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008			220601s2021 sz | s |||| 0|eng d
020			_a9783031015915
024	7		_a10.1007/978-3-031-01591-5 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQ325.6 _b2021 EB
100	1		_aLeno Silva, Felipe _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686644
245	1	0	_aTransfer Learning for Multiagent Reinforcement Learning Systems _cby Felipe Leno Silva, Anna Helena Reali Costa
250			_a1st edition 2021
264		1	_aCham _bSpringer International Publishing _c2021
300			_a1 recurso en línea (XVII, 111 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		_aPreface -- Acknowledgments -- Introduction -- Background -- Taxonomy -- Intra-Agent Transfer Methods -- Inter-Agent Transfer Methods -- Experiment Domains and Applications -- Current Challenges -- Resources -- Conclusion -- Bibliography -- Authors' Biographies .
520			_aLearning to solve sequential decision-making tasks is difficult. Humans take years exploring the environment essentially in a random way until they are able to reason, solve difficult tasks, and collaborate with other humans towards a common goal. Artificial Intelligent agents are like humans in this aspect. Reinforcement Learning (RL) is a well-known technique to train autonomous agents through interactions with the environment. Unfortunately, the learning process has a high sample complexity to infer an effective actuation policy, especially when multiple agents are simultaneously actuating in the environment. However, previous knowledge can be leveraged to accelerate learning and enable solving harder tasks. In the same way humans build skills and reuse them by relating different tasks, RL agents might reuse knowledge from previously solved tasks and from the exchange of knowledge with other agents in the environment. In fact, virtually all of the most challenging tasks currently solved by RL rely on embedded knowledge reuse techniques, such as Imitation Learning, Learning from Demonstration, and Curriculum Learning. This book surveys the literature on knowledge reuse in multiagent RL. The authors define a unifying taxonomy of state-of-the-art solutions for reusing knowledge, providing a comprehensive discussion of recent progress in the area. In this book, readers will find a comprehensive discussion of the many ways in which knowledge can be reused in multiagent sequential decision-making tasks, as well as in which scenarios each of the approaches is more efficient. The authors also provide their view of the current low-hanging fruit developments of the area, as well as the still-open big questions that could result in breakthrough developments. Finally, the book provides resources to researchers who intend to join this area or leverage those techniques, including a list of conferences, journals, and implementation tools. This book will be useful for a wide audience; and will hopefully promote new dialogues across communities and novel developments in the area.
988			_aSynthesis Collection of Technology_2021
650		7	_2embne _9160930 _aAgentes inteligentes (Programas de ordenador)
650		7	_2embne _9166090 _aAprendizaje automático
700	1		_aReali Costa, Anna Helena _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686645
776	0	8	_iPrinted edition: _z9783031000362
776	0	8	_iPrinted edition: _z9783031004636
776	0	8	_iPrinted edition: _z9783031027192
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-01591-5 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b02/2023 _dz _esc _zSI