Reinforcement learning : an introduction / Richard S. Sutton and Andrew G. Barto.
| Author/creator | Sutton, Richard S. author. |
| Other author | Barto, Andrew G., author. |
| Format | Electronic |
| Edition | Second edition. |
| Publication | Cambridge, Massachusetts : The MIT Press, [2018] |
| Copyright Date | ©2018 |
| Description | 1 online resource (xxii, 526 pages). |
| Supplemental Content | ProQuest Ebook Central |
| Subjects |
| Series | Adaptive computation and machine learning Adaptive computation and machine learning. ^A474767 |
| Contents | 1. Introduction -- I. Tabular Solution Methods: 2. Multi-armed Bandits -- 3. Finite Markov Decision processes -- 4. Dynamic programming -- 5. Monte Carlo methods -- 6. Temporal-difference learning -- 7. n-step Bootstrapping -- 8. Planning and learning with tabular methods-- I. Approximate Solution Methods: 9. On-policy Prediction with Approximation-- 10. On-policy Control with Approximation-- 11. Oæ-policy Methods with Approximation -- 12. Eligibility Traces-- 13. Policy Gradient Methods-- III. Looking Deeper: 14. Psychology -- 15. Neuroscience -- 16. Applications and Case Studies -- 17. Frontiers |
| Abstract | "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 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."-- Provided by publisher |
| Bibliography note | Includes bibliographical references and index. |
| Issued in other form | Print version: Sutton, Richard S. Reinforcement learning. Second edition. Cambridge, Massachusetts : The MIT Press, [2018] 0262039249 9780262039246 |
| ISBN | 9780262352703 (electronic bk.) |
| ISBN | 0262352702 (electronic bk.) |
| ISBN | (hardcover alkaline paper) |
| ISBN | (hardcover alkaline paper) |