R. Sutton

R. S. Sutton, J. P. Carey, P. M. Pilarski, 2012, 2012 4th IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob).

Toward Efficient Gradient-Based Value Estimation

R. Sutton, Arsalan Sharifnassab, 2023, ICML.

A History of Meta-gradient: Gradient Methods for Meta-learning

R. Sutton, 2022, ArXiv.

Scaling life-long off-policy learning

Richard S. Sutton, Adam White, Joseph Modayil, 2012, 2012 IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL).

Planning by Prioritized Sweeping with Small Backups pdf

Richard S. Sutton, Harm van Seijen, R. Sutton, 2013, ICML.

Multi-step Off-policy Learning Without Importance Sampling Ratios pdf

Richard S. Sutton, Huizhen Yu, Ashique Rupam Mahmood, 2017, ArXiv.

Understanding the Pathologies of Approximate Policy Evaluation when Combined with Greedification in Reinforcement Learning pdf

Richard S. Sutton, Kenny J. Young, Kenny Young, 2020, ArXiv.

Fixed-Horizon Temporal Difference Methods for Stable Reinforcement Learning

Kristopher De Asis, R. Sutton, D. Graves, 2019, AAAI.

Per-decision Multi-step Temporal Difference Learning with Control Variates pdf

Richard S. Sutton, Kristopher De Asis, R. Sutton, 2018, UAI.

Universal Option Models

Shalabh Bhatnagar, Richard S. Sutton, Csaba Szepesvári, 2014, NIPS.

Between MDPs and semi-MDPs: A framework for temporal abstraction in reinforcement learning

R. Sutton, Satinder Singh, Doina Precup, 1999 .

Value-aware Importance Weighting for Off-policy Reinforcement Learning

Kristopher De Asis, R. Sutton, Eric Graves, 2023, arXiv.org.

An Empirical Comparison of Off-policy Prediction Learning Algorithms in the Four Rooms Environment pdf

Richard S. Sutton, Sina Ghiassian, R. Sutton, 2021, ArXiv.

An Empirical Comparison of Off-policy Prediction Learning Algorithms on the Collision Task

R. Sutton, Sina Ghiassian, 2021, ArXiv.

Planning with Expectation Models

Martha White, Yi Wan, Richard S. Sutton, 2019, IJCAI.

Online Off-policy Prediction pdf

Martha White, Richard S. Sutton, Andrew Patterson, 2018, ArXiv.

Predicting Periodicity with Temporal Difference Learning pdf

Richard S. Sutton, Brendan Bennett, Kristopher De Asis, 2018, ArXiv.

Acquiring a broad range of empirical knowledge in real time by temporal-difference learning

Patrick M. Pilarski, Richard S. Sutton, Adam White, 2012, 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

Multi-timescale nexting in a reinforcement learning robot

R. Sutton, Joseph Modayil, Adam White, 2011, Adapt. Behav..

Beyond Reward: The Problem of Knowledge and Data

Richard S. Sutton, R. Sutton, 2011, ILP.

TD(λ) networks: temporal-difference networks with eligibility traces

Richard S. Sutton, Brian Tanner, R. Sutton, 2005, ICML.

Acquiring Diverse Predictive Knowledge in Real Time by Temporal-difference Learning

R. Sutton, Joseph Modayil, P. Pilarski, 2012 .

Reinforcement Learning Algorithms in Markov Decision Processes AAAI-10 Tutorial Part IV: Take home message

Doina Precup, Richard S. Sutton, Csaba Szepesvári, 2010 .

Reinforcement Learning Algorithms in Markov Decision Processes AAAI-10 Tutorial Part II: Learning to predict values

Doina Precup, Richard S. Sutton, Csaba Szepesvári, 2010 .

The PEAK Project

R. Sutton, 2007 .

Improved Switching among Temporally Abstract Actions

R. Sutton, Satinder Singh, Doina Precup, 1998 .

GQ($\lambda$) Quick Reference and Implementation Guide

Richard S. Sutton, Adam White, R. Sutton, 2017, 1705.03967.

True Online Emphatic TD(λ): Quick Reference and Implementation Guide pdf

Richard S. Sutton, R. Sutton, 2015, ArXiv.

True Online Emphatic TD($\lambda$): Quick Reference and Implementation Guide

Richard S. Sutton, R. Sutton, 2015 .

An Empirical Evaluation of True Online TD({\lambda})

Patrick M. Pilarski, Richard S. Sutton, Harm van Seijen, 2015, 1507.00353.

A new Q(lambda) with interim forward view and Monte Carlo equivalence

Doina Precup, Richard S. Sutton, Hado van Hasselt, 2014, ICML.

An Empirical Evaluation of True Online TD(λ) pdf

Patrick M. Pilarski, Richard S. Sutton, Harm van Seijen, 2015, ArXiv.

Loss of Plasticity in Deep Continual Learning

R. Sutton, A. Mahmood, Shibhansh Dohare, 2023 .

Scalable Real-Time Recurrent Learning Using Columnar-Constructive Networks

R. Sutton, Martha White, Haseeb Shah, 2023 .

Directly Estimating the Variance of the λ-Return Using Temporal-Difference Methods pdf

Martha White, Richard S. Sutton, Adam White, 2018, ArXiv.

Inverse Policy Evaluation for Value-based Sequential Decision-making pdf

Richard S. Sutton, Alan Chan, Kristopher De Asis, 2020, ArXiv.

Model-Free reinforcement learning with continuous action in practice

R. Sutton, T. Degris, P. Pilarski, 2012, American Control Conference.

Multi-timescale nexting in a reinforcement learning robot

R. Sutton, Joseph Modayil, Adam White, 2011, Adapt. Behav..

Online human training of a myoelectric prosthesis controller via actor-critic reinforcement learning

M. R. Dawson, R. Sutton, T. Degris, 2011, 2011 IEEE International Conference on Rehabilitation Robotics.

Incremental Least-Squares Temporal Difference Learning

Alborz Geramifard, Michael H. Bowling, Richard S. Sutton, 2006, AAAI.

Scalable Real-Time Recurrent Learning Using Columnar-Constructive Networks

R. Sutton, Martha White, Haseeb Shah, 2023, J. Mach. Learn. Res..

Reward-Respecting Subtasks for Model-Based Reinforcement Learning

Marlos C. Machado, R. Sutton, Adam White, 2022, Artif. Intell..

Some Recent Applications of Reinforcement Learning

R. S. Sutton, P. S. Thomas, R. Sutton, 2017 .

Timing in trace conditioning of the nictitating membrane response of the rabbit (Oryctolagus cuniculus): scalar, nonscalar, and adaptive features.

Elliot A. Ludvig, R. Sutton, E. Kehoe, 2010, Learning & memory.

Temporal abstraction in reinforcement learning

R. Sutton, Doina Precup, 2000, ICML 2000.

Integrating Episodic Memory into a Reinforcement Learning Agent using Reservoir Sampling pdf

Shuo Yang, Richard S. Sutton, Kenny J. Young, 2018, ArXiv.

Reinforcement Learning Architectures

R. Sutton, 1992 .