Statistical Reinforcement Learning (gnv64)

Statistical Reinforcement Learning: Modern Machine Learning Approaches
by Masashi Sugiyama
Chapman and Hall/CRC | Mar 2015 | ISBN: 1439856893 | 208 Pages | ePUB/MOBI| 10.7 mb
http://www.amazon.com/Statistical-Reinforcement-Learning-Approaches-Recognition/dp/1439856893

Reinforcement learning (RL) is a framework for decision making in unknown environments based on a large amount of data. Several practical RL applications for business intelligence, plant control, and game players have been successfully explored in recent years. Providing an accessible introduction to the field, this book covers model-based and model-free approaches, policy iteration, and policy search methods. It presents illustrative examples and state-of-the-art results, including dimensionality reduction in RL and risk-sensitive RLm. The book provides a bridge between RL and data mining and machine learning research.

About the Author

Masashi Sugiyamareceived his bachelor, master, and doctor of engineering degrees in computer science from the Tokyo Institute of Technology, Japan. In 2001 he was appointed assistant professor at the Tokyo Institute of Technology and he was promoted to associate professor in 2003. He moved to the University of Tokyo as professor in 2014.
He received an Alexander von Humboldt Foundation Research Fellowship and researched at Fraunhofer Institute, Berlin, Germany, from 2003 to 2004. In 2006, he received a European Commission Program Erasmus Mundus Scholarship and researched at the University of Edinburgh, Scotland. He received the Faculty Award from IBM in 2007 for his contribution to machine learning under non-stationarity, the Nagao Special Researcher Award from the Information Processing Society of Japan in 2011, and the Young Scientists' Prize from the Commendation for Science and Technology by the Minister of Education, Culture, Sports, Science and Technology for his contribution to the density-ratio paradigm of machine learning.
His research interests include theories and algorithms of machine learning and data mining, and a wide range of applications such as signal processing, image processing, and robot control. He published Density Ratio Estimation in Machine Learning(Cambridge University Press, 2012) and Machine Learning in Non-Stationary Environments: Introduction to Covariate Shift Adaptation(MIT Press, 2012).

CONTENTS
Foreword ix
Preface xi
Author xiii
I Introduction 1
1 Introduction to Reinforcement Learning 3
II Model-Free Policy Iteration 15
2 Policy Iteration with Value Function Approximation 17
3 Basis Design for Value Function Approximation 27
4 Sample Reuse in Policy Iteration 47
5 Active Learning in Policy Iteration 65
6 Robust Policy Iteration 79
III Model-Free Policy Search 93
7 Direct Policy Search by Gradient Ascent 95
8 Direct Policy Search by Expectation-Maximization 117
9 Policy-Prior Search 133
IV Model-Based Reinforcement Learning 155
10 Transition Model Estimation 157
11 Dimensionality Reduction for Transition Model Estimation173
References 183
Index 19

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