Recent Advances in Reinforcement Learning: 9th European Workshop, EWRL 2011, Athens, Greece, September 9-11, 2011, Revised and Selected Papers | SpringerLink
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Recent Advances in Reinforcement Learning

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

  • Conference proceedings
  • © 2012

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Editors:
  1. Scott Sanner

    1. NICTA and the Australian National University, Canberra, Australia

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  2. Marcus Hutter

    1. Research School of Computer Science, Australian National University, Canberra, Australia

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    You can also search for this editor in PubMed   Google Scholar

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

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

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Conference proceedings info: EWRL 2011.

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About this book

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.

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Table of contents (32 papers)

  1. Front Matter

    Download chapter PDF

  3. Online Reinforcement Learning

    1. Automatic Discovery of Ranking Formulas for Playing with Multi-armed Bandits

      • Francis Maes, Louis Wehenkel, Damien Ernst
      Pages 5-17

    2. Goal-Directed Online Learning of Predictive Models

      • Sylvie C. W. Ong, Yuri Grinberg, Joelle Pineau
      Pages 18-29

    3. Gradient Based Algorithms with Loss Functions and Kernels for Improved On-Policy Control

      • Matthew Robards, Peter Sunehag
      Pages 30-41

  4. Learning and Exploring MDPs

    1. Active Learning of MDP Models

      • Mauricio Araya-López, Olivier Buffet, Vincent Thomas, François Charpillet
      Pages 42-53

    2. Handling Ambiguous Effects in Action Learning

      • Boris Lesner, Bruno Zanuttini
      Pages 54-65

    3. Feature Reinforcement Learning in Practice

      • Phuong Nguyen, Peter Sunehag, Marcus Hutter
      Pages 66-77

  5. Function Approximation Methods for Reinforcement Learning

    1. Reinforcement Learning with a Bilinear Q Function

      • Charles Elkan
      Pages 78-88

    2. ℓ1-Penalized Projected Bellman Residual

      • Matthieu Geist, Bruno Scherrer
      Pages 89-101

    3. Regularized Least Squares Temporal Difference Learning with Nested ℓ2 and ℓ1 Penalization

      • Matthew W. Hoffman, Alessandro Lazaric, Mohammad Ghavamzadeh, Rémi Munos
      Pages 102-114

    4. Recursive Least-Squares Learning with Eligibility Traces

      • Bruno Scherrer, Matthieu Geist
      Pages 115-127

    5. Value Function Approximation through Sparse Bayesian Modeling

      • Nikolaos Tziortziotis, Konstantinos Blekas
      Pages 128-139

  6. Macro-actions in Reinforcement Learning

    1. Automatic Construction of Temporally Extended Actions for MDPs Using Bisimulation Metrics

      • Pablo Samuel Castro, Doina Precup
      Pages 140-152

    2. Unified Inter and Intra Options Learning Using Policy Gradient Methods

      • Kfir Y. Levy, Nahum Shimkin
      Pages 153-164

    3. Options with Exceptions

      • Munu Sairamesh, Balaraman Ravindran
      Pages 165-176

  7. Policy Search and Bounds

    1. Robust Bayesian Reinforcement Learning through Tight Lower Bounds

      • Christos Dimitrakakis
      Pages 177-188
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Other volumes

  1. Recent Advances in Reinforcement Learning

Editors and Affiliations

  • NICTA and the Australian National University, Canberra, Australia

    Scott Sanner

  • Research School of Computer Science, Australian National University, Canberra, Australia

    Marcus Hutter

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