : Cambridge University Press
Release : 2020-07-16
Page : 536
Category : Business & Economics
ISBN 13 : 1108486827
Description :


A comprehensive and rigorous introduction for graduate students and researchers, with applications in sequential decision-making problems.


Author : John White
Publisher : "O'Reilly Media, Inc."
Release : 2012-12-24
Page : 73
Category : Computers
ISBN 13 : 1449341330
Description :


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


Author : Aleksandrs Slivkins
Publisher :
Release : 2019-10-31
Page : 306
Category : Computers
ISBN 13 : 9781680836202
Description :


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.


Author : Tor Lattimore
Csaba Szepesvári
Publisher : Cambridge University Press
Release : 2020-06-30
Page :
Category : Computers
ISBN 13 : 1108687490
Description :


Decision-making in the face of uncertainty is a significant challenge in machine learning, and the multi-armed bandit model is a commonly used framework to address it. This comprehensive and rigorous introduction to the multi-armed bandit problem examines all the major settings, including stochastic, adversarial, and Bayesian frameworks. A focus on both mathematical intuition and carefully worked proofs makes this an excellent reference for established researchers and a helpful resource for graduate students in computer science, engineering, statistics, applied mathematics and economics. Linear bandits receive special attention as one of the most useful models in applications, while other chapters are dedicated to combinatorial bandits, ranking, non-stationary problems, Thompson sampling and pure exploration. The book ends with a peek into the world beyond bandits with an introduction to partial monitoring and learning in Markov decision processes.


Author : Donald A. Berry
Bert Fristedt
Publisher : Springer Science & Business Media
Release : 2013-04-17
Page : 275
Category : Science
ISBN 13 : 9401537119
Description :


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.


Author : Vittorio Maniezzo
Roberto Battiti
Publisher : Springer Science & Business Media
Release : 2008-12-18
Page : 243
Category : Computers
ISBN 13 : 3540926941
Description :


This book constitutes the thoroughly refereed post-conference proceedings of the Second International Conference on Learning and Intelligent Optimization, LION 2007 II, held in Trento, Italy, in December 2007. The 18 revised full papers were carefully reviewed and selected from 48 submissions for inclusion in the book. The papers cover current issues of machine learning, artificial intelligence, mathematical programming and algorithms for hard optimization problems and are organized in topical sections on improving optimization through learning, variable neighborhood search, insect colony optimization, applications, new paradigms, cliques, stochastic optimization, combinatorial optimization, fitness and landscapes, and particle swarm optimization.


Author : Csaba Szepesvari
Publisher : Morgan & Claypool Publishers
Release : 2010
Page : 89
Category : Computers
ISBN 13 : 1608454924
Description :


Reinforcement learning is a learning paradigm concerned with learning to control a system so as to maximize a numerical performance measure that expresses a long-term objective. What distinguishes reinforcement learning from supervised learning is that only partial feedback is given to the learner about the learner's predictions. Further, the predictions may have long term effects through influencing the future state of the controlled system. Thus, time plays a special role. The goal in reinforcement learning is to develop efficient learning algorithms, as well as to understand the algorithms' merits and limitations. Reinforcement learning is of great interest because of the large number of practical applications that it can be used to address, ranging from problems in artificial intelligence to operations research or control engineering. In this book, we focus on those algorithms of reinforcement learning that build on the powerful theory of dynamic programming.We give a fairly comprehensive catalog of learning problems, describe the core ideas, note a large number of state of the art algorithms, followed by the discussion of their theoretical properties and limitations.


Author : Sébastien Bubeck
Nicolò Cesa-Bianchi
Publisher : Now Pub
Release : 2012
Page : 138
Category : Computers
ISBN 13 : 9781601986269
Description :


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.


Author : John Gittins
Kevin Glazebrook
Publisher : John Wiley & Sons
Release : 2011-02-18
Page : 312
Category : Mathematics
ISBN 13 : 9781119990215
Description :


In 1989 the first edition of this book set out Gittins' pioneering index solution to the multi-armed bandit problem and his subsequent investigation of a wide of sequential resource allocation and stochastic scheduling problems. Since then there has been a remarkable flowering of new insights, generalizations and applications, to which Glazebrook and Weber have made major contributions. This second edition brings the story up to date. There are new chapters on the achievable region approach to stochastic optimization problems, the construction of performance bounds for suboptimal policies, Whittle's restless bandits, and the use of Lagrangian relaxation in the construction and evaluation of index policies. Some of the many varied proofs of the index theorem are discussed along with the insights that they provide. Many contemporary applications are surveyed, and over 150 new references are included. Over the past 40 years the Gittins index has helped theoreticians and practitioners to address a huge variety of problems within chemometrics, economics, engineering, numerical analysis, operational research, probability, statistics and website design. This new edition will be an important resource for others wishing to use this approach.


