Reinforcement Learning: An Introduction -2/E

Richard S. Sutton, Andrew G. Barto

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This book is designed to be used as the primary text for a one- or two-semester course on reinforcement learning. For a one-semester course, the first ten chapters should be covered in order and form a good core, to which can be added material from the other chapters, from other books such as Bertsekas and Tsitsiklis (1996), Wiering and van Otterlo (2012), and Szepesv´ari (2010), or from the literature, according to taste. Depending of the students’ background, some additional material on online supervised learning may be helpful. The ideas of options and option models are a natural addition (Sutton, Precup and Singh, 1999). A two-semester course can cover all the chapters as well as supplementary material. The book can also be used as part of broader courses on machine learning, artificial intelligence, or neural networks. In this case, it may be desirable to cover only a subset of the material. We recommend covering Chapter 1 for a brief overview, Chapter 2 through Section 2.4, Chapter 3, and then selecting sections from the remaining chapters according to time and interests. Chapter 6 is the most important for the subject and for the rest of the book. A course focusing on machine learning or neural networks should cover Chapters 9 and 10, and a course focusing on artificial intelligence or planning should cover Chapter 8. Throughout the book, sections and chapters that are more dicult and not essential to the rest of the book are marked with a ?. These can be omitted on first reading without creating problems later on. Some exercises are also marked with a ? to indicate that they are more advanced and not essential to understanding the basic material of the chapter

Subjek

PROGRAMMING
LEARNING,

Katalog

Reinforcement Learning: An Introduction -2/E
978-0-262-19398-6
526p.; pdf file.; 88 MB
English

Sirkulasi

Rp. 0
Rp. 0
Tidak

Pengarang

Richard S. Sutton, Andrew G. Barto
Perorangan
 
 

Penerbit

The MIT Press
London
2018

Koleksi

Kompetensi

 

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