Daniel Kudenko

Reinforcement Learning · 2002–2019

A researcher in the Science corpus: 8 papers; trusts 58 researchers and is trusted by 19 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 68 citations into it; #609 of 2,242 by trust here, #1,055 by citations
• Artificial Intelligence — 33 citations into it; #34,174 of 35,345 by trust here, #23,077 by citations
• Game & Decision Theory — 9 citations into it
• Management Science and Operations Research — 9 citations into it
• Model Architectures — 8 citations into it
• Computer Vision and Pattern Recognition — 6 citations into it
• Mathematical Optimization — 6 citations into it
• Control and Systems Engineering — 5 citations into it

Whom Daniel Kudenko trusts overall

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

1. Richard S. Sutton
2. Dimitri P. Bertsekas
3. Michael L. Littman
4. Ming Tan
5. Andrew Barto
6. Satinder Singh
7. Andrew Y. Ng
8. Geoffrey E. Hinton
9. Maxim Egorov Stanford
10. Kaiming He

Who trusts Daniel Kudenko overall

1. Liviu Panait
2. Sean Luke
3. Karl Tuyls
4. Sam Devlin
5. Patrick Mannion
6. Ermo Wei
7. Laëtitia Matignon
8. Georgios Chalkiadakis
9. Ann Nowé
10. Alex Peysakhovich

Papers from Daniel Kudenko's perspective

Ordered by how strongly the researchers Daniel Kudenko 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Reinforcement Learning: An Introduction
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Parallel reinforcement learning with linear function approximation (2007) — 45 citations
• Potential-based reward shaping for finite horizon online POMDP planning (2016) — 39 citations
• Context-sensitive reward shaping for sparse interaction multi-agent systems (2016) — 7 citations
• MAGNet: Multi-agent Graph Network for Deep Multi-agent Reinforcement Learning (2019) — 25 citations
• Deep Multi-Agent Reinforcement Learning with Relevance Graphs (2018) — 50 citations
• Potential-based difference rewards for multiagent reinforcement learning (2014) — 118 citations
• Theoretical considerations of potential-based reward shaping for multi-agent systems (2011) — 139 citations
• Reinforcement learning of coordination in cooperative multi-agent systems (2002) — 234 citations