David Earl Hostallero

Reinforcement Learning · 2019

A researcher in the Science corpus: 2 papers; trusts 20 researchers and is trusted by 16 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 61 citations into it; #894 of 2,242 by trust here, #1,139 by citations
• Artificial Intelligence — 45 citations into it; #10,739 of 35,345 by trust here, #12,387 by citations
• Game & Decision Theory — 14 citations into it
• Computational Theory and Mathematics — 9 citations into it
• Computer Networks and Communications — 8 citations into it
• LLM Agents & Tool Use — 7 citations into it

Whom David Earl Hostallero trusts overall

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

1. Jakob N. Foerster
2. Geoffrey E. Hinton
3. Richard S. Sutton
4. Peter Stone
5. G. Tesauro
6. Carlos Guestrin
7. David Silver
8. Daphne Koller
9. Jayesh K. Gupta
10. Volodymyr Mnih

Who trusts David Earl Hostallero overall

1. Sai Qian Zhang
2. Tonghan Wang
3. Kyunghwan Son
4. Jianhao Wang
5. Christian Schröder de Witt

Papers from David Earl Hostallero's perspective

Ordered by how strongly the researchers David Earl Hostallero trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Reinforcement Learning: An Introduction
3. Human-level control through deep reinforcement learning
4. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
5. Asynchronous Methods for Deep Reinforcement Learning
6. Long Short-Term Memory
7. Gradient-based learning applied to document recognition
8. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
9. Attention is All you Need (Neural Information Processing Systems)
10. Continuous control with deep reinforcement learning (International Conference on Learning Representations)

Their papers

• QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning (2019) — 1,068 citations
• Learning to Schedule Communication in Multi-agent Reinforcement Learning (2019) — 257 citations