R. Sutton

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Policy Gradient Methods for Reinforcement Learning with Function Approximation

Yishay Mansour, Richard S. Sutton, David A. McAllester, 1999, NIPS.

Convergent Temporal-Difference Learning with Arbitrary Smooth Function Approximation

Shalabh Bhatnagar, Doina Precup, Richard S. Sutton, 2009, NIPS.

A convergent O ( n ) algorithm for off-policy temporal-difference learning with linear function approximation

R. Sutton, H. Maei, Csaba Szepesvari, 2008, NIPS 2008.

Neuronlike adaptive elements that can solve difficult learning control problems

Richard S. Sutton, Andrew G. Barto, Charles W. Anderson, 1983, IEEE Transactions on Systems, Man, and Cybernetics.

Simulation of anticipatory responses in classical conditioning by a neuron-like adaptive element

R. Sutton, A. Barto, 1982, Behavioural Brain Research.

Horde: a scalable real-time architecture for learning knowledge from unsupervised sensorimotor interaction

Patrick M. Pilarski, Doina Precup, Richard S. Sutton, 2011, AAMAS.

GQ(λ): A general gradient algorithm for temporal-difference prediction learning with eligibility traces

R. Sutton, H. Maei, 2010 .

Temporal credit assignment in reinforcement learning

Richard S. Sutton, R. Sutton, 1984 .

Sequential Decision Problems and Neural Networks

Richard S. Sutton, Andrew G. Barto, Chris Watkins, 1989, NIPS 1989.

Gain Adaptation Beats Least Squares

R. Sutton, 2006 .

Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming

Richard S. Sutton, R. Sutton, 1990, ML.

Planning by Incremental Dynamic Programming

Richard S. Sutton, R. Sutton, 1991, ML.

Toward a modern theory of adaptive networks: expectation and prediction.

A G Barto, R S Sutton, R. Sutton, 1981, Psychological review.

Training and Tracking in Robotics

Richard S. Sutton, Andrew G. Barto, Oliver G. Selfridge, 1985, IJCAI.

Gradient temporal-difference learning algorithms

R. Sutton, H. Maei, 2011 .

Connectionist Learning for Control: An Overview

R. Sutton, O. Selfridge, 1989 .

Dyna, an integrated architecture for learning, planning, and reacting

Richard S. Sutton, R. Sutton, 1990, SGAR.

Learning and Sequential Decision Making

Richard S. Sutton, Andrew G. Barto, Chris Watkins, 1989 .

Reinforcement Learning is Direct Adaptive Optimal Control

Richard S. Sutton, Andrew G. Barto, Ronald J. Williams, 1992, 1991 American Control Conference.

Time-Derivative Models of Pavlovian Reinforcement

Richard S. Sutton, Andrew G. Barto, R. Sutton, 1990 .

Simulation of the classically conditioned nictitating membrane response by a neuron-like adaptive element: Response topography, neuronal firing, and interstimulus intervals

R. Sutton, A. Barto, JOHN W. Moore, 1986, Behavioural Brain Research.

Goal Seeking Components for Adaptive Intelligence: An Initial Assessment.

Richard S. Sutton, Andrew G. Barto, R. Sutton, 1981 .

Reinforcement Learning: An Introduction

Richard S. Sutton, Andrew G. Barto, R. Sutton, 2005, IEEE Transactions on Neural Networks.

Synthesis of nonlinear control surfaces by a layered associative search network

R. Sutton, A. Barto, C. Anderson, 2004, Biological Cybernetics.

On The Virtues of Linear Learning and Trajectory Distributions

R. Sutton, 2007 .

Learning to Predict by the Methods of Temporal Differences

Richard S. Sutton, R. Sutton, 1988, Machine Learning.

Introduction: The challenge of reinforcement learning

R. Sutton, 2004, Machine Learning.

Reinforcement learning with replacing eligibility traces

Richard S. Sutton, Satinder P. Singh, R. Sutton, 2004, Machine Learning.

