Christopher Painter-Wakefield

Reinforcement Learning · 2007–2008

A researcher in the Science corpus: 2 papers; trusts 16 researchers and is trusted by 32 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 14 citations into it; #523 of 2,242 by trust here, #916 by citations
• Artificial Intelligence — 7 citations into it; #13,303 of 35,345 by trust here, #13,544 by citations
• Control and Systems Engineering — 6 citations into it; #2,013 of 8,257 by trust here, #2,782 by citations
• Representation & Self-Supervised Learning — 5 citations into it

Whom Christopher Painter-Wakefield trusts overall

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

1. Richard S. Sutton
2. Justin A. Boyan
3. R´emi Munos
4. Sridhar Mahadevan
5. Stéphane Mallat
6. Ronald E. Parr
7. Michail G. Lagoudakis
8. Andrew W. Moore
9. Andrew Barto
10. Vladimir Naumovich Vapnik

Who trusts Christopher Painter-Wakefield overall

1. Csaba Szepesvari
2. Michael L. Littman
3. Shimon Whiteson
4. Christoph Dann
5. Marc G. Bellemare
6. Lihong Li
7. Amir-massoud Farahmand
8. Wendelin Böhmer
9. Sridhar Mahadevan
10. Ronald E. Parr

Papers from Christopher Painter-Wakefield's perspective

Ordered by how strongly the researchers Christopher Painter-Wakefield trusts endorse each paper. Same corpus, a different order for every researcher.

1. Reinforcement Learning: An Introduction
2. ImageNet classification with deep convolutional neural networks
3. Gradient-based learning applied to document recognition
4. Least-Squares Temporal Difference Learning
5. Least-Squares Policy Iteration
6. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
7. Error Bounds for Approximate Policy Iteration
8. A decision-theoretic generalization of on-line learning and an application to boosting
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Analyzing feature generation for value-function approximation

Their papers

• An analysis of linear models, linear value-function approximation, and feature selection for reinforcement learning (2008) — 224 citations
• Analyzing feature generation for value-function approximation (2007) — 156 citations