Richard S. Sutton

196 papers A* 35A 8B 2C 4Misc 1Journal 105Unranked 38

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Average-reward reinforcement learning in semi-Markov decision processes via relative value iteration. CoRR Huizhen Yu, Yi Wan, Richard S. Sutton
2025	A*	`conf`	MetaOptimize: A Framework for Optimizing Step Sizes and Other Meta-parameters. ICML Arsalan Sharifnassab, Saber Salehkaleybar, Richard S. Sutton
2025	J	`jnl`	Off-Policy Prediction Learning: An Empirical Study of Online Algorithms. IEEE Trans. Neural Networks Learn. Syst. Sina Ghiassian, Banafsheh Rafiee, Richard S. Sutton
2025	J	`jnl`	Reinitializing weights vs units for maintaining plasticity in neural networks. CoRR J. Fernando Hernandez-Garcia, Shibhansh Dohare, Jun Luo, Richard S. Sutton
2025	J	`jnl`	Swift-Sarsa: Fast and Robust Linear Control. CoRR Khurram Javed, Richard S. Sutton
2024	J	`jnl`	An Idiosyncrasy of Time-discretization in Reinforcement Learning. RLJ Kris De Asis, Richard S. Sutton
2024	J	`jnl`	An Idiosyncrasy of Time-discretization in Reinforcement Learning. CoRR Kris De Asis, Richard S. Sutton
2024	J	`jnl`	Asynchronous Stochastic Approximation and Average-Reward Reinforcement Learning. CoRR Huizhen Yu, Yi Wan, Richard S. Sutton
2024	J	`jnl`	Loss of plasticity in deep continual learning. Nat. Shibhansh Dohare, J. Fernando Hernandez-Garcia, Qingfeng Lan, Parash Rahman, A. Rupam Mahmood, Richard S. Sutton
2024	J	`jnl`	MetaOptimize: A Framework for Optimizing Step Sizes and Other Meta-parameters. CoRR Arsalan Sharifnassab, Saber Salehkaleybar, Richard S. Sutton
2024	J	`jnl`	On Convergence of Average-Reward Q-Learning in Weakly Communicating Markov Decision Processes. CoRR Yi Wan, Huizhen Yu, Richard S. Sutton
2024	J	`jnl`	Reward Centering. RLJ Abhishek Naik, Yi Wan, Manan Tomar, Richard S. Sutton
2024	J	`jnl`	Reward Centering. CoRR Abhishek Naik, Yi Wan, Manan Tomar, Richard S. Sutton
2024	A*	`conf`	Reward-Respecting Subtasks for Model-Based Reinforcement Learning (Abstract Reprint). AAAI Richard S. Sutton, Marlos C. Machado, G. Zacharias Holland, David Szepesvari, Finbarr Timbers, Brian Tanner, Adam White
2024	J	`jnl`	Step-size Optimization for Continual Learning. CoRR Thomas Degris, Khurram Javed, Arsalan Sharifnassab, Yuxin Liu, Richard S. Sutton
2024	J	`jnl`	SwiftTD: A Fast and Robust Algorithm for Temporal Difference Learning. RLJ Khurram Javed, Arsalan Sharifnassab, Richard S. Sutton
2023	J	`jnl`	A Note on Stability in Asynchronous Stochastic Approximation without Communication Delays. CoRR Huizhen Yu, Yi Wan, Richard S. Sutton
2023	—	`conf`	Auxiliary task discovery through generate-and-test. CoLLAs Banafsheh Rafiee, Sina Ghiassian, Jun Jin, Richard S. Sutton, Jun Luo, Adam White
2023	J	`jnl`	Communicative capital: a key resource for human-machine shared agency and collaborative capacity. Neural Comput. Appl. Kory W. Mathewson, Adam S. R. Parker, Craig Sherstan, Ann L. Edwards, Richard S. Sutton, Patrick M. Pilarski
2023	J	`jnl`	From eye-blinks to state construction: Diagnostic benchmarks for online representation learning. Adapt. Behav. Banafsheh Rafiee, Zaheer Abbas, Sina Ghiassian, Raksha Kumaraswamy, Richard S. Sutton, Elliot A. Ludvig, Adam White
2023	J	`jnl`	Iterative Option Discovery for Planning, by Planning. CoRR Kenny Young, Richard S. Sutton
2023	J	`jnl`	Maintaining Plasticity in Deep Continual Learning. CoRR Shibhansh Dohare, J. Fernando Hernandez-Garcia, Parash Rahman, Richard S. Sutton, A. Rupam Mahmood
