James MacGlashan

Reinforcement Learning · 2014–2022

A researcher in the Science corpus: 8 papers; trusts 16 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 67 citations into it; #2,118 of 2,242 by trust here, #778 by citations
• Artificial Intelligence — 58 citations into it
• Control and Systems Engineering — 21 citations into it
• Natural Language Processing — 17 citations into it

Whom James MacGlashan trusts overall

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

1. W. B. Knox
2. Richard S. Sutton
3. Andrew Barto
4. Peter Stone
5. S. Branavan
6. Peter F. Brown
7. Luke S. Zettlemoyer
8. Christopher J. C. H. Watkins
9. Andrea Lockerd Thomaz
10. Raymond J. Mooney

Who trusts James MacGlashan overall

1. Dilip Arumugam
2. Jan Leike
3. Siddharth Karamcheti
4. Ruohan Zhang
5. Edward C. Williams
6. Anis Najar
7. Nakul Gopalan
8. Julia Kreutzer
9. Banghua Zhu
10. Theodore R. Sumers

Papers from James MacGlashan's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Reinforcement Learning: An Introduction
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Interactive Learning from Policy-Dependent Human Feedback (2017) — 346 citations
• Grounding English Commands to Reward Functions (2015) — 104 citations
• Learning behaviors via human-delivered discrete feedback: modeling implicit feedback strategies to speed up learning (2015) — 109 citations
• Outracing champion Gran Turismo drivers with deep reinforcement learning (2022) — 497 citations
• Environment-Independent Task Specifications via GLTL (2017) — 162 citations
• Showing versus doing: Teaching by demonstration (2016) — 118 citations
• Portable Option Discovery for Automated Learning Transfer in Object-Oriented Markov Decision Processes (2015) — 34 citations
• Learning something from nothing: Leveraging implicit human feedback strategies (2014) — 31 citations