Neuronlike adaptive elements that can solve difficult learning control problems. - Andrew G. Barto; Richard S. Sutton; Charles W. Anderson (IEEE Trans. Syst. Man Cybern., 1983)

Training and Tracking in Robotics. - Oliver G. Selfridge; Richard S. Sutton; Andrew G. Barto (IJCAI, 1985)

Learning to Predict by the Methods of Temporal Differences. - Richard S. Sutton (Mach. Learn., 1988)

Sequential Decision Probelms and Neural Networks. - Andrew G. Barto; Richard S. Sutton; Christopher J. C. H. Watkins (NIPS, 1989)

Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming. - Richard S. Sutton (ML, 1990)

Integrated Modeling and Control Based on Reinforcement Learning. - Richard S. Sutton (NIPS, 1990)

Planning by Incremental Dynamic Programming. - Richard S. Sutton (ML, 1991)

Learning Polynomial Functions by Feature Construction. - Richard S. Sutton; Christopher J. Matheus (ML, 1991)

Iterative Construction of Sparse Polynomial Approximations. - Terence D. Sanger; Richard S. Sutton; Christopher J. Matheus (NIPS, 1991)

Dyna, an Integrated Architecture for Learning, Planning, and Reacting. - Richard S. Sutton (SIGART Bull., 1991)

Adapting Bias by Gradient Descent: An Incremental Version of Delta-Bar-Delta. - Richard S. Sutton (AAAI, 1992)

Online Learning with Random Representations. - Richard S. Sutton; Steven D. Whitehead (ICML, 1993)

TD Models: Modeling the World at a Mixture of Time Scales. - Richard S. Sutton (ICML, 1995)

Generalization in Reinforcement Learning: Successful Examples Using Sparse Coarse Coding. - Richard S. Sutton (NIPS, 1995)

Reinforcement Learning with Replacing Eligibility Traces. - Satinder P. Singh; Richard S. Sutton (Mach. Learn., 1996)

On the Significance of Markov Decision Processes. - Richard S. Sutton (ICANN, 1997)

Exponentiated Gradient Methods for Reinforcement Learning. - Doina Precup; Richard S. Sutton (ICML, 1997)

Multi-time Models for Temporally Abstract Planning. - Doina Precup; Richard S. Sutton (NIPS, 1997)

Experiments with Reinforcement Learning in Problems with Continuous State and Action Spaces. - Juan Carlos Santamaría; Richard S. Sutton; Ashwin Ram 001 (Adapt. Behav., 1997)

Reinforcement learning - an introduction. - Richard S. Sutton; Andrew G. Barto (MIT Press, 1998)

Theoretical Results on Reinforcement Learning with Temporally Abstract Options. - Doina Precup; Richard S. Sutton; Satinder Singh 001 (ECML, 1998)

Intra-Option Learning about Temporally Abstract Actions. - Richard S. Sutton; Doina Precup; Satinder Singh 001 (ICML, 1998)

Learning Instance-Independent Value Functions to Enhance Local Search. - Robert Moll; Andrew G. Barto; Theodore J. Perkins; Richard S. Sutton (NIPS, 1998)

Improved Switching among Temporally Abstract Actions. - Richard S. Sutton; Satinder Singh 001; Doina Precup; Balaraman Ravindran (NIPS, 1998)

Reinforcement Learning: Past, Present and Future. - Richard S. Sutton (SEAL, 1998)

Reinforcement Learning: An Introduction. - Richard S. Sutton; Andrew G. Barto (IEEE Trans. Neural Networks, 1998)

Open Theoretical Questions in Reinforcement Learning. - Richard S. Sutton (EuroCOLT, 1999)

Policy Gradient Methods for Reinforcement Learning with Function Approximation. - Richard S. Sutton; David A. McAllester; Satinder Singh 001; Yishay Mansour (NIPS, 1999)

Between MDPs and Semi-MDPs: A Framework for Temporal Abstraction in Reinforcement Learning. - Richard S. Sutton; Doina Precup; Satinder Singh 001 (Artif. Intell., 1999)

Eligibility Traces for Off-Policy Policy Evaluation. - Doina Precup; Richard S. Sutton; Satinder Singh 001 (ICML, 2000)

