The Resource Algorithms for reinforcement learning, Csaba Szepesvári, (electronic book)
Algorithms for reinforcement learning, Csaba Szepesvári, (electronic book)
Resource Information
The item Algorithms for reinforcement learning, Csaba Szepesvári, (electronic book) represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in University of Liverpool.This item is available to borrow from 1 library branch.
Resource Information
The item Algorithms for reinforcement learning, Csaba Szepesvári, (electronic book) represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in University of Liverpool.
This item is available to borrow from 1 library branch.
 Summary
 Reinforcement learning is a learning paradigm concerned with learning to control a system so as to maximize a numerical performance measure that expresses a longterm 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
 Language
 eng
 Extent
 1 electronic text (xii, 89 p. : ill.)
 Contents

 1. Markov decision processes  Preliminaries  Markov decision processes  Value functions  Dynamic programming algorithms for solving MDPs 
 2. Value prediction problems  Temporal difference learning in finite state spaces  Tabular TD(0)  Everyvisit MonteCarlo  TD([lambda]): unifying MonteCarlo and TD(0)  Algorithms for large state spaces  TD([lambda]) with function approximation  Gradient temporal difference learning  Leastsquares methods  The choice of the function space 
 3. Control  A catalog of learning problems  Closedloop interactive learning  Online learning in bandits  Active learning in bandits  Active learning in Markov decision processes  Online learning in Markov decision processes  Direct methods  Qlearning in finite MDPs  Qlearning with function approximation  Actorcritic methods  Implementing a critic  Implementing an actor 
 4. For further exploration  Further reading  Applications  Software 
 A. The theory of discounted Markovian decision processes  A.1. Contractions and Banach's fixedpoint theorem  A.2. Application to MDPs  Bibliography  Author's biography
 Isbn
 9781608454938
 Label
 Algorithms for reinforcement learning
 Title
 Algorithms for reinforcement learning
 Statement of responsibility
 Csaba Szepesvári
 Language
 eng
 Summary
 Reinforcement learning is a learning paradigm concerned with learning to control a system so as to maximize a numerical performance measure that expresses a longterm 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
 Cataloging source
 CaBNvSL
 http://library.link/vocab/creatorName
 Szepesvári, Csaba.
 Illustrations
 illustrations
 Index
 no index present
 Literary form
 non fiction
 Nature of contents

 dictionaries
 abstracts summaries
 bibliography
 Series statement

 Synthesis digital library of engineering and computer science
 Synthesis lectures on artificial intelligence and machine learning
 Series volume
 9
 http://library.link/vocab/subjectName
 Reinforcement learning
 Target audience

 adult
 specialized
 Label
 Algorithms for reinforcement learning, Csaba Szepesvári, (electronic book)
 Antecedent source
 file reproduced from original
 Bibliography note
 Includes bibliographical references (p. 7388)
 Color
 multicolored
 Contents

 1. Markov decision processes  Preliminaries  Markov decision processes  Value functions  Dynamic programming algorithms for solving MDPs 
 2. Value prediction problems  Temporal difference learning in finite state spaces  Tabular TD(0)  Everyvisit MonteCarlo  TD([lambda]): unifying MonteCarlo and TD(0)  Algorithms for large state spaces  TD([lambda]) with function approximation  Gradient temporal difference learning  Leastsquares methods  The choice of the function space 
 3. Control  A catalog of learning problems  Closedloop interactive learning  Online learning in bandits  Active learning in bandits  Active learning in Markov decision processes  Online learning in Markov decision processes  Direct methods  Qlearning in finite MDPs  Qlearning with function approximation  Actorcritic methods  Implementing a critic  Implementing an actor 
 4. For further exploration  Further reading  Applications  Software 
 A. The theory of discounted Markovian decision processes  A.1. Contractions and Banach's fixedpoint theorem  A.2. Application to MDPs  Bibliography  Author's biography
 Dimensions
 unknown
 Extent
 1 electronic text (xii, 89 p. : ill.)
 File format
 multiple file formats
 Form of item
 electronic
 Isbn
 9781608454938
 Level of compression
 unknown
 Other physical details
 digital file. ;
 Quality assurance targets
 unknown
 Reformatting quality
 access
 Specific material designation
 remote
 System details
 System requirements: Adobe Acrobat Reader
 Label
 Algorithms for reinforcement learning, Csaba Szepesvári, (electronic book)
 Antecedent source
 file reproduced from original
 Bibliography note
 Includes bibliographical references (p. 7388)
 Color
 multicolored
 Contents

 1. Markov decision processes  Preliminaries  Markov decision processes  Value functions  Dynamic programming algorithms for solving MDPs 
 2. Value prediction problems  Temporal difference learning in finite state spaces  Tabular TD(0)  Everyvisit MonteCarlo  TD([lambda]): unifying MonteCarlo and TD(0)  Algorithms for large state spaces  TD([lambda]) with function approximation  Gradient temporal difference learning  Leastsquares methods  The choice of the function space 
 3. Control  A catalog of learning problems  Closedloop interactive learning  Online learning in bandits  Active learning in bandits  Active learning in Markov decision processes  Online learning in Markov decision processes  Direct methods  Qlearning in finite MDPs  Qlearning with function approximation  Actorcritic methods  Implementing a critic  Implementing an actor 
 4. For further exploration  Further reading  Applications  Software 
 A. The theory of discounted Markovian decision processes  A.1. Contractions and Banach's fixedpoint theorem  A.2. Application to MDPs  Bibliography  Author's biography
 Dimensions
 unknown
 Extent
 1 electronic text (xii, 89 p. : ill.)
 File format
 multiple file formats
 Form of item
 electronic
 Isbn
 9781608454938
 Level of compression
 unknown
 Other physical details
 digital file. ;
 Quality assurance targets
 unknown
 Reformatting quality
 access
 Specific material designation
 remote
 System details
 System requirements: Adobe Acrobat Reader
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<div class="citation" vocab="http://schema.org/"><i class="fa faexternallinksquare fafw"></i> Data from <span resource="http://link.liverpool.ac.uk/portal/AlgorithmsforreinforcementlearningCsaba/2CCNeKf5O7k/" typeof="Book http://bibfra.me/vocab/lite/Item"><span property="name http://bibfra.me/vocab/lite/label"><a href="http://link.liverpool.ac.uk/portal/AlgorithmsforreinforcementlearningCsaba/2CCNeKf5O7k/">Algorithms for reinforcement learning, Csaba Szepesvári, (electronic book)</a></span>  <span property="potentialAction" typeOf="OrganizeAction"><span property="agent" typeof="LibrarySystem http://library.link/vocab/LibrarySystem" resource="http://link.liverpool.ac.uk/"><span property="name http://bibfra.me/vocab/lite/label"><a property="url" href="http://link.liverpool.ac.uk/">University of Liverpool</a></span></span></span></span></div>