Wan Ju Kang

Reinforcement Learning · 2019

A researcher in the Science corpus: 2 papers; trusts 20 researchers and is trusted by 16 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 61 citations into it; #895 of 2,242 by trust here, #1,139 by citations
• Artificial Intelligence — 45 citations into it; #10,740 of 35,345 by trust here, #12,387 by citations
• Game & Decision Theory — 14 citations into it
• Computational Theory and Mathematics — 9 citations into it
• Computer Networks and Communications — 8 citations into it
• LLM Agents & Tool Use — 7 citations into it

Whom Wan Ju Kang trusts overall

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

1. Jakob N. Foerster
2. Carlos Guestrin
3. Richard S. Sutton
4. Geoffrey E. Hinton
5. Peter Stone
6. Daphne Koller
7. Jayesh K. Gupta
8. G. Tesauro
9. Ronald E. Parr
10. David Silver

Who trusts Wan Ju Kang overall

1. Tonghan Wang
2. Sai Qian Zhang
3. Kyunghwan Son
4. Jianhao Wang
5. Christian Schröder de Witt
6. Tabish Rashid
7. Chao Yu
8. Wendelin Böhmer
9. Bei Peng

Papers from Wan Ju Kang's perspective

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

1. Reinforcement Learning: An Introduction
2. Human-level control through deep reinforcement learning
3. ImageNet classification with deep convolutional neural networks
4. Asynchronous Methods for Deep Reinforcement Learning
5. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
6. Gradient-based learning applied to document recognition
7. Long Short-Term Memory
8. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
9. Attention is All you Need (Neural Information Processing Systems)
10. Continuous control with deep reinforcement learning (International Conference on Learning Representations)

Their papers

• QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning (2019) — 1,068 citations
• Learning to Schedule Communication in Multi-agent Reinforcement Learning (2019) — 257 citations