Reinforcement Learning for 3 vs. 2 Keepaway - Peter Stone 001; Richard S. Sutton; Satinder Singh 001 (RoboCup, 2000)

Off-Policy Temporal Difference Learning with Function Approximation. - Doina Precup; Richard S. Sutton; Sanjoy Dasgupta (ICML, 2001)

Scaling Reinforcement Learning toward RoboCup Soccer. - Peter Stone 001; Richard S. Sutton (ICML, 2001)

Predictive Representations of State. - Michael L. Littman; Richard S. Sutton; Satinder Singh 001 (NIPS, 2001)

Keepaway Soccer: A Machine Learning Testbed. - Peter Stone 001; Richard S. Sutton (RoboCup, 2001)

Temporal-Difference Networks. - Richard S. Sutton; Brian Tanner (NIPS, 2004)

TD(lambda) networks: temporal-difference networks with eligibility traces. - Brian Tanner; Richard S. Sutton (ICML, 2005)

Using Predictive Representations to Improve Generalization in Reinforcement Learning. - Eddie J. Rafols; Mark B. Ring; Richard S. Sutton; Brian Tanner (IJCAI, 2005)

Temporal-Difference Networks with History. - Brian Tanner; Richard S. Sutton (IJCAI, 2005)

Off-policy Learning with Options and Recognizers. - Doina Precup; Richard S. Sutton; Cosmin Paduraru; Anna Koop; Satinder Singh 001 (NIPS, 2005)

Temporal Abstraction in Temporal-difference Networks. - Richard S. Sutton; Eddie J. Rafols; Anna Koop (NIPS, 2005)

Reinforcement Learning for RoboCup Soccer Keepaway. - Peter Stone 001; Richard S. Sutton; Gregory Kuhlmann (Adapt. Behav., 2005)

Incremental Least-Squares Temporal Difference Learning. - Alborz Geramifard; Michael H. Bowling; Richard S. Sutton (AAAI, 2006)

iLSTD: Eligibility Traces and Convergence Analysis. - Alborz Geramifard; Michael H. Bowling; Martin Zinkevich; Richard S. Sutton (NIPS, 2006)

On the role of tracking in stationary environments. - Richard S. Sutton; Anna Koop; David Silver (ICML, 2007)

Reinforcement Learning of Local Shape in the Game of Go. - David Silver; Richard S. Sutton; Martin Müller 003 (IJCAI, 2007)

Incremental Natural Actor-Critic Algorithms. - Shalabh Bhatnagar; Richard S. Sutton; Mohammad Ghavamzadeh; Mark Lee (NIPS, 2007)

Agent Learning using Action-Dependent Learning Rates in Computer Role-Playing Games. - Maria Cutumisu; Duane Szafron; Michael H. Bowling; Richard S. Sutton (AIIDE, 2008)

Sample-based learning and search with permanent and transient memories. - David Silver; Richard S. Sutton; Martin Müller 003 (ICML, 2008)

A computational model of hippocampal function in trace conditioning. - Elliot A. Ludvig; Richard S. Sutton; Eric Verbeek 002; E. James Kehoe (NIPS, 2008)

A Convergent O(n) Temporal-difference Algorithm for Off-policy Learning with Linear Function Approximation. - Richard S. Sutton; Csaba Szepesvári; Hamid Reza Maei (NIPS, 2008)

Dyna-Style Planning with Linear Function Approximation and Prioritized Sweeping. - Richard S. Sutton; Csaba Szepesvári; Alborz Geramifard; Michael H. Bowling (UAI, 2008)

Stimulus Representation and the Timing of Reward-Prediction Errors in Models of the Dopamine System. - Elliot A. Ludvig; Richard S. Sutton; E. James Kehoe (Neural Comput., 2008)

Fast gradient-descent methods for temporal-difference learning with linear function approximation. - Richard S. Sutton; Hamid Reza Maei; Doina Precup; Shalabh Bhatnagar; David Silver; Csaba Szepesvári; Eric Wiewiora (ICML, 2009)

