Patrick M. Pilarski

Reinforcement Learning · 2011–2017

A researcher in the Science corpus: 5 papers; trusts 18 researchers and is trusted by 18 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 60 citations into it; #736 of 2,242 by trust here, #535 by citations
• Artificial Intelligence — 35 citations into it; #14,011 of 35,345 by trust here, #7,531 by citations
• Control and Systems Engineering — 12 citations into it
• Biomedical Engineering — 10 citations into it
• Cellular and Molecular Neuroscience — 6 citations into it
• Optimization & Training — 6 citations into it
• Management Science and Operations Research — 5 citations into it

Whom Patrick M. Pilarski trusts overall

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

1. Richard S. Sutton
2. Andrew Barto
3. Ronald J. Williams
4. Kevin B. Englehart
5. Kenji Doya
6. Dimitri P. Bertsekas
7. W. B. Knox
8. Ben J. A. Kröse
9. George Dimitri Konidaris
10. Hamid Reza Maei

Who trusts Patrick M. Pilarski overall

1. Adam White
2. Philip S. Thomas
3. Julia Kreutzer
4. A. Mahmood
5. Saminda Abeyruwan
6. Kamil Andrzej Ciosek
7. Shane Griffith
8. W. B. Knox
9. David Silver
10. Wei Zhang

Papers from Patrick M. Pilarski's perspective

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

1. Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning
2. ImageNet classification with deep convolutional neural networks
3. Reinforcement Learning: An Introduction
4. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
5. Human-level control through deep reinforcement learning
6. Gradient-based learning applied to document recognition
7. The Arcade Learning Environment: An Evaluation Platform for General Agents
8. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
9. Long Short-Term Memory
10. Learning to predict by the methods of temporal differences

Their papers

• Horde: a scalable real-time architecture for learning knowledge from unsupervised sensorimotor interaction (2011) — 543 citations
• True Online Temporal-Difference Learning (2015) — 105 citations
• Actor-Critic Reinforcement Learning with Simultaneous Human Control and Feedback (2017) — 9 citations
• Online human training of a myoelectric prosthesis controller via actor-critic reinforcement learning (2011) — 185 citations
• Model-Free reinforcement learning with continuous action in practice (2012)