Wei Yang

Reinforcement Learning · with Shi · 2019–2023

A researcher in the Science corpus: 3 papers; trusts 2 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 37 citations into it

Whom Wei Yang trusts

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

1. Richard S. Sutton (in Reinforcement Learning)
2. Andrew Barto (in Reinforcement Learning)

Papers from Wei Yang's perspective

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

1. Attention is All you Need (Neural Information Processing Systems)
2. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. Language Models are Unsupervised Multitask Learners
4. Language Models are Few-Shot Learners (Neural Information Processing Systems)
5. Reinforcement Learning: An Introduction
6. Human-level control through deep reinforcement learning
7. Proximal Policy Optimization Algorithms (arXiv.org)
8. Mastering the game of Go with deep neural networks and tree search
9. IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures
10. Mastering the game of Go without human knowledge

Their papers

• Mastering Complex Control in MOBA Games with Deep Reinforcement Learning (2019) — 410 citations
• Supervised Learning Achieves Human-Level Performance in MOBA Games: A Case Study of Honor of Kings (2020) — 60 citations
• RLTF: Reinforcement Learning from Unit Test Feedback (2023) — 134 citations