Convergent Temporal-Difference Learning with Arbitrary Smooth Function Approximation. - Hamid Reza Maei; Csaba Szepesvári; Shalabh Bhatnagar; Doina Precup; David Silver; Richard S. Sutton (NIPS, 2009)

Multi-Step Dyna Planning for Policy Evaluation and Control. - Hengshuai Yao; Richard S. Sutton; Shalabh Bhatnagar; Diao Dongcui; Csaba Szepesvári (NIPS, 2009)

Natural actor-critic algorithms. - Shalabh Bhatnagar; Richard S. Sutton; Mohammad Ghavamzadeh; Mark Lee (Autom., 2009)

Toward Off-Policy Learning Control with Function Approximation. - Hamid Reza Maei; Csaba Szepesvári; Shalabh Bhatnagar; Richard S. Sutton (ICML, 2010)

Horde: a scalable real-time architecture for learning knowledge from unsupervised sensorimotor interaction. - Richard S. Sutton; Joseph Modayil; Michael Delp; Thomas Degris; Patrick M. Pilarski; Adam White 001; Doina Precup (AAMAS, 2011)

Beyond Reward: The Problem of Knowledge and Data. - Richard S. Sutton (ILP, 2011)

Between Instruction and Reward: Human-Prompted Switching. - Patrick M. Pilarski; Richard S. Sutton (AAAI Fall Symposium: Robots Learning Interactively from Human Teachers, 2012)

Model-Free reinforcement learning with continuous action in practice. - Thomas Degris; Patrick M. Pilarski; Richard S. Sutton (ACC, 2012)

Tuning-free step-size adaptation. - Ashique Rupam Mahmood; Richard S. Sutton; Thomas Degris; Patrick M. Pilarski (ICASSP, 2012)

Scaling life-long off-policy learning. - Adam White 001; Joseph Modayil; Richard S. Sutton (ICDL-EPIROB, 2012)

Linear Off-Policy Actor-Critic. - Thomas Degris; Martha White; Richard S. Sutton (ICML, 2012)

Acquiring a broad range of empirical knowledge in real time by temporal-difference learning. - Joseph Modayil; Adam White 001; Patrick M. Pilarski; Richard S. Sutton (SMC, 2012)

Off-Policy Actor-Critic - Thomas Degris; Martha White; Richard S. Sutton (CoRR, 2012)

Temporal-difference search in computer Go. - David Silver; Richard S. Sutton; Martin Müller 003 (Mach. Learn., 2012)

Representation Search through Generate and Test. - Ashique Rupam Mahmood; Richard S. Sutton (AAAI Workshop: Learning Rich Representations from Low-Level Sensors, 2013)

Planning by Prioritized Sweeping with Small Backups. - Harm van Seijen; Richard S. Sutton (ICML (3), 2013)

Real-time prediction learning for the simultaneous actuation of multiple prosthetic joints. - Patrick M. Pilarski; Travis B. Dick; Richard S. Sutton (ICORR, 2013)

Position Paper: Representation Search through Generate and Test. - Ashique Rupam Mahmood; Richard S. Sutton (SARA, 2013)

Temporal-Difference Learning to Assist Human Decision Making during the Control of an Artificial Limb. - Ann L. Edwards; Alexandra Kearney; Michael Rory Dawson; Richard S. Sutton; Patrick M. Pilarski (CoRR, 2013)

Adaptive Artificial Limbs: A Real-Time Approach to Prediction and Anticipation. - Patrick M. Pilarski; Michael Rory Dawson; Thomas Degris; Jason P. Carey; K. Ming Chan; Jacqueline S. Hebert; Richard S. Sutton (IEEE Robotics Autom. Mag., 2013)

True Online TD(lambda). - Harm van Seijen; Richard S. Sutton (ICML, 2014)

A new Q(lambda) with interim forward view and Monte Carlo equivalence. - Richard S. Sutton; Ashique Rupam Mahmood; Doina Precup; Hado van Hasselt (ICML, 2014)

Weighted importance sampling for off-policy learning with linear function approximation. - Ashique Rupam Mahmood; Hado van Hasselt; Richard S. Sutton (NIPS, 2014)

