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: | 34 |
| Created: | 2020-08-30 |
| License: | CC BY-NC-ND 2.0 |
| Author(s): | Richard S. Sutton and Andrew G. Barto |
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