Daniel Hein

Reinforcement Learning · 2016–2017

A researcher in the Science corpus: 4 papers; trusts 24 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 45 citations into it
• Artificial Intelligence — 21 citations into it; #14,233 of 35,345 by trust here, #17,412 by citations
• Control and Systems Engineering — 9 citations into it
• Statistical Learning Theory — 8 citations into it
• Optimization & Training — 6 citations into it
• Probabilistic & Bayesian Methods — 6 citations into it

Whom Daniel Hein trusts overall

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

1. Richard S. Sutton
2. Martin A. Riedmiller
3. Geoffrey J. Gordon
4. Ralph Neuneier
5. Carl E. Rasmussen
6. Stefan Depeweg
7. John R. Koza
8. Steffen Udluft
9. Andrew Barto
10. Anton Maximilian Schäfer

Who trusts Daniel Hein overall

1. Ramitha Sundar
2. Steffen Udluft
3. Yashesh D. Dhebar
4. Justin Fu
5. Phillip Swazinna
6. Stefan Depeweg
7. Thomas A. Runkler
8. Kalyanmoy Deb
9. Aviral Kumar
10. David R Ha

Papers from Daniel Hein's perspective

Ordered by how strongly the researchers Daniel Hein 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. Human-level control through deep reinforcement learning
6. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
7. Long Short-Term Memory
8. Learning Multiple Layers of Features from Tiny Images
9. Continuous control with deep reinforcement learning (International Conference on Learning Representations)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• A benchmark environment motivated by industrial control problems (2017) — 68 citations
• Batch reinforcement learning on the industrial benchmark: First experiences (2017) — 12 citations
• Particle swarm optimization for generating interpretable fuzzy reinforcement learning policies (2016) — 89 citations
• Interpretable Policies for Reinforcement Learning by Genetic Programming (2017) — 156 citations