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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… mais…
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ISBN: 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 developm… mais…
ISBN: 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 developm… mais…
ISBN: 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 developm… mais…
ISBN: 9783642018824
*Transfer in Reinforcement Learning Domains* / pdf eBook für 96.49 € / Aus dem Bereich: eBooks, Sachthemen & Ratgeber, Computer & Internet Medien > Bücher nein eBook als pdf eBooks > Sach… mais…
ISBN: 9783642018824
Transfer in Reinforcement Learning Domains: ab 96.49 € eBooks > Sachthemen & Ratgeber > Computer & Internet Springer-Verlag GmbH eBook als pdf, Springer-Verlag GmbH
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Dados detalhados do livro - Transfer in Reinforcement Learning Domains
EAN (ISBN-13): 9783642018824
Ano de publicação: 2009
Editor/Editora: Springer-Verlag GmbH
12 Páginas
Língua: eng/Englisch
Livro na base de dados desde 2011-11-17T02:13:22-02:00 (Sao Paulo)
Página de detalhes modificada pela última vez em 2024-02-24T16:43:12-03:00 (Sao Paulo)
Número ISBN/EAN: 9783642018824
Número ISBN - Ortografia alternativa:
978-3-642-01882-4
Ortografia alternativa e termos de pesquisa relacionados:
Autor do livro: springer, walter greiner, taylor, greiner maiungaischer
Título do livro: transfer, domain, learning
Dados da editora
Autor: Matthew Taylor
Título: Studies in Computational Intelligence; Transfer in Reinforcement Learning Domains
Editora: Springer; Springer Berlin
230 Páginas
Ano de publicação: 2009-05-19
Berlin; Heidelberg; DE
Língua: Inglês
96,29 € (DE)
99,00 € (AT)
118,00 CHF (CH)
Available
XII, 230 p.
EA; E107; eBook; Nonbooks, PBS / Technik/Allgemeines, Lexika; Künstliche Intelligenz; Verstehen; Computational Intelligence; Data Mining; Distributed Environments; Information Retrieval; Signal; agents; algorithm; algorithms; computer science; development; knowledge; learning; reinforcement learning; C; Computational Intelligence; Artificial Intelligence; Engineering; BB
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 ScienceIntroductory book to the new concept of transfer learning Recent research in transfer learning which is a current important topic in the field of Computational Intelligence
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