Includes bibliographical references (pages 291-312) and index.
Contents
1. Introduction -- 2. Evaluative feedback -- 3. The reinforcement learning problem -- 4. Dynamic programming -- 5. Monte carlo methods -- 6. Temporal-difference learning -- 7. Eligibility traces -- 8. Generalization and function approximation -- 9. Planning and learning -- 10. Dimensions of reinforcement learning -- 11. Case studies.
Summary
Presents the book "Reinforcement Learning: An Introduction," written by Richard S. Sutton and Andrew G. Barto and published by the Massachusetts Institute of Technology (MIT) Press in 1998. The book is a textbook targeted toward engineers and scientists in artificial intelligence, operations research, neural networks, and control systems. Examines a computation approach to learning from interaction with environment.