Multi-Agent Machine Learning

Discover the fascinating world of artificial intelligence with Multi-Agent Machine Learning by H. M. Schwartz. Published by John Wiley & Sons Inc in 2014, this hardback edition spans 256 pages and provides a comprehensive overview of advanced learning techniques.

The book kicks off with an insightful chapter on traditional supervised learning methods, delving into recursive least squares learning, mean square error methods, and stochastic approximation. In the following chapter, Schwartz explores single agent reinforcement learning, covering essential topics such as learning value functions, Markov games, and TD learning with eligibility traces.

Whether you're a student, researcher, or professional in the field of machine learning, this book serves as an invaluable resource to deepen your understanding of multi-agent systems. Enhance your knowledge and skills in this rapidly evolving domain with Multi-Agent Machine Learning.