Reinforcement Learning [electronic resource] : State-of-the-Art / edited by Marco Wiering, Martijn Otterlo.

By: Wiering, Marco [editor.]Contributor(s): Otterlo, Martijn [editor.] | SpringerLink (Online service)Material type: TextTextLanguage: English Series: Adaptation, Learning, and Optimization: 12Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2012Description: XXXIV, 638 p. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9783642276453Subject(s): Engineering | Artificial intelligence | Engineering | Computational Intelligence | Artificial Intelligence (incl. Robotics)Additional physical formats: Printed edition:: No titleDDC classification: 006.3 LOC classification: Q342Online resources: Click here to access online
Contents:
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.
In: Springer eBooksSummary: 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.
Tags from this library: No tags from this library for this title. Log in to add tags.
    Average rating: 0.0 (0 votes)
No physical items for this record

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.

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.

There are no comments on this title.

to post a comment.

Implemented and Maintained by Biju Patnaik Central Library.
For any Suggestions/Query Contact to library or Email: library@nitrkl.ac.in OR bpcl-cir@nitrkl.ac.in. Ph:91+6612462103
Website/OPAC best viewed in Mozilla Browser in 1366X768 Resolution.

Powered by Koha