Christian Schröder de Witt

Reinforcement Learning · 2018–2020

A researcher in the Science corpus: 10 papers; trusts 166 researchers and is trusted by 58 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 304 citations into it; #355 of 2,242 by trust here, #368 by citations
• Artificial Intelligence — 291 citations into it; #5,197 of 35,345 by trust here, #5,281 by citations
• Game & Decision Theory — 68 citations into it; #245 of 249 by trust here, #162 by citations
• Computational Theory and Mathematics — 48 citations into it
• Computer Vision and Pattern Recognition — 41 citations into it
• Management Science and Operations Research — 40 citations into it; #1,740 of 11,946 by trust here, #932 by citations
• Sociology and Political Science — 33 citations into it; #22,321 of 22,756 by trust here, #11,934 by citations
• Computer Networks and Communications — 31 citations into it

Whom Christian Schröder de Witt trusts overall

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

1. Tabish Rashid
2. Jakob N. Foerster
3. Richard S. Sutton
4. Geoffrey E. Hinton
5. Mikayel Samvelyan
6. Yoshua Bengio
7. Volodymyr Mnih
8. Ming Tan
9. Diederik P. Kingma
10. John Schulman

Who trusts Christian Schröder de Witt overall

1. Tabish Rashid
2. Tonghan Wang
3. Jakub Grudzien Kuba
4. Wendelin Böhmer
5. Bei Peng
6. Shimon Whiteson
7. Filippos Christianos
8. Shariq Iqbal
9. Sai Qian Zhang
10. Yaodong Yang

Papers from Christian Schröder de Witt's perspective

Ordered by how strongly the researchers Christian Schröder de Witt 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. Long Short-Term Memory
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Learning Multiple Layers of Features from Tiny Images
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning (2018) — 2,072 citations
• Multi-Agent Common Knowledge Reinforcement Learning (2018) — 119 citations
• FACMAC: Factored Multi-Agent Centralised Policy Gradients (2020) — 370 citations
• Randomized Entity-wise Factorization for Multi-Agent Reinforcement Learning (2020) — 88 citations
• Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning (2020) — 1,503 citations
• AI-QMIX: Attention and Imagination for Dynamic Multi-Agent Reinforcement Learning (2020) — 23 citations
• Deep Multi-Agent Reinforcement Learning for Decentralized Continuous Cooperative Control (2020) — 100 citations
• Is Independent Learning All You Need in the StarCraft Multi-Agent Challenge? (2020) — 584 citations
• The StarCraft Multi-Agent Challenge (2019) — 1,262 citations
• Stratospheric Aerosol Injection as a Deep Reinforcement Learning Problem (2019) — 9 citations