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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 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 http://www.ms.k.u-tokyo.ac.jp/sugi/pics/Sugiyama(2008)-small.jpg 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 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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statistical reinforcement learning gnv64