Gabriel Dulac-Arnold

Reinforcement Learning · 2015–2025

A researcher in the Science corpus: 11 papers; trusts 257 researchers and is trusted by 37 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 370 citations into it; #506 of 2,242 by trust here, #287 by citations
• Artificial Intelligence — 353 citations into it; #5,310 of 35,345 by trust here, #2,604 by citations
• Management Science and Operations Research — 86 citations into it
• Control and Systems Engineering — 77 citations into it; #1,421 of 8,257 by trust here, #4,722 by citations
• Computational Theory and Mathematics — 67 citations into it
• Computer Vision and Pattern Recognition — 63 citations into it
• Electrical and Electronic Engineering — 34 citations into it
• Cognitive Neuroscience — 32 citations into it

Whom Gabriel Dulac-Arnold trusts overall

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

1. Richard S. Sutton
2. Andrew Barto
3. Sergey Levine
4. Geoffrey E. Hinton
5. David Silver
6. Diederik P. Kingma
7. Pieter Abbeel
8. Yoshua Bengio
9. Timothy P. Lillicrap
10. Volodymyr Mnih

Who trusts Gabriel Dulac-Arnold overall

1. Cosmin Paduraru