2023	J	`jnl`	Online Real-Time Recurrent Learning Using Sparse Connections and Selective Learning. CoRR Khurram Javed, Haseeb Shah, Richard S. Sutton, Martha White
2023	J	`jnl`	Reward-respecting subtasks for model-based reinforcement learning. Artif. Intell. Richard S. Sutton, Marlos C. Machado, G. Zacharias Holland, David Szepesvari, Finbarr Timbers, Brian Tanner, Adam White
2023	J	`jnl`	Scalable Real-Time Recurrent Learning Using Columnar-Constructive Networks. J. Mach. Learn. Res. Khurram Javed, Haseeb Shah, Richard S. Sutton, Martha White
2023	A*	`conf`	Toward Efficient Gradient-Based Value Estimation. ICML Arsalan Sharifnassab, Richard S. Sutton
2023	—	`conf`	Value-aware Importance Weighting for Off-policy Reinforcement Learning. CoLLAs Kristopher De Asis, Eric Graves, Richard S. Sutton
2023	J	`jnl`	Value-aware Importance Weighting for Off-policy Reinforcement Learning. CoRR Kristopher De Asis, Eric Graves, Richard S. Sutton
2022	J	`jnl`	A History of Meta-gradient: Gradient Methods for Meta-learning. CoRR Richard S. Sutton
2022	J	`jnl`	Auxiliary task discovery through generate-and-test. CoRR Banafsheh Rafiee, Sina Ghiassian, Jun Jin, Richard S. Sutton, Jun Luo, Adam White
2022	A*	`conf`	Doubly-Asynchronous Value Iteration: Making Value Iteration Asynchronous in Actions. NeurIPS Tian Tian, Kenny Young, Richard S. Sutton
2022	J	`jnl`	Doubly-Asynchronous Value Iteration: Making Value Iteration Asynchronous in Actions. CoRR Tian Tian, Kenny Young, Richard S. Sutton
2022	J	`jnl`	On Convergence of Average-Reward Off-Policy Control Algorithms in Weakly-Communicating MDPs. CoRR Yi Wan, Richard S. Sutton
2022	J	`jnl`	Reward-Respecting Subtasks for Model-Based Reinforcement Learning. CoRR Richard S. Sutton, Marlos C. Machado, G. Zacharias Holland, David Szepesvari, Finbarr Timbers, Brian Tanner, Adam White
2022	J	`jnl`	The Alberta Plan for AI Research. CoRR Richard S. Sutton, Michael H. Bowling, Patrick M. Pilarski
2022	J	`jnl`	The Quest for a Common Model of the Intelligent Decision Maker. CoRR Richard S. Sutton
2022	J	`jnl`	Toward Discovering Options that Achieve Faster Planning. CoRR Yi Wan, Richard S. Sutton
2021	J	`jnl`	An Empirical Comparison of Off-policy Prediction Learning Algorithms in the Four Rooms Environment. CoRR Sina Ghiassian, Richard S. Sutton
2021	J	`jnl`	An Empirical Comparison of Off-policy Prediction Learning Algorithms on the Collision Task. CoRR Sina Ghiassian, Richard S. Sutton
2021	A*	`conf`	Average-Reward Learning and Planning with Options. NeurIPS Yi Wan, Abhishek Naik, Richard S. Sutton
2021	J	`jnl`	Average-Reward Learning and Planning with Options. CoRR Yi Wan, Abhishek Naik, Richard S. Sutton
2021	A*	`conf`	Average-Reward Off-Policy Policy Evaluation with Function Approximation. ICML Shangtong Zhang, Yi Wan, Richard S. Sutton, Shimon Whiteson
2021	J	`jnl`	Average-Reward Off-Policy Policy Evaluation with Function Approximation. CoRR Shangtong Zhang, Yi Wan, Richard S. Sutton, Shimon Whiteson
2021	J	`jnl`	Continual Backprop: Stochastic Gradient Descent with Persistent Randomness. CoRR Shibhansh Dohare, A. Rupam Mahmood, Richard S. Sutton
2021	J	`jnl`	Does Standard Backpropagation Forget Less Catastrophically Than Adam? CoRR Dylan R. Ashley, Sina Ghiassian, Richard S. Sutton
2021	J	`jnl`	Learning Agent State Online with Recurrent Generate-and-Test. CoRR Amir Samani, Richard S. Sutton
2021	A*	`conf`	Learning and Planning in Average-Reward Markov Decision Processes. ICML Yi Wan, Abhishek Naik, Richard S. Sutton
