Transfer in Reinforcement Learning Domains
Transfer in Reinforcement Learning Domains (Studies in Computational Intelligence) by Matthew E. Taylor
Publisher: Springer | Number Of Pages: 235 | Publication Date: 2009-08-01 | ISBN-10: 3642018815 | PDF | 2 Mb
Publisher: Springer | Number Of Pages: 235 | Publication Date: 2009-08-01 | ISBN-10: 3642018815 | PDF | 2 Mb
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.
Download from Uploading
or
Download from Depositfiles
or
Download from FileFactory
No Mirrors below, please! Follow Rules!
More serious and useful books!: Part I. 2000 ebooks, 27 Gb | Part II. 1000 ebooks, 4 Gb
or
Download from Depositfiles
or
Download from FileFactory
No Mirrors below, please! Follow Rules!
More serious and useful books!: Part I. 2000 ebooks, 27 Gb | Part II. 1000 ebooks, 4 Gb