Donghwan Lee

Reinforcement Learning · 2018–2019

A researcher in the Science corpus: 3 papers; trusts 56 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 41 citations into it; #2,076 of 2,242 by trust here, #2,125 by citations
• Artificial Intelligence — 36 citations into it; #27,536 of 35,345 by trust here, #30,347 by citations
• Computer Networks and Communications — 18 citations into it; #4,400 of 9,886 by trust here, #8,543 by citations
• Optimization & Training — 13 citations into it; #2,475 of 2,601 by trust here, #2,428 by citations
• Management Science and Operations Research — 10 citations into it
• Statistical and Nonlinear Physics — 8 citations into it
• Computational Mechanics — 7 citations into it
• Computational Theory and Mathematics — 5 citations into it; #9,757 of 12,182 by trust here, #9,306 by citations

Whom Donghwan Lee trusts overall

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

1. Richard S. Sutton
2. Angelia Nedić
3. Dimitri P. Bertsekas
4. Yichen Chen
5. Leemon C. Baird III
6. Doina Precup
7. Mengdi Wang
8. Jing Wang
9. David Freedman
10. Andrew Barto

Who trusts Donghwan Lee overall

1. Afshin Oroojlooyjadid
2. Yan Zhang
3. Kaiqing Zhang
4. Kevin Black
5. Davood Hajinezhad
6. Michael M. Zavlanos
7. Niao He
8. Zhuoran Yang
9. Sergey Levine
10. A. Perera

Papers from Donghwan Lee's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Reinforcement Learning: An Introduction
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. Learning Multiple Layers of Features from Tiny Images
9. ImageNet: A large-scale hierarchical image database
10. GENERATIVE ADVERSARIAL NETS

Their papers

• Stochastic Primal-Dual Q-Learning (2018) — 17 citations
• Stochastic Primal-Dual Q-Learning Algorithm For Discounted MDPs (2019) — 13 citations
• Primal-Dual Algorithm for Distributed Reinforcement Learning: Distributed GTD (2018) — 49 citations