Overview
- Introductory 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
Part of the book series: Studies in Computational Intelligence (SCI, volume 216)
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About this book
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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Keywords
Table of contents (9 chapters)
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Front Matter
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Back Matter
Authors and Affiliations
Bibliographic Information
Book Title: Transfer in Reinforcement Learning Domains
Authors: Matthew E. Taylor
Series Title: Studies in Computational Intelligence
DOI: https://doi.org/10.1007/978-3-642-01882-4
Publisher: Springer Berlin, Heidelberg
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer-Verlag Berlin Heidelberg 2009
Hardcover ISBN: 978-3-642-01881-7Published: 05 June 2009
Softcover ISBN: 978-3-642-10186-1Published: 28 October 2010
eBook ISBN: 978-3-642-01882-4Published: 19 May 2009
Series ISSN: 1860-949X
Series E-ISSN: 1860-9503
Edition Number: 1
Number of Pages: XII, 230