Author : Dorota Głowacka
Publisher :
Release : 2019
Page : 134
Category : Algorithms
ISBN 13 : 9781680835755
Description :


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.


Author : Marcus Hutter
Rocco A. Servedio
Publisher : Springer Science & Business Media
Release : 2007-09-17
Page : 402
Category : Computers
ISBN 13 : 3540752242
Description :


This volume contains the papers presented at the 18th International Conf- ence on Algorithmic Learning Theory (ALT 2007), which was held in Sendai (Japan) during October 1–4, 2007. The main objective of the conference was to provide an interdisciplinary forum for high-quality talks with a strong theore- cal background and scienti?c interchange in areas such as query models, on-line learning, inductive inference, algorithmic forecasting, boosting, support vector machines, kernel methods, complexity and learning, reinforcement learning, - supervised learning and grammatical inference. The conference was co-located with the Tenth International Conference on Discovery Science (DS 2007). This volume includes 25 technical contributions that were selected from 50 submissions by the ProgramCommittee. It also contains descriptions of the ?ve invited talks of ALT and DS; longer versions of the DS papers are available in the proceedings of DS 2007. These invited talks were presented to the audience of both conferences in joint sessions.


Author : Nicolo Cesa-Bianchi
Gabor Lugosi
Publisher : Cambridge University Press
Release : 2006-03-13
Page :
Category : Computers
ISBN 13 : 113945482X
Description :


This important text and reference for researchers and students in machine learning, game theory, statistics and information theory offers a comprehensive treatment of the problem of predicting individual sequences. Unlike standard statistical approaches to forecasting, prediction of individual sequences does not impose any probabilistic assumption on the data-generating mechanism. Yet, prediction algorithms can be constructed that work well for all possible sequences, in the sense that their performance is always nearly as good as the best forecasting strategy in a given reference class. The central theme is the model of prediction using expert advice, a general framework within which many related problems can be cast and discussed. Repeated game playing, adaptive data compression, sequential investment in the stock market, sequential pattern analysis, and several other problems are viewed as instances of the experts' framework and analyzed from a common nonstochastic standpoint that often reveals new and intriguing connections.


Author : Yuxi (Hayden) Liu
Publisher : Packt Publishing Ltd
Release : 2019-10-31
Page : 340
Category : Computers
ISBN 13 : 1838553231
Description :


This book presents practical solutions to the most common reinforcement learning problems. The recipes in this book will help you understand the fundamental concepts to develop popular RL algorithms. You will gain practical experience in the RL domain using the modern offerings of the PyTorch 1.x library.


Author : Dai Shi
Publisher :
Release : 2014
Page :
Category :
ISBN 13 :
Description :


Multi-armed bandit (MAB) problem is derived from slot machines in the casino. It is about how a gambler could pull the arms in order to maximize total reward. In this sense, the gambler needs to decide which arm to explore in order to gain more knowledge, and which arm to exploit in order to guarantee the total payoff. This problem is also very common in real world, such as automatic content selection. The website is like a gambler. It needs to select proper content to recommend to the visitors, trying to maximize click through rate (CTR). Bandit algorithms are very suitable for this kind of issue. Because they are able to deal with exploration and exploitation trade-off with high churning data. When context is considered during content selection, we model it as contextual bandit problems. In this thesis, we evaluate several popular bandit algorithms in different bandit settings. And we propose our own approach to solve a real world automatic content selection case. Our experiments demonstrate that bandit algorithms are efficient in automatic content selection.


