Richard S. Sutton

ORCID

Publication Activity (10 Years)

Years Active: 1983-2024
Publications (10 Years): 88

Top Venues

Publications

Kris De Asis, Richard S. Sutton
An Idiosyncrasy of Time-discretization in Reinforcement Learning. CoRR (2024)
Thomas Degris, Khurram Javed, Arsalan Sharifnassab, Yuxin Liu, Richard S. Sutton
Step-size Optimization for Continual Learning. CoRR (2024)
Abhishek Naik, Yi Wan, Manan Tomar, Richard S. Sutton
Reward Centering. CoRR (2024)
Arsalan Sharifnassab, Saber Salehkaleybar, Richard S. Sutton
MetaOptimize: A Framework for Optimizing Step Sizes and Other Meta-parameters. CoRR (2024)
Richard S. Sutton, Marlos C. Machado, G. Zacharias Holland, David Szepesvari, Finbarr Timbers, Brian Tanner, Adam White
Reward-Respecting Subtasks for Model-Based Reinforcement Learning (Abstract Reprint). AAAI (2024)
Kristopher De Asis, Eric Graves, Richard S. Sutton
Value-aware Importance Weighting for Off-policy Reinforcement Learning. CoLLAs (2023)
Banafsheh Rafiee, Zaheer Abbas, Sina Ghiassian, Raksha Kumaraswamy, Richard S. Sutton, Elliot A. Ludvig, Adam White
From eye-blinks to state construction: Diagnostic benchmarks for online representation learning. Adapt. Behav. 31 (1) (2023)
Khurram Javed, Haseeb Shah, Richard S. Sutton, Martha White
Scalable Real-Time Recurrent Learning Using Columnar-Constructive Networks. J. Mach. Learn. Res. 24 (2023)
Kristopher De Asis, Eric Graves, Richard S. Sutton
Value-aware Importance Weighting for Off-policy Reinforcement Learning. CoRR (2023)
Kory W. Mathewson, Adam S. R. Parker, Craig Sherstan, Ann L. Edwards, Richard S. Sutton, Patrick M. Pilarski
Communicative capital: a key resource for human-machine shared agency and collaborative capacity. Neural Comput. Appl. 35 (23) (2023)
Khurram Javed, Haseeb Shah, Richard S. Sutton, Martha White
Online Real-Time Recurrent Learning Using Sparse Connections and Selective Learning. CoRR (2023)
Banafsheh Rafiee, Sina Ghiassian, Jun Jin, Richard S. Sutton, Jun Luo, Adam White
Auxiliary task discovery through generate-and-test. CoLLAs (2023)
Richard S. Sutton, Marlos C. Machado, G. Zacharias Holland, David Szepesvari, Finbarr Timbers, Brian Tanner, Adam White
Reward-respecting subtasks for model-based reinforcement learning. Artif. Intell. 324 (2023)
Arsalan Sharifnassab, Richard S. Sutton
Toward Efficient Gradient-Based Value Estimation. ICML (2023)
Shibhansh Dohare, J. Fernando Hernandez-Garcia, Parash Rahman, Richard S. Sutton, A. Rupam Mahmood
Maintaining Plasticity in Deep Continual Learning. CoRR (2023)
Huizhen Yu, Yi Wan, Richard S. Sutton
A Note on Stability in Asynchronous Stochastic Approximation without Communication Delays. CoRR (2023)
Kenny Young, Richard S. Sutton
Iterative Option Discovery for Planning, by Planning. CoRR (2023)
Yi Wan, Richard S. Sutton
Toward Discovering Options that Achieve Faster Planning. CoRR (2022)
Tian Tian, Kenny Young, Richard S. Sutton
Doubly-Asynchronous Value Iteration: Making Value Iteration Asynchronous in Actions. CoRR (2022)
Banafsheh Rafiee, Sina Ghiassian, Jun Jin, Richard S. Sutton, Jun Luo, Adam White
Auxiliary task discovery through generate-and-test. CoRR (2022)
Richard S. Sutton
A History of Meta-gradient: Gradient Methods for Meta-learning. CoRR (2022)
Richard S. Sutton
The Quest for a Common Model of the Intelligent Decision Maker. CoRR (2022)
Richard S. Sutton, Michael H. Bowling, Patrick M. Pilarski
The Alberta Plan for AI Research. CoRR (2022)
Tian Tian, Kenny Young, Richard S. Sutton
Doubly-Asynchronous Value Iteration: Making Value Iteration Asynchronous in Actions. NeurIPS (2022)
Yi Wan, Richard S. Sutton
On Convergence of Average-Reward Off-Policy Control Algorithms in Weakly-Communicating MDPs. CoRR (2022)
