Lucian Busoniu

Reinforcement Learning · 2008–2018

A researcher in the Science corpus: 7 papers; trusts 127 researchers and is trusted by 76 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 216 citations into it; #318 of 2,242 by trust here, #291 by citations
• Artificial Intelligence — 143 citations into it; #5,571 of 35,345 by trust here, #4,577 by citations
• Management Science and Operations Research — 47 citations into it; #2,889 of 11,946 by trust here, #2,472 by citations
• Control and Systems Engineering — 42 citations into it
• Computational Theory and Mathematics — 28 citations into it; #1,447 of 12,182 by trust here, #1,027 by citations
• Optimization & Training — 19 citations into it
• Computer Vision and Pattern Recognition — 15 citations into it
• Statistical Learning Theory — 15 citations into it

Whom Lucian Busoniu trusts overall

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

1. Richard S. Sutton
2. Michael H. Bowling
3. Satinder Singh
4. Michael L. Littman
5. Andrew Barto
6. Dimitri P. Bertsekas
7. Csaba Szepesvari
8. Martin A. Riedmiller
9. R´emi Munos
10. Ronald J. Williams

Who trusts Lucian Busoniu overall

1. Karl Tuyls
2. Hado van Hasselt
3. Haitham Bou-Ammar
4. Jakob N. Foerster
5. Glen Berseth
6. Patrick Mannion
7. J. Urgen Schmidhuber
8. Daan Bloembergen
9. Huy Xuan Pham
10. Felix Leibfried

Papers from Lucian Busoniu's perspective

Ordered by how strongly the researchers Lucian Busoniu 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. Reinforcement Learning: An Introduction
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• A Comprehensive Survey of Multiagent Reinforcement Learning (2008) — 2,254 citations
• Multi-agent Reinforcement Learning: An Overview (2010) — 788 citations
• Reinforcement Learning and Dynamic Programming Using Function Approximators (2010) — 922 citations
• Experience Replay for Real-Time Reinforcement Learning Control (2012) — 260 citations
• Reinforcement learning for control: Performance, stability, and deep approximators (2018) — 446 citations
• Reinforcement learning in continuous state and action spaces (2009) — 164 citations
• Ieee Transactions on Systems, Man and Cybernetics—part C: Applications and Reviews 1 a Survey of Actor-critic Reinforcement Learning: Standard and Natural Policy Gradients