000			03030nam a2200505 i 4500
001			6267343
003			IEEE
005			20220712204635.0
006			m o d
007			cr |n|||||||||
008			151223s1998 maua ob 001 eng d
010			_z 97026416 (print)
020			_z9780262193986 _qprint
020			_a9780262257053 _qelectronic
020			_z0262193981 _qalk. paper
035			_a(CaBNVSL)mat06267343
035			_a(IDAMS)0b000064818b431d
040			_aCaBNVSL _beng _erda _cCaBNVSL _dCaBNVSL
050		4	_aQ325.6 _b.S88 1998eb
082	0	0	_a006.3/1 _221
100	1		_aSutton, Richard S., _eauthor. _922249
245	1	0	_aReinforcement learning : _ban introduction / _cRichard S. Sutton and Andrew G. Barto.
264		1	_aCambridge, Massachusetts : _bMIT Press, _cc1998.
264		2	_a[Piscataqay, New Jersey] : _bIEEE Xplore, _c[1998]
300			_a1 PDF (xviii, 322 pages) : _billustrations.
336			_atext _2rdacontent
337			_aelectronic _2isbdmedia
338			_aonline resource _2rdacarrier
490	1		_aAdaptive computation and machine learning series
504			_aIncludes bibliographical references (p. [291]-312) and index.
506	1		_aRestricted to subscribers or individual electronic text purchasers.
520			_aReinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability.The book is divided into three parts. Part I defines the reinforcement learning problem in terms of Markov decision processes. Part II provides basic solution methods: dynamic programming, Monte Carlo methods, and temporal-difference learning. Part III presents a unified view of the solution methods and incorporates artificial neural networks, eligibility traces, and planning; the two final chapters present case studies and consider the future of reinforcement learning.
530			_aAlso available in print.
538			_aMode of access: World Wide Web
588			_aDescription based on PDF viewed 12/23/2015.
650		0	_aReinforcement learning. _99427
655		0	_aElectronic books. _93294
700	1		_aBarto, Andrew G. _922250
710	2		_aIEEE Xplore (Online Service), _edistributor. _922251
710	2		_aMIT Press, _epublisher. _922252
776	0	8	_iPrint version _z9780262193986
830		0	_aAdaptive computation and machine learning series _921885
856	4	2	_3Abstract with links to resource _uhttps://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=6267343
942			_cEBK
999			_c72998 _d72998