2021	J	`jnl`	Looking Back on the Actor-Critic Architecture. IEEE Trans. Syst. Man Cybern. Syst. Andrew G. Barto, Richard S. Sutton, Charles W. Anderson
2021	J	`jnl`	Planning with Expectation Models for Control. CoRR Katya Kudashkina, Yi Wan, Abhishek Naik, Richard S. Sutton
2021	J	`jnl`	Policy iterations for reinforcement learning problems in continuous time and space - Fundamental theory and methods. Autom. Jae Young Lee, Richard S. Sutton
2021	J	`jnl`	Reward is enough. Artif. Intell. David Silver, Satinder Singh, Doina Precup, Richard S. Sutton
2021	J	`jnl`	Scalable Online Recurrent Learning Using Columnar Neural Networks. CoRR Khurram Javed, Martha White, Richard S. Sutton
2020	A*	`conf`	Behaviour Suite for Reinforcement Learning. ICLR 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
2020	J	`jnl`	Document-editing Assistants and Model-based Reinforcement Learning as a Path to Conversational AI. CoRR Katya Kudashkina, Patrick M. Pilarski, Richard S. Sutton
2020	A*	`conf`	Fixed-Horizon Temporal Difference Methods for Stable Reinforcement Learning. AAAI Kristopher De Asis, Alan Chan, Silviu Pitis, Richard S. Sutton, Daniel Graves
2020	J	`jnl`	Inverse Policy Evaluation for Value-based Sequential Decision-making. CoRR Alan Chan, Kristopher De Asis, Richard S. Sutton
2020	J	`jnl`	Learning and Planning in Average-Reward Markov Decision Processes. CoRR Yi Wan, Abhishek Naik, Richard S. Sutton
2020	J	`jnl`	Special Issue "On Defining Artificial Intelligence" - Commentaries and Author's Response. J. Artif. Gen. Intell. 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
2020	J	`jnl`	Understanding the Pathologies of Approximate Policy Evaluation when Combined with Greedification in Reinforcement Learning. CoRR Kenny Young, Richard S. Sutton
2019	J	`jnl`	Behaviour Suite for Reinforcement Learning. CoRR 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
2019	J	`jnl`	Discounted Reinforcement Learning is Not an Optimization Problem. CoRR Abhishek Naik, Roshan Shariff, Niko Yasui, Richard S. Sutton
2019	A*	`conf`	Extending Sliding-Step Importance Weighting from Supervised Learning to Reinforcement Learning. IJCAI Tian Tian, Richard S. Sutton
2019	J	`jnl`	Fixed-Horizon Temporal Difference Methods for Stable Reinforcement Learning. CoRR Kristopher De Asis, Alan Chan, Silviu Pitis, Richard S. Sutton, Daniel Graves
2019	J	`jnl`	Learning Feature Relevance Through Step Size Adaptation in Temporal-Difference Learning. CoRR Alexandra Kearney, Vivek Veeriah, Jaden B. Travnik, Patrick M. Pilarski, Richard S. Sutton
2019	J	`jnl`	Learning Sparse Representations Incrementally in Deep Reinforcement Learning. CoRR J. Fernando Hernandez-Garcia, Richard S. Sutton
2019	A*	`conf`	Planning with Expectation Models. IJCAI Yi Wan, Muhammad Zaheer, Adam White, Martha White, Richard S. Sutton
2019	J	`jnl`	Planning with Expectation Models. CoRR Yi Wan, Muhammad Zaheer, Adam White, Martha White, Richard S. Sutton
2019	A	`conf`	Prediction in Intelligence: An Empirical Comparison of Off-policy Algorithms on Robots. AAMAS Banafsheh Rafiee, Sina Ghiassian, Adam White, Richard S. Sutton
2019	J	`jnl`	Should All Temporal Difference Learning Use Emphasis? CoRR Xiang Gu, Sina Ghiassian, Richard S. Sutton
2019	J	`jnl`	Understanding Multi-Step Deep Reinforcement Learning: A Systematic Study of the DQN Target. CoRR J. Fernando Hernandez-Garcia, Richard S. Sutton
2018	A	`conf`	Comparing Direct and Indirect Temporal-Difference Methods for Estimating the Variance of the Return. UAI Craig Sherstan, Dylan R. Ashley, Brendan Bennett, Kenny Young, Adam White, Martha White, Richard S. Sutton
