000			03055nam a2200421 i 4500
999			_c387725 _d387725
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003			ES-MaUEC
005			20240111050231.0
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007			cr nn 008mamaa
008			220601s2010 sz | o |||| 0|eng d
020			_a9783031015519
024	7		_a10.1007/978-3-031-01551-9 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQ325.6 _b2010 EB
100	1		_aSzepesvári, Csaba _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687798
245	1	0	_aAlgorithms for Reinforcement Learning _cby Csaba Szepesvári
250			_a1st edition 2010
264		1	_aCham _bSpringer International Publishing _c2010
300			_a1 recurso en línea (XIII, 89 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSynthesis Lectures on Artificial Intelligence and Machine Learning _x1939-4616
505	0		_aMarkov Decision Processes -- Value Prediction Problems -- Control -- For Further Exploration.
520			_aReinforcement learning is a learning paradigm concerned with learning to control a system so as to maximize a numerical performance measure that expresses a long-term objective. What distinguishes reinforcement learning from supervised learning is that only partial feedback is given to the learner about the learner's predictions. Further, the predictions may have long term effects through influencing the future state of the controlled system. Thus, time plays a special role. The goal in reinforcement learning is to develop efficient learning algorithms, as well as to understand the algorithms' merits and limitations. Reinforcement learning is of great interest because of the large number of practical applications that it can be used to address, ranging from problems in artificial intelligence to operations research or control engineering. In this book, we focus on those algorithms of reinforcement learning that build on the powerful theory of dynamic programming. We give a fairly comprehensive catalog of learning problems, describe the core ideas, note a large number of state of the art algorithms, followed by the discussion of their theoretical properties and limitations. Table of Contents: Markov Decision Processes / Value Prediction Problems / Control / For Further Exploration.
988			_aSynthesis Collection of Technology_2010
650		7	_2embne _9166090 _aAprendizaje automático
650		7	_2embne _aInteligencia artificial _9413115
776	0	8	_iPrinted edition: _z9783031000218
776	0	8	_iPrinted edition: _z9783031004230
776	0	8	_iPrinted edition: _z9783031026799
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-01551-9 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b03/2023 _dz _eb _zSI