Gregory Palmer

Reinforcement Learning · 2017–2018

A researcher in the Science corpus: 2 papers; trusts 41 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 51 citations into it; #1,114 of 2,242 by trust here, #1,545 by citations
• Artificial Intelligence — 39 citations into it; #14,167 of 35,345 by trust here, #18,186 by citations
• Management Science and Operations Research — 17 citations into it; #6,813 of 11,946 by trust here, #9,931 by citations
• Computational Theory and Mathematics — 9 citations into it
• Game & Decision Theory — 9 citations into it; #195 of 249 by trust here, #149 by citations
• Control and Systems Engineering — 7 citations into it

Whom Gregory Palmer trusts overall

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

1. Richard S. Sutton
2. Diederik P. Kingma
3. Hado van Hasselt
4. Laëtitia Matignon
5. Ermo Wei
6. Liviu Panait
7. Shayegan Omidshafiei
8. Sean Luke
9. Karl Tuyls
10. Jayesh K. Gupta

Who trusts Gregory Palmer overall

1. Pablo Hernandez-Leal
2. Sven Gronauer
3. Xueguang Lyu
4. Bilal Kartal
5. Yan Zheng
6. Maximilian Hüttenrauch
7. Chris Amato
8. Klaus-Jürgen Diepold
9. Jianye Hao
10. Matthew E. Taylor

Papers from Gregory Palmer's perspective

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

1. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
2. ImageNet classification with deep convolutional neural networks
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Long Short-Term Memory
5. Reinforcement Learning: An Introduction
6. Gradient-based learning applied to document recognition
7. Attention is All you Need (Neural Information Processing Systems)
8. Human-level control through deep reinforcement learning
9. ImageNet: A large-scale hierarchical image database
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Lenient Multi-Agent Deep Reinforcement Learning (2017) — 168 citations
• Negative Update Intervals in Deep Multi-Agent Reinforcement Learning (2018) — 28 citations