Charles Lee Isbell Jr.

Reinforcement Learning · 2001–2018

A researcher in the Science corpus: 11 papers; trusts 128 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 114 citations into it; #2,207 of 2,242 by trust here, #856 by citations
• Artificial Intelligence — 90 citations into it; #18,698 of 35,345 by trust here, #10,430 by citations
• Control and Systems Engineering — 40 citations into it
• Computer Vision and Pattern Recognition — 27 citations into it; #14,299 of 21,575 by trust here, #13,815 by citations
• Management Science and Operations Research — 15 citations into it
• Cognitive Neuroscience — 12 citations into it
• Robotics & Control — 9 citations into it
• Computer Networks and Communications — 7 citations into it

Whom Charles Lee Isbell Jr. trusts overall

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

1. Richard S. Sutton
2. Andrew Barto
3. Andrew Y. Ng
4. Geoffrey E. Hinton
5. Pieter Abbeel
6. Csaba Szepesvari
7. Thomas G. Dietterich
8. Christian R. Shelton
9. J. Urgen Schmidhuber
10. Diederik P. Kingma

Who trusts Charles Lee Isbell Jr. overall

1. Andrea Lockerd Thomaz
2. Anis Najar
3. Faraz Torabi
4. Robert Tyler Loftin
5. Ruohan Zhang
6. Todd Hester
7. Ashley D. Edwards
8. Yannick Schroecker
9. Cynthia Lynn Breazeal
10. Bei Peng

Papers from Charles Lee Isbell Jr.'s perspective

Ordered by how strongly the researchers Charles Lee Isbell Jr. 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. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Reinforcement Learning: An Introduction
9. GENERATIVE ADVERSARIAL NETS
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Imitating Latent Policies from Observation (2018) — 179 citations
• Learning From Explanations Using Sentiment and Advice in RL (2017) — 76 citations
• Learning to Compose Skills (2017) — 44 citations
• Object focused q-learning for autonomous agents (2013) — 29 citations
• A social reinforcement learning agent (2001) — 170 citations
• State Aware Imitation Learning (2017) — 28 citations
• Environment-Independent Task Specifications via GLTL (2017) — 162 citations
• Exploration from Demonstration for Interactive Reinforcement Learning (2016) — 91 citations
• Policy Shaping with Human Teachers (2015) — 91 citations
• A Physics-Based Model Prior for Object-Oriented MDPs (2014) — 67 citations
• Policy Shaping: Integrating Human Feedback with Reinforcement Learning (2013) — 484 citations