Fast gradient-descent methods for temporal-difference learning with linear function approximation

Shalabh Bhatnagar, Doina Precup, Richard S. Sutton, 2009, ICML '09.

Off-policy Learning with Recognizers

R. Sutton, Doina Precup, Cosmin Paduraru, 2000 .

Off-Policy Actor-Critic pdf

Martha White, Richard S. Sutton, Thomas Degris, 2012, ICML 2012.

Intra-Option Learning about Temporally Abstract Actions

Doina Precup, Richard S. Sutton, Satinder P. Singh, 1998, ICML.

A Deeper Look at Experience Replay pdf

Richard S. Sutton, Shangtong Zhang, R. Sutton, 2017, ArXiv.

Roles of Macro-Actions in Accelerating Reinforcement Learning

Richard S. Sutton, Andrew H. Fagg, Amy McGovern, 1998 .

Temporal abstraction in reinforcement learning

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

Connectionist Learning for Control

Richard S. Sutton, W. Thomas Miller, Paul J. Werbos, 1995 .

Connectionist Learning Control at GTE Laboratories

Richard S. Sutton, Oliver G. Selfridge, Charles W. Anderson, 1990, Other Conferences.

Actor-critic Algorithms 1. Policy Gradient Methods for Reinforcement Learning with Function Average Reward Td Actor-critic Algorithm Using Func- Tion Approximation

R. Sutton, Y. Mansour, Satinder Singh, .

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

R. Sutton, A. Mahmood, 2014 .

Challenging Control Problems

Richard S. Sutton, W. Thomas Miller, Paul J. Werbos, 1995 .

Off-Policy Temporal Difference Learning with Function Approximation

Sanjoy Dasgupta, Doina Precup, Richard S. Sutton, 2001, ICML.

Dimensions of Reinforcement Learning

Richard S. Sutton, Andrew G. Barto, R. Sutton, 1998 .

Incremental Natural Actor-Critic Algorithms

Shalabh Bhatnagar, Mark Lee, Richard S. Sutton, 2007, NIPS.

Between MDPs and Semi-MDPs: A Framework for Temporal Abstraction in Reinforcement Learning

Doina Precup, Richard S. Sutton, Satinder P. Singh, 1999, Artif. Intell..

Macro-Actions in Reinforcement Learning: An Empirical Analysis

R. Sutton, A. McGovern, 1998 .

Associative search network: A reinforcement learning associative memory

Richard S. Sutton, Andrew G. Barto, Peter S. Brouwer, 1981, Biological Cybernetics.

Model-Free reinforcement learning with continuous action in practice

Patrick M. Pilarski, Richard S. Sutton, Thomas Degris, 2012, 2012 American Control Conference (ACC).

A Menu of Designs for Reinforcement Learning Over Time

Richard S. Sutton, W. Thomas Miller, Paul J. Werbos, 1995 .

Generalization in Reinforcement Learning: Successful Examples Using Sparse Coarse Coding

Richard S. Sutton, R. Sutton, 1995, NIPS.

Reinforcement Learning with Replacing Eligibility Traces

Richard S. Sutton, Satinder P. Singh, R. Sutton, 1996, Machine Learning.

Eligibility Traces for Off-Policy Policy Evaluation

Doina Precup, Richard S. Sutton, Satinder P. Singh, 2000, ICML.

Reinforcement Learning of Local Shape in the Game of Go

Richard S. Sutton, David Silver, Martin Müller, 2007, IJCAI.

Reinforcement Learning

Richard S. Sutton, R. Sutton, 1992, Handbook of Machine Learning.

Toward Off-Policy Learning Control with Function Approximation

Shalabh Bhatnagar, Richard S. Sutton, Csaba Szepesvári, 2010, ICML.

Experiments with Reinforcement Learning in Problems with Continuous State and Action Spaces

Ashwin Ram, Richard S. Sutton, Juan Carlos Santamaría, 1997, Adapt. Behav..

Adapting Bias by Gradient Descent: An Incremental Version of Delta-Bar-Delta

Richard S. Sutton, R. Sutton, 1992, AAAI.

