30 Nov Reinforcement Learning: An Introduction, Second Edition
This textbook provides a clear and simple account of the key ideas and algorithms of reinforcement learning that is accessible to readers in all the related disciplines. Familiarity with elementary concepts of probability is required.
Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. This book provides a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability.
Our goal in writing this book was to provide a clear and simple account of the key ideas and algorithms of reinforcement learning. We wanted our treatment to be accessible to readers in all of the related disciplines, but we could not cover all of these perspectives in detail. For the most part, our treatment takes the point of view of artificial intelligence and engineering. Coverage of connections to other fields we leave to others or to another time.
We also chose not to produce a rigorous formal treatment of reinforcement learning. We did not reach for the highest possible level of mathematical abstraction and did not rely on a theorem–proof format. We tried to choose a level of mathematical detail that points the mathematically inclined in the right directions without distracting from the simplicity and potential generality of the underlying ideas.
The book is largely self-contained. The only mathematical background assumed is familiarity with elementary concepts of probability, such as expectations of random variables. Chapter 9 is substantially easier to digest if the reader has some knowledge of artificial neural networks or some other kind of supervised learning method, but it can be read without prior background. We strongly recommend working the exercises provided throughout the book. Solution manuals are available to instructors. This and other related and timely material is available via the Internet.
About the Authors
- Richard S. Sutton is Professor and iCORE chair Department of Computing Science at University of Alberta.
- Andrew Barto is Professor Emeritus in the College of Information and Computer Sciences at University of Massachusetts Amherst.
|Authors||Richard S. Sutton and Andrew G. Barto|
|Publisher||The MIT Press; University of Alberta (Draft, 2017)|
|Hardcover||322 pages (1st Edition)|
|ISBN-10||0262193981 (1st Edition)|
|ISBN-13||978-0262193986 (1st Edition)|