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Recent Advances in Reinforcement Learning

8th European Workshop, EWRL 2008, Villeneuve d'Ascq, France, June 30-July 3, 2008, Revised and Selected Papers

  • Conference proceedings
  • © 2008

Overview

Editors:
  1. Sertan Girgin

    1. INRIA Lille-Nord Europe, Villeneuve d’Ascq, France

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  2. Manuel Loth

    1. INRIA, LIFL, CNRS, Université de Lille, Villeneuve d’Ascq, France

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  3. Rémi Munos

    1. INRIA, LIFL, CNRS, Université de Lille, Villeneuve d’Ascq, France

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  4. Philippe Preux

    1. INRIA, LIFL, CNRS, Université de Lille, Villeneuve d’Ascq, France

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  5. Daniil Ryabko

    1. INRIA, LIFL, CNRS, Université de Lille, Villeneuve d’Ascq, France

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 5323)

Part of the book sub series: Lecture Notes in Artificial Intelligence (LNAI)

Included in the following conference series:

Conference proceedings info: EWRL 2008.

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Table of contents (21 papers)

  1. Front Matter

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  2. Lazy Planning under Uncertainty by Optimizing Decisions on an Ensemble of Incomplete Disturbance Trees

    • Boris Defourny, Damien Ernst, Louis Wehenkel
    Pages 1-14

  3. Exploiting Additive Structure in Factored MDPs for Reinforcement Learning

    • Thomas Degris, Olivier Sigaud, Pierre-Henri Wuillemin
    Pages 15-26

  4. Algorithms and Bounds for Rollout Sampling Approximate Policy Iteration

    • Christos Dimitrakakis, Michail G. Lagoudakis
    Pages 27-40

  5. Efficient Reinforcement Learning in Parameterized Models: Discrete Parameter Case

    • Kirill Dyagilev, Shie Mannor, Nahum Shimkin
    Pages 41-54

  6. Regularized Fitted Q-Iteration: Application to Planning

    • Amir massoud Farahmand, Mohammad Ghavamzadeh, Csaba Szepesvári, Shie Mannor
    Pages 55-68

  7. A Near Optimal Policy for Channel Allocation in Cognitive Radio

    • Sarah Filippi, Olivier Cappé, Fabrice Clérot, Eric Moulines
    Pages 69-81

  8. Evaluation of Batch-Mode Reinforcement Learning Methods for Solving DEC-MDPs with Changing Action Sets

    • Thomas Gabel, Martin Riedmiller
    Pages 82-95

  9. Bayesian Reward Filtering

    • Matthieu Geist, Olivier Pietquin, Gabriel Fricout
    Pages 96-109

  10. Basis Expansion in Natural Actor Critic Methods

    • Sertan Girgin, Philippe Preux
    Pages 110-123

  11. Reinforcement Learning with the Use of Costly Features

    • Robby Goetschalckx, Scott Sanner, Kurt Driessens
    Pages 124-135

  12. Variable Metric Reinforcement Learning Methods Applied to the Noisy Mountain Car Problem

    • Verena Heidrich-Meisner, Christian Igel
    Pages 136-150

  13. Optimistic Planning of Deterministic Systems

    • Jean-François Hren, Rémi Munos
    Pages 151-164

  14. Policy Iteration for Learning an Exercise Policy for American Options

    • Yuxi Li, Dale Schuurmans
    Pages 165-178

  15. Tile Coding Based on Hyperplane Tiles

    • Daniele Loiacono, Pier Luca Lanzi
    Pages 179-190

  16. Use of Reinforcement Learning in Two Real Applications

    • José D. Martín-Guerrero, Emilio Soria-Olivas, Marcelino Martínez-Sober, Antonio J. Serrrano-López, Rafael Magdalena-Benedito, Juan Gómez-Sanchis
    Pages 191-204

  17. Applications of Reinforcement Learning to Structured Prediction

    • Francis Maes, Ludovic Denoyer, Patrick Gallinari
    Pages 205-219

  18. Policy Learning – A Unified Perspective with Applications in Robotics

    • Jan Peters, Jens Kober, Duy Nguyen-Tuong
    Pages 220-228

  19. Probabilistic Inference for Fast Learning in Control

    • Carl Edward Rasmussen, Marc Peter Deisenroth
    Pages 229-242

  20. United We Stand: Population Based Methods for Solving Unknown POMDPs

    • Noel Welsh, Jeremy Wyatt
    Pages 243-252
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Other volumes

  1. Recent Advances in Reinforcement Learning

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About this book

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.

Editors and Affiliations

  • INRIA Lille-Nord Europe, Villeneuve d’Ascq, France

    Sertan Girgin

  • INRIA, LIFL, CNRS, Université de Lille, Villeneuve d’Ascq, France

    Manuel Loth, Rémi Munos, Philippe Preux, Daniil Ryabko

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