Multi-time Models for Temporally Abstract Planning

Doina Precup, Richard S. Sutton, R. Sutton, 1997, NIPS.

Reinforcement Learning for RoboCup Soccer Keepaway

Peter Stone, Richard S. Sutton, Gregory Kuhlmann, 2005, Adapt. Behav..

Surprise and Curiosity for Big Data Robotics

R. Sutton, Joseph Modayil, Adam White, 2014 .

Online Learning with Random Representations

Richard S. Sutton, Steven D. Whitehead, R. Sutton, 1993, ICML.

Improved Switching among Temporally Abstract Actions

Balaraman Ravindran, Doina Precup, Richard S. Sutton, 1998, NIPS.

Temporal-difference search in computer Go

Richard S. Sutton, David Silver, Martin Müller, 2012, Machine Learning.

A Batch, Off-Policy, Actor-Critic Algorithm for Optimizing the Average Reward pdf

Susan A. Murphy, Richard S. Sutton, Hamid Reza Maei, 2016, ArXiv.

Theoretical Results on Reinforcement Learning with Temporally Abstract Behaviors

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

Application of connectionist learning methods to manufacturing process monitoring

Richard S. Sutton, Charles W. Anderson, Judy A. Franklin, 1988, Proceedings IEEE International Symposium on Intelligent Control 1988.

Open Theoretical Questions in Reinforcement Learning

Richard S. Sutton, R. Sutton, 1999, EuroCOLT.

A Convergent O(n) Temporal-difference Algorithm for Off-policy Learning with Linear Function Approximation

Richard S. Sutton, Csaba Szepesvári, Hamid Reza Maei, 2008, NIPS.

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

Farbod Fahimi, Patrick M. Pilarski, Richard S. Sutton, 2011, 2011 IEEE International Conference on Rehabilitation Robotics.

Landmark learning: An illustration of associative search

Richard S. Sutton, Andrew G. Barto, R. Sutton, 1981, Biological Cybernetics.

Planning with Closed Loop Macro Actions

Doina Precup, Richard S. Sutton, Satinder Singh, 2008 .

Weighted importance sampling for off-policy learning with linear function approximation

Richard S. Sutton, Hado van Hasselt, Ashique Rupam Mahmood, 2014, NIPS.

Predictive Representations of State

Richard S. Sutton, Michael L. Littman, Satinder P. Singh, 2001, NIPS.

Reinforcement learning in board games

R. Sutton, G. Tesauro, A. Samuel, 2004 .

Iterative Construction of Sparse Polynomial Approximations

Richard S. Sutton, Terence D. Sanger, Christopher J. Matheus, 1991, NIPS.

Communicative Capital for Prosthetic Agents pdf

Patrick M. Pilarski, Richard S. Sutton, Craig Sherstan, 2017, ArXiv.

Temporal-Difference Learning to Assist Human Decision Making during the Control of an Artificial Limb pdf

Patrick M. Pilarski, Richard S. Sutton, Michael Rory Dawson, 2013, ArXiv.

iLSTD: Eligibility Traces and Convergence Analysis

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

The Reinforcement Learning Problem

Richard S. Sutton, Andrew G. Barto, R. Sutton, 1998 .

Representation Search through Generate and Test

Richard S. Sutton, Ashique Rupam Mahmood, R. Sutton, 2013, AAAI Workshop: Learning Rich Representations from Low-Level Sensors.

On the role of tracking in stationary environments

Richard S. Sutton, David Silver, Anna Koop, 2007, ICML '07.

Adaptive State Representation and Estimation Using Recurrent Connectionist Networks

Richard S. Sutton, W. Thomas Miller, Paul J. Werbos, 1995 .

Using Predictive Representations to Improve Generalization in Reinforcement Learning

Richard S. Sutton, Brian Tanner, Eddie J. Rafols, 2005, IJCAI.

On the Significance of Markov Decision Processes

Richard S. Sutton, R. Sutton, 1997, ICANN.

