Nolan Wagener

Reinforcement Learning · 2015–2022

A researcher in the Science corpus: 5 papers; trusts 82 researchers and is trusted by 77 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 77 citations into it; #492 of 2,242 by trust here, #805 by citations
• Artificial Intelligence — 67 citations into it; #5,116 of 35,345 by trust here, #8,283 by citations
• Control and Systems Engineering — 26 citations into it; #607 of 8,257 by trust here, #618 by citations
• Management Science and Operations Research — 19 citations into it
• Computational Theory and Mathematics — 15 citations into it
• Optimization & Training — 15 citations into it
• Biomedical Engineering — 12 citations into it
• Computer Vision and Pattern Recognition — 12 citations into it; #3,674 of 21,575 by trust here, #8,722 by citations

Whom Nolan Wagener trusts overall

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

1. Sergey Levine
2. Jan Peters
3. Pieter Abbeel
4. Stefan Schaal
5. John Schulman
6. Richard S. Sutton
7. Peter W. Glynn
8. Geoffrey E. Hinton
9. Elad Hazan
10. Andrew Y. Ng

Who trusts Nolan Wagener overall

1. Sergey Levine
2. Vikash Kumar
3. Chelsea Finn
4. Pieter Abbeel
5. Oliver Kroemer
6. Hado van Hasselt
7. Arthur Argenson
8. Yevgen Chebotar
9. Ke Li
10. Aravind Rajeswaran

Papers from Nolan Wagener's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Learning contact-rich manipulation skills with guided policy search (2015) — 358 citations
• Information theoretic MPC for model-based reinforcement learning (2017) — 671 citations
• Fast Policy Learning through Imitation and Reinforcement (2018) — 90 citations
• Consistent Dropout for Policy Gradient Reinforcement Learning (2022) — 14 citations
• An Online Learning Approach to Model Predictive Control (2019) — 84 citations