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008			221015s2022 si | s |||| 0|eng d
020			_a9789811906381
024	7		_a10.1007/978-981-19-0638-1 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQ325.6 _b2022 EB
100	1		_aPlaat, Aske _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9684996
245	1	0	_aDeep Reinforcement Learning _cby Aske Plaat
250			_aFirst edition 2022
264		1	_aSingapore _bSpringer International Publising _c2022
300			_a1 recurso en línea (XV, 406 páginas) _b1 ilustraciones
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
505	0		_a1. Introduction -- 2. Tabular Value-Based Methods -- 3. Approximating the Value Function -- 4. Policy-Based Methods -- 5. Model-Based Methods -- 6. Two-Agent Reinforcement Learning -- 7. Multi-Agent Reinforcement Learning -- 8. Hierarchical Reinforcement Learning -- 9. Meta Learning -- 10. Further Developments -- A. Deep Reinforcement Learning Suites -- B. Deep Learning -- C. Mathematical Background.
520			_aDeep reinforcement learning has attracted considerable attention recently. Impressive results have been achieved in such diverse fields as autonomous driving, game playing, molecular recombination, and robotics. In all these fields, computer programs have taught themselves to understand problems that were previously considered to be very difficult. In the game of Go, the program AlphaGo has even learned to outmatch three of the world's leading players.Deep reinforcement learning takes its inspiration from the fields of biology and psychology. Biology has inspired the creation of artificial neural networks and deep learning, while psychology studies how animals and humans learn, and how subjects' desired behavior can be reinforced with positive and negative stimuli. When we see how reinforcement learning teaches a simulated robot to walk, we are reminded of how children learn, through playful exploration. Techniques that are inspired by biology and psychology work amazingly well in computers: animal behavior and the structure of the brain as new blueprints for science and engineering. In fact, computers truly seem to possess aspects of human behavior; as such, this field goes to the heart of the dream of artificial intelligence. These research advances have not gone unnoticed by educators. Many universities have begun offering courses on the subject of deep reinforcement learning. The aim of this book is to provide an overview of the field, at the proper level of detail for a graduate course in artificial intelligence. It covers the complete field, from the basic algorithms of Deep Q-learning, to advanced topics such as multi-agent reinforcement learning and meta learning.
988			_aSpringer_Computer_2022
650		7	_2embne _9166090 _aAprendizaje automático
776	0	8	_iPrinted edition: _z9789811906374
776	0	8	_iPrinted edition: _z9789811906398
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-19-0638-1 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b10/2022 _dz _eIG _zSI