000			01831cam a22003135i 4500
003			EG-CaNGU
005			20250326124831.0
008			250326t2018 mau fo m eng d
020			_a9780262039246
040			_aDLC _beng _cDLC _erda _dDLC _dEG-CaNGU
100	1		_aSutton, Richard S., _eauthor. _96575
245	1	0	_aReinforcement learning : _ban introduction /
250			_aSecond edition.
264		1	_aCambridge, Massachusetts : _bThe MIT Press, _c[2018]
264		4	_c©2018
300			_a1 online resource (xxii, 526 pages) : _billustrations.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
490	0		_aAdaptive computation and machine learning series
504			_aIncludes bibliographical references and index.
505	0	0	_aPreface to the Second Edition -- Preface to the First Edition -- Summary of Notation -- 1. Introduction -- I. Tabular Solution Methods -- II. Approximate Solution Methods -- III. Looking Deeper.
506			_aAvailable on-campus and off-campus.
520			_a"Reinforcement 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 while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms."-- _cProvided by publisher.
650		7	_aReinforcement learning. _2NGU-sh _96576
655		7	_aElectronic books. _2NGU-sh _91203
700	1		_aBarto, Andrew G., _eauthor. _96577
856	4	0	_aOnline resource. _uhttps://mitpress.ublish.com/ebook/reinforcement-learning-an-introduction-2-preview/2351/Cover _zOnline resource.
999			_c2179 _d2179