Reinforcement Learning: State-of-the-Art: Adaptation, Learning, and Optimization, cartea 12
Editat de Marco Wiering, Martijn van Otterloen Limba Engleză Paperback – 16 apr 2014
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 knowledge
representation in reinforcement learning settings.
Toate formatele și edițiile | Preț | Express |
---|---|---|
Paperback (1) | 2201.85 lei 6-8 săpt. | |
Springer Berlin, Heidelberg – 16 apr 2014 | 2201.85 lei 6-8 săpt. | |
Hardback (1) | 2205.12 lei 6-8 săpt. | |
Springer Berlin, Heidelberg – 14 mar 2012 | 2205.12 lei 6-8 săpt. |
Din seria Adaptation, Learning, and Optimization
- 15% Preț: 641.71 lei
- 15% Preț: 634.32 lei
- 20% Preț: 983.85 lei
- 20% Preț: 648.44 lei
- 20% Preț: 984.18 lei
- 20% Preț: 985.35 lei
- 20% Preț: 653.38 lei
- 20% Preț: 648.59 lei
- 15% Preț: 640.37 lei
- 20% Preț: 649.43 lei
- 20% Preț: 651.42 lei
- 18% Preț: 1837.57 lei
- 20% Preț: 646.62 lei
- 20% Preț: 927.45 lei
- 20% Preț: 651.75 lei
- 20% Preț: 646.47 lei
- 20% Preț: 986.66 lei
- 20% Preț: 638.55 lei
- 20% Preț: 983.39 lei
- 20% Preț: 991.14 lei
- 15% Preț: 638.57 lei
- 15% Preț: 640.88 lei
- 20% Preț: 637.23 lei
- 20% Preț: 1449.13 lei
- 20% Preț: 1284.47 lei
- 20% Preț: 985.35 lei
Preț: 2201.85 lei
Preț vechi: 2752.31 lei
-20% Nou
Puncte Express: 3303
Preț estimativ în valută:
421.41€ • 438.25$ • 352.64£
421.41€ • 438.25$ • 352.64£
Carte tipărită la comandă
Livrare economică 15-29 martie
Preluare comenzi: 021 569.72.76
Specificații
ISBN-13: 9783642446856
ISBN-10: 364244685X
Pagini: 672
Ilustrații: XXXIV, 638 p.
Dimensiuni: 155 x 235 x 35 mm
Greutate: 0.95 kg
Ediția:2012
Editura: Springer Berlin, Heidelberg
Colecția Springer
Seria Adaptation, Learning, and Optimization
Locul publicării:Berlin, Heidelberg, Germany
ISBN-10: 364244685X
Pagini: 672
Ilustrații: XXXIV, 638 p.
Dimensiuni: 155 x 235 x 35 mm
Greutate: 0.95 kg
Ediția:2012
Editura: Springer Berlin, Heidelberg
Colecția Springer
Seria Adaptation, Learning, and Optimization
Locul publicării:Berlin, Heidelberg, Germany
Public țintă
ResearchCuprins
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.
Textul de pe ultima copertă
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 knowledge
representation in reinforcement learning settings.
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 knowledge
representation in reinforcement learning settings.
Caracteristici
Covers all important recent developments in reinforcement learning Very good introduction and explanation of the different emerging areas in Reinforcement Learning Includes a survey of previous papers written on the topic