Richard S. Sutton

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Temporal abstraction in reinforcement learning

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

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

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

Reinforcement Learning is Direct Adaptive Optimal Control

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

Some New Directions for Adaptive Control Theory in Robotics

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

Dimensions of Reinforcement Learning

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

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

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

Toward Off-Policy Learning Control with Function Approximation

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

Linear Off-Policy Actor-Critic pdf

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

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

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

Adaptive State Representation and Estimation Using Recurrent Connectionist Networks

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

Improved Switching among Temporally Abstract Actions

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

Extending Sliding-Step Importance Weighting from Supervised Learning to Reinforcement Learning

Richard S. Sutton, Tian Tian, 2019, IJCAI.

Communicative Capital for Prosthetic Agents pdf

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

Convergent Temporal-Difference Learning with Arbitrary Smooth Function Approximation

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

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

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

Elementary Solution Methods

Richard S. Sutton, Andrew G. Barto, 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.

Pavlovian control of intraspinal microstimulation to produce over-ground walking

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

On the Significance of Markov Decision Processes

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

Keepaway Soccer: A Machine Learning Testbed

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

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.

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

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

Learning to Predict Independent of Span pdf

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

Connectionist Learning for Control

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

Reinforcement Learning with Replacing Eligibility Traces

Richard S. Sutton, Satinder P. Singh, R. Sutton, 2005, Machine Learning.

Multi-time Models for Temporally Abstract Planning

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

Iterative Construction of Sparse Polynomial Approximations

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

Beyond Reward: The Problem of Knowledge and Data

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

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 .

Understanding Multi-Step Deep Reinforcement Learning: A Systematic Study of the DQN Target pdf

Richard S. Sutton, J. Fernando Hernandez-Garcia, R. Sutton, 2019, ArXiv.

Comparing Policy-Gradient Algorithms

Richard S. Sutton, David A. McAllester, Satinder Singh, 2001 .

Eligibility Traces for Off-Policy Policy Evaluation

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

Learning Polynomial Functions by Feature Construction

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

Connectionist Learning Control at GTE Laboratories

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

Comparing Direct and Indirect Temporal-Difference Methods for Estimating the Variance of the Return

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

Reinforcement Learning for RoboCup Soccer Keepaway

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

Challenging Control Problems

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

Learning Sparse Representations Incrementally in Deep Reinforcement Learning pdf

Richard S. Sutton, J. Fernando Hernandez-Garcia, 2019, ArXiv.

Multi-Time Models for Reinforcement Learning

Doina Precup, Richard S. Sutton, R. Sutton, 2007 .

Natural actor-critic algorithms

Shalabh Bhatnagar, Mark Lee, Richard S. Sutton, 2009, Autom..

Open Theoretical Questions in Reinforcement Learning

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

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

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

True Online TD(lambda)

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

Using Predictive Representations to Improve Generalization in Reinforcement Learning

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

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

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

Planning with Closed Loop Macro Actions

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

Reinforcement Learning of Local Shape in the Game of Go

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

Sequential Decision Probelms and Neural Networks

Richard S. Sutton, Andrew G. Barto, Christopher J. C. H. Watkins, 1989, NIPS.

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

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

Incremental Natural Actor-Critic Algorithms

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

Adaptive Control Using Neural Networks

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

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

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

Predictive Representations of State

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

Forward Actor-Critic for Nonlinear Function Approximation in Reinforcement Learning

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

Policy Gradient Methods for Reinforcement Learning with Function Approximation

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

Associative search network: A reinforcement learning associative memory

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

Planning by Prioritized Sweeping with Small Backups pdf

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

Using Associative Content- Addressable Memories to Control Robots

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

Introduction to Reinforcement Learning

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

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.

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

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

Intra-Option Learning about Temporally Abstract Actions

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

Representation Search through Generate and Test

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

Learning and Sequential Decision Making

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

Between MOPs and Semi-MOP: Learning, Planning & Representing Knowledge at Multiple Temporal Scales

Doina Precup, Richard S. Sutton, Samar Singh, 1998 .

Temporal Abstraction in TD Networks

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

Temporal-difference search in computer Go

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

Associative Learning from Replayed Experience

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

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.

Average-Reward Off-Policy Policy Evaluation with Function Approximation pdf

Shimon Whiteson, Richard S. Sutton, Shangtong Zhang, 2021, ArXiv.

Reinforcement Learning and Artificial Intelligence

Richard S. Sutton, R. Sutton, 2003 .

Directly Estimating the Variance of the {\lambda}-Return Using Temporal-Difference Methods

Richard S. Sutton, Adam White, Craig Sherstan, 2018 .

Fixed-Horizon Temporal Difference Methods for Stable Reinforcement Learning pdf

Richard S. Sutton, Silviu Pitis, Daniel Graves, 2020, AAAI.

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

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

A Deeper Look at Experience Replay pdf

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

Landmark learning: An illustration of associative search

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

A Menu of Designs for Reinforcement Learning Over Time

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

Adaptive Switching in Practice: Improving Myoelectric Prosthesis Performance through Reinforcement Learning

Patrick M. Pilarski, Richard S. Sutton, K. Ming Chan, 2014 .

