Richard S. SuttonFRS is a Canadian computer scientist. Currently, he is a distinguished research scientist at DeepMind and a professor of computing science at the University of Alberta. Sutton is considered one of the founders of modern computational reinforcement learning, having several significant contributions to the field, including temporal difference learning and policy gradient methods.
Sutton received his B.A. in psychology from Stanford University in 1978 before taking an M.S. (1980) and Ph.D. (1984) in computer science from the University of Massachusetts Amherst under the supervision of Andrew Barto. His doctoral dissertation, Temporal Credit Assignment in Reinforcement Learning, introduced actor-critic architectures and temporal credit assignment.
In 1984, Sutton was a postdoctoral researcher at the University of Massachusetts.
From 1985 to 1994, he was a principal member of technical staff in the Computer and Intelligent Systems Laboratory at GTE in Waltham, Massachusetts. In 1995, he returned to the University of Massachusetts as a senior research scientist.
Since 2003, he has been a professor of computing science at the University of Alberta. He led the institution’s Reinforcement Learning and Artificial Intelligence Laboratory until 2018.
Sutton became a Canadian citizen in 2015 and renounced his US citizenship in 2017.
- Sutton, R. S., Barto, A. G., Reinforcement Learning: An Introduction. MIT Press, 1998. Also translated into Japanese and Russian. Second edition MIT Press 2018.
- Miller, W. T., Sutton, R. S., Werbos, P. J. (Eds.), Neural Networks for Control. MIT Press, 1991.
- Sutton, R. S. (Ed.), Reinforcement Learning. Reprinting of a special issue of Machine Learning Journal. Kluwer Academic Press, 1992
Sutton is fellow of the Association for the Advancement of Artificial Intelligence (AAAI) since 2001. In 2003 he received the President’s Award from the International Neural Network Society and in 2013, the Outstanding Achievement in Research award from the University of Massachusetts Amherst.
Sutton’s nomination as a AAAI fellow reads:
For significant contributions to many topics in machine learning, including reinforcement learning, temporal difference techniques, and neural networks.
In 2021, Sutton was elected Fellow of the Royal Society.