This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics. Author: Richard S. Sutton, Andrew G. Barto. Like the first edition, this second edition focuses on core, online learning algorithms, with the more mathematical material set off in shaded boxes.
Author: Richard S. Sutton, Andrew G. Barto. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics. Like the first edition, this second edition focuses on core, online learning algorithms, with the more mathematical material set off in shaded boxes.
The product is a second edition textbook titled "Reinforcement Learning: An Introduction" by Richard S. Sutton and Andrew G. Barto. Published by MIT Press in 2018 as part of the Adaptive Computation and Machine Learning series, the book covers topics in computer programming, algorithms, neural networks, and artificial intelligence. With 552 pages and illustrated in English, this textbook is intended for trade audiences and provides a comprehensive introduction to the field of reinforcement learning.
Reinforcement Learning, Second 2nd edition: An Introduction, Sutton & Barto
The product is a hardcover textbook titled "Reinforcement Learning, Second Edition: An Introduction" by Richard S. Sutton and Andrew G. Barto. Published by MIT Press in 2018 as part of the Adaptive Computation and Machine Learning series, it covers subjects such as programming, algorithms, neural networks, and artificial intelligence. With a total of 552 pages, the book is intended for a trade audience and is illustrated with diagrams. The authors provide a comprehensive introduction to the field of reinforcement learning, making it a valuable resource for students and professionals in the field.
Covers foundational concepts and modern RL techniques with clear explanations and practical examples. • Authoritative RL text widely used in university machine learning courses. • Temporal-Difference Learning.
Covers foundational concepts and modern RL techniques with clear explanations and practical examples. • Authoritative RL text widely used in university machine learning courses. • Temporal-Difference Learning.
Reinforcement Learning, second edition: An Introduction (Adaptive Computation us
Covers foundational concepts and modern RL techniques with clear explanations and practical examples. • Authoritative RL text widely used in university machine learning courses. • Temporal-Difference Learning.
Covers foundational concepts and modern RL techniques with clear explanations and practical examples. • Authoritative RL text widely used in university machine learning courses. • Temporal-Difference Learning.
