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Multi-Agent Machine Learning: A Reinforcement Approach H. M. Schwartz 1st edition
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Multi-Agent Machine Learning: A Reinforcement Approach
H. M. Schwartz
The book begins with a chapter on traditional methods of supervised learning, covering recursive least squares learning, mean square error methods, and stochastic approximation. Chapter 2 covers single agent reinforcement learning. Topics include learning value functions, Markov games, and TD learning with eligibility traces.
256 pages
| Media | Books Hardcover Book (Book with hard spine and cover) |
| Released | September 26, 2014 |
| ISBN13 | 9781118362082 |
| Publishers | John Wiley & Sons Inc |
| Pages | 256 |
| Dimensions | 238 × 163 × 18 mm · 478 g |
| Language | English |
