Statistical Reinforcement Learning: Modern Machine Learning Approaches
暫譯: 統計強化學習：現代機器學習方法

Sugiyama, Masashi

Statistical Reinforcement Learning: Modern Machine Learning Approaches

商品描述

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



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  • Lays out the associated optimization problems for each reinforcement learning scenario covered



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  • 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.

商品描述(中文翻譯)

強化學習是一種數學框架，用於開發能夠通過將一般獎勵信號與其過去行為相關聯來學習最佳行為的計算機代理。強化學習在商業智慧、工廠控制和遊戲等領域有著眾多成功的應用，該框架非常適合在未知環境中進行決策，尤其是在處理大量數據時。

本書《統計強化學習：現代機器學習方法》提供了該領域的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、策略迭代和策略搜尋方法。

- 從現代的角度涵蓋了各種強化學習算法
- 為每個涵蓋的強化學習場景列出了相關的優化問題
- 提供了引人深思的強化學習算法的統計處理

本書涵蓋了最近在數據挖掘和機器學習領域引入的方法，為強化學習與數據挖掘/機器學習研究者之間提供了一個系統性的橋樑。它展示了最先進的結果，包括強化學習中的降維和風險敏感的強化學習。書中包含了大量的示例，以幫助讀者理解強化學習技術的直覺和實用性。

本書是計算機科學和應用統計研究生課程的理想資源，適合相關領域的研究人員和工程師。

作者簡介

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).

作者簡介(中文翻譯)

杉山雅史於日本東京工業大學獲得計算機科學的學士、碩士及工程博士學位。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，他轉任東京大學教授。

他曾獲得亞歷山大·馮·洪堡基金會研究獎學金，並於2003年至2004年在德國柏林的弗勞恩霍夫研究所進行研究。2006年，他獲得歐洲委員會的Erasmus Mundus獎學金，並在蘇格蘭的愛丁堡大學進行研究。他於2007年因在非平穩性下對機器學習的貢獻而獲得IBM的院系獎，2011年獲得日本資訊處理學會的長尾特別研究者獎，以及因對機器學習的密度比範式的貢獻而獲得教育、文化、體育、科學與技術大臣的科學技術表彰的青年科學家獎。

他的研究興趣包括機器學習和數據挖掘的理論與算法，以及信號處理、圖像處理和機器人控制等廣泛應用。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《非平穩環境中的機器學習：協變移適應導論》（麻省理工學院出版社，2012年）。

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