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Foundations and Trends in Machine Learning Ser.: Bayesian Reinforcement Learning : A Survey by Joelle Pineau, Aviv Tamar, Mohammad Ghavamzadeh and Shie Mannor (2015, Trade Paperback)

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Bayesian Reinforcement Learning : A Survey, Paperback by Ghavamzadeh, Mohammad; Mannor, Shie, ISBN 1680830880, ISBN-13 9781680830880, Brand New, Free shipping in the US

About this product

Product Identifiers

PublisherNow Publishers

ISBN-101680830880

ISBN-139781680830880

eBay Product ID (ePID)219082006

Product Key Features

Number of Pages146 Pages

LanguageEnglish

Publication NameBayesian Reinforcement Learning : a Survey

SubjectMachine Theory, Probability & Statistics / Bayesian Analysis

Publication Year2015

TypeTextbook

AuthorJoelle Pineau, Aviv Tamar, Mohammad Ghavamzadeh, Shie Mannor

Subject AreaMathematics, Computers

SeriesFoundations and Trends in Machine Learning Ser.

FormatTrade Paperback

Dimensions

Item Height0.3 in

Item Weight7.6 Oz

Item Length9.2 in

Item Width6.1 in

Additional Product Features

Intended AudienceScholarly & Professional

Series Volume NumberVol. 27

IllustratedYes

Table Of Content1: Introduction 2: Technical Background 3: Bayesian Bandits 4: Model-based Bayesian Reinforcement Learning 5: Model-free Bayesian Reinforcement Learning 6: Risk-aware Bayesian Reinforcement Learning 7: BRL Extensions 8: Outlook. Acknowledgements. Appendices. References.

SynopsisBayesian methods for machine learning have been widely investigated, yielding principled methods for incorporating prior information into inference algorithms. This monograph provides the reader with an in-depth review of the role of Bayesian methods for the reinforcement learning (RL) paradigm. The major incentives for incorporating Bayesian reasoning in RL are that it provides an elegant approach to action-selection (exploration/exploitation) as a function of the uncertainty in learning, and it provides a machinery to incorporate prior knowledge into the algorithms. Bayesian Reinforcement Learning: A Survey first discusses models and methods for Bayesian inference in the simple single-step Bandit model. It then reviews the extensive recent literature on Bayesian methods for model-based RL, where prior information can be expressed on the parameters of the Markov model. It also presents Bayesian methods for model-free RL, where priors are expressed over the value function or policy class. Bayesian Reinforcement Learning: A Survey is a comprehensive reference for students and researchers with an interest in Bayesian RL algorithms and their theoretical and empirical properties., Bayesian Reinforcement Learning: A Survey is a comprehensive reference for students and researchers with an interest in Bayesian RL algorithms and their theoretical and empirical properties. It first discusses models and methods for Bayesian inference in the simple single-step Bandit model. It then reviews the extensive recent literature on Bayesian methods for model-based RL, where prior information can be expressed on the parameters of the Markov model. It also presents Bayesian methods for model-free RL, where priors are expressed over the value function or policy class., A comprehensive reference for students and researchers with an interest in Bayesian RL algorithms and their theoretical and empirical properties.