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008			190117s2019 gw a o |||| 0|eng d
020			_a9783030105464
024	7		_a10.1007/978-3-030-10546-4 _2doi
040			_bspa _dES-MaUEC _cES-MaUEC
050		4	_aQ325.6 _b2019 EB
100	1		_aYu, F. Richard _eautor _9670023
245	1	0	_aDeep Reinforcement Learning for Wireless Networks _cF. Richard Yu, Ying He
264		1	_aCham _bSpringer International Publishing : _bImprint: Springer _c2019
300			_a1 recurso en línea (VIII, 71 páginas) _b28 ilustraciones
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_atext file _bPDF
490	0		_aEngineering (Springer-11647)
490	0		_aSpringerBriefs in Electrical and Computer Engineering _x2191-8112
520	3		_aThis Springerbrief presents a deep reinforcement learning approach to wireless systems to improve system performance. Particularly, deep reinforcement learning approach is used in cache-enabled opportunistic interference alignment wireless networks and mobile social networks. Simulation results with different network parameters are presented to show the effectiveness of the proposed scheme. There is a phenomenal burst of research activities in artificial intelligence, deep reinforcement learning and wireless systems. Deep reinforcement learning has been successfully used to solve many practical problems. For example, Google DeepMind adopts this method on several artificial intelligent projects with big data (e.g., AlphaGo), and gets quite good results.. Graduate students in electrical and computer engineering, as well as computer science will find this brief useful as a study guide. Researchers, engineers, computer scientists, programmers, and policy makers will also find this brief to be a useful tool
650		7	_2embne _aSistemas de comunicación inalámbricos _9158044
650		7	_2embne _aAprendizaje automático _9166090
650		7	_2embne _aInteligencia artificial _9413115
700	1		_aHe, Ying. _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut
776	0	8	_iPrinted edition: _z9783030105457
776	0	8	_iPrinted edition: _z9783030105471
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-10546-4 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
988			_aPrimersemestre_2019_Engineering
998			_aSI _cm _dz _feng _ggw _h0 _b07/2019 _eel _zSI