An Adaptive Sensorimotor Network Inspired by the Anatomy and Physiology of the Cerebellum

Richard S. Sutton, W. Thomas Miller, Paul J. Werbos, 1995 .

Keepaway Soccer: A Machine Learning Testbed

Peter Stone, Richard S. Sutton, R. Sutton, 2001, RoboCup.

Learning Polynomial Functions by Feature Construction

Richard S. Sutton, Christopher J. Matheus, R. Sutton, 1991, ML.

TD Models: Modeling the World at a Mixture of Time Scales

Richard S. Sutton, R. Sutton, 1995, ICML.

Pavlovian Control of Intraspinal Microstimulation to Produce Over-Ground Walking

Richard S. Sutton, Ashley N Dalrymple, David A Roszko, 2019 .

Learning to Predict Independent of Span pdf

Richard S. Sutton, Hado van Hasselt, R. Sutton, 2015, ArXiv.

Adaptive artificial limbs: a real-time approach to prediction and anticipation

Patrick M. Pilarski, Richard S. Sutton, Thomas Degris, 2013, IEEE Robotics & Automation Magazine.

Stimulus Representation and the Timing of Reward-Prediction Errors in Models of the Dopamine System

Richard S. Sutton, E. James Kehoe, Elliot A. Ludvig, 2008, Neural Computation.

Investigating Experience: Temporal Coherence and Empirical Knowledge Representation

R. Sutton, M. Spetch, 2007 .

The Grand Challenge of Predictive Empirical Abstract Knowledge

R. Sutton, 2009 .

Policy iterations for reinforcement learning problems in continuous time and space - Fundamental theory and methods

Richard S. Sutton, Jaeyoung Lee, R. Sutton, 2020, Autom..

Policy Iteration for Discounted Reinforcement Learning Problems in Continuous Time and Space

Jae Young Lee, R. Sutton, 2017 .

Forward Actor-Critic for Nonlinear Function Approximation in Reinforcement Learning

Richard S. Sutton, Harm van Seijen, Vivek Veeriah, 2017, AAMAS.

Associative Learning from Replayed Experience

Richard S. Sutton, E. James Kehoe, Elliot Andrew Ludvig, 2017 .

Evaluating the TD model of classical conditioning

Elliot A. Ludvig, R. Sutton, E. Kehoe, 2012, Learning & behavior.

Scaling Reinforcement Learning toward RoboCup Soccer

Peter Stone, Richard S. Sutton, R. Sutton, 2001, ICML.

Neural networks for control

Richard S. Sutton, W. Thomas Miller, Paul J. Werbos, 1990 .

Looking Back on the Actor–Critic Architecture

Richard S. Sutton, Andrew G. Barto, Charles W. Anderson, 2021, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

Learning Instance-Independent Value Functions to Enhance Local Search

Richard S. Sutton, Andrew G. Barto, Theodore J. Perkins, 1998, NIPS.

An Emphatic Approach to the Problem of Off-policy Temporal-Difference Learning pdf

Martha White, Richard S. Sutton, Ashique Rupam Mahmood, 2015, J. Mach. Learn. Res..

Behaviour Suite for Reinforcement Learning pdf

Tor Lattimore, Richard S. Sutton, Csaba Szepesvári, 2019, ICLR.

Computational Schemes and Neural Network Models for Formation and Control of Multijoint Arm Trajectory

Richard S. Sutton, W. Thomas Miller, Paul J. Werbos, 1995 .

Introduction: The Challenge of Reinforcement Learning

R. Sutton, 1992 .

On the Signi � cance of Markov Decision Processes

R. Sutton, 1997 .

Emphatic Temporal-Difference Learning pdf

Martha White, Richard S. Sutton, Huizhen Yu, 2015, ArXiv.

Theoretical Results on Reinforcement Learning with Temporally Abstract Options

Doina Precup, Richard S. Sutton, Satinder P. Singh, 1998, ECML.

