Algorithms for Reinforcement Learning

Morgan & Claypool Publishers

In this book, we focus on those algorithms of reinforcement learning that build on the powerful theory of dynamic programming. We give a fairly comprehensive catalog of learning problems, describe the core ideas, note a large number of state of the art algorithms, followed by the discussion of their theoretical properties and limitations. ISBN 9781608454921, 103 pages.

Introduction to Semi-Supervised Learning

Morgan & Claypool Publishers

In this introductory book, we present some popular semi-supervised learning models, including self-training, mixture models, co-training and multiview learning, graph-based methods, and semi-supervised support vector machines. ISBN 9781598295474, 130 pages.

Metric Learning

Morgan & Claypool Publishers

This book is devoted to metric learning, a set of techniques to automatically learn similarity and distance functions from data that has attracted a lot of interest in machine learning and related fields in the past ten years. ISBN 9781627053655, 151 pages.

When reinforcement learning should not be used?


While reinforcement learning has achieved many successes, there are situations when it use is problematic. We describe the issues and how to work around them.