Universal Option Models. - Hengshuai Yao; Csaba Szepesvári; Richard S. Sutton; Joseph Modayil; Shalabh Bhatnagar (NIPS, 2014)

Off-policy TD( l) with a true online equivalence. - Hado van Hasselt; Ashique Rupam Mahmood; Richard S. Sutton (UAI, 2014)

Multi-timescale nexting in a reinforcement learning robot. - Joseph Modayil; Adam White 001; Richard S. Sutton (Adapt. Behav., 2014)

A Deeper Look at Planning as Learning from Replay. - Harm Vanseijen; Richard S. Sutton (ICML, 2015)

Off-policy learning based on weighted importance sampling with linear computational complexity. - Ashique Rupam Mahmood; Richard S. Sutton (UAI, 2015)

Learning to Predict Independent of Span. - Hado van Hasselt; Richard S. Sutton (CoRR, 2015)

Emphatic Temporal-Difference Learning. - Ashique Rupam Mahmood; Huizhen Yu; Martha White; Richard S. Sutton (CoRR, 2015)

An Empirical Evaluation of True Online TD(λ). - Harm van Seijen; Ashique Rupam Mahmood; Patrick M. Pilarski; Richard S. Sutton (CoRR, 2015)

True Online Emphatic TD(λ): Quick Reference and Implementation Guide. - Richard S. Sutton (CoRR, 2015)

A Batch, Off-Policy, Actor-Critic Algorithm for Optimizing the Average Reward. - Susan A. Murphy; Yanzhen Deng; Eric B. Laber; Hamid Reza Maei; Richard S. Sutton; Katie Witkiewitz (CoRR, 2016)

Learning representations through stochastic gradient descent in cross-validation error. - Richard S. Sutton; Vivek Veeriah (CoRR, 2016)

Face valuing: Training user interfaces with facial expressions and reinforcement learning. - Vivek Veeriah; Patrick M. Pilarski; Richard S. Sutton (CoRR, 2016)

True Online Temporal-Difference Learning. - Harm van Seijen; Ashique Rupam Mahmood; Patrick M. Pilarski; Marlos C. Machado; Richard S. Sutton (J. Mach. Learn. Res., 2016)

An Emphatic Approach to the Problem of Off-policy Temporal-Difference Learning. - Richard S. Sutton; Ashique Rupam Mahmood; Martha White (J. Mach. Learn. Res., 2016)

Forward Actor-Critic for Nonlinear Function Approximation in Reinforcement Learning. - Vivek Veeriah; Harm van Seijen; Richard S. Sutton (AAMAS, 2017)

Crossprop: Learning Representations by Stochastic Meta-Gradient Descent in Neural Networks. - Vivek Veeriah; Shangtong Zhang; Richard S. Sutton (ECML/PKDD (1), 2017)

A First Empirical Study of Emphatic Temporal Difference Learning. - Sina Ghiassian; Banafsheh Rafiee; Richard S. Sutton (CoRR, 2017)

Integral Policy Iterations for Reinforcement Learning Problems in Continuous Time and Space. - Jae Young Lee; Richard S. Sutton (CoRR, 2017)

Multi-step Off-policy Learning Without Importance Sampling Ratios. - Ashique Rupam Mahmood; Huizhen Yu; Richard S. Sutton (CoRR, 2017)

GQ($λ$) Quick Reference and Implementation Guide. - Adam White 001; Richard S. Sutton (CoRR, 2017)

Communicative Capital for Prosthetic Agents. - Patrick M. Pilarski; Richard S. Sutton; Kory W. Mathewson; Craig Sherstan; Adam S. R. Parker; Ann L. Edwards (CoRR, 2017)

A Deeper Look at Experience Replay. - Shangtong Zhang; Richard S. Sutton (CoRR, 2017)

Reinforcement learning - an introduction, 2nd Edition. - Richard S. Sutton; Andrew G. Barto (MIT Press, 2018)

Multi-Step Reinforcement Learning: A Unifying Algorithm. - Kristopher De Asis; J. Fernando Hernandez-Garcia; G. Zacharias Holland; Richard S. Sutton (AAAI, 2018)