Richard S. Sutton, Marlos C. Machado, G. Zacharias Holland, David Szepesvari, Finbarr Timbers, Brian Tanner, Adam White
Reward-Respecting Subtasks for Model-Based Reinforcement Learning. CoRR (2022)
Shangtong Zhang, Yi Wan, Richard S. Sutton, Shimon Whiteson
Average-Reward Off-Policy Policy Evaluation with Function Approximation. CoRR (2021)
Sina Ghiassian, Richard S. Sutton
An Empirical Comparison of Off-policy Prediction Learning Algorithms on the Collision Task. CoRR (2021)
Shibhansh Dohare, A. Rupam Mahmood, Richard S. Sutton
Continual Backprop: Stochastic Gradient Descent with Persistent Randomness. CoRR (2021)
Amir Samani, Richard S. Sutton
Learning Agent State Online with Recurrent Generate-and-Test. CoRR (2021)
David Silver, Satinder P. Singh, Doina Precup, Richard S. Sutton
Reward is enough. Artif. Intell. 299 (2021)
Shangtong Zhang, Yi Wan, Richard S. Sutton, Shimon Whiteson
Average-Reward Off-Policy Policy Evaluation with Function Approximation. ICML (2021)
Dylan R. Ashley, Sina Ghiassian, Richard S. Sutton
Does Standard Backpropagation Forget Less Catastrophically Than Adam? CoRR (2021)
Sina Ghiassian, Richard S. Sutton
An Empirical Comparison of Off-policy Prediction Learning Algorithms in the Four Rooms Environment. CoRR (2021)
Yi Wan, Abhishek Naik, Richard S. Sutton
Average-Reward Learning and Planning with Options. CoRR (2021)
Yi Wan, Abhishek Naik, Richard S. Sutton
Learning and Planning in Average-Reward Markov Decision Processes. ICML (2021)
Katya Kudashkina, Yi Wan, Abhishek Naik, Richard S. Sutton
Planning with Expectation Models for Control. CoRR (2021)
Yi Wan, Abhishek Naik, Richard S. Sutton
Average-Reward Learning and Planning with Options. NeurIPS (2021)
Jae Young Lee, Richard S. Sutton
Policy iterations for reinforcement learning problems in continuous time and space - Fundamental theory and methods. Autom. 126 (2021)
Khurram Javed, Martha White, Richard S. Sutton
Scalable Online Recurrent Learning Using Columnar Neural Networks. CoRR (2021)
Andrew G. Barto, Richard S. Sutton, Charles W. Anderson
Looking Back on the Actor-Critic Architecture. IEEE Trans. Syst. Man Cybern. Syst. 51 (1) (2021)
Ian Osband, Yotam Doron, Matteo Hessel, John Aslanides, Eren Sezener, Andre Saraiva, Katrina McKinney, Tor Lattimore, Csaba Szepesvári, Satinder Singh, Benjamin Van Roy, Richard S. Sutton, David Silver, Hado van Hasselt
Behaviour Suite for Reinforcement Learning. ICLR (2020)
Yi Wan, Abhishek Naik, Richard S. Sutton
Learning and Planning in Average-Reward Markov Decision Processes. CoRR (2020)
Kenny Young, Richard S. Sutton
Understanding the Pathologies of Approximate Policy Evaluation when Combined with Greedification in Reinforcement Learning. CoRR (2020)
Dagmar Monett, Colin W. P. Lewis, Kristinn R. Thórisson, Joscha Bach, Gianluca Baldassarre, Giovanni Granato, Istvan S. N. Berkeley, François Chollet, Matthew Crosby, Henry Shevlin, John F. Sowa, John E. Laird, Shane Legg, Peter Lindes, Tomás Mikolov, William J. Rapaport, Raúl Rojas, Marek Rosa, Peter Stone, Richard S. Sutton, Roman V. Yampolskiy, Pei Wang, Roger C. Schank, Aaron Sloman, Alan F. T. Winfield
Special Issue "On Defining Artificial Intelligence" - Commentaries and Author's Response. J. Artif. Gen. Intell. 11 (2) (2020)
Alan Chan, Kristopher De Asis, Richard S. Sutton
Inverse Policy Evaluation for Value-based Sequential Decision-making. CoRR (2020)
Katya Kudashkina, Patrick M. Pilarski, Richard S. Sutton
Document-editing Assistants and Model-based Reinforcement Learning as a Path to Conversational AI. CoRR (2020)
Kristopher De Asis, Alan Chan, Silviu Pitis, Richard S. Sutton, Daniel Graves
Fixed-Horizon Temporal Difference Methods for Stable Reinforcement Learning. AAAI (2020)
Yi Wan, Muhammad Zaheer, Adam White, Martha White, Richard S. Sutton
Planning with Expectation Models. IJCAI (2019)