2018	J	`jnl`	Directly Estimating the Variance of the λ-Return Using Temporal-Difference Methods. CoRR Craig Sherstan, Brendan Bennett, Kenny Young, Dylan R. Ashley, Adam White, Martha White, Richard S. Sutton
2018	J	`jnl`	Integrating Episodic Memory into a Reinforcement Learning Agent using Reservoir Sampling. CoRR Kenny J. Young, Richard S. Sutton, Shuo Yang
2018	A*	`conf`	Multi-Step Reinforcement Learning: A Unifying Algorithm. AAAI Kristopher De Asis, J. Fernando Hernandez-Garcia, G. Zacharias Holland, Richard S. Sutton
2018	J	`jnl`	On Generalized Bellman Equations and Temporal-Difference Learning. J. Mach. Learn. Res. Huizhen Yu, Ashique Rupam Mahmood, Richard S. Sutton
2018	J	`jnl`	Online Off-policy Prediction. CoRR Sina Ghiassian, Andrew Patterson, Martha White, Richard S. Sutton, Adam White
2018	A	`conf`	Per-decision Multi-step Temporal Difference Learning with Control Variates. UAI Kristopher De Asis, Richard S. Sutton
2018	J	`jnl`	Per-decision Multi-step Temporal Difference Learning with Control Variates. CoRR Kristopher De Asis, Richard S. Sutton
2018	J	`jnl`	Predicting Periodicity with Temporal Difference Learning. CoRR Kristopher De Asis, Brendan Bennett, Richard S. Sutton
2018	J	`jnl`	Reactive Reinforcement Learning in Asynchronous Environments. CoRR Jaden B. Travnik, Kory W. Mathewson, Richard S. Sutton, Patrick M. Pilarski
2018	J	`jnl`	Reactive Reinforcement Learning in Asynchronous Environments. Frontiers Robotics AI Jaden B. Travnik, Kory W. Mathewson, Richard S. Sutton, Patrick M. Pilarski
2018	—	`book`	Reinforcement learning - an introduction, 2nd Edition. Richard S. Sutton, Andrew G. Barto
2018	J	`jnl`	TIDBD: Adapting Temporal-difference Step-sizes Through Stochastic Meta-descent. CoRR Alexandra Kearney, Vivek Veeriah, Jaden B. Travnik, Richard S. Sutton, Patrick M. Pilarski
2018	J	`jnl`	Two geometric input transformation methods for fast online reinforcement learning with neural nets. CoRR Sina Ghiassian, Huizhen Yu, Banafsheh Rafiee, Richard S. Sutton
2017	J	`jnl`	A Deeper Look at Experience Replay. CoRR Shangtong Zhang, Richard S. Sutton
2017	J	`jnl`	A First Empirical Study of Emphatic Temporal Difference Learning. CoRR Sina Ghiassian, Banafsheh Rafiee, Richard S. Sutton
2017	J	`jnl`	Communicative Capital for Prosthetic Agents. CoRR Patrick M. Pilarski, Richard S. Sutton, Kory W. Mathewson, Craig Sherstan, Adam S. R. Parker, Ann L. Edwards
2017	—	`conf`	Crossprop: Learning Representations by Stochastic Meta-Gradient Descent in Neural Networks. ECML/PKDD (1) Vivek Veeriah, Shangtong Zhang, Richard S. Sutton
2017	A	`conf`	Forward Actor-Critic for Nonlinear Function Approximation in Reinforcement Learning. AAMAS Vivek Veeriah, Harm van Seijen, Richard S. Sutton
2017	J	`jnl`	GQ($λ$) Quick Reference and Implementation Guide. CoRR Adam White, Richard S. Sutton
2017	J	`jnl`	Integral Policy Iterations for Reinforcement Learning Problems in Continuous Time and Space. CoRR Jae Young Lee, Richard S. Sutton
2017	J	`jnl`	Multi-step Off-policy Learning Without Importance Sampling Ratios. CoRR Ashique Rupam Mahmood, Huizhen Yu, Richard S. Sutton
2017	J	`jnl`	Multi-step Reinforcement Learning: A Unifying Algorithm. CoRR Kristopher De Asis, J. Fernando Hernandez-Garcia, G. Zacharias Holland, Richard S. Sutton
2017	—	`conf`	On Generalized Bellman Equations and Temporal-Difference Learning. Canadian AI Huizhen Yu, Ashique Rupam Mahmood, Richard S. Sutton
