| Reinforcement Learning: An Introduction 2nd Ed (Bradford Book) |
| Wednesday, 21 November 2018 | |||
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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 the new edition of this classic book, authors 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. It focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes <ASIN: 0262039249> Author: Richard S. Sutton and Andrew G. Barto
Algorithms new in this second edition include UCB, Expected Sarsa, and Double Learning. There are new sections on artificial neural networks and the Fourier basis, and expanded treatment of off-policy learning and policy-gradient methods. This new edition also has new chapters on reinforcement learning's relationships to psychology and neuroscience.
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