Learning a nonlinear model of a manufacturing process using multilayer connectionist networks

Richard S. Sutton, Charles W. Anderson, Judy A. Franklin, 1990, Proceedings. 5th IEEE International Symposium on Intelligent Control 1990.

Theoretical Results on Reinforcement Learning with Temporally Abstract Options

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

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

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

Crossprop: Learning Representations by Stochastic Meta-Gradient Descent in Neural Networks

Richard S. Sutton, Shangtong Zhang, Vivek Veeriah, 2017, ECML/PKDD.

Integrated Modeling and Control Based on Reinforcement Learning and Dynamic Programming

Richard S. Sutton, R. Sutton, 1990, NIPS 1990.

A Neural Network Baseline Problem for Control of Aircraft Flare and Touchdown

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

GQ($λ$) Quick Reference and Implementation Guide pdf

Richard S. Sutton, Adam White, 2017, ArXiv.

True online TD(λ)

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

Off-Policy Temporal Difference Learning with Function Approximation

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

Prediction in Intelligence: An Empirical Comparison of Off-policy Algorithms on Robots

Richard S. Sutton, Adam White, Sina Ghiassian, 2019, AAMAS.

True Online Temporal-Difference Learning pdf

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

Model-Based Reinforcement Learning with an Approximate, Learned Model

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

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).

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

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

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

Richard S. Sutton, Hamid Reza Maei, R. Sutton, 2010, Artificial General Intelligence.

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

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

Tuning-free step-size adaptation

Patrick M. Pilarski, Richard S. Sutton, Thomas Degris, 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

A Unified View

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

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.

Incremental Least-Squares Temporal Difference Learning

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

Multi-step Reinforcement Learning: A Unifying Algorithm

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

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

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

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

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

Recent Advances in Numerical Techniques for Large-Scale Optimization

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

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

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

Learning Instance-Independent Value Functions to Enhance Local Search

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

Planning by Incremental Dynamic Programming

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

Integrated Modeling and Control Based on Reinforcement Learning

Richard S. Sutton, R. Sutton, 1990, NIPS.

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).

Off-policy Learning with Options and Recognizers

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

Off-policy learning based on weighted importance sampling with linear computational complexity

Richard S. Sutton, Ashique Rupam Mahmood, 2015, UAI.

Emphatic Temporal-Difference Learning pdf

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

Between Instruction and Reward: Human-Prompted Switching

Patrick M. Pilarski, Richard S. Sutton, R. Sutton, 2012, AAAI Fall Symposium: Robots Learning Interactively from Human Teachers.

Should All Temporal Difference Learning Use Emphasis? pdf

Richard S. Sutton, Xiang Gu, Sina Ghiassian, 2019, ArXiv.

Vision-Based Robot Motion Planning

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

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..

On Generalized Bellman Equations and Temporal-Difference Learning pdf

Richard S. Sutton, Huizhen Yu, Ashique Rupam Mahmood, 2017, Canadian Conference on AI.

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

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

A First Empirical Study of Emphatic Temporal Difference Learning pdf

Richard S. Sutton, Sina Ghiassian, Banafsheh Rafiee, 2017, ArXiv.

Exponentiated Gradient Methods for Reinforcement Learning

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

Sequential Decision Problems and Neural Networks

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

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

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

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

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

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.

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

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

Multi-Step Dyna Planning for Policy Evaluation and Control

Shalabh Bhatnagar, Richard S. Sutton, Csaba Szepesvári, 2009, NIPS.

Planning and Learning

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

Behaviour Suite for Reinforcement Learning pdf

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

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

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

Summary of Notation

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

An Empirical Evaluation of True Online TD(λ) pdf

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

Multi-timescale nexting in a reinforcement learning robot

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

Multi-timescale Nexting in a Reinforcement Learning Robot

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

SELECTED BIBLIOGRAPHY ON CONNECTIONISM

Richard S. Sutton, Oliver G. Selfridge, Charles W. Anderson, 1989 .

Sample-based learning and search with permanent and transient memories

Richard S. Sutton, David Silver, Martin Müller, 2008, ICML '08.

Reinforcement learning with replacing eligibility traces

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

Linear Off-Policy Actor-Critic

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

Temporal Abstraction in Temporal-difference Networks

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

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).

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

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

Time-Derivative Models of Pavlovian Reinforcement

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

On the role of tracking in stationary environments

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

Learning and Planning in Average-Reward Markov Decision Processes pdf

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

Online Off-policy Prediction pdf

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

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.

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

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

Incremental Policy Gradients for Online Reinforcement Learning Control

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

Intelligent Control for Multiple Autonomous Undersea Vehicles

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

Reinforcement Learning: An Introduction

Richard S. Sutton, Andrew G. Barto, R. Sutton, 1998, IEEE Trans. Neural Networks.

