Recent Advances in Reinforcement Learning

  • Leslie Pack Kaelbling

Table of contents

  1. Front Matter
    Pages I-III
  2. Thomas G. Dietterich
    Pages 5-6
  3. Leslie Pack Kaelbling
    Pages 7-9
  4. David E. Moriarty, Risto Miikkulainen
    Pages 11-32
  5. Steven J. Bradtke, Andrew G. Barto
    Pages 33-57
  6. John N. Tsitsiklis, Benjamin Van Roy
    Pages 59-94
  7. Robert E. Schapire, Manfred K. Warmuth
    Pages 95-121
  8. Satinder P. Singh, Richard S. Sutton
    Pages 123-158
  9. Richard Maclin, Jude W. Shavlik
    Pages 251-281
  10. Jing Peng, Ronald J. Williams
    Pages 283-290
  11. Back Matter
    Pages 291-292

About this book


Recent Advances in Reinforcement Learning addresses current research in an exciting area that is gaining a great deal of popularity in the Artificial Intelligence and Neural Network communities.
Reinforcement learning has become a primary paradigm of machine learning. It applies to problems in which an agent (such as a robot, a process controller, or an information-retrieval engine) has to learn how to behave given only information about the success of its current actions. This book is a collection of important papers that address topics including the theoretical foundations of dynamic programming approaches, the role of prior knowledge, and methods for improving performance of reinforcement-learning techniques. These papers build on previous work and will form an important resource for students and researchers in the area.
Recent Advances in Reinforcement Learning is an edited volume of peer-reviewed original research comprising twelve invited contributions by leading researchers. This research work has also been published as a special issue of Machine Learning (Volume 22, Numbers 1, 2 and 3).


Performance algorithms artificial intelligence intelligence learning machine learning programming proving reinforcement learning robot

Editors and affiliations

  • Leslie Pack Kaelbling
    • 1
  1. 1.Brown UniversityUSA

Bibliographic information