2017	J	`jnl`	On Generalized Bellman Equations and Temporal-Difference Learning. CoRR Huizhen Yu, Ashique Rupam Mahmood, Richard S. Sutton
2016	J	`jnl`	A Batch, Off-Policy, Actor-Critic Algorithm for Optimizing the Average Reward. CoRR Susan A. Murphy, Yanzhen Deng, Eric B. Laber, Hamid Reza Maei, Richard S. Sutton, Katie Witkiewitz
2016	J	`jnl`	An Emphatic Approach to the Problem of Off-policy Temporal-Difference Learning. J. Mach. Learn. Res. Richard S. Sutton, Ashique Rupam Mahmood, Martha White
2016	J	`jnl`	Face valuing: Training user interfaces with facial expressions and reinforcement learning. CoRR Vivek Veeriah, Patrick M. Pilarski, Richard S. Sutton
2016	J	`jnl`	Learning representations through stochastic gradient descent in cross-validation error. CoRR Richard S. Sutton, Vivek Veeriah
2016	J	`jnl`	True Online Temporal-Difference Learning. J. Mach. Learn. Res. Harm van Seijen, Ashique Rupam Mahmood, Patrick M. Pilarski, Marlos C. Machado, Richard S. Sutton
2015	A*	`conf`	A Deeper Look at Planning as Learning from Replay. ICML Harm Vanseijen, Richard S. Sutton
2015	J	`jnl`	An Emphatic Approach to the Problem of Off-policy Temporal-Difference Learning. CoRR Richard S. Sutton, Ashique Rupam Mahmood, Martha White
2015	J	`jnl`	An Empirical Evaluation of True Online TD(λ). CoRR Harm van Seijen, Ashique Rupam Mahmood, Patrick M. Pilarski, Richard S. Sutton
2015	J	`jnl`	Emphatic Temporal-Difference Learning. CoRR Ashique Rupam Mahmood, Huizhen Yu, Martha White, Richard S. Sutton
2015	J	`jnl`	Learning to Predict Independent of Span. CoRR Hado van Hasselt, Richard S. Sutton
2015	A	`conf`	Off-policy learning based on weighted importance sampling with linear computational complexity. UAI Ashique Rupam Mahmood, Richard S. Sutton
2015	J	`jnl`	Temporal-Difference Networks. CoRR Richard S. Sutton, Brian Tanner
2015	J	`jnl`	True Online Emphatic TD(λ): Quick Reference and Implementation Guide. CoRR Richard S. Sutton
2015	J	`jnl`	True Online Temporal-Difference Learning. CoRR Harm van Seijen, Ashique Rupam Mahmood, Patrick M. Pilarski, Marlos C. Machado, Richard S. Sutton
2014	A*	`conf`	A new Q(lambda) with interim forward view and Monte Carlo equivalence. ICML Richard S. Sutton, Ashique Rupam Mahmood, Doina Precup, Hado van Hasselt
2014	J	`jnl`	Multi-timescale nexting in a reinforcement learning robot. Adapt. Behav. Joseph Modayil, Adam White, Richard S. Sutton
2014	A	`conf`	Off-policy TD( l) with a true online equivalence. UAI Hado van Hasselt, Ashique Rupam Mahmood, Richard S. Sutton
2014	A*	`conf`	True Online TD(lambda). ICML Harm van Seijen, Richard S. Sutton
2014	—	`conf`	Universal Option Models. NIPS Hengshuai Yao, Csaba Szepesvári, Richard S. Sutton, Joseph Modayil, Shalabh Bhatnagar
2014	—	`conf`	Weighted importance sampling for off-policy learning with linear function approximation. NIPS Ashique Rupam Mahmood, Hado van Hasselt, Richard S. Sutton
2013	J	`jnl`	Adaptive Artificial Limbs: A Real-Time Approach to Prediction and Anticipation. IEEE Robotics Autom. Mag. Patrick M. Pilarski, Michael Rory Dawson, Thomas Degris, Jason P. Carey, K. Ming Chan, Jacqueline S. Hebert, Richard S. Sutton
2013	J	`jnl`	Planning by Prioritized Sweeping with Small Backups CoRR Harm van Seijen, Richard S. Sutton
2013	—	`conf`	Planning by Prioritized Sweeping with Small Backups. ICML (3) Harm van Seijen, Richard S. Sutton
2013	—	`conf`	Position Paper: Representation Search through Generate and Test. SARA Ashique Rupam Mahmood, Richard S. Sutton
