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Algorithms for Reinforcement Learning (Synthesis Lectures on Artificial Intelligence and Machine Learning) - Tapa blanda
Sinopsis
Páginas:106Géneros:12:UG:Graphical&digitalmediaapplications12:UYQ:ArtificialintelligenceSinopsis:Reinforcementlearningisalearningparadigmconcernedwithlearningtocontrolasystemsoastomaximizeanumericalperformancemeasurethatexpressesalong-termobjective.Whatdistinguishesreinforcementlearningfromsupervisedlearningisthatonlypartialfeedbackisgiventothelearneraboutthelearnerspredictions.Further,thepredictionsmayhavelongtermeffectsthroughinfluencingthefuturestateofthecontrolledsystem.Thus,timeplaysaspecialrole.Thegoalinreinforcementlearningistodevelopefficientlearningalgorithms,aswellastounderstandthealgorithmsmeritsandlimitations.Reinforcementlearningisofgreatinterestbecauseofthelargenumberofpracticalapplicationsthatitcanbeusedtoaddress,rangingfromproblemsinartificialintelligencetooperationsresearchorcontrolengineering.Inthisbook,wefocusonthosealgorithmsofreinforcementlearningthatbuildonthepowerfultheoryofdynamicprogramming.Wegiveafairlycomprehensivecatalogoflearningproblems,describethecoreideas,notealargenumberofstateoftheartalgorithms,followedbythediscussionoftheirtheoreticalpropertiesandlimitations._,
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Reseña del editor
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.
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- EditorialMorgan and Claypool Publishers
- Año de publicación2010
- ISBN 10 1608454924
- ISBN 13 9781608454921
- EncuadernaciónTapa blanda
- IdiomaInglés
- Número de páginas104
EUR 31,03
EUR 5,09 gastos de envío desde Reino Unido a Estados Unidos de America
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Algorithms for Reinforcement Learning (Synthesis Lectures on Artificial Intelligence and Machine Learning)
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Morgan and Claypool Publishers, 2010
ISBN 10: 1608454924
ISBN 13: 9781608454921
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Algorithms for Reinforcement Learning (Synthesis Lectures on Artificial Intelligence and Machine Learning)
Publicado por
Morgan and Claypool Publishers, 2010
ISBN 10: 1608454924
ISBN 13: 9781608454921
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Algorithms for Reinforcement Learning (Synthesis Lectures on Artificial Intelligence and Machine Learning)
Publicado por
Morgan and Claypool Publishers, 2010
ISBN 10: 1608454924
ISBN 13: 9781608454921
Antiguo o usado
Tapa blanda
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