Recent Advances in Reinforcement Learning 8th European Workshop,
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
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Subseries of Lecture Notes in Computer Science
5323
Sertan Girgin Manuel Loth Rémi Munos Philippe Preux Daniil Ryabko (Eds.)
Recent Advances in Reinforcement Learning 8th European Workshop, EWRL 2008 Villeneuve d’Ascq, France, June 30-July 3, 2008 Revised and Selected Papers
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Series Editors Randy Goebel, University of Alberta, Edmonton, Canada Jörg Siekmann, University of Saarland, Saarbrücken, Germany Wolfgang Wahlster, DFKI and University of Saarland, Saarbrücken, Germany Volume Editors Sertan Girgin INRIA Lille-Nord Europe, 59650 Villeneuve d’Ascq, France E-mail: [email protected] Manuel Loth Rémi Munos Philippe Preux Daniil Ryabko INRIA, LIFL, CNRS, Université de Lille Villeneuve d’Ascq, France E-mail: {manuel.loth,remi.munos,philippe.preux,daniil.ryabko}@inria.fr
Library of Congress Control Number: 2008940489
CR Subject Classification (1998): I.2.6, I.2, F.1, G.3, C.1.3 LNCS Sublibrary: SL 7 – Artificial Intelligence ISSN ISBN-10 ISBN-13
0302-9743 3-540-89721-6 Springer Berlin Heidelberg New York 978-3-540-89721-7 Springer Berlin Heidelberg New York
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Foreword
In the summer of 2008, reinforcement learning researchers from around the world gathered in the north of France for a week of talks and discussions on reinforcement learning, on how it could be made more efficient, 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 previous 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 fitted 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 workshop and the papers collected here provide evidence that the field of reinforcement learning remains vigorous and varied. It is appropriate to reflect on some of the rea
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