Recent Advances In Reinforcement Learning

Girgin, S.; Loth, M.; Munos, R.; Preux, P.; Ryabko, D. ,

ISBN 10: 3540897216 / ISBN 13: 9783540897217

Published by Springer, 2008

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Bibliographic Details

• Title: Recent Advances In Reinforcement Learning
• Publisher: Springer
• Publication Date: 2008
• Language: English
• ISBN 10: 3540897216
• ISBN 13: 9783540897217
• Binding: Soft cover
• Condition: New

About this title

Synopsis

Inthesummerof2008,reinforcementlearningresearchersfromaroundtheworld gathered in the north of France for a week of talks and discussions on reinfor- ment learning, on how it could be made more e?cient, applied to a broader range of applications, and utilized at more abstract and symbolic levels. As a participant in this 8th European Workshop on Reinforcement Learning, I was struck by both the quality and quantity of the presentations. There were four full days of short talks, over 50 in all, far more than there have been at any p- vious meeting on reinforcement learning in Europe, or indeed, anywhere else in the world. There was an air of excitement as substantial progress was reported in many areas including Computer Go, robotics, and ?tted methods. Overall, the work reported seemed to me to be an excellent, broad, and representative sample of cutting-edge reinforcement learning research. Some of the best of it is collected and published in this volume. The workshopandthe paperscollectedhere provideevidence thatthe ?eldof reinforcement learning remains vigorous and varied. It is appropriate to re?ect on some of the reasons for this. One is that the ?eld remains focused on a pr- lem ― sequential decision making ― without prejudice as to solution methods. Another is the existence of a common terminology and body of theory.

From the Back Cover

This book constitutes revised and selected papers of the 8th European Workshop on Reinforcement Learning, EWRL 2008, which took place in Villeneuve d'Ascq, France, during June 30 - July 3, 2008.

The 21 papers presented were carefully reviewed and selected from 61 submissions. They are dedicated to the field of and current researches in reinforcement learning.