| 書目名稱 | Transfer in Reinforcement Learning Domains |
| 編輯 | Matthew E. Taylor |
| 視頻video | http://file.papertrans.cn/929/928206/928206.mp4 |
| 概述 | 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 |
| 叢書名稱 | Studies in Computational Intelligence |
| 圖書封面 |  |
| 描述 | .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 |
| 出版日期 | Book 2009 |
| 關鍵詞 | Computational Intelligence; Data Mining; Distributed Environments; Information Retrieval; Signal; agents; |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-642-01882-4 |
| isbn_softcover | 978-3-642-10186-1 |
| isbn_ebook | 978-3-642-01882-4Series ISSN 1860-949X Series E-ISSN 1860-9503 |
| issn_series | 1860-949X |
| copyright | Springer-Verlag Berlin Heidelberg 2009 |
|Archiver|手機版|小黑屋|
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