Statistical Reinforcement Learning: Modern Machine Learning Approaches

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.

強化學習是一種數學框架，用於開發能夠通過將通用獎勵信號與過去的行動相關聯來學習最佳行為的計算機代理。強化學習框架在商業智能、工廠控制和遊戲等領域有著眾多成功的應用，對於在未知環境中進行決策並處理大量數據非常理想。

《統計強化學習：現代機器學習方法》提供了一個最新且易於理解的介紹，從現代機器學習的角度介紹了統計強化學習的基本概念和實用算法。它涵蓋了各種類型的強化學習方法，包括基於模型和無模型的方法、策略迭代和策略搜索方法。

本書從數據挖掘和機器學習領域引入了最近介紹的方法，以提供強化學習和數據挖掘/機器學習研究人員之間的系統橋樑。它呈現了最新的結果，包括強化學習中的降維和風險敏感強化學習。書中包含了許多實例，以幫助讀者理解強化學習技術的直觀和實用性。

本書是計算機科學和應用統計學碩士學生以及相關領域的研究人員和工程師的理想資源。

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).

Masashi Sugiyama在日本東京工業大學獲得計算機科學的學士、碩士和博士學位。2001年，他被任命為東京工業大學的助理教授，並於2003年晉升為副教授。2014年，他轉到東京大學擔任教授。

他曾獲得亞歷山大·馮·洪堡基金會的研究獎學金，在2003年至2004年期間在德國柏林的Fraunhofer研究所進行研究。2006年，他獲得歐洲委員會的Erasmus Mundus獎學金，在蘇格蘭愛丁堡大學進行研究。他於2007年獲得IBM的教職員獎，以表彰他在非穩態機器學習方面的貢獻；2011年獲得日本信息處理學會的長尾特別研究員獎；以及因其對機器學習中密度比範式的貢獻而獲得日本教育文化體育科學技術部部長的科學技術表彰中的青年科學家獎。

他的研究興趣包括機器學習和數據挖掘的理論和算法，以及信號處理、圖像處理和機器人控制等各種應用。他出版了《機器學習中的密度比估計》（劍橋大學出版社，2012年）和《非穩態環境中的機器學習：協變量轉移適應入門》（麻省理工學院出版社，2012年）。