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Transfer in Reinforcement Learning Domains Matthew Taylor Author
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In reinforcement learning (RL) problems, learning agents sequentially execute actions with the goal of maximizing a reward signal. The RL framework has gained popularity with the developm… Más…

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In reinforcement learning (RL) problems, learning agents sequentially execute actions with the goal of maximizing a reward signal. The RL framework has gained popularity with the developm… Más…

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Transfer in Reinforcement Learning Domains / Matthew Taylor / Buch / XII / Englisch / 2009 / Springer / EAN 9783642018817 - Taylor, Matthew
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Taylor, Matthew:
Transfer in Reinforcement Learning Domains / Matthew Taylor / Buch / XII / Englisch / 2009 / Springer / EAN 9783642018817 - libro nuevo

2009

ISBN: 9783642018817

[PU: Springer], In reinforcement learning (RL) problems, learning agents sequentially execute actions with the goal of maximizing a reward signal. The RL framework has gained popularity w… Más…

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Taylor, Matthew E.:
Transfer in Reinforcement Learning Domains - encuadernado, tapa blanda

2009, ISBN: 9783642018817

Erscheinungsdatum: 05.06.2009, Medium: Buch, Einband: Gebunden, Titel: Transfer in Reinforcement Learning Domains, Autor: Taylor, Matthew E., Verlag: Springer-Verlag GmbH // Springer Berl… Más…

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Transfer in Reinforcement Learning Domains - Taylor, Matthew E.
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Taylor, Matthew E.:
Transfer in Reinforcement Learning Domains - encuadernado, tapa blanda

2009, ISBN: 3642018815

Gebundene Ausgabe Intelligenz / Künstliche Intelligenz, KI, Künstliche Intelligenz - AI, Roboter - Robotik - Industrieroboter, Künstliche Intelligenz, Ingenieurswesen, Maschinenbau allge… Más…

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Transfer in Reinforcement Learning Domains Matthew Taylor Author

In reinforcement learning (RL) problems, learning agents sequentially execute actions with the goal of maximizing a reward signal. The RL framework has gained popularity with the development of algorithms capable of mastering increasingly complex problems, but learning difficult tasks is often slow or infeasible when RL agents begin with no prior knowledge. The key insight behind "transfer learning" is that generalization may occur not only within tasks, but also across tasks. While transfer has been studied in the psychological literature for many years, the RL community has only recently begun to investigate the benefits of transferring knowledge. This book provides an introduction to the RL transfer problem and discusses methods which demonstrate the promise of this exciting area of research. The key contributions of this book are: Definition of the transfer problem in RL domains Background on RL, sufficient to allow a wide audience to understand discussed transfer concepts Taxonomy for transfer methods in RL Survey of existing approaches In-depth presentation of selected transfer methods Discussion of key open questions By way of the research presented in this book, the author has established himself as the pre-eminent worldwide expert on transfer learning in sequential decision making tasks. A particular strength of the research is its very thorough and methodical empirical evaluation, which Matthew presents, motivates, and analyzes clearly in prose throughout the book. Whether this is your initial introduction to the concept of transfer learning, or whether you are a practitioner in the field looking for nuanced details, I trust that you will find this book to be an enjoyable and enlightening read. Peter Stone, Associate Professor of Computer Science

Detalles del libro - Transfer in Reinforcement Learning Domains Matthew Taylor Author


EAN (ISBN-13): 9783642018817
ISBN (ISBN-10): 3642018815
Tapa dura
Año de publicación: 2009
Editorial: Springer Berlin Heidelberg Core >2 >T
229 Páginas
Peso: 0,511 kg
Idioma: eng/Englisch

Libro en la base de datos desde 2009-07-27T13:42:32-05:00 (Mexico City)
Página de detalles modificada por última vez el 2024-02-24T13:43:12-06:00 (Mexico City)
ISBN/EAN: 3642018815

ISBN - escritura alterna:
3-642-01881-5, 978-3-642-01881-7
Mode alterno de escritura y términos de búsqueda relacionados:
Autor del libro: taylor
Título del libro: transfer, domai, learning englisch


Datos del la editorial

Autor: Matthew Taylor
Título: Studies in Computational Intelligence; Transfer in Reinforcement Learning Domains
Editorial: Springer; Springer Berlin
230 Páginas
Año de publicación: 2009-06-05
Berlin; Heidelberg; DE
Impreso en
Peso: 1,150 kg
Idioma: Inglés
106,99 € (DE)
109,99 € (AT)
118,00 CHF (CH)
POD
XII, 230 p.

BB; Computational Intelligence; Hardcover, Softcover / Technik/Allgemeines, Lexika; Künstliche Intelligenz; Verstehen; Informatik; Computational Intelligence; Data Mining; Distributed Environments; Information Retrieval; Signal; agents; algorithm; algorithms; computer science; development; knowledge; learning; reinforcement learning; Artificial Intelligence; Computational Intelligence; Artificial Intelligence; BC; EA

In reinforcement learning (RL) problems, learning agents sequentially execute actions with the goal of maximizing a reward signal. The RL framework has gained popularity with the development of algorithms capable of mastering increasingly complex problems, but learning difficult tasks is often slow or infeasible when RL agents begin with no prior knowledge. The key insight behind "transfer learning" is that generalization may occur not only within tasks, but also across tasks. While transfer has been studied in the psychological literature for many years, the RL community has only recently begun to investigate the benefits of transferring knowledge. This book provides an introduction to the RL transfer problem and discusses methods which demonstrate the promise of this exciting area of research. The key contributions of this book are: Definition of the transfer problem in RL domains Background on RL, sufficient to allow a wide audience to understand discussed transfer concepts Taxonomy for transfer methods in RL Survey of existing approaches In-depth presentation of selected transfer methods Discussion of key open questions By way of the research presented in this book, the author has established himself as the pre-eminent worldwide expert on transfer learning in sequential decision making tasks. A particular strength of the research is its very thorough and methodical empirical evaluation, which Matthew presents, motivates, and analyzes clearly in prose throughout the book. Whether this is your initial introduction to the concept of transfer learning, or whether you are a practitioner in the field looking for nuanced details, I trust that you will find this book to be an enjoyable and enlightening read. Peter Stone, Associate Professor of Computer Science

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