Series Forward

I am pleased to have this book by Richard Sutton and Andrew Barto as one of the first books in the new Adaptive Computation and Machine Learning series. This textbook presents a comprehensive introduction to the exciting field of reinforcement learning. Written by two of the pioneers in this field, it provides students, practitioners, and researchers with an intuitive understanding of the central concepts of reinforcement learning as well as a precise presentation of the underlying mathematics. The book also communicates the excitement of recent practical applications of reinforcement learning and the relationship of reinforcement learning to the core questions in artifical intelligence. Reinforcement learning promises to be an extremely important new technology with immense practical impact and important scientific insights into the organization of intelligent systems.

The goal of building systems that can adapt to their environments and learn from their experience has attracted researchers from many fields, including computer science, engineering, mathematics, physics, neuroscience, and cognitive science. Out of this research has come a wide variety of learning techniques that have the potential to transform many industrial and scientific fields. Recently, several research communities have begun to converge on a common set of issues surrounding supervised, unsupervised, and reinforcement learning problems. The MIT Press series on Adaptive Computation and Machine Learning seeks to unify the many diverse strands of machine learning research and to foster high quality research and innovative applications.

Thomas Diettrich