The Truck Backer-Upper: An Example of Self-Learning in Neural Networks

Richard S. Sutton, W. Thomas Miller, Paul J. Werbos, 1995 .

Reinforcement learning architectures for animats

Richard S. Sutton, R. Sutton, 1991 .

True Online Temporal-Difference Learning pdf

Patrick M. Pilarski, Marlos C. Machado, Richard S. Sutton, 2015, J. Mach. Learn. Res..

Reinforcement learning is direct adaptive optimal control

R.J. Williams, R.S. Sutton, A.G. Barto, 1991, IEEE Control Systems.

Model-Based Reinforcement Learning with an Approximate, Learned Model

Richard S. Sutton, Leonid Kuvayev, R. Sutton, 1996 .

A Unified View

Richard S. Sutton, Andrew G. Barto, R. Sutton, 1998 .

Planning and Learning

Richard S. Sutton, Andrew G. Barto, R. Sutton, 1998 .

Multi-timescale nexting in a reinforcement learning robot

Richard S. Sutton, Adam White, Joseph Modayil, 2011, Adapt. Behav..

Temporal Abstraction in Temporal-difference Networks

Richard S. Sutton, Anna Koop, Eddie J. Rafols, 2005, NIPS.

Reactive Reinforcement Learning in Asynchronous Environments

Patrick M. Pilarski, Richard S. Sutton, Kory Wallace Mathewson, 2018, Front. Robot. AI.

Reinforcement Learning for 3 vs. 2 Keepaway

Peter Stone, Richard S. Sutton, Satinder P. Singh, 2000, RoboCup.

Dyna-Style Planning with Linear Function Approximation and Prioritized Sweeping

Alborz Geramifard, Michael H. Bowling, Richard S. Sutton, 2008, UAI.

A Bioreactor Benchmark for Adaptive Network-based Process Control

Richard S. Sutton, W. Thomas Miller, Paul J. Werbos, 1995 .

A Deeper Look at Planning as Learning from Replay

Rich Sutton, Harm Vanseijen, R. Sutton, 2015, ICML.

Off-Policy Knowledge Maintenance for Robots

R. Sutton, Joseph Modayil, T. Degris, 2010 .

Real-time prediction learning for the simultaneous actuation of multiple prosthetic joints

Patrick M. Pilarski, Richard S. Sutton, Travis B. Dick, 2013, 2013 IEEE 13th International Conference on Rehabilitation Robotics (ICORR).

Multi-step Reinforcement Learning: A Unifying Algorithm

Richard S. Sutton, J. Fernando Hernandez-Garcia, G. Zacharias Holland, 2017, AAAI.

Prediction Driven Behavior: Learning Predictions that Drive Fixed Responses

Richard S. Sutton, Joseph Modayil, R. Sutton, 2014, AAAI 2014.

Agent Learning using Action-Dependent Learning Rates in Computer Role-Playing Games

Duane Szafron, Michael H. Bowling, Richard S. Sutton, 2008, AIIDE.

Special Issue “On Defining Artificial Intelligence”—Commentaries and Author’s Response

Raúl Rojas, Aaron Sloman, Shane Legg, 2020, J. Artif. Gen. Intell..

Reinforcement Learning: Past, Present and Future

Richard S. Sutton, R. Sutton, 1998, SEAL.

Advances in reinforcement learning and their implications for intelligent control

Richard S. Sutton, Steven D. Whitehead, D. H. Ballard, 1990, Proceedings. 5th IEEE International Symposium on Intelligent Control 1990.

Face valuing: Training user interfaces with facial expressions and reinforcement learning pdf

Patrick M. Pilarski, Richard S. Sutton, Vivek Veeriah, 2016, ArXiv.

A computational model of hippocampal function in trace conditioning

Richard S. Sutton, Eric Verbeek, E. James Kehoe, 2008, NIPS.

Document-editing Assistants and Model-based Reinforcement Learning as a Path to Conversational AI pdf

Patrick M. Pilarski, Richard S. Sutton, Katya Kudashkina, 2020, ArXiv.

