Statistical Reinforcement Learning : Modern Machine Learning Approaches

Masashi Sugiyama

ISBN 10: 1439856893 / ISBN 13: 9781439856895

Verlag: Chapman And Hall/CRC, 2015

Beschreibung

nach der Bestellung gedruckt Neuware - Printed after ordering - 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. 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 RL. The book provides a bridge between RL and data mining and machine learning research. Bestandsnummer des Verkäufers 9781439856895

Bibliografische Details

Titel: Statistical Reinforcement Learning : Modern ...

Verlag: Chapman And Hall/CRC

Erscheinungsdatum: 2015

Einband: Buch

Zustand: Neu

Über diesen Titel

Inhaltsangabe:

Reinforcement learning is a mathematical framework for developing computer agents that can learn an optimal behavior by relating generic reward signals with its past actions. With numerous successful applications in business intelligence, plant control, and gaming, the RL framework is ideal for decision making in unknown environments with large amounts of data.

Supplying an up-to-date and accessible introduction to the field, Statistical Reinforcement Learning: Modern Machine Learning Approaches presents fundamental concepts and practical algorithms of statistical reinforcement learning from the modern machine learning viewpoint. It covers various types of RL approaches, including model-based and model-free approaches, policy iteration, and policy search methods.

• Covers the range of reinforcement learning algorithms from a modern perspective
• Lays out the associated optimization problems for each reinforcement learning scenario covered
• Provides thought-provoking statistical treatment of reinforcement learning algorithms

The book covers approaches recently introduced in the data mining and machine learning fields to provide a systematic bridge between RL and data mining/machine learning researchers. It presents state-of-the-art results, including dimensionality reduction in RL and risk-sensitive RL. Numerous illustrative examples are included to help readers understand the intuition and usefulness of reinforcement learning techniques.

This book is an ideal resource for graduate-level students in computer science and applied statistics programs, as well as researchers and engineers in related fields.

Über die Autorin bzw. den Autor:

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).