rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Reinforcement Learning
    sub title: State-of-the-Art
  name:
    name part: Wiering, Marco.
    role:
      role term: editor.
  name:
    name part: Otterlo, Martijn van.
    role:
      role term: editor.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2012
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XXXIV, 638 p. online resource.
  abstract: 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.
  table of 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.
  note: edited by Marco Wiering, Martijn van Otterlo.
  subject:
    topic: Engineering
  subject:
    topic: Artificial intelligence
  subject:
    topic: Computational intelligence
  subject:
    topic: Engineering
  subject:
    topic: Computational Intelligence
  subject:
    topic: Artificial Intelligence (incl. Robotics)
  classification: Q342
  classification: 006.3
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Adaptation, Learning, and Optimization, 12
  identifier: 9783642276453
  identifier: http://dx.doi.org/10.1007/978-3-642-27645-3
  record info:
    record creation date: 120305
    record change date: 20151125201532.0
    record identifier: 978-3-642-27645-3