Yang Yu

Artificial Intelligence · 1 papers · 2021

A researcher in the Science corpus: 1 paper; trusts 10 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 33 citations into it
• Reinforcement Learning — 33 citations into it
• Evaluation & Benchmarks — 8 citations into it

Whom Yang Yu trusts

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

1. Sergey Levine (in Artificial Intelligence)
2. Richard S. Sutton (in Reinforcement Learning)
3. Pieter Abbeel (in Artificial Intelligence)
4. George Tucker (in Artificial Intelligence)
5. Andrew Barto (in Reinforcement Learning)
6. Doina Precup (in Reinforcement Learning)
7. Chelsea Finn (in Artificial Intelligence)
8. Aviral Kumar (in Artificial Intelligence)
9. Jaka Demsar (in Evaluation & Benchmarks)
10. Diederik P. Kingma (in Artificial Intelligence)

Papers from Yang Yu's perspective

Ordered by how strongly the researchers Yang Yu 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. MuJoCo: A physics engine for model-based control
4. Playing Atari with Deep Reinforcement Learning (arXiv.org)
5. Proximal Policy Optimization Algorithms (arXiv.org)
6. Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
7. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
8. Mastering the game of Go without human knowledge
9. ImageNet classification with deep convolutional neural networks
10. Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (International Conference on Machine Learning)

Their papers

• NeoRL: A Near Real-World Benchmark for Offline Reinforcement Learning (2021) — 99 citations