Phillip Wang

Reinforcement Learning · 2019–2021

A researcher in the Science corpus: 2 papers; trusts 1 researcher and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 36 citations into it

Whom Phillip Wang trusts

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

1. OpenAI (in Reinforcement Learning)

Papers from Phillip Wang's perspective

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

1. ImageNet: A large-scale hierarchical image database
2. Et al
3. Asynchronous Methods for Deep Reinforcement Learning
4. Human-level control through deep reinforcement learning
5. ImageNet classification with deep convolutional neural networks
6. The Arcade Learning Environment: An Evaluation Platform for General Agents
7. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
8. Gradient-based learning applied to document recognition
9. Mastering the game of Go without human knowledge
10. Long Short-Term Memory

Their papers

• The MineRL 2020 Competition on Sample Efficient Reinforcement Learning using Human Priors (2021) — 73 citations
• MineRL: A Large-Scale Dataset of Minecraft Demonstrations (2019) — 305 citations