Richard Stuart Sutton, an American computer scientist and AI-researcher. Since 2003, Richard S. Sutton is a professor in the Department of Computing Science [1] at the University of Alberta and is principal investigator of the RLAI [2] group. 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 [3] and, with Andrew Barto, of the textbook Reinforcement Learning: An Introduction [4] . 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 [5] .
Richard Sutton [6]

Selected Publications

1978

• Richard Sutton (1978). Single channel theory: A neuronal theory of learning. Brain Theory Newsletter 3, No. 3/4, pp. 72-75. pdf

1980 ...

• Richard Sutton, Andrew Barto (1981). Toward a modern theory of adaptive networks: Expectation and prediction. Psychological Review, Vol. 88, pp. 135-170. pdf
• Richard Sutton (1984). Temporal Credit Assignment in Reinforcement Learning. Ph.D. dissertation, University of Massachusetts
• Richard Sutton (1988). Learning to Predict by the Methods of Temporal Differences. Machine Learning, Vol. 3, No. 1, pdf

1990 ...

• Richard Sutton, Andrew Barto (1990). Time-Derivative Models of Pavlovian Reinforcement. in Michael Gabriel, John Moore (eds.) (1990). Learning and Computational Neuroscience: Foundations of Adaptive Networks. MIT Press, pdf
• Doina Precup, Richard Sutton (1997). Multi-time Models for Temporally Abstract Planning. NIPS 1997
• Richard Sutton, Andrew Barto (1998). Reinforcement Learning: An Introduction. MIT Press

2000 ...

• Michael L. Littman, Richard Sutton, Satinder Singh (2001). Predictive Representations of State. NIPS 2001, pdf
• Richard Sutton, Brian Tanner (2005). Temporal-Difference Networks. Advances in Neural Information Processing Systems 17, pages 1377-1384. pdf
• David Silver, Richard Sutton, Martin Müller (2007). Reinforcement learning of local shape in the game of Go. In Twentieth International Joint Conference on Artificial Intelligence (IJCAI), pages 1053-1058, Hyderabad, India. pdf
• Richard Sutton, Csaba Szepesvári, Hamid Reza Maei (2008). A Convergent O(n) Algorithm for Off-policy Temporal-difference Learning with Linear Function Approximation, available as pdf (draft)
• David Silver, Richard Sutton, Martin Müller (2008). Sample-Based Learning and Search with Permanent and Transient Memories. In Proceedings of the 25th International Conference on Machine Learning, pdf
• Maria Cutumisu, Michael Bowling, Duane Szafron, Richard Sutton (2008). Agent Learning using Action-Dependent Learning Rates in Computer Role-Playing Games. Proceedings of the Fourth Artificial Intelligence and Interactive Digital Entertainment Conference, pdf
• Hamid Reza Maei, Csaba Szepesvári, Shalabh Bhatnagar, Doina Precup, David Silver, Richard Sutton (2009). Convergent Temporal-Difference Learning with Arbitrary Smooth Function Approximation. Accepted in Advances in Neural Information Processing Systems 22, Vancouver, BC. December 2009. MIT Press. pdf
• Richard Sutton, Hamid Reza Maei, Doina Precup, Shalabh Bhatnagar, David Silver, Csaba Szepesvári, Eric Wiewiora. (2009). Fast Gradient-Descent Methods for Temporal-Difference Learning with Linear Function Approximation. In Proceedings of the 26th International Conference on Machine Learning (ICML-09). pdf

2010

• Hamid Reza Maei, Richard Sutton (2010). GQ(λ): A general gradient algorithm for temporal-difference prediction learning with eligibility traces. In Proceedings of the Third Conference on Artificial General Intelligence
• David Silver, Richard Sutton, Martin Mueller (2013). Temporal-Difference Search in Computer Go. Proceedings of the ICAPS-13 Workshop on Planning and Learning, pdf
• Huizhen Yu, A. Rupam Mahmood, Richard Sutton (2017). On Generalized Bellman Equations and Temporal-Difference Learning. Canadian Conference on AI 2017, arXiv:1704.04463

External Links

• Rich Sutton's Home Page
• The Mathematics Genealogy Project - Richard Sutton
• Reinforcement Learning: An Introduction ebook by Richard Sutton and Andrew Barto
• Interview with Richard S. Sutton, by Verena Heidrich-Meisner of Kuenstliche Intelligenz. (2009). pp. 41-43. (pdf)
• Deconstructing Reinforcement Learning, videolecture by Richard Sutton, June 2009
• Fast Gradient-Descent Methods for Temporal-Difference Learning with Linear Function Approximation, videolecture by Richard Sutton, June 2009
• DeepMind expands to Canada with new research office in Edmonton, Alberta by Demis Hassabis, DeepMind, July 5, 2017

References

1. ^ Home | Department of Computing Science
2. ^ Reinforcement Learning and Artificial Intelligence (RLAI)
3. ^ Sutton, R.S. (1988). Learning to Predict by the Methods of Temporal Differences. Machine Learning, Vol. 3, No. 1, pp. 9-44. Kluwer Academic Publishers, Boston. ISSN 0885-6125. pdf
4. ^ Reinforcement Learning: An Introduction ebook by Richard Sutton and Andrew Barto
5. ^ Brief Biography for Richard Sutton
6. ^ Rich Sutton's Home Page
7. ^ Rich Sutton's Publications
8. ^ dblp: Richard S. Sutton
9. ^ ICGA Reference Database (pdf)

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