Transfer in Reinforcement Learning Domains

by Matthew Taylor

Overview

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

Product Details

ISBN-13:	9783642018817
Publisher:	Springer Berlin Heidelberg
Publication date:	05/27/2009
Series:	Studies in Computational Intelligence , #216
Edition description:	2009
Pages:	230
Product dimensions:	6.10(w) x 9.25(h) x 0.02(d)

Table of Contents

Reinforcement Learning Background.- Related Work.- Empirical Domains.- Value Function Transfer via Inter-Task Mappings.- Extending Transfer via Inter-Task Mappings.- Transfer between Different Reinforcement Learning Methods.- Learning Inter-Task Mappings.- Conclusion and Future Work.