000			03989nam a22004935i 4500
999			_c330771 _d330771 _x1
001			330771
003			ES-MaUEC
005			20240111050207.0
006			a||||fo|||| 00| 0
007			cr nn nnnaamaa
008			210102s2021 gw | s |||| 0|eng d
020			_a9783030411886
024	7		_a10.1007/978-3-030-41188-6 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _erda _dES-MaUEC
050		4	_aQ325.6 _b2021 EB
245	0	0	_aReinforcement Learning Algorithms : _bAnalysis and Applications _cedited by Boris Belousov, Hany Abdulsamad, Pascal Klink, Simone Parisi, Jan Peters.
250			_aFirst edition 2021
264		1	_aCham _bSpringer International Pulishing _c2021
300			_a1 recurso en línea (VIII, 206 páginas) _b45 ilustraciones, 35 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_atext file _bPDF _2
490	0		_aStudies in Computational Intelligence _x1860-949X _v883
490	0		_aIntelligent Technologies and Robotics (SpringerNature-42732)
490	0		_aIntelligent Technologies and Robotics (R0) (SpringerNature-43728)
505	0		_aPrediction Error and Actor-Critic Hypotheses in the Brain -- Reviewing on-policy / off-policy critic learning in the context of Temporal Differences and Residual Learning -- Reward Function Design in Reinforcement Learning -- Exploration Methods In Sparse Reward Environments -- A Survey on Constraining Policy Updates Using the KL Divergence -- Fisher Information Approximations in Policy Gradient Methods -- Benchmarking the Natural gradient in Policy Gradient Methods and Evolution Strategies -- Information-Loss-Bounded Policy Optimization -- Persistent Homology for Dimensionality Reduction -- Model-free Deep Reinforcement Learning - Algorithms and Applications -- Actor vs Critic -- Bring Color to Deep Q-Networks -- Distributed Methods for Reinforcement Learning -- Model-Based Reinforcement Learning -- Challenges of Model Predictive Control in a Black Box Environment -- Control as Inference?
520	3		_aThis book reviews research developments in diverse areas of reinforcement learning such as model-free actor-critic methods, model-based learning and control, information geometry of policy searches, reward design, and exploration in biology and the behavioral sciences. Special emphasis is placed on advanced ideas, algorithms, methods, and applications. The contributed papers gathered here grew out of a lecture course on reinforcement learning held by Prof. Jan Peters in the winter semester 2018/2019 at Technische Universität Darmstadt. The book is intended for reinforcement learning students and researchers with a firm grasp of linear algebra, statistics, and optimization. Nevertheless, all key concepts are introduced in each chapter, making the content self-contained and accessible to a broader audience.
988			_aSpringer_Robotics_2021
650		7	_aAprendizaje automático _2embne _9166090
650		7	_aAlgoritmos _2embne _9141162
650		7	_aInteligencia artificial _2embne _9413115
700	1		_aBelousov, Boris _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aAbdulsamad, Hany _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aKlink, Pascal _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aParisi, Simone _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aPeters, Jan _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
776	0	8	_iPrinted edition: _z9783030411879
776	0	8	_iPrinted edition: _z9783030411893
776	0	8	_iPrinted edition: _z9783030411909
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-41188-6 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE _n0
998			_b04/2021 _dz _eo _zSI