Sutton, Richard S.

Reinforcement Learning: An Introduction

Sutton, R. S., & Barto, A. G. (1998) · IEEE Transactions on Neural Networks

Reinforcement Learning in RoboticsMachine Learning and Algorithms

Cited by 27,225

Learning to predict by the methods of temporal differences

Sutton, R. S. (1988) · Machine Learning

Reinforcement Learning in RoboticsMachine Learning and Algorithms

Cited by 5,148

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

Sutton, R. S., Precup, D., & Singh, S. (1999) · Artificial Intelligence

Reinforcement Learning in RoboticsAI-based Problem Solving and Planning

Cited by 3,119

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

Sutton, R. S., & Barto, A. G. (1981) · Psychological Review

Complex Network Analysis TechniquesNeural Networks and Applications

Cited by 1,450

Dyna, an Integrated Architecture for Learning, Planning, and Reacting

Sutton, R. S. (1991) · ACM SIGART Bulletin

AI-based Problem Solving and PlanningReinforcement Learning in Robotics

Cited by 777

Reward is enough

Silver, D., Singh, S., Precup, D., & Sutton, R. S. (2021) · Artificial Intelligence

Reinforcement Learning in RoboticsPsychiatry, Mental Health, Neuroscience

Cited by 445

Model-Free reinforcement learning with continuous action in practice

Degris, T., Pilarski, P. M., & Sutton, R. S. (2012) · 2012 American Control Conference (ACC)

Reinforcement Learning in RoboticsAdaptive Dynamic Programming Control

Cited by 239

Loss of plasticity in deep continual learning

Dohare, S., Hernandez-Garcia, J. F., Lan, Q., Rahman, P., Mahmood, A. R., & Sutton, R. S. (2024) · Nature

Domain Adaptation and Few-Shot LearningNeural Networks and Applications

Cited by 172

Stimulus representation and the timing of reward-prediction errors in models of the dopamine system

Ludvig, E. A., Sutton, R. S., & Kehoe, E. J. (2008) · Neural Computation

Neurotransmitter Receptor Influence on BehaviorNeural and Behavioral Psychology Studies

Cited by 148

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

Sutton, R. S., Modayil, J., Delp, M., Degris, T., Pilarski, P. M., White, A., & Precup, D. (2011) · International Joint Conference on Autonomous Agents and Multiagent Systems

Reinforcement Learning in RoboticsAI-based Problem Solving and Planning

Cited by 15