Articles liés à Algorithms for Reinforcement Learning
Synopsis
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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Présentation de l'éditeur
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.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
- ÉditeurMorgan and Claypool Publishers
- Date d'édition2010
- ISBN 10 1608454924
- ISBN 13 9781608454921
- ReliureBroché
- Langueanglais
- Nombre de pages104
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Algorithms for Reinforcement Learning (Synthesis Lectures on Artificial Intelligence and Machine Learning)
Edité par
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)
Edité par
Morgan and Claypool Publishers, 2010
ISBN 10 : 1608454924
ISBN 13 : 9781608454921
Ancien ou d'occasion
Couverture souple
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Algorithms for Reinforcement Learning (Synthesis Lectures on Artificial Intelligence and Machine Learning)
Edité par
Morgan and Claypool Publishers, 2010
ISBN 10 : 1608454924
ISBN 13 : 9781608454921
Ancien ou d'occasion
Couverture souple
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Etat : Very Good. Item in very good condition! Textbooks may not include supplemental items i.e. CDs, access codes etc. N° de réf. du vendeur 00087521207
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