Rachel Gardner

Control and Systems Engineering · 2019–2021

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

Subfields

• Control and Systems Engineering — 22 citations into it
• Reinforcement Learning — 15 citations into it
• Biomedical Engineering — 11 citations into it
• Robotics & Control — 8 citations into it

Whom Rachel Gardner trusts overall

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

1. Oussama Khatib
2. John Schulman
3. Christian Ott
4. Jonas Buchli
5. Stefan Schaal

Papers from Rachel Gardner's perspective

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

1. Reinforcement Learning: An Introduction
2. Asynchronous Methods for Deep Reinforcement Learning
3. Trust Region Policy Optimization (International Conference on Machine Learning)
4. Continuous control with deep reinforcement learning (International Conference on Learning Representations)
5. End-to-End Training of Deep Visuomotor Policies
6. Proximal Policy Optimization Algorithms (arXiv.org)
7. ImageNet classification with deep convolutional neural networks
8. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
9. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
10. Attention is All you Need (Neural Information Processing Systems)

Their papers

• Variable Impedance Control in End-Effector Space: An Action Space for Reinforcement Learning in Contact-Rich Tasks (2019) — 238 citations
• Vision-Only Robot Navigation in a Neural Radiance World (2021) — 302 citations