2013	—	`conf`	Real-time prediction learning for the simultaneous actuation of multiple prosthetic joints. ICORR Patrick M. Pilarski, Travis B. Dick, Richard S. Sutton
2013	—	`conf`	Representation Search through Generate and Test. AAAI Workshop: Learning Rich Representations from Low-Level Sensors Ashique Rupam Mahmood, Richard S. Sutton
2013	J	`jnl`	Temporal-Difference Learning to Assist Human Decision Making during the Control of an Artificial Limb. CoRR Ann L. Edwards, Alexandra Kearney, Michael Rory Dawson, Richard S. Sutton, Patrick M. Pilarski
2013	A*	`conf`	Temporal-Difference Search in Computer Go. ICAPS David Silver, Richard S. Sutton, Martin Müller
2012	B	`conf`	Acquiring a broad range of empirical knowledge in real time by temporal-difference learning. SMC Joseph Modayil, Adam White, Patrick M. Pilarski, Richard S. Sutton
2012	—	`conf`	Between Instruction and Reward: Human-Prompted Switching. AAAI Fall Symposium: Robots Learning Interactively from Human Teachers Patrick M. Pilarski, Richard S. Sutton
2012	J	`jnl`	Dyna-Style Planning with Linear Function Approximation and Prioritized Sweeping CoRR Richard S. Sutton, Csaba Szepesvári, Alborz Geramifard, Michael Bowling
2012	A*	`conf`	Linear Off-Policy Actor-Critic. ICML Thomas Degris, Martha White, Richard S. Sutton
2012	C	`conf`	Model-Free reinforcement learning with continuous action in practice. ACC Thomas Degris, Patrick M. Pilarski, Richard S. Sutton
2012	—	`conf`	Multi-timescale Nexting in a Reinforcement Learning Robot. SAB Joseph Modayil, Adam White, Richard S. Sutton
2012	J	`jnl`	Off-Policy Actor-Critic CoRR Thomas Degris, Martha White, Richard S. Sutton
2012	J	`jnl`	Scaling Life-long Off-policy Learning CoRR Adam White, Joseph Modayil, Richard S. Sutton
2012	—	`conf`	Scaling life-long off-policy learning. ICDL-EPIROB Adam White, Joseph Modayil, Richard S. Sutton
2012	J	`jnl`	Temporal-difference search in computer Go. Mach. Learn. David Silver, Richard S. Sutton, Martin Müller
2012	Misc	`conf`	Tuning-free step-size adaptation. ICASSP Ashique Rupam Mahmood, Richard S. Sutton, Thomas Degris, Patrick M. Pilarski
2011	B	`conf`	Beyond Reward: The Problem of Knowledge and Data. ILP Richard S. Sutton
2011	A	`conf`	Horde: a scalable real-time architecture for learning knowledge from unsupervised sensorimotor interaction. AAMAS Richard S. Sutton, Joseph Modayil, Michael Delp, Thomas Degris, Patrick M. Pilarski, Adam White, Doina Precup
2011	J	`jnl`	Multi-timescale Nexting in a Reinforcement Learning Robot CoRR Joseph Modayil, Adam White, Richard S. Sutton
2010	A*	`conf`	Toward Off-Policy Learning Control with Function Approximation. ICML Hamid Reza Maei, Csaba Szepesvári, Shalabh Bhatnagar, Richard S. Sutton
2009	—	`conf`	Convergent Temporal-Difference Learning with Arbitrary Smooth Function Approximation. NIPS Hamid Reza Maei, Csaba Szepesvári, Shalabh Bhatnagar, Doina Precup, David Silver, Richard S. Sutton
2009	A*	`conf`	Fast gradient-descent methods for temporal-difference learning with linear function approximation. ICML Richard S. Sutton, Hamid Reza Maei, Doina Precup, Shalabh Bhatnagar, David Silver, Csaba Szepesvári, Eric Wiewiora
2009	—	`conf`	Multi-Step Dyna Planning for Policy Evaluation and Control. NIPS Hengshuai Yao, Richard S. Sutton, Shalabh Bhatnagar, Diao Dongcui, Csaba Szepesvári
2009	J	`jnl`	Natural actor-critic algorithms. Autom. Shalabh Bhatnagar, Richard S. Sutton, Mohammad Ghavamzadeh, Mark Lee