J. Fernando Hernandez-Garcia, Richard S. Sutton
Understanding Multi-Step Deep Reinforcement Learning: A Systematic Study of the DQN Target. CoRR (2019)
Kristopher De Asis, Alan Chan, Silviu Pitis, Richard S. Sutton, Daniel Graves
Fixed-Horizon Temporal Difference Methods for Stable Reinforcement Learning. CoRR (2019)
Tian Tian, Richard S. Sutton
Extending Sliding-Step Importance Weighting from Supervised Learning to Reinforcement Learning. IJCAI (2019)
Xiang Gu, Sina Ghiassian, Richard S. Sutton
Should All Temporal Difference Learning Use Emphasis? CoRR (2019)
Yi Wan, Muhammad Zaheer, Adam White, Martha White, Richard S. Sutton
Planning with Expectation Models. CoRR (2019)
Alexandra Kearney, Vivek Veeriah, Jaden B. Travnik, Patrick M. Pilarski, Richard S. Sutton
Learning Feature Relevance Through Step Size Adaptation in Temporal-Difference Learning. CoRR (2019)
Ian Osband, Yotam Doron, Matteo Hessel, John Aslanides, Eren Sezener, Andre Saraiva, Katrina McKinney, Tor Lattimore, Csaba Szepesvári, Satinder Singh, Benjamin Van Roy, Richard S. Sutton, David Silver, Hado van Hasselt
Behaviour Suite for Reinforcement Learning. CoRR (2019)
Banafsheh Rafiee, Sina Ghiassian, Adam White, Richard S. Sutton
Prediction in Intelligence: An Empirical Comparison of Off-policy Algorithms on Robots. AAMAS (2019)
J. Fernando Hernandez-Garcia, Richard S. Sutton
Learning Sparse Representations Incrementally in Deep Reinforcement Learning. CoRR (2019)
Abhishek Naik, Roshan Shariff, Niko Yasui, Richard S. Sutton
Discounted Reinforcement Learning is Not an Optimization Problem. CoRR (2019)
Alexandra Kearney, Vivek Veeriah, Jaden B. Travnik, Richard S. Sutton, Patrick M. Pilarski
TIDBD: Adapting Temporal-difference Step-sizes Through Stochastic Meta-descent. CoRR (2018)
Jaden B. Travnik, Kory W. Mathewson, Richard S. Sutton, Patrick M. Pilarski
Reactive Reinforcement Learning in Asynchronous Environments. CoRR (2018)
Kristopher De Asis, Richard S. Sutton
Per-decision Multi-step Temporal Difference Learning with Control Variates. CoRR (2018)
Huizhen Yu, Ashique Rupam Mahmood, Richard S. Sutton
On Generalized Bellman Equations and Temporal-Difference Learning. J. Mach. Learn. Res. 19 (2018)
Craig Sherstan, Brendan Bennett, Kenny Young, Dylan R. Ashley, Adam White, Martha White, Richard S. Sutton
Directly Estimating the Variance of the λ-Return Using Temporal-Difference Methods. CoRR (2018)
Sina Ghiassian, Huizhen Yu, Banafsheh Rafiee, Richard S. Sutton
Two geometric input transformation methods for fast online reinforcement learning with neural nets. CoRR (2018)
Kristopher De Asis, J. Fernando Hernandez-Garcia, G. Zacharias Holland, Richard S. Sutton
Multi-Step Reinforcement Learning: A Unifying Algorithm. AAAI (2018)
Kristopher De Asis, Brendan Bennett, Richard S. Sutton
Predicting Periodicity with Temporal Difference Learning. CoRR (2018)
Sina Ghiassian, Andrew Patterson, Martha White, Richard S. Sutton, Adam White
Online Off-policy Prediction. CoRR (2018)
Kristopher De Asis, Richard S. Sutton
Per-decision Multi-step Temporal Difference Learning with Control Variates. UAI (2018)
Craig Sherstan, Dylan R. Ashley, Brendan Bennett, Kenny Young, Adam White, Martha White, Richard S. Sutton
Comparing Direct and Indirect Temporal-Difference Methods for Estimating the Variance of the Return. UAI (2018)
Kenny J. Young, Richard S. Sutton, Shuo Yang
Integrating Episodic Memory into a Reinforcement Learning Agent using Reservoir Sampling. CoRR (2018)
Jaden B. Travnik, Kory W. Mathewson, Richard S. Sutton, Patrick M. Pilarski
Reactive Reinforcement Learning in Asynchronous Environments. Frontiers Robotics AI 5 (2018)
Huizhen Yu, Ashique Rupam Mahmood, Richard S. Sutton
On Generalized Bellman Equations and Temporal-Difference Learning. CoRR (2017)
