Brief Biography


Richard S. Sutton is a distinguished research scientist at DeepMind and a professor in the department of computing science at the University of Alberta. Before joining the University of Alberta in 2003, he worked in industry at AT&T and GTE Labs, and in academia at the University of Massachusetts. Rich received a PhD in computer science from the University of Massachusetts in 1984 and a BA in psychology from Stanford University in 1978. He is co-author of the textbook Reinforcement Learning: An Introduction from MIT Press. He is also a fellow of the Royal Society of Canada and of the Association for the Advancement of Artificial Intelligence. At the University of Alberta, Rich directs the Reinforcement Learning and Artificial Intelligence Laboratory and is a principal investigator of the Alberta Machine Intelligence Institute. His research interests center on the learning problems facing a decision-maker interacting with its environment, which he sees as central to artificial intelligence.  He is also interested in animal learning psychology, in connectionist networks, and generally in systems that continually improve their representations and models of the world.

or, more humbly,  

Richard S. Sutton was born in Ohio, and grew up in Oak Brook, Illinois, a suburb of Chicago. He received the B.A. degree in psychology from Stanford University in 1978, and the M.S. and Ph.D. degrees in Computer Science from the University of Massachusetts in 1980 and 1984. He worked for nine years at GTE Laboratories in Waltham as principal investigator of their connectionist machine learning project, and for three years at the University of Massachusetts in Amherst as a research scientist in the computer science department. In 1998-2002 Rich worked at AT&T Labs in Florham Park, New Jersey, since August of 2003 he has been a professor of computing science at the University of Alberta, and since 2017 he has also worked for DeepMind in their Edmonton office.

Rich's research interests center on the learning problems facing a decision-maker interacting with its environment, which he sees as central to artificial intelligence. He is the author of the original paper on temporal-difference learning and, with Andrew Barto, of the textbook Reinforcement Learning: An Introduction. He is also interested in animal learning psychology, in connectionist networks, and generally in systems that continually improve their representations and models of the world.