New Results - Learning Algorithms for Autonomous Robots: Concepts and Algorithms

Pierre-Yves Oudeyer, Manuel Lopes, Richard S. Sutton, 2011 .

Position Paper: Representation Search through Generate and Test

Richard S. Sutton, Ashique Rupam Mahmood, R. Sutton, 2013, SARA.

Reinforcement Learning for 3 vs. 2 Keepaway

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

Neural networks for control

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

Temporal-Difference Networks

Richard S. Sutton, Brian Tanner, R. Sutton, 2004, NIPS.

Learning Feature Relevance Through Step Size Adaptation in Temporal-Difference Learning pdf

Patrick M. Pilarski, Richard S. Sutton, Vivek Veeriah, 2019, ArXiv.

Predicting Periodicity with Temporal Difference Learning pdf

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

Online Learning with Random Representations

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

Universal Option Models

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

Temporal-Difference Networks with History

Richard S. Sutton, Brian Tanner, 2005, IJCAI.

Scaling Reinforcement Learning toward RoboCup Soccer

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

Training and Tracking in Robotics

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

Roles of Macro-Actions in Accelerating Reinforcement Learning

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

Model-Free reinforcement learning with continuous action in practice

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

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.

Temporal credit assignment in reinforcement learning

Richard S. Sutton, R. Sutton, 1984 .

Reactive Reinforcement Learning in Asynchronous Environments

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

Reinforcement Learning

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

A Bioreactor Benchmark for Adaptive Network-based Process Control

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

Off-policy TD( l) with a true online equivalence

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

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

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

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

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

Learning representations through stochastic gradient descent in cross-validation error pdf

Richard S. Sutton, Vivek Veeriah, 2016, ArXiv.

Prediction Driven Behavior: Learning Predictions that Drive Fixed Responses

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

Learning to predict by the methods of temporal differences

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

Goal Seeking Components for Adaptive Intelligence: An Initial Assessment.

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

Natural actorcritic algorithms.

Shalabh Bhatnagar, Richard S. Sutton, Mohammad Ghavamzadeh, 2009 .

Incremental natural-gradient actor-critic algorithms

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

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

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

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.

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

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

Dyna-Style Planning with Linear Function Approximation and Prioritized Sweeping

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

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

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

TIDBD: Adapting Temporal-difference Step-sizes Through Stochastic Meta-descent pdf

Patrick M. Pilarski, Richard S. Sutton, Vivek Veeriah, 2018, ArXiv.

Reinforcement learning architectures for animats

Richard S. Sutton, R. Sutton, 1991 .

Applications of Neural Networks in Robotics and Automation for Manufacturing

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

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.

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

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

Efficient planning in MDPs by small backups

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

A computational model of hippocampal function in trace conditioning

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

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 .

Two geometric input transformation methods for fast online reinforcement learning with neural nets pdf

Richard S. Sutton, Huizhen Yu, Sina Ghiassian, 2018, ArXiv.

Discounted Reinforcement Learning is Not an Optimization Problem pdf

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

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

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

Generalization in Reinforcement Learning: Successful Examples Using Sparse Coarse Coding

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

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

Richard S. Sutton, R. Sutton, 2015 .

Learning to predict by the methods of temporal differences

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

Reinforcement Learning: Past, Present and Future

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

Planning with Expectation Models

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

Average-Reward Off-Policy Policy Evaluation with Function Approximation

Shimon Whiteson, Richard S. Sutton, Shangtong Zhang, 2021, ICML.

Average-Reward Learning and Planning with Options pdf

Richard S. Sutton, Abhishek Naik, Yi Wan, 2021, ArXiv.

Planning with Expectation Models for Control pdf

Richard S. Sutton, Katya Kudashkina, Yi Wan, 2021, ArXiv.

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

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

Does Standard Backpropagation Forget Less Catastrophically Than Adam? pdf

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

Learning and Planning in Average-Reward Markov Decision Processes

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

Learning Agent State Online with Recurrent Generate-and-Test pdf

Richard S. Sutton, Amir Samani, R. Sutton, 2021, ArXiv.

Continual Backprop: Stochastic Gradient Descent with Persistent Randomness pdf

Richard S. Sutton, Shibhansh Dohare, A. Rupam Mahmood, 2021, ArXiv.

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

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

Does the Adam Optimizer Exacerbate Catastrophic Forgetting?

Richard S. Sutton, Sina Ghiassian, Dylan R. Ashley, 2021 .

Generalization and Function Approximation

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

Prediction and Anticipation for Adaptive Artificial Limbs

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

NADALINE : A Normalized Adaptive Linear Element that Learns Efficiently

Richard S. Sutton, R. Sutton, 2001 .

This Excerpt from Reinforcement Learning. Introduction 1.2 Examples 1.3 Elements of Reinforcement Learning 1.3 Elements of Reinforcement Learning

Richard S. Sutton, Andrew G. Barto, .

Solutions to Selected Problems In : Reinforcement Learning : An Introduction by

Richard S. Sutton, Andrew G. Barto, 2008 .