Jay Lemmon

Reinforcement Learning · 2017–2020

A researcher in the Science corpus: 3 papers; trusts 3 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 52 citations into it; #1,401 of 2,242 by trust here, #546 by citations

Whom Jay Lemmon trusts overall

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

1. Igor Mordatch
2. John Schulman

Who trusts Jay Lemmon overall

1. Josh Merel
2. Xue Bin Peng
3. Faraz Torabi
4. Jiahui Yu

Papers from Jay Lemmon's perspective

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

1. GENERATIVE ADVERSARIAL NETS
2. Asynchronous Methods for Deep Reinforcement Learning
3. Human-level control through deep reinforcement learning
4. Reinforcement Learning: An Introduction
5. Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning
6. ImageNet classification with deep convolutional neural networks
7. Trust Region Policy Optimization (International Conference on Machine Learning)
8. Continuous control with deep reinforcement learning (International Conference on Learning Representations)
9. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
10. End-to-End Training of Deep Visuomotor Policies

Their papers

• Emergence of Locomotion Behaviours in Rich Environments (2017) — 1,015 citations
• Learning human behaviors from motion capture by adversarial imitation (2017) — 221 citations
• Using Unity to Help Solve Intelligence (2020) — 23 citations