Faculty profile

Rich Sutton

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ProfessorComputing ScienceUniversity of Alberta

Rich Sutton

Professor

Computing Science

Faculty of Science

University of Alberta

Edmonton, Alberta, Canada

Supervision signals

Professors usually supervise graduate students in their department's programs.
20 publications in the last five years, latest in 2024.
Senior (last) author on 11 recent papers, a sign of leading a research group.

These signals come from public records, including federal and provincial funding decisions. Ask the professor or the graduate program whether they are taking new students.

Academic metrics

267

Publications

68,152

Citations

Collaborators

Publications by year

Counts cover the publications linked to this profile. Citations and the h-index appear when the source records them.

Profiles and links

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Signals and details

Research

Read how they describe their research on their University of Alberta profile.

Latest papers

Loss of plasticity in deep continual learning
Nature · 2024 · senior author
Off-Policy Prediction Learning: An Empirical Study of Online Algorithms
IEEE Transactions on Neural Networks and Learning Systems · 2024 · senior author
An Idiosyncrasy of Time-discretization in Reinforcement Learning
arXiv (Cornell University) · 2024 · senior author

All 267 publications

From the 150 most recent of 267 publications.

Loss of plasticity in deep continual learning

Shibhansh Dohare, Juan Hernandez-Garcia, Qingfeng Lan, Parash Rahman, Ashique Rupam Mahmood, Richard S. Sutton

Nature2024157 citationsSenior authorOpen Access

DOI: 10.1038/s41586-024-07711-7 PMID: 39169245

Off-Policy Prediction Learning: An Empirical Study of Online Algorithms

Sina Ghiassian, Banafsheh Rafiee, Richard S. Sutton

IEEE Transactions on Neural Networks and Learning Systems20243 citationsSenior authorOpen Access

DOI: 10.1109/tnnls.2024.3373749 PMID: 38857133

An Idiosyncrasy of Time-discretization in Reinforcement Learning

Kris De Asis, Richard S. Sutton

arXiv (Cornell University)20241 citationsSenior authorOpen Access

DOI: 10.48550/arxiv.2406.14951

Reward-Respecting Subtasks for Model-Based Reinforcement Learning (Abstract Reprint)

Richard S. Sutton, Marlos C. Machado, Gerhard Holland, David Szepesvári, Finbarr Timbers, Brian K Tanner, Adam John White

Proceedings of the AAAI Conference on Artificial Intelligence2024First authorOpen Access

DOI: 10.1609/aaai.v38i20.30613

On Convergence of Average-Reward Q-Learning in Weakly Communicating Markov Decision Processes

Yi Wan, Huizhen Yu, Richard S. Sutton

arXiv (Cornell University)2024Senior authorOpen Access

DOI: 10.48550/arxiv.2408.16262

Reward-respecting subtasks for model-based reinforcement learning

Richard S. Sutton, Marlos C. Machado, Gerhard Holland, David Szepesvári, Finbarr Timbers, Brian K Tanner, Adam John White

Artificial Intelligence202317 citationsFirst authorOpen Access

DOI: 10.1016/j.artint.2023.104001

Loss of Plasticity in Deep Continual Learning

Shibhansh Dohare, Juan Hernandez-Garcia, Parash Rahman, Richard S. Sutton, A. Rupam Mahmood

Research Square20237 citationsOpen Access

DOI: 10.21203/rs.3.rs-3256479/v1

From eye-blinks to state construction: Diagnostic benchmarks for online representation learning.

Rafiee B, Abbas Z, Ghiassian S, Kumaraswamy R, Sutton RS, Ludvig EA, White A

Adaptive behavior20233 citationsOpen Access

DOI: 10.1177/10597123221085039 PMID: 36618906

Maintaining Plasticity in Deep Continual Learning

Shibhansh Dohare, J. Fernando Hernandez-Garcia, Parash Rahman, A. Rupam Mahmood, Richard S. Sutton

arXiv (Cornell University)2023Senior authorOpen Access

DOI: 10.48550/arxiv.2306.13812

Value-aware Importance Weighting for Off-policy Reinforcement Learning

Kristopher De Asis, Eric Graves, Richard S. Sutton

arXiv (Cornell University)2023Senior authorOpen Access

DOI: 10.48550/arxiv.2306.15625

Profile data last refreshed on September 29, 2026 from the university directory, publication records and public research funding records.

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Rich Sutton

Research

Latest papers

Loss of plasticity in deep continual learning

Off-Policy Prediction Learning: An Empirical Study of Online Algorithms

An Idiosyncrasy of Time-discretization in Reinforcement Learning

Reward-Respecting Subtasks for Model-Based Reinforcement Learning (Abstract Reprint)

On Convergence of Average-Reward Q-Learning in Weakly Communicating Markov Decision Processes

Reward-respecting subtasks for model-based reinforcement learning

Loss of Plasticity in Deep Continual Learning

From eye-blinks to state construction: Diagnostic benchmarks for online representation learning.

Maintaining Plasticity in Deep Continual Learning

Value-aware Importance Weighting for Off-policy Reinforcement Learning

Frequent collaborators

Richard Sutton

Patrick Pilarski

Doina Precup

Ashique Mahmood

Adam White

Csaba Szepesvari

Martha White

Huizhen Yu

Michael Bowling

Jacqueline Hebert

Jason Carey

Martin Müller

Vivian Mushahwar

Ashley Dalrymple

Theodore Perkins

Maria Cutumisu