引用次数
引用: 150,478
H-指数: 90
I10-指数 : 352
出版物: 537
A temporal-difference model of classical conditioning
Richard S Sutton , Andrew G Barto
Smpte Journal 355 -378
264
1987
Model-based reinforcement learning with an approximate, learned model
Leonid Kuvayev , Richard S Sutton
Smpte Journal 101 -105
109
1996
Toward off-policy learning control with function approximation.
Hamid Reza Maei , Csaba Szepesvári , Shalabh Bhatnagar , Richard S Sutton
ICML 10 719 -726
333
2010
Machine Learning in a Dynamic World: Panel Discussion
Panos J Antsaklis , Kenneth A DeJong , Alan L Meyrowitz , Alexander M Meystel
1988
Reinforcement Learning of Local Shape in the Game of Go.
David Silver , Richard S Sutton , Martin Müller
IJCAI 7 1053 -1058
183
2007
Online off-policy prediction
Sina Ghiassian , Andrew Patterson , Martha White , Richard S Sutton
arXiv preprint arXiv:1811.02597
30
2018
Comparing Direct and Indirect Temporal-Difference Methods for Estimating the Variance of the Return.
Craig Sherstan , Dylan R Ashley , Brendan Bennett , Kenny Young
UAI 63 -72
19
2018
Model-based reinforcement learning with non-linear expectation models and stochastic environments
Yi Wan , Zaheer Abbas , Martha White , Richard S Sutton
FAIM Workshop on Prediction and Generative Modeling in Reinforcement Learning 1 -5
6
2018
Scalable Real-Time Recurrent Learning Using Columnar-Constructive Networks
Khurram Javed , Haseeb Shah , Richard S Sutton , Martha White
Journal of Machine Learning Research 24 1 -34
5
2023
Evaluating Predictive Knowledge
Alex Kearney , Anna Koop , Craig Sherstan , Johannes Gunther
AAAI Fall Symposium on Reasoning and Learning In Real-World Systems For Long-Term Autonomy
1
2018
Comparing policy-gradient algorithms
Richard S Sutton , Satinder Singh , David McAllester
IEEE Transactions on Systems, Man, and Cybernetics
31
2000
Hierarchical optimal control of MDPs
Amy McGovern , Doina Precup , Balaraman Ravindran , Satinder Singh
Proceedings of the Tenth Yale Workshop on Adaptive and Learning Systems 186 -191
35
1998
Off-policy temporal-difference learning with function approximation
Doina Precup , Richard S Sutton , Sanjoy Dasgupta
ICML 417 -424
462
2001
Intra-Option Learning about Temporally Abstract Actions.
Richard S Sutton , Doina Precup , Satinder Singh
ICML 98 556 -564
228
1998
Planning with closed-loop macro actions
Doina Precup , Richard S Sutton , Satinder P Singh
Working notes of the 1997 AAAI Fall Symposium on Model-directed Autonomous Systems 70 -76
44
1997
Exponentiated gradient methods for reinforcement learning
Doina Precup , Richard S Sutton
ICML 272 -277
31
1997
Multi-time models for reinforcement learning
Doina Precup , Richard S Sutton
Proceedings of the ICML’97 Workshop on Modelling in Reinforcement Learning
18
1997
NEURON-LIKE ADAPTIVE ELEMENTS THAT CAN SOLVE DIFFICULT LEARNING CONTROL-PROBLEMS
Andrew G Barto , Richard S Sutton , Charles W Anderson
Behavioural Processes 9 ( 1)
4,653
1984
Introduction to reinforcement learning. Vol. 135
Richard S Sutton , Andrew G Barto
MIT press Cambridge 5 21 -22
1,099
1998
Andrew Barto Satinder Singh Doina Precup Patrick M. Pilarski Charles Anderson Adam White Csaba Szepesvari Shalabh Bhatnagar A. Rupam Mahmood Thomas Degris Hamid Maei David Silver Martha White David McAllester Joseph Modayil Yishay Mansour Elliot A. Ludvig E James Kehoe Mohammad Ghavamzadeh Peter Stone