Author : Daniel J. Russo
Benjamin van Roy
Publisher : Now Publishers
Release : 2018-07-12
Page : 114
Category : Computers
ISBN 13 : 9781680834703
Description :


Thompson sampling is an algorithm for online decision problems where actions are taken sequentially in a manner that must balance between exploiting what is known to maximize immediate performance and investing to accumulate new information that may improve future performance. The algorithm addresses a broad range of problems in a computationally efficient manner and is therefore enjoying wide use. A Tutorial on Thompson Sampling covers the algorithm and its application, illustrating concepts through a range of examples, including Bernoulli bandit problems, shortest path problems, product recommendation, assortment, active learning with neural networks, and reinforcement learning in Markov decision processes. Most of these problems involve complex information structures, where information revealed by taking an action informs beliefs about other actions. It also discusses when and why Thompson sampling is or is not effective and relations to alternative algorithms.


Author : L. R. M. Dorard
Publisher :
Release : 2012
Page :
Category :
ISBN 13 :
Description :


Bandit games consist of single-state environments in which an agent must sequentially choose actions to take, for which rewards are given. The objective being to maximise the cumulated reward, the agent naturally seeks to build a model of the relationship between actions and rewards. The agent must both choose uncertain actions in order to improve its model (exploration), and actions that are believed to yield high rewards according to the model (exploitation). The choice of an action to take is called a play of an arm of the bandit, and the total number of plays may or may not be known in advance. Algorithms designed to handle the exploration-exploitation dilemma were initially motivated by problems with rather small numbers of actions. But the ideas they were based on have been extended to cases where the number of actions to choose from is much larger than the maximum possible number of plays. Several problems fall into this setting, such as information retrieval with relevance feedback, where the system must learn what a user is looking for while serving relevant documents often enough, but also global optimisation, where the search for an optimum is done by selecting where to acquire potentially expensive samples of a target function. All have in common the search of large spaces. In this thesis, we focus on an algorithm based on the Gaussian Processes probabilistic model, often used in Bayesian optimisation, and the Upper Confidence Bound action-selection heuristic that is popular in bandit algorithms. In addition to demonstrating the advantages of the GP-UCB algorithm on an image retrieval problem, we show how it can be adapted in order to search tree-structured spaces. We provide an efficient implementation, theoretical guarantees on the algorithm's performance, and empirical evidence that it handles large branching factors better than previous bandit-based algorithms, on synthetic trees.


Author : Sudharsan Ravichandiran
Publisher : Packt Publishing Ltd
Release : 2018-06-28
Page : 318
Category : Computers
ISBN 13 : 178883691X
Description :


Reinforcement learning is a self-evolving type of machine learning that takes us closer to achieving true artificial intelligence. This easy-to-follow guide explains everything from scratch using rich examples written in Python.


Author : Richard S. Sutton
Andrew G. Barto
Publisher : MIT Press
Release : 2018-11-13
Page : 552
Category : Computers
ISBN 13 : 0262352702
Description :


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.


Author : Harald Baumeister
Christian Montag
Publisher : Springer Nature
Release : 2019-12-26
Page : 291
Category : Medical
ISBN 13 : 3030316203
Description :


This book offers a snapshot of cutting-edge applications of mobile sensing for digital phenotyping in the field of Psychoinformatics. The respective chapters, written by authoritative researchers, cover various aspects related to the use of these technologies in health, education, and cognitive science research. They share insights both into established applications of mobile sensing (such as predicting personality or mental and behavioral health on the basis of smartphone usage patterns) and emerging trends. Machine learning and deep learning approaches are discussed, and important considerations regarding privacy risks and ethical issues are assessed. In addition to essential background information on various technologies and theoretical methods, the book also presents relevant case studies and good scientific practices, thus addressing researchers and professionals alike. To cite Thomas R. Insel, who wrote the foreword to this book: “Patients will only use digital phenotyping if it solves a problem, perhaps a digital smoke alarm that can prevent a crisis. Providers will only use digital phenotyping if it fits seamlessly into their crowded workflow. If we can earn public trust, there is every reason to be excited about this new field. Suddenly, studying human behavior at scale, over months and years, is feasible.”


Author : Rmi Munos
Publisher : Now Pub
Release : 2014
Page : 146
Category : Computers
ISBN 13 : 9781601987662
Description :


Covers the optimism in the face of uncertainty principle applied to large scale optimization problems under finite numerical budget. The initial motivation for this research originated from the empirical success of the Monte-Carlo Tree Search method popularized in Computer Go and further extended to other games, optimization, and planning problems.