Sina Ghiassian, Banafsheh Rafiee, Richard S. Sutton
A First Empirical Study of Emphatic Temporal Difference Learning. CoRR (2017)
Shangtong Zhang, Richard S. Sutton
A Deeper Look at Experience Replay. CoRR (2017)
Ashique Rupam Mahmood, Huizhen Yu, Richard S. Sutton
Multi-step Off-policy Learning Without Importance Sampling Ratios. CoRR (2017)
Patrick M. Pilarski, Richard S. Sutton, Kory W. Mathewson, Craig Sherstan, Adam S. R. Parker, Ann L. Edwards
Communicative Capital for Prosthetic Agents. CoRR (2017)
Huizhen Yu, Ashique Rupam Mahmood, Richard S. Sutton
On Generalized Bellman Equations and Temporal-Difference Learning. Canadian Conference on AI (2017)
Kristopher De Asis, J. Fernando Hernandez-Garcia, G. Zacharias Holland, Richard S. Sutton
Multi-step Reinforcement Learning: A Unifying Algorithm. CoRR (2017)
Adam White, Richard S. Sutton
GQ($λ$) Quick Reference and Implementation Guide. CoRR (2017)
Jae Young Lee, Richard S. Sutton
Integral Policy Iterations for Reinforcement Learning Problems in Continuous Time and Space. CoRR (2017)
Vivek Veeriah, Harm van Seijen, Richard S. Sutton
Forward Actor-Critic for Nonlinear Function Approximation in Reinforcement Learning. AAMAS (2017)
Vivek Veeriah, Shangtong Zhang, Richard S. Sutton
Crossprop: Learning Representations by Stochastic Meta-Gradient Descent in Neural Networks. ECML/PKDD (1) (2017)
Richard S. Sutton, Vivek Veeriah
Learning representations through stochastic gradient descent in cross-validation error. CoRR (2016)
Richard S. Sutton, Ashique Rupam Mahmood, Martha White
An Emphatic Approach to the Problem of Off-policy Temporal-Difference Learning. J. Mach. Learn. Res. 17 (2016)
Susan A. Murphy, Yanzhen Deng, Eric B. Laber, Hamid Reza Maei, Richard S. Sutton, Katie Witkiewitz
A Batch, Off-Policy, Actor-Critic Algorithm for Optimizing the Average Reward. CoRR (2016)
Harm van Seijen, Ashique Rupam Mahmood, Patrick M. Pilarski, Marlos C. Machado, Richard S. Sutton
True Online Temporal-Difference Learning. J. Mach. Learn. Res. 17 (2016)
Vivek Veeriah, Patrick M. Pilarski, Richard S. Sutton
Face valuing: Training user interfaces with facial expressions and reinforcement learning. CoRR (2016)
Ashique Rupam Mahmood, Richard S. Sutton
Off-policy learning based on weighted importance sampling with linear computational complexity. UAI (2015)
Richard S. Sutton, Ashique Rupam Mahmood, Martha White
An Emphatic Approach to the Problem of Off-policy Temporal-Difference Learning. CoRR (2015)
Richard S. Sutton
True Online Emphatic TD(λ): Quick Reference and Implementation Guide. CoRR (2015)
Richard S. Sutton, Brian Tanner
Temporal-Difference Networks. CoRR (2015)
Harm van Seijen, Ashique Rupam Mahmood, Patrick M. Pilarski, Marlos C. Machado, Richard S. Sutton
True Online Temporal-Difference Learning. CoRR (2015)
Ashique Rupam Mahmood, Huizhen Yu, Martha White, Richard S. Sutton
Emphatic Temporal-Difference Learning. CoRR (2015)
Hado van Hasselt, Richard S. Sutton
Learning to Predict Independent of Span. CoRR (2015)
Harm van Seijen, Ashique Rupam Mahmood, Patrick M. Pilarski, Richard S. Sutton
An Empirical Evaluation of True Online TD(λ). CoRR (2015)
Hado van Hasselt, Ashique Rupam Mahmood, Richard S. Sutton
Off-policy TD( l) with a true online equivalence. UAI (2014)
Ashique Rupam Mahmood, Hado van Hasselt, Richard S. Sutton
Weighted importance sampling for off-policy learning with linear function approximation. NIPS (2014)
Richard S. Sutton, Ashique Rupam Mahmood, Doina Precup, Hado van Hasselt
A new Q(lambda) with interim forward view and Monte Carlo equivalence. ICML (2014)
Harm van Seijen, Richard S. Sutton
True Online TD(lambda). ICML (2014)

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Publication Activity (10 Years)

Top Topics

Top Venues

Richard S. Sutton