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020 _a9783642276446
040 _cIIT Kanpur
041 _aeng
082 _a006.31
_bR275
245 _aReinforcement learning
_bstate-of-the-art
_cedited by Marco Wiering and Martijn van Otterlo
260 _aBerlin
_bSpringer
_c2012
300 _axxxiv, 638p
440 _aAdaptation, learning, and optimization
490 _a/ edited by Meng-Hiot Lim; v.12
520 _aReinforcement 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.
650 _aReinforcement learning
650 _aArtificial intelligence
650 _aReinforcement (Psychology)
700 _aWiering, Marco [ed.]
700 _aOtterlo, Martijn van [ed.]
942 _cBK
999 _c560554
_d560554