© 2012

Recent Advances in Reinforcement Learning

9th European Workshop, EWRL 2011, Athens, Greece, September 9-11, 2011, Revised Selected Papers

  • Scott Sanner
  • Marcus Hutter


  • Fast-track conference proceedings

  • State-of-the-art research

  • Up-to-date results

Conference proceedings EWRL 2011

Part of the Lecture Notes in Computer Science book series (LNCS, volume 7188)

Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 7188)

Table of contents

  1. Front Matter
  2. Invited Talk Abstracts

  3. Online Reinforcement Learning

    1. Francis Maes, Louis Wehenkel, Damien Ernst
      Pages 5-17
    2. Sylvie C. W. Ong, Yuri Grinberg, Joelle Pineau
      Pages 18-29
  4. Learning and Exploring MDPs

    1. Mauricio Araya-López, Olivier Buffet, Vincent Thomas, François Charpillet
      Pages 42-53
    2. Boris Lesner, Bruno Zanuttini
      Pages 54-65
    3. Phuong Nguyen, Peter Sunehag, Marcus Hutter
      Pages 66-77
  5. Function Approximation Methods for Reinforcement Learning

    1. Matthieu Geist, Bruno Scherrer
      Pages 89-101
    2. Matthew W. Hoffman, Alessandro Lazaric, Mohammad Ghavamzadeh, Rémi Munos
      Pages 102-114
    3. Bruno Scherrer, Matthieu Geist
      Pages 115-127
    4. Nikolaos Tziortziotis, Konstantinos Blekas
      Pages 128-139
  6. Macro-actions in Reinforcement Learning

    1. Munu Sairamesh, Balaraman Ravindran
      Pages 165-176
  7. Policy Search and Bounds

About these proceedings


This book constitutes revised and selected papers of the 9th European Workshop on Reinforcement Learning, EWRL 2011, which took place in Athens, Greece in September 2011. The papers presented were carefully reviewed and selected from 40 submissions. The papers are organized in topical sections online reinforcement learning, learning and exploring MDPs, function approximation methods for reinforcement learning, macro-actions in reinforcement learning, policy search and bounds, multi-task and transfer reinforcement learning, multi-agent reinforcement learning, apprenticeship and inverse reinforcement learning and real-world reinforcement learning.


bayesian inference multitask learning predictive state representation real-time control recommender system

Editors and affiliations

  • Scott Sanner
    • 1
  • Marcus Hutter
    • 2
  1. 1.NICTA and the Australian National UniversityCanberraAustralia
  2. 2.Research School of Computer ScienceAustralian National UniversityCanberraAustralia

Bibliographic information

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