Rich Sutton is a computer scientist who studies artificial intelligence.

Rich is known for developing elegant algorithms for learning to predict and control that have become influential in machine learning and, separately, as models of natural learning. For example, he authored the original scientific papers on temporal-difference learning and policy-gradient algorithms, which were used by DeepMind's AlphaZero to learn to play Go and chess better than any human or previous computer program. The same algorithms have also become influential in psychology and neuroscience as models of Pavlovian conditioning and reward prediction (Dopamine).

Rich's work is part of the interdisciplinary field of reinforcement learning, which spans artificial intelligence, psychology, neuroscience, and control-systems engineering. Rich is co-author of the field's most popular textbook and founder of one of the field's largest academic research laboratories, at the University of Alberta.

Rich is also a libertarian, a chess player, and a cancer survivor.