Hangyu Mao

Reinforcement Learning · 2019

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 — 16 citations into it

Whom Hangyu Mao trusts overall

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

1. Ming Tan
2. Richard S. Sutton
3. Jakob N. Foerster
4. Daniel S. Bernstein
5. Andrew Barto
6. Ryan J. Lowe
7. Tabish Rashid
8. Gregory Farquhar
9. Peter Sunehag
10. Geoffrey E. Hinton

Papers from Hangyu Mao's perspective

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

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

Their papers

• Neighborhood Cognition Consistent Multi-Agent Reinforcement Learning (2019) — 85 citations