000			03814nam a22004575i 4500
001			978-3-642-27645-3
003			DE-He213
005			20140220083309.0
007			cr nn 008mamaa
008			120305s2012 gw | s |||| 0|eng d
020			_a9783642276453 _9978-3-642-27645-3
024	7		_a10.1007/978-3-642-27645-3 _2doi
050		4	_aQ342
072		7	_aUYQ _2bicssc
072		7	_aCOM004000 _2bisacsh
082	0	4	_a006.3 _223
100	1		_aWiering, Marco. _eeditor.
245	1	0	_aReinforcement Learning _h[electronic resource] : _bState-of-the-Art / _cedited by Marco Wiering, Martijn Otterlo.
264		1	_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg : _bImprint: Springer, _c2012.
300			_aXXXIV, 638 p. 87 illus. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aAdaptation, Learning, and Optimization, _x1867-4534 ; _v12
505	0		_aContinous 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.
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		0	_aEngineering.
650		0	_aArtificial intelligence.
650	1	4	_aEngineering.
650	2	4	_aComputational Intelligence.
650	2	4	_aArtificial Intelligence (incl. Robotics).
700	1		_aOtterlo, Martijn. _eeditor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9783642276446
830		0	_aAdaptation, Learning, and Optimization, _x1867-4534 ; _v12
856	4	0	_uhttp://dx.doi.org/10.1007/978-3-642-27645-3
912			_aZDB-2-ENG
999			_c102644 _d102644