Multi-timescale Nexting in a Reinforcement Learning Robot

Richard S. Sutton, Adam White, Joseph Modayil, 2012, SAB.

Learning to predict by the methods of temporal differences

Richard S. Sutton, R. Sutton, 2004, Machine Learning.

Discounted Reinforcement Learning is Not an Optimization Problem pdf

Richard S. Sutton, Roshan Shariff, Niko Yasui, 2019, ArXiv.

Integral Policy Iterations for Reinforcement Learning Problems in Continuous Time and Space pdf

Jae Young Lee, Richard S. Sutton, Jae Young Lee, 2017, ArXiv.

Oryctolagus cuniculus membrane response of the rabbit ( Timing and cue competition in conditioning of the nictitating

Richard S. Sutton, E. James Kehoe, Elliot Andrew Ludvig, 2013 .

Magnitude and timing of nictitating membrane movements during classical conditioning of the rabbit (Oryctolagus cuniculus).

Elliot A. Ludvig, R. Sutton, E. Kehoe, 2008, Behavioral neuroscience.

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.

Scalar timing varies with response magnitude in classical conditioning of the nictitating membrane response of the rabbit (Oryctolagus cuniculus).

Kirk N. Olsen, Elliot A. Ludvig, R. Sutton, 2009, Behavioral neuroscience.

Empirical Comparison of Gradient Descent and Exponentiated Gradient Descent in Supervised and Reinforcement Learning

R. Sutton, Doina Precup, 1996 .

Artificial Intelligence as a Control Problem: Comments on the Relationship between Machine Learning and Intelligent Control

R. Sutton, .

Reinforcement and Local Searc: A Case Study TITLE2:

R. Sutton, A. Barto, T. Perkins, 1997 .

Natural Actor – Crit ic Algorithms

R. Sutton, S. Bhatnagar, M. Ghavamzadeh, 2009 .

Natural-Gradient Actor-Critic Algorithms

R. Sutton, S. Bhatnagar, M. Ghavamzadeh, 2007 .

Research Grant Renewal Proposal Reinforcement Learning and Artificial Intelligence chair :

R. Sutton, 2007 .

iCORE Research Grant Proposal Reinforcement Learning and Artificial Intelligence

R. Sutton, 2003 .

5.2. Improvement through Adding New Learning Methods 19 0

R. Sutton, R. Yee, S. Saxena, 2007 .

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

Elliot A. Ludvig, R. Sutton, Adam White, 2020 .

Model-based Reinforcement Learning

R. Sutton, Leonid Kuvayev, 2007 .

Summary of Proposal for Public Release

R. Sutton, 2004 .

Reinforcement Learning in Artificial Intelligence

R. Sutton, A. Barto, 1997 .

Opening remarks 9 : 15-9 : 45 Active Sequential Estimation of Object Dynamics with Tactile Sensory Feedback -

R. Sutton, T. Degris, P. Pilarski, 2010 .

Title of Thesis: Reinforcement Learning in Environments with Independent Delayed-sense Dynamics Reinforcement Learning in Environments with Independent Delayed-sense Dynamics

Sirish L. Shah, R. Sutton, Martin Jägersand, 2008 .

Timing and cue competition in conditioning of the nictitating membrane response of the rabbit (Oryctolagus cuniculus).

Elliot A. Ludvig, R. Sutton, E. Kehoe, 2013, Learning & memory.

Category : Reinforcement Learning and Control ; ORAL presentation Improved Switching among Temporally Abstract Actions

R. Sutton, 1999 .

On the Signiicance of Markov Decision Processes

R. Sutton, 1997 .

Appeared in Proceedings of the Seventh Yale Workshop on Adaptive and Learning Systems pp Gain Adaptation Beats Least Squares

R. Sutton, 2004 .

Prediction problems inspired by animal learning

Elliot A. Ludvig, R. Sutton, Adam White, 2022 .

cient Planning in MDPs by Small Backups

R. Sutton, 2013 .

E � cient Planning in MDPs by Small Backups

R. Sutton, H. V. Seijen, 2013 .

Iterations for Reinforcement Learning Problems in Continuous Time and Space ?

