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020			_a9783031791673
024	7		_a10.1007/978-3-031-79167-3 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQ325.5 _b2022 EB
100	1		_aOsborne, Philip _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9688419
245	1	0	_aApplying Reinforcement Learning on Real-World Data with Practical Examples in Python _cby Philip Osborne, Kajal Singh, Matthew E. Taylor
250			_a1st edition 2022
264		1	_aCham _bSpringer International Publishing _c2022
300			_a1 recurso en línea (XVII, 92 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		_aBackground and Definitions -- Reinforcement Learning Theory -- A Robot Cleaner Example -- The Classroom Environment -- Industry Applications -- Conclusion -- Bibliography -- Authors' Biographies.
520			_aReinforcement learning is a powerful tool in artificial intelligence in which virtual or physical agents learn to optimize their decision making to achieve long-term goals. In some cases, this machine learning approach can save programmers time, outperform existing controllers, reach super-human performance, and continually adapt to changing conditions. This book argues that these successes show reinforcement learning can be adopted successfully in many different situations, including robot control, stock trading, supply chain optimization, and plant control. However, reinforcement learning has traditionally been limited to applications in virtual environments or simulations in which the setup is already provided. Furthermore, experimentation may be completed for an almost limitless number of attempts risk-free. In many real-life tasks, applying reinforcement learning is not as simple as (1) data is not in the correct form for reinforcement learning, (2) data is scarce, and (3) automation has limitations in the real-world. Therefore, this book is written to help academics, domain specialists, and data enthusiast alike to understand the basic principles of applying reinforcement learning to real-world problems. This is achieved by focusing on the process of taking practical examples and modeling standard data into the correct form required to then apply basic agents. To further assist with readers gaining a deep and grounded understanding of the approaches, the book shows hand-calculated examples in full and then how this can be achieved in a more automated manner with code. For decision makers who are interested in reinforcement learning as a solution but are not technically proficient we include simple, non-technical examples in the introduction and case studies section. These provide context of what reinforcement learning offer but also the challenges and risks associated with applying it in practice. Specifically, the book illustrates the differences between reinforcement learning and other machine learning approaches as well as how well-known companies have found success using the approach to their problems.
988			_aSynthesis Collection of Technology_2022
650		7	_2embne _9166090 _aAprendizaje automático
650		7	_2embne _aInteligencia artificial _9413115
650		7	_2embne _9413145 _aProcesos cognitivos _xModelos matemáticos
700	1		_aSingh, Kajal _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9688418
700	1		_aTaylor, Matthew E. _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9688417
776	0	8	_iPrinted edition: _z9783031791680
776	0	8	_iPrinted edition: _z9783031791666
776	0	8	_iPrinted edition: _z9783031791697
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-79167-3 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b05/2023 _dz _eb _zSI