Todd Hester

Reinforcement Learning · 2008–2021

A researcher in the Science corpus: 16 papers; trusts 286 researchers and is trusted by 52 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 376 citations into it; #845 of 2,242 by trust here, #280 by citations
• Artificial Intelligence — 350 citations into it; #4,142 of 35,345 by trust here, #2,350 by citations
• Management Science and Operations Research — 85 citations into it
• Control and Systems Engineering — 66 citations into it; #2,940 of 8,257 by trust here, #585 by citations
• Computational Theory and Mathematics — 60 citations into it
• Computer Vision and Pattern Recognition — 52 citations into it; #7,942 of 21,575 by trust here, #15,955 by citations
• Cognitive Neuroscience — 30 citations into it
• Robotics & Control — 25 citations into it

Whom Todd Hester trusts overall

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

1. Richard S. Sutton
2. Ben J. A. Kröse
3. Andrew Barto
4. Ronen I. Brafman
5. J. Ross Quinlan
6. David Silver
7. Andrew Y. Ng
8. Levente Kocsis
9. Geoffrey E. Hinton
10. Michael Kearns

Who trusts Todd Hester overall

1. Peter Stone
2. Sergey Levine
3. W. B. Knox
4. Scott Fujimoto
5. Matthew J. Hausknecht
6. Ashvin Nair
7. Chao Yu
8. Javier García
9. Josiah P. Hanna
10. Gabriel Dulac-Arnold

Papers from Todd Hester's perspective

Ordered by how strongly the researchers Todd Hester 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. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Reinforcement Learning: An Introduction
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

• Deep Q-learning From Demonstrations (2017) — 1,289 citations
• Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards (2017) — 764 citations
• Observe and Look Further: Achieving Consistent Performance on Atari (2018) — 127 citations
• Learning from Demonstrations for Real World Reinforcement Learning (2017) — 160 citations
• Challenges of Real-World Reinforcement Learning (2019) — 673 citations
• A Practical Approach to Insertion with Variable Socket Position Using Deep Reinforcement Learning (2018) — 118 citations
• Safe Exploration in Continuous Action Spaces (2018) — 522 citations
• RTMBA: A Real-Time Model-Based Reinforcement Learning Architecture for robot control (2011) — 88 citations
• Generalized model learning for Reinforcement Learning on a humanoid robot (2010) — 113 citations
• The Open-Source TEXPLORE Code Release for Reinforcement Learning on Robots (2013) — 6 citations
• Challenges of real-world reinforcement learning: definitions, benchmarks and analysis (2021) — 780 citations
• Real time targeted exploration in large domains (2010) — 17 citations
• Learning and Using Models (2012) — 49 citations
• ETA Prediction with Graph Neural Networks in Google Maps (2021) — 295 citations
• An empirical investigation of the challenges of real-world reinforcement learning (2020) — 140 citations
• The utility of temporal abstraction in reinforcement learning (2008) — 89 citations