Jae Young Lee, R. Sutton, 2018 .

Model-based Reinforcement Learning with Non-linear Expectation Models and Stochastic Environments

R. Sutton, Martha White, Yi Wan, 2018 .

Magnitude and timing of conditioned responses in delay and trace classical conditioning of the nictitating membrane response of the rabbit (Oryctolagus cuniculus).

Elliot A. Ludvig, R. Sutton, E. Kehoe, 2009, Behavioral neuroscience.

Online Representation Search and Its Interactions with Unsupervised Learning

R. Sutton, A. Mahmood, 2012 .

Reinforcement Learning for 3 vs. 2 Keepaway

R. Sutton, Satinder Singh, P. Stone, 2000, RoboCup.

True Online TD ( λ ) Harm

R. Sutton, van Seijen, 2014 .

Off-policy Learning with Options and Recognizers

Doina Precup, Richard S. Sutton, Satinder P. Singh, 2005, NIPS.

Continual Backprop: Stochastic Gradient Descent with Persistent Randomness

R. Sutton, A. Mahmood, Shibhansh Dohare, 2021, ArXiv.

Time course of the rabbit's conditioned nictitating membrane movements during acquisition, extinction, and reacquisition.

Elliot A. Ludvig, R. Sutton, E. Kehoe, 2014, Learning & memory.

movements during acquisition, extinction, and reacquisition Time course of the rabbit's conditioned nictitating membrane

Elliot A. Ludvig, R. Sutton, E. Kehoe, 2014 .

Using Associative Content- Addressable Memories to Control Robots

Richard S. Sutton, W. Thomas Miller, Paul J. Werbos, 1995 .

Off-policy Learning with Recognizers

R. Sutton, Doina Precup, Cosmin Paduraru, 2000 .

Between MDPs and Semi-MDPs : Learning , Planning , and Representing Knowledge at Multiple Temporal Scales

R. Sutton, 1998 .

Recent Advances in Numerical Techniques for Large-Scale Optimization

Richard S. Sutton, W. Thomas Miller, Paul J. Werbos, 1995 .

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

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

The PEAK Project

R. Sutton, 2007 .

Incremental Policy Gradients for Online Reinforcement Learning Control

Richard S. Sutton, Yi Wan, Alan Chan, 2020 .

GQ(lambda): A general gradient algorithm for temporal-difference prediction learning with eligibility traces

Richard S. Sutton, Hamid Reza Maei, R. Sutton, 2010, AGI 2010.

Continual Backprop: Stochastic Gradient Descent with Persistent Randomness

R. Sutton, A. Mahmood, Shibhansh Dohare, 2021, ArXiv.

Prediction and Anticipation for Adaptive Artificial Limbs

Patrick M. Pilarski, Richard S. Sutton, Thomas Degris, 2012 .

Learning and Planning in Average-Reward Markov Decision Processes

Richard S. Sutton, Abhishek Naik, Yi Wan, 2020, ICML.

Vision-Based Robot Motion Planning

Richard S. Sutton, W. Thomas Miller, Paul J. Werbos, 1995 .

Intelligent Control for Multiple Autonomous Undersea Vehicles

Richard S. Sutton, W. Thomas Miller, Paul J. Werbos, 1995 .

Toward Discovering Options that Achieve Faster Planning

R. Sutton, Yi Wan, 2022, ArXiv.

True Online TD(lambda)

Richard S. Sutton, Harm van Seijen, R. Sutton, 2014, ICML.

Learning to predict by the methods of temporal differences

R. Sutton, 2004, Machine Learning.

Discounted Reinforcement Learning is Not an Optimization Problem

R. Sutton, R. Shariff, Abhishek Naik, 2019, ArXiv.

A Summary Comparison of CMAC Neural Network and Traditional Adaptive Control Systems

Richard S. Sutton, W. Thomas Miller, Paul J. Werbos, 1995 .