Peter Vrancx

Reinforcement Learning · 2008–2018

A researcher in the Science corpus: 9 papers; trusts 69 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 102 citations into it
• Artificial Intelligence — 63 citations into it; #14,402 of 35,345 by trust here, #15,236 by citations
• Computer Vision and Pattern Recognition — 16 citations into it
• Management Science and Operations Research — 13 citations into it; #4,115 of 11,946 by trust here, #3,977 by citations
• Computational Theory and Mathematics — 10 citations into it
• Game & Decision Theory — 10 citations into it
• Optimization & Training — 8 citations into it

Whom Peter Vrancx trusts overall

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

1. Richard S. Sutton
2. Michael L. Littman
3. Ben J. A. Kröse
4. Diederik P. Kingma
5. Andrew Barto
6. Ronald J. Williams
7. Satinder Singh
8. Geoffrey E. Hinton
9. Yoshua Bengio
10. Craig Boutilier

Who trusts Peter Vrancx overall

1. Yann-Michaël De Hauwere
2. Ann Nowé
3. Yujing Hu
4. R´emi Munos
5. B. '. Pires
6. Y. Liu
7. Yang Gao
8. Yasin Abbasi-Yadkori
9. Sam Devlin
10. Zhang-Wei Hong

Papers from Peter Vrancx's perspective

Ordered by how strongly the researchers Peter Vrancx 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Game Theory and Multi-agent Reinforcement Learning (2012) — 210 citations
• Expressing Arbitrary Reward Functions as Potential-Based Advice (2015) — 134 citations
• Solving Sparse Delayed Coordination Problems in Multi-Agent Reinforcement Learning (2011) — 14 citations
• Decentralized Learning in Markov Games (2008) — 80 citations
• Model-Based Regularization for Deep Reinforcement Learning with Transcoder Networks (2018) — 7 citations
• Model-Based Stabilisation of Deep Reinforcement Learning (2018)
• Soft Q-Learning with Mutual-Information Regularization (2018) — 68 citations
• Reinforcement Learning: State-of-the-Art (2012) — 546 citations
• Learning multi-agent state space representations (2010) — 74 citations