David Abel

Reinforcement Learning · 2016–2020

A researcher in the Science corpus: 8 papers; trusts 82 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 135 citations into it; #1,278 of 2,242 by trust here, #1,253 by citations
• Artificial Intelligence — 83 citations into it; #17,342 of 35,345 by trust here, #13,544 by citations
• Computational Theory and Mathematics — 20 citations into it
• Management Science and Operations Research — 17 citations into it
• Control and Systems Engineering — 13 citations into it; #7,160 of 8,257 by trust here, #6,083 by citations
• Statistical Learning Theory — 9 citations into it; #878 of 1,038 by trust here, #817 by citations
• Cognitive Neuroscience — 6 citations into it
• Game & Decision Theory — 6 citations into it

Whom David Abel trusts overall

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

1. Richard S. Sutton
2. Andrew Barto
3. Lihong Li
4. Michael L. Littman
5. Doina Precup
6. Thomas G. Dietterich
7. Martin L. Puterman
8. Satinder Singh
9. Leslie Pack Kaelbling
10. Geoffrey E. Hinton

Who trusts David Abel overall

1. Ofir Nachum
2. Eric
3. Carles Gelada
4. Iulian Serban
5. Khimya Khetarpal
6. Weitong Zhang
7. William Saunders
8. Dilip Arumugam
9. Cameron S. Allen
10. Kamyar Azizzadenesheli

Papers from David Abel's perspective

Ordered by how strongly the researchers David Abel 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Reinforcement Learning: An Introduction
7. Attention is All you Need (Neural Information Processing Systems)
8. ImageNet: A large-scale hierarchical image database
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Near Optimal Behavior via Approximate State Abstraction (2016) — 186 citations
• Exploratory Gradient Boosting for Reinforcement Learning in Complex Domains (2016) — 43 citations
• Agent-Agnostic Human-in-the-Loop Reinforcement Learning (2017) — 66 citations
• Value Preserving State-Action Abstractions (2020) — 59 citations
• What can I do here? A Theory of Affordances in Reinforcement Learning (2020) — 64 citations
• Discovering Options for Exploration by Minimizing Cover Time (2019) — 60 citations
• State Abstractions for Lifelong Reinforcement Learning (2018) — 140 citations
• Toward Good Abstractions for Lifelong Learning (2017)