Thomas Degris

Reinforcement Learning · 2006–2018

A researcher in the Science corpus: 9 papers; trusts 38 researchers and is trusted by 133 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 99 citations into it; #202 of 2,242 by trust here, #159 by citations
• Artificial Intelligence — 55 citations into it; #4,548 of 35,345 by trust here, #3,348 by citations
• Optimization & Training — 17 citations into it
• Computational Theory and Mathematics — 16 citations into it; #4,917 of 12,182 by trust here, #3,339 by citations
• Control and Systems Engineering — 13 citations into it
• Management Science and Operations Research — 8 citations into it

Whom Thomas Degris trusts overall

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

1. Richard S. Sutton
2. Andrew Barto
3. Hamid Reza Maei
4. Shalabh Bhatnagar
5. Paul E. Utgoff
6. Kenji Doya
7. Stephen W. Carden
8. Vivek Shripad Borkar
9. J. Ross Quinlan
10. Carlos Guestrin

Who trusts Thomas Degris overall

1. Todd Hester
2. Adam White
3. Philip S. Thomas
4. Kamil Andrzej Ciosek
5. Sergey Levine
6. David Silver
7. Peter Stone
8. Shimon Whiteson
9. Chris HomesParker
10. Nicolas Manfred Otto Heess

Papers from Thomas Degris's perspective

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

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

Their papers

• The Predictron: End-To-End Learning and Planning (2016) — 310 citations
• Horde: a scalable real-time architecture for learning knowledge from unsupervised sensorimotor interaction (2011) — 543 citations
• Linear Off-Policy Actor-Critic (2012) — 213 citations
• Deep Reinforcement Learning in Large Discrete Action Spaces (2015) — 640 citations
• Vector-based navigation using grid-like representations in artificial agents (2018) — 716 citations
• Learning the structure of Factored Markov Decision Processes in reinforcement learning problems (2006) — 130 citations
• Online human training of a myoelectric prosthesis controller via actor-critic reinforcement learning (2011) — 185 citations
• Deterministic Policy Gradient Algorithms (2014) — 4,557 citations
• Model-Free reinforcement learning with continuous action in practice (2012)