Mikayel Samvelyan

Reinforcement Learning · 2018–2024

A researcher in the Science corpus: 5 papers; trusts 93 researchers and is trusted by 62 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 188 citations into it; #319 of 2,242 by trust here, #388 by citations
• Artificial Intelligence — 171 citations into it; #4,608 of 35,345 by trust here, #5,359 by citations
• Game & Decision Theory — 43 citations into it
• Management Science and Operations Research — 32 citations into it; #1,666 of 11,946 by trust here, #932 by citations
• Computational Theory and Mathematics — 23 citations into it
• Computer Vision and Pattern Recognition — 19 citations into it
• Computer Networks and Communications — 17 citations into it
• Statistical and Nonlinear Physics — 14 citations into it

Whom Mikayel Samvelyan trusts overall

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

1. Jakob N. Foerster
2. Ming Tan
3. Geoffrey E. Hinton
4. Diederik P. Kingma
5. Sepp Hochreiter
6. Richard S. Sutton
7. Yoshua Bengio
8. J. Urgen Schmidhuber
9. Radford M. Neal
10. Ben J. A. Kröse

Who trusts Mikayel Samvelyan overall

1. Christian Schröder de Witt
2. Tonghan Wang
3. Tabish Rashid
4. Wendelin Böhmer
5. Filippos Christianos
6. Shimon Whiteson
7. Bei Peng
8. Jianhao Wang
9. Kyunghwan Son
10. Georgios Papoudakis

Papers from Mikayel Samvelyan's perspective

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

Their papers

• QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning (2018) — 2,072 citations
• MAVEN: Multi-Agent Variational Exploration (2019) — 444 citations
• The Llama 3 Herd of Models (2024) — 17,085 citations
• Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning (2020) — 1,503 citations
• The StarCraft Multi-Agent Challenge (2019) — 1,262 citations