Richard C. Yee

Reinforcement Learning · 1994

A researcher in the Science corpus: 1 paper; trusts 1 researcher and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 5 citations into it

Whom Richard C. Yee trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Richard S. Sutton
2. G. Tesauro
3. Paul J. Werbos

Who trusts Richard C. Yee overall

1. Bruno Scherrer
2. Tengyang Xie
3. Lihong Li
4. Nan Jiang
5. Michael L. Littman
6. Boris Lesner
7. Lin F. Yang
8. Sham M. Kakade
9. Alekh Agarwal

Papers from Richard C. Yee's perspective

Ordered by how strongly the researchers Richard C. Yee trusts endorse each paper. Same corpus, a different order for every researcher.

1. Learning to predict by the methods of temporal differences
2. Practical Issues in Temporal Difference Learning
3. Learning from delayed rewards
4. Neuronlike adaptive elements that can solve difficult learning control problems
5. Building and Understanding Adaptive Systems: A Statistical/Numerical Approach to Factory Automation and Brain Research
6. Residual Algorithms: Reinforcement Learning with Function Approximation
7. Learning internal representations by error propagation
8. Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning
9. Between MDPs and Semi-MDPs: A Framework for Temporal Abstraction in Reinforcement Learning
10. Continuous control with deep reinforcement learning (International Conference on Learning Representations)

Their papers

• An upper bound on the loss from approximate optimal-value functions (1994)