Reinforcement Learning (RL), 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 while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. 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 treatment to be accessible to readers in all of the related disciplines.
Pages : | : 522 pages |
Size : | : PDF (548 pages) |
Downloads: | 46 |
Created: | 2020-08-30 |
License: | CC BY-NC-ND 2.0 |
Author(s): | Richard S. Sutton and Andrew G. Barto |
Warning: Trying to access array offset on false in /home/tutovnfz/public_html/amp/article-amp.php on line 263
Others related eBooks about Reinforcement Learning: An Introduction, Second Edition
AI is transforming numerous industries. Machine Learning Yearning, a free ebook from Andre..., download free Machine Learning tutorial in PDF (118 pages) created by Andrew Ng .
Download Understanding Machine Learning tutorial, a complete eBook created by Shai Shalev-Shwartz and Shai Ben-David.
This book is about making machine learning models and their decisions interpretable. After..., download free Machine Learning tutorial in PDF (312 pages) created by Christoph Molnar .
Download free course Python Machine Learning Projects, pdf file on 135 pages by Lisa Tagliaferri, Michelle Morales, Ellie Birkbeck, Alvin Wan.
Download free course Automated Machine Learning, pdf file on 223 pages by by Frank Hutter, Lars Kotthoff, Joaquin Vanschoren.