Masashi Sugiyama

Statistical Reinforcement Learning (eBook, PDF)

Modern Machine Learning Approaches

• Format: PDF

Produktbeschreibung

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 gaming have been successfully explored in recent years.

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 206
• Erscheinungstermin: 16. März 2015
• Englisch
• ISBN-13: 9781439856901
• Artikelnr.: 42509664

Autorenporträt

Masashi Sugiyama received 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).

Inhaltsangabe

Introduction to Reinforcement Learning. Model-Free Policy Iteration. Policy
Iteration with Value Function Approximation. Basis Design for Value
Function Approximation. Sample Reuse in Policy Iteration. Active Learning
in Policy Iteration. Robust Policy Iteration. Model-Free Policy Search.
Direct Policy Search by Gradient Ascent. Direct Policy Search by
Expectation-Maximization. Policy-Prior Search. Model-Based Reinforcement
Learning. Transition Model Estimation. Dimensionality Reduction for
Transition Model Estimation.