Steffen Udluft

Reinforcement Learning · 2005–2020

A researcher in the Science corpus: 13 papers; trusts 154 researchers and is trusted by 28 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 125 citations into it; #823 of 2,242 by trust here, #1,331 by citations
• Artificial Intelligence — 112 citations into it; #8,226 of 35,345 by trust here, #11,072 by citations
• Control and Systems Engineering — 31 citations into it; #3,182 of 8,257 by trust here, #3,145 by citations
• Probabilistic & Bayesian Methods — 31 citations into it; #579 of 1,525 by trust here, #543 by citations
• Management Science and Operations Research — 26 citations into it
• Computational Theory and Mathematics — 25 citations into it
• Optimization & Training — 20 citations into it
• Statistical Learning Theory — 18 citations into it

Whom Steffen Udluft trusts overall

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

1. Richard S. Sutton
2. Martin A. Riedmiller
3. Diederik P. Kingma
4. J. Urgen Schmidhuber
5. Geoffrey E. Hinton
6. Stefan Depeweg
7. Ronald J. Williams
8. Yoshua Bengio
9. Andrew Barto
10. Geoffrey J. Gordon

Who trusts Steffen Udluft overall

1. Daniel Hein
2. Stefan Depeweg
3. J. Urgen Schmidhuber
4. Lucy R. Chai
5. Thomas A. Runkler
6. Phillip Swazinna
7. Javier Antorán Cabiscol
8. Ramitha Sundar
9. José Miguel Hernández-Lobato
10. Justin Fu

Papers from Steffen Udluft's perspective

Ordered by how strongly the researchers Steffen Udluft 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Reinforcement Learning: An Introduction
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Overcoming Model Bias for Robust Offline Deep Reinforcement Learning (2020) — 95 citations
• Uncertainty Decomposition in Bayesian Neural Networks with Latent Variables (2017) — 46 citations
• A benchmark environment motivated by industrial control problems (2017) — 68 citations
• Batch reinforcement learning on the industrial benchmark: First experiences (2017) — 12 citations
• Learning Long Term Dependencies with Recurrent Neural Networks (2006) — 64 citations
• Learning and Policy Search in Stochastic Dynamical Systems with Bayesian Neural Networks (2016) — 168 citations
• Particle swarm optimization for generating interpretable fuzzy reinforcement learning policies (2016) — 89 citations
• Sensitivity analysis for predictive uncertainty (2017) — 8 citations
• Interpretable Policies for Reinforcement Learning by Genetic Programming (2017) — 156 citations
• Decomposition of Uncertainty for Active Learning and Reliable Reinforcement Learning in Stochastic Systems (2017) — 27 citations
• Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning (2017) — 521 citations
• Safe exploration for reinforcement learning (2008) — 146 citations
• SOLVING PARTIALLY OBSERVABLE REINFORCEMENT LEARNING PROBLEMS WITH RECURRENT NEURAL NETWORKS (2005)