Reinforcement Learning : State-of-the-Art
Reinforcement learning encompasses both a science of adaptive behavior of rational beings in uncertain environments and a computational methodology for finding optimal behaviors for challenging problems in control, optimization and adaptive behavior of intelligent agents. As a field, reinforcement l...
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| المؤلفون الرئيسيون: | , |
|---|---|
| مؤلفون آخرون: | |
| التنسيق: | Livre numérique |
| اللغة: | Anglais |
| منشور في: |
Berlin, Heidelberg :
Springer Berlin Heidelberg
2012.
Cham : Springer Nature |
| الطبعة: | 1st ed. 2012. |
| سلاسل: | Adaptation, Learning, and Optimization
12 |
| الوصول للمادة أونلاين: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| ملاحظة: |
Numérisation de l'édition de Springer La pagination de l'édition imprimée correspondante est de : XXXIV-638 p. lectorat : scientifique Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Reinforcement learning, state-of-the-art, Marco Wiering and Martijn van Otterlo (eds.), Heidelberg, Springer, 2012, 1 volume (xxxiv-638 pages), Adaptation, learning, and optimization, 978-3-642-27644-6 • Reinforcement Learning, Texte imprimé, 9783642446856 • Reinforcement Learning, Texte imprimé, 9783642276460 |
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| 008 | 130325s2012 xx ||| |||| 00| 0 eng d | ||
| 009 | PPN168310996 | ||
| 020 | |a 9783642276453 | ||
| 024 | |a 9783642276446 | ||
| 041 | 0 | |a eng | |
| 082 | |a 006.3 | ||
| 100 | 1 | |a Wiering, Marco. | |
| 245 | 1 | 0 | |a Reinforcement Learning : |b State-of-the-Art |c edited by Marco Wiering, Martijn Otterlo. |
| 250 | |a 1st ed. 2012. | ||
| 256 | |a Données textuelles | ||
| 260 | |a Berlin, Heidelberg : |b Springer Berlin Heidelberg. | ||
| 260 | |a Cham : |b Springer Nature, |c 2012. | ||
| 490 | 0 | |a Adaptation, Learning, and Optimization |v 12 |x 1867-4542 | |
| 500 | |a Numérisation de l'édition de Springer | ||
| 500 | |a La pagination de l'édition imprimée correspondante est de : XXXIV-638 p. | ||
| 500 | |a lectorat : scientifique | ||
| 500 | |a Archives Springer e-books (Licence nationale) | ||
| 500 | |a Archives Springer e-books (Licence nationale) | ||
| 505 | 1 | |a Continous State and Action Spaces Relational and First-Order Knowledge Representation Hierarchical Approaches Predictive Approaches Multi-Agent Reinforcement Learning Partially Observable Markov Decision Processes (POMDPs) Decentralized POMDPs (DEC-POMDPs) Features and Function Approximation RL as Supervised Learning (or batch learning) Bounds and complexity RL for Games RL in Robotics Policy Gradient Techniques Least Squares Value Iteration Models and Model Induction Model-based RL Transfer Learning in RL Using of and extracting Knowledge in RL Biological or Psychological Background Evolutionary Approaches Closing chapter, prospects, future issues | |
| 506 | |a Accès en ligne pour les établissements français bénéficiaires des licences nationales | ||
| 506 | |a Accès soumis à abonnement pour tout autre établissement | ||
| 506 | |a Conditions particulières de réutilisation pour les bénéficiaires des licences nationales. https://www.licencesnationales.fr/springer-nature-ebooks-contrat-licence-ln-2017 | ||
| 520 | |a Reinforcement learning encompasses both a science of adaptive behavior of rational beings in uncertain environments and a computational methodology for finding optimal behaviors for challenging problems in control, optimization and adaptive behavior of intelligent agents. As a field, reinforcement learning has progressed tremendously in the past decade.The main goal of this book is to present an up-to-date series of survey articles on the main contemporary sub-fields of reinforcement learning. This includes surveys on partially observable environments, hierarchical task decompositions, relational knowledge representation and predictive state representations. Furthermore, topics such as transfer, evolutionary methods and continuous spaces in reinforcement learning are surveyed. In addition, several chapters review reinforcement learning methods in robotics, in games, and in computational neuroscience. In total seventeen different subfields are presented by mostly young experts in those areas, and together they truly represent a state-of-the-art of current reinforcement learning research.Marco Wiering works at the artificial intelligence department of the University of Groningen in the Netherlands. He has published extensively on various reinforcement learning topics. Martijn van Otterlo works in the cognitive artificial intelligence group at the Radboud University Nijmegen in The Netherlands. He has mainly focused on expressive knowledgerepresentation in reinforcement learning settings | ||
| 538 | |a Nécessite un lecteur de fichier PDF | ||
| 700 | 1 | |a van Otterlo, Martijn. |4 edt | |
| 700 | 1 | |a Otterlo, Martijn. |4 aut | |
| 776 | 0 | |0 165852186 |t Reinforcement learning |o state-of-the-art |f Marco Wiering and Martijn van Otterlo (eds.) |c Heidelberg |n Springer |d 2012 |p 1 volume (xxxiv-638 pages) |s Adaptation, learning, and optimization |z 978-3-642-27644-6 | |
| 776 | 0 | |t Reinforcement Learning |b Texte imprimé |z 9783642446856 | |
| 776 | 0 | |t Reinforcement Learning |b Texte imprimé |z 9783642276460 | |
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| 856 | 4 | |q PDF |u https://revue-sommaire.istex.fr/ark:/67375/8Q1-Z5XNWVH7-F |z Accès sur la plateforme Istex | |
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