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Transfer in Reinforcement Learning Domains - Matthew Taylor
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Matthew Taylor:
Transfer in Reinforcement Learning Domains - neues Buch

ISBN: 9783642018824

ID: 9783642018824

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 Transfer in Reinforcement Learning Domains: 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 agents algorithm algorithms Computational Intelligence computer science Data Mining development Distributed Environments Information Retrieval knowledge learning reinforcement learning Signal, Springer Berlin

Neues Buch Rheinberg-Buch.de
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Transfer in Reinforcement Learning Domains - Taylor, Matthew E.
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Taylor, Matthew E.:
Transfer in Reinforcement Learning Domains - neues Buch

2009, ISBN: 9783642018824

ID: 9783642018824

In englischer Sprache. Verlag: Springer Berlin, 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 questionsBy 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, PC-PDF, 12 Seiten, 12 Seiten, [GR: 9681 - Nonbooks, PBS / Technik/Allgemeines, Lexika], [SW: - Computational Intelligence], [DRM: hard-drm], [Ausgabe: 1]

Neues Buch Libreka.de
Libreka
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Transfer in Reinforcement Learning Domains - Matthew Taylor
Vergriffenes Buch, derzeit bei uns nicht verfügbar.
(*)
Matthew Taylor:
Transfer in Reinforcement Learning Domains - neues Buch

2009, ISBN: 9783642018824

ID: 1004438335

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 Transfer in Reinforcement Learning Domains eBook PDF 19.05.2009 eBooks>Fremdsprachige eBooks>Englische eBooks>Sach- & Fachthemen>Informatik, Springer, .200

Neues Buch Orellfuessli.ch
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Transfer in Reinforcement Learning Domains - Taylor, Matthew E.
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Taylor, Matthew E.:
Transfer in Reinforcement Learning Domains - Erstausgabe

2009, ISBN: 9783642018824

ID: 9783642018824

In englischer Sprache. Verlag: Springer Berlin, PC-PDF, 12 Seiten, 12 Seiten, [GR: 9681 - Nonbooks, PBS / Technik/Allgemeines, Lexika], [SW: - Computational Intelligence], [Ausgabe: 1]

Neues Buch DE Libreka.de
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Transfer in Reinforcement Learning Domains - Matthew E. Taylor
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Matthew E. Taylor:
Transfer in Reinforcement Learning Domains - Erstausgabe

2009, ISBN: 9783642018824

ID: 21814464

[ED: 1], eBook Download (PDF), eBooks, [PU: Springer Berlin]

Neues Buch Lehmanns.de
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