Lukas Schäfer

Reinforcement Learning · 2020

A researcher in the Science corpus: 3 papers; trusts 67 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 81 citations into it
• Artificial Intelligence — 70 citations into it; #31,575 of 35,345 by trust here, #29,412 by citations
• Management Science and Operations Research — 15 citations into it; #10,587 of 11,946 by trust here, #10,392 by citations
• Computational Theory and Mathematics — 12 citations into it
• LLM Agents & Tool Use — 10 citations into it
• Computer Science Applications — 8 citations into it
• Game & Decision Theory — 7 citations into it
• Electrical and Electronic Engineering — 6 citations into it

Whom Lukas Schäfer trusts overall

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

1. Ming Tan
2. Richard S. Sutton
3. Ronald J. Williams
4. Volodymyr Mnih
5. Jakob N. Foerster
6. Geoffrey E. Hinton
7. Tabish Rashid
8. Stefano V. Albrecht
9. Diederik P. Kingma
10. Yoshua Bengio

Who trusts Lukas Schäfer overall

1. Georgios Papoudakis
2. Filippos Christianos
3. Chao Yu

Papers from Lukas Schäfer's perspective

Ordered by how strongly the researchers Lukas Schäfer 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. 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. Reinforcement Learning: An Introduction
9. Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning
10. Human-level control through deep reinforcement learning

Their papers

• Shared Experience Actor-Critic for Multi-Agent Reinforcement Learning (2020) — 230 citations
• Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks (2020) — 367 citations
• Comparative Evaluation of Multi-Agent Deep Reinforcement Learning Algorithms (2020) — 33 citations