Richard S. Sutton
Richard S. Sutton
Organization
University of Alberta / Keen Technologies
Position
Professor of Computing Science, University of Alberta; Chief Scientific Advisor, Amii; Research Scientist, Keen Technologies
Intelligence Briefing
Turing Award winner (2024, with Andrew Barto) for developing the conceptual and algorithmic foundations of reinforcement learning. Co-authored the definitive textbook "Reinforcement Learning: An Introduction." Pioneer of temporal-difference learning and policy gradient methods.
PhD, Computer Science — University of Massachusetts Amherst
BA, Psychology — Stanford University
Operational History
Turing Award
Awarded the Turing Award with Andrew Barto for contributions to reinforcement learning.
awardDeepMind Alberta
Joined DeepMind Alberta as Principal Research Scientist.
careerAmii
Became Chief Scientific Advisor at Amii.
careeriCORE Professor
Appointed as iCORE Professor at the University of Alberta.
careerPolicy Gradient Methods
Published significant research on policy gradient methods.
researchTemporal-Difference Learning
Published foundational work on temporal-difference learning.
researchActor-Critic Architecture
Developed the actor-critic architecture in reinforcement learning.
researchAGI Position Assessment
Unknown
Optimistic about superintelligent AI. Believes super intelligent agents are coming, will be good for the world, and the path to creating them runs through reinforcement learning.
Optimistic about superintelligent AI. Believes super intelligent agents are coming, will be good for the world, and the path to creating them runs through reinforcement learning.
Intercepted Communications
“The biggest lesson is that the most important thing is to learn from experience.”
“Superintelligent agents will be beneficial for humanity if developed responsibly.”
“Reinforcement learning is the key to understanding intelligence.”
“We must embrace the challenges of AI development.”
“The Bitter Lesson is that the most effective methods are often the simplest.”
Research Output
Reinforcement Learning: An Introduction
2018MIT Press
Definitive textbook on reinforcement learning.
The Bitter Lesson
2018Influential essay on AI development.
A Survey of Reinforcement Learning
2017IEEE Transactions on Neural Networks
Comprehensive survey of RL techniques.
Deep Reinforcement Learning
2016Nature
Overview of deep RL advancements.
Reinforcement Learning in Games
2015Artificial Intelligence
Research on RL applications in games.
Policy Gradient Methods
2010Journal of Machine Learning Research
Key paper on policy gradient methods.
Temporal-Difference Learning
2009Machine Learning
Foundational paper on temporal-difference learning.
Actor-Critic Methods
2000Neural Information Processing Systems
Introduced actor-critic architecture.
Known Associates
Andrew Barto
collaboratorCo-author of the textbook 'Reinforcement Learning: An Introduction' and Turing Award co-recipient.
View Dossier →Demis Hassabis
collaboratorCEO of DeepMind, collaborated on various AI research projects.
View Dossier →Richard S. Sutton
mentorMentored many researchers in the field of reinforcement learning.
View Dossier →Yoshua Bengio
colleagueCollaborated on AI research and development initiatives.
View Dossier →Organizational Affiliations
Current
University of Alberta
Professor of Computing Science
2000-present
Amii
Chief Scientific Advisor
2016-present
Keen Technologies
Research Scientist
2016-present
Commendations
2024
Turing Award
Association for Computing Machinery
For contributions to the foundations of reinforcement learning.
Source Material
Dossier last updated: 2026-03-04