2008	—	`conf`	A Convergent O(n) Temporal-difference Algorithm for Off-policy Learning with Linear Function Approximation. NIPS Richard S. Sutton, Csaba Szepesvári, Hamid Reza Maei
2008	—	`conf`	A computational model of hippocampal function in trace conditioning. NIPS Elliot A. Ludvig, Richard S. Sutton, Eric Verbeek, E. James Kehoe
2008	—	`conf`	Agent Learning using Action-Dependent Learning Rates in Computer Role-Playing Games. AIIDE Maria Cutumisu, Duane Szafron, Michael H. Bowling, Richard S. Sutton
2008	A	`conf`	Dyna-Style Planning with Linear Function Approximation and Prioritized Sweeping. UAI Richard S. Sutton, Csaba Szepesvári, Alborz Geramifard, Michael H. Bowling
2008	A*	`conf`	Sample-based learning and search with permanent and transient memories. ICML David Silver, Richard S. Sutton, Martin Müller
2008	J	`jnl`	Stimulus Representation and the Timing of Reward-Prediction Errors in Models of the Dopamine System. Neural Comput. Elliot A. Ludvig, Richard S. Sutton, E. James Kehoe
2007	—	`conf`	Incremental Natural Actor-Critic Algorithms. NIPS Shalabh Bhatnagar, Richard S. Sutton, Mohammad Ghavamzadeh, Mark Lee
2007	A*	`conf`	On the role of tracking in stationary environments. ICML Richard S. Sutton, Anna Koop, David Silver
2007	A*	`conf`	Reinforcement Learning of Local Shape in the Game of Go. IJCAI David Silver, Richard S. Sutton, Martin Müller
2006	A*	`conf`	Incremental Least-Squares Temporal Difference Learning. AAAI Alborz Geramifard, Michael H. Bowling, Richard S. Sutton
2006	—	`conf`	iLSTD: Eligibility Traces and Convergence Analysis. NIPS Alborz Geramifard, Michael H. Bowling, Martin Zinkevich, Richard S. Sutton
2005	—	`conf`	Off-policy Learning with Options and Recognizers. NIPS Doina Precup, Richard S. Sutton, Cosmin Paduraru, Anna Koop, Satinder Singh
2005	J	`jnl`	Reinforcement Learning for RoboCup Soccer Keepaway. Adapt. Behav. Peter Stone, Richard S. Sutton, Gregory Kuhlmann
2005	A*	`conf`	TD(lambda) networks: temporal-difference networks with eligibility traces. ICML Brian Tanner, Richard S. Sutton
2005	—	`conf`	Temporal Abstraction in Temporal-difference Networks. NIPS Richard S. Sutton, Eddie J. Rafols, Anna Koop
2005	A*	`conf`	Temporal-Difference Networks with History. IJCAI Brian Tanner, Richard S. Sutton
2005	A*	`conf`	Using Predictive Representations to Improve Generalization in Reinforcement Learning. IJCAI Eddie J. Rafols, Mark B. Ring, Richard S. Sutton, Brian Tanner
2004	—	`conf`	Temporal-Difference Networks. NIPS Richard S. Sutton, Brian Tanner
2002	—	`ed.`	Proceedings of the Eighteenth National Conference on Artificial Intelligence and Fourteenth Conference on Innovative Applications of Artificial Intelligence, July 28 - August 1, 2002, Edmonton, Alberta, Canada. AAAI Rina Dechter, Michael J. Kearns, Richard S. Sutton
2001	C	`conf`	Keepaway Soccer: A Machine Learning Testbed. RoboCup Peter Stone, Richard S. Sutton
2001	A*	`conf`	Off-Policy Temporal Difference Learning with Function Approximation. ICML Doina Precup, Richard S. Sutton, Sanjoy Dasgupta
2001	—	`conf`	Predictive Representations of State. NIPS Michael L. Littman, Richard S. Sutton, Satinder Singh
2001	A*	`conf`	Scaling Reinforcement Learning toward RoboCup Soccer. ICML Peter Stone, Richard S. Sutton
2000	A*	`conf`	Eligibility Traces for Off-Policy Policy Evaluation. ICML Doina Precup, Richard S. Sutton, Satinder Singh
2000	C	`conf`	Reinforcement Learning for 3 vs. 2 Keepaway RoboCup Peter Stone, Richard S. Sutton, Satinder Singh
