Richard S. Sutton

See: Temporal Difference Learning, Reinforcement Learning, MaskGAN, Q-Learning.

References

(Wikipedia, 2023) ⇒ https://en.wikipedia.org/wiki/Richard_S._Sutton Retrieved:2023-9-13.
- Richard S. Sutton is a Canadian computer scientist. 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, 2022) => Richard S. Sutton. (2022). “The Quest for a Common Model of the Intelligent Decision Maker".

(Sutton, 2019) ⇒ Richard S. Sutton. (2019). “The Bitter Lesson." Blog post
- QUOTE: "The biggest lesson that can be read from 70 years of AI research is that general methods that leverage computation are ultimately the most effective, and by a large margin."
- QUOTE: "Most AI research has been conducted as if the computation available to the agent were constant ... but, over a slightly longer time than a typical research project, massively more computation inevitably becomes available."
- QUOTE: "The only thing that matters in the long run is the leveraging of computation."
- QUOTE: "There were many examples of AI researchers' belated learning of this bitter lesson, and it is instructive to review some of the most prominent."

(Sutton & Barto, 2018) ⇒ Richard S. Sutton, and A.G. Barto. (2018). “Reinforcement Learning: An Introduction, 2nd Edition.” In: MIT Press.
- NOTE: It serves as a foundational text on the subject of Reinforcement Learning, extensively cited by academics and professionals in the field.

(Precup et al., 2001) ⇒ Doina Precup, Richard S. Sutton, and Sanjoy Dasgupta. (2001). “Off-Policy Temporal-Difference Learning with Function Approximation.” In: Proceedings of ICML-2001 (ICML-2001).

(Sutton, Precup & Singh, 1999) ⇒ Richard S. Sutton, D. Precup, and S. Singh. (1999). “Between MDPs and Semi-MDPs: A Framework for Temporal Abstraction in Reinforcement Learning.” In: Artificial Intelligence 112(1-2), pp. 181-211.
- NOTE: It offers a framework for understanding temporal abstractions in Reinforcement Learning, facilitating the study of more complex learning tasks.

(Sutton et al., 1999) ⇒ Richard S. Sutton, D. McAllester, S. Singh, and Y. Mansour. (1999). “Policy Gradient Methods for Reinforcement Learning with Function Approximation.” In: Advances in Neural Information Processing Systems 12.
- NOTE: It presents an in-depth study of Policy Gradient Methods, another foundational concept in Reinforcement Learning.

(Sutton & Barto, 1998) ⇒ Richard S. Sutton, and A.G. Barto. (1998). “Reinforcement Learning: An Introduction, 1st Edition.” In: MIT Press.
- NOTE: It is the first edition of a seminal work in Reinforcement Learning, setting the stage for future research and applications in the field.

(Sutton, 1988) ⇒ Richard S. Sutton. (1988). “Learning to Predict by the Methods of Temporal Differences.” In: Machine Learning 3, pp. 9-44.

(Barto, Sutton & Anderson, 1983) ⇒ A.G. Barto, Richard S. Sutton, and C.W. Anderson. (1983). “Neuronlike Adaptive Elements that Can Solve Difficult Learning Control Problems.” In: IEEE Transactions on Systems, Man, and Cybernetics 13(5), pp. 834-846.