Per-decision Multi-step Temporal Difference Learning with Control Variates. - Kristopher De Asis; Richard S. Sutton (UAI, 2018)

Comparing Direct and Indirect Temporal-Difference Methods for Estimating the Variance of the Return. - Craig Sherstan; Dylan R. Ashley; Brendan Bennett; Kenny Young; Adam White 001; Martha White; Richard S. Sutton (UAI, 2018)

Directly Estimating the Variance of the λ-Return Using Temporal-Difference Methods. - Craig Sherstan; Brendan Bennett; Kenny Young; Dylan R. Ashley; Adam White 001; Martha White; Richard S. Sutton (CoRR, 2018)

TIDBD: Adapting Temporal-difference Step-sizes Through Stochastic Meta-descent. - Alexandra Kearney; Vivek Veeriah; Jaden B. Travnik; Richard S. Sutton; Patrick M. Pilarski (CoRR, 2018)

Two geometric input transformation methods for fast online reinforcement learning with neural nets. - Sina Ghiassian; Huizhen Yu; Banafsheh Rafiee; Richard S. Sutton (CoRR, 2018)

Integrating Episodic Memory into a Reinforcement Learning Agent using Reservoir Sampling. - Kenny J. Young; Richard S. Sutton; Shuo Yang (CoRR, 2018)

Predicting Periodicity with Temporal Difference Learning. - Kristopher De Asis; Brendan Bennett; Richard S. Sutton (CoRR, 2018)

Online Off-policy Prediction. - Sina Ghiassian; Andrew Patterson; Martha White; Richard S. Sutton; Adam White 001 (CoRR, 2018)

Reactive Reinforcement Learning in Asynchronous Environments. - Jaden B. Travnik; Kory W. Mathewson; Richard S. Sutton; Patrick M. Pilarski (Frontiers Robotics AI, 2018)

On Generalized Bellman Equations and Temporal-Difference Learning. - Huizhen Yu; Ashique Rupam Mahmood; Richard S. Sutton (J. Mach. Learn. Res., 2018)

Prediction in Intelligence: An Empirical Comparison of Off-policy Algorithms on Robots. - Banafsheh Rafiee; Sina Ghiassian; Adam White 001; Richard S. Sutton (AAMAS, 2019)

Extending Sliding-Step Importance Weighting from Supervised Learning to Reinforcement Learning. - Tian Tian; Richard S. Sutton (IJCAI, 2019)

Planning with Expectation Models. - Yi Wan 004; Muhammad Zaheer; Adam White 001; Martha White; Richard S. Sutton (IJCAI, 2019)

Understanding Multi-Step Deep Reinforcement Learning: A Systematic Study of the DQN Target. - J. Fernando Hernandez-Garcia; Richard S. Sutton (CoRR, 2019)

Should All Temporal Difference Learning Use Emphasis? - Xiang Gu 002; Sina Ghiassian; Richard S. Sutton (CoRR, 2019)

Learning Feature Relevance Through Step Size Adaptation in Temporal-Difference Learning. - Alexandra Kearney; Vivek Veeriah; Jaden B. Travnik; Patrick M. Pilarski; Richard S. Sutton (CoRR, 2019)

Discounted Reinforcement Learning is Not an Optimization Problem. - Abhishek Naik; Roshan Shariff; Niko Yasui; Richard S. Sutton (CoRR, 2019)

Learning Sparse Representations Incrementally in Deep Reinforcement Learning. - J. Fernando Hernandez-Garcia; Richard S. Sutton (CoRR, 2019)

Fixed-Horizon Temporal Difference Methods for Stable Reinforcement Learning. - Kristopher De Asis; Alan Chan 001; Silviu Pitis; Richard S. Sutton; Daniel Graves (AAAI, 2020)

Behaviour Suite for Reinforcement Learning. - Ian Osband; Yotam Doron; Matteo Hessel; John Aslanides; Eren Sezener; Andre Saraiva 001; Katrina McKinney; Tor Lattimore; Csaba Szepesvári; Satinder Singh 001; Benjamin Van Roy; Richard S. Sutton; et al. (ICLR, 2020)

Inverse Policy Evaluation for Value-based Sequential Decision-making. - Alan Chan 001; Kristopher De Asis; Richard S. Sutton (CoRR, 2020)

Document-editing Assistants and Model-based Reinforcement Learning as a Path to Conversational AI. - Katya Kudashkina; Patrick M. Pilarski; Richard S. Sutton (CoRR, 2020)

Understanding the Pathologies of Approximate Policy Evaluation when Combined with Greedification in Reinforcement Learning. - Kenny Young; Richard S. Sutton (CoRR, 2020)

Special Issue "On Defining Artificial Intelligence" - Commentaries and Author's Response. - 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; et al. (J. Artif. Gen. Intell., 2020)

Learning and Planning in Average-Reward Markov Decision Processes. - Yi Wan 004; Abhishek Naik; Richard S. Sutton (ICML, 2021)

Average-Reward Off-Policy Policy Evaluation with Function Approximation. - Shangtong Zhang; Yi Wan 004; Richard S. Sutton; Shimon Whiteson (ICML, 2021)

Average-Reward Learning and Planning with Options. - Yi Wan 004; Abhishek Naik; Richard S. Sutton (NeurIPS, 2021)

Reward is enough. - David Silver; Satinder Singh 001; Doina Precup; Richard S. Sutton (Artif. Intell., 2021)

Policy iterations for reinforcement learning problems in continuous time and space - Fundamental theory and methods. - Jae Young Lee; Richard S. Sutton (Autom., 2021)

Does Standard Backpropagation Forget Less Catastrophically Than Adam? - Dylan R. Ashley; Sina Ghiassian; Richard S. Sutton (CoRR, 2021)

Scalable Online Recurrent Learning Using Columnar Neural Networks. - Khurram Javed; Martha White; Richard S. Sutton (CoRR, 2021)

Planning with Expectation Models for Control. - Katya Kudashkina; Yi Wan 004; Abhishek Naik; Richard S. Sutton (CoRR, 2021)

An Empirical Comparison of Off-policy Prediction Learning Algorithms on the Collision Task. - Sina Ghiassian; Richard S. Sutton (CoRR, 2021)

Continual Backprop: Stochastic Gradient Descent with Persistent Randomness. - Shibhansh Dohare; A. Rupam Mahmood; Richard S. Sutton (CoRR, 2021)

An Empirical Comparison of Off-policy Prediction Learning Algorithms in the Four Rooms Environment. - Sina Ghiassian; Richard S. Sutton (CoRR, 2021)

Learning Agent State Online with Recurrent Generate-and-Test. - Amir Samani; Richard S. Sutton (CoRR, 2021)

Looking Back on the Actor-Critic Architecture. - Andrew G. Barto; Richard S. Sutton; Charles W. Anderson (IEEE Trans. Syst. Man Cybern. Syst., 2021)

Doubly-Asynchronous Value Iteration: Making Value Iteration Asynchronous in Actions. - Tian Tian; Kenny Young; Richard S. Sutton (NeurIPS, 2022)

A History of Meta-gradient: Gradient Methods for Meta-learning. - Richard S. Sutton (CoRR, 2022)

The Quest for a Common Model of the Intelligent Decision Maker. - Richard S. Sutton (CoRR, 2022)

Toward Discovering Options that Achieve Faster Planning. - Yi Wan 004; Richard S. Sutton (CoRR, 2022)

The Alberta Plan for AI Research. - Richard S. Sutton; Michael H. Bowling; Patrick M. Pilarski (CoRR, 2022)

On Convergence of Average-Reward Off-Policy Control Algorithms in Weakly-Communicating MDPs. - Yi Wan 004; Richard S. Sutton (CoRR, 2022)

Value-aware Importance Weighting for Off-policy Reinforcement Learning. - Kristopher De Asis; Eric Graves 002; Richard S. Sutton (CoLLAs, 2023)

Auxiliary task discovery through generate-and-test. - Banafsheh Rafiee; Sina Ghiassian; Jun Jin 001; Richard S. Sutton; Jun Luo 009; Adam White 001 (CoLLAs, 2023)

Toward Efficient Gradient-Based Value Estimation. - Arsalan Sharifnassab; Richard S. Sutton (ICML, 2023)

Online Real-Time Recurrent Learning Using Sparse Connections and Selective Learning. - Khurram Javed; Haseeb Shah; Richard S. Sutton; Martha White (CoRR, 2023)

Maintaining Plasticity in Deep Continual Learning. - Shibhansh Dohare; J. Fernando Hernandez-Garcia; Parash Rahman; Richard S. Sutton; A. Rupam Mahmood (CoRR, 2023)

Iterative Option Discovery for Planning, by Planning. - Kenny Young; Richard S. Sutton (CoRR, 2023)

A Note on Stability in Asynchronous Stochastic Approximation without Communication Delays. - Huizhen Yu; Yi Wan 004; Richard S. Sutton (CoRR, 2023)

Scalable Real-Time Recurrent Learning Using Columnar-Constructive Networks. - Khurram Javed; Haseeb Shah; Richard S. Sutton; Martha White (J. Mach. Learn. Res., 2023)

From eye-blinks to state construction: Diagnostic benchmarks for online representation learning. - Banafsheh Rafiee; Zaheer Abbas; Sina Ghiassian; Raksha Kumaraswamy; Richard S. Sutton; Elliot A. Ludvig; Adam White 001 (Adapt. Behav., 2023)

Communicative capital: a key resource for human-machine shared agency and collaborative capacity. - Kory W. Mathewson; Adam S. R. Parker; Craig Sherstan; Ann L. Edwards; Richard S. Sutton; Patrick M. Pilarski (Neural Comput. Appl., 2023)

Reward-respecting subtasks for model-based reinforcement learning. - Richard S. Sutton; Marlos C. Machado; G. Zacharias Holland; David Szepesvari; Finbarr Timbers; Brian Tanner; Adam White 001 (Artif. Intell., 2023)

Reward-Respecting Subtasks for Model-Based Reinforcement Learning (Abstract Reprint). - Richard S. Sutton; Marlos C. Machado; G. Zacharias Holland; David Szepesvari; Finbarr Timbers; Brian Tanner; Adam White 001 (AAAI, 2024)

An Idiosyncrasy of Time-discretization in Reinforcement Learning. - Kris De Asis; Richard S. Sutton (RLJ, 2024)

SwiftTD: A Fast and Robust Algorithm for Temporal Difference Learning. - Khurram Javed; Arsalan Sharifnassab; Richard S. Sutton (RLJ, 2024)

Reward Centering. - Abhishek Naik; Yi Wan 004; Manan Tomar; Richard S. Sutton (RLJ, 2024)

Step-size Optimization for Continual Learning. - Thomas Degris; Khurram Javed; Arsalan Sharifnassab; Yuxin Liu; Richard S. Sutton (CoRR, 2024)

MetaOptimize: A Framework for Optimizing Step Sizes and Other Meta-parameters. - Arsalan Sharifnassab; Saber Salehkaleybar; Richard S. Sutton (CoRR, 2024)

On Convergence of Average-Reward Q-Learning in Weakly Communicating Markov Decision Processes. - Yi Wan 004; Huizhen Yu; Richard S. Sutton (CoRR, 2024)

Asynchronous Stochastic Approximation and Average-Reward Reinforcement Learning. - Huizhen Yu; Yi Wan 004; Richard S. Sutton (CoRR, 2024)

Loss of plasticity in deep continual learning. - Shibhansh Dohare; J. Fernando Hernandez-Garcia; Qingfeng Lan; Parash Rahman; A. Rupam Mahmood; Richard S. Sutton (Nat., 2024)

Off-Policy Prediction Learning: An Empirical Study of Online Algorithms. - Sina Ghiassian; Banafsheh Rafiee; Richard S. Sutton (IEEE Trans. Neural Networks Learn. Syst., 2025)

Swift-Sarsa: Fast and Robust Linear Control. - Khurram Javed; Richard S. Sutton (CoRR, 2025)

Reinitializing weights vs units for maintaining plasticity in neural networks. - J. Fernando Hernandez-Garcia; Shibhansh Dohare; Jun Luo 009; Richard S. Sutton (CoRR, 2025)