1999	J	`jnl`	Between MDPs and Semi-MDPs: A Framework for Temporal Abstraction in Reinforcement Learning. Artif. Intell. Richard S. Sutton, Doina Precup, Satinder Singh
1999	—	`conf`	Open Theoretical Questions in Reinforcement Learning. EuroCOLT Richard S. Sutton
1999	—	`conf`	Policy Gradient Methods for Reinforcement Learning with Function Approximation. NIPS Richard S. Sutton, David A. McAllester, Satinder Singh, Yishay Mansour
1998	—	`conf`	Improved Switching among Temporally Abstract Actions. NIPS Richard S. Sutton, Satinder Singh, Doina Precup, Balaraman Ravindran
1998	A*	`conf`	Intra-Option Learning about Temporally Abstract Actions. ICML Richard S. Sutton, Doina Precup, Satinder Singh
1998	—	`conf`	Learning Instance-Independent Value Functions to Enhance Local Search. NIPS Robert Moll, Andrew G. Barto, Theodore J. Perkins, Richard S. Sutton
1998	J	`jnl`	Reinforcement Learning: An Introduction. IEEE Trans. Neural Networks Richard S. Sutton, Andrew G. Barto
1998	—	`conf`	Reinforcement Learning: Past, Present and Future. SEAL Richard S. Sutton
1998	—	`book`	Reinforcement learning - an introduction. Richard S. Sutton, Andrew G. Barto
1998	—	`conf`	Theoretical Results on Reinforcement Learning with Temporally Abstract Options. ECML Doina Precup, Richard S. Sutton, Satinder Singh
1997	J	`jnl`	Experiments with Reinforcement Learning in Problems with Continuous State and Action Spaces. Adapt. Behav. Juan Carlos Santamaría, Richard S. Sutton, Ashwin Ram
1997	A*	`conf`	Exponentiated Gradient Methods for Reinforcement Learning. ICML Doina Precup, Richard S. Sutton
1997	—	`conf`	Multi-time Models for Temporally Abstract Planning. NIPS Doina Precup, Richard S. Sutton
1997	C	`conf`	On the Significance of Markov Decision Processes. ICANN Richard S. Sutton
1996	J	`jnl`	Reinforcement Learning with Replacing Eligibility Traces. Mach. Learn. Satinder P. Singh, Richard S. Sutton
1995	—	`conf`	Generalization in Reinforcement Learning: Successful Examples Using Sparse Coarse Coding. NIPS Richard S. Sutton
1995	A*	`conf`	TD Models: Modeling the World at a Mixture of Time Scales. ICML Richard S. Sutton
1993	A*	`conf`	Online Learning with Random Representations. ICML Richard S. Sutton, Steven D. Whitehead
1992	A*	`conf`	Adapting Bias by Gradient Descent: An Incremental Version of Delta-Bar-Delta. AAAI Richard S. Sutton
1991	J	`jnl`	Dyna, an Integrated Architecture for Learning, Planning, and Reacting. SIGART Bull. Richard S. Sutton
1991	—	`conf`	Iterative Construction of Sparse Polynomial Approximations. NIPS Terence D. Sanger, Richard S. Sutton, Christopher J. Matheus
1991	—	`conf`	Learning Polynomial Functions by Feature Construction. ML Richard S. Sutton, Christopher J. Matheus
1991	—	`conf`	Planning by Incremental Dynamic Programming. ML Richard S. Sutton
1990	—	`conf`	Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming. ML Richard S. Sutton
1990	—	`conf`	Integrated Modeling and Control Based on Reinforcement Learning. NIPS Richard S. Sutton
1989	—	`conf`	Sequential Decision Probelms and Neural Networks. NIPS Andrew G. Barto, Richard S. Sutton, Christopher J. C. H. Watkins
1988	J	`jnl`	Learning to Predict by the Methods of Temporal Differences. Mach. Learn. Richard S. Sutton
1985	A*	`conf`	Training and Tracking in Robotics. IJCAI Oliver G. Selfridge, Richard S. Sutton, Andrew G. Barto
1983	J	`jnl`	Neuronlike adaptive elements that can solve difficult learning control problems. IEEE Trans. Syst. Man Cybern. Andrew G. Barto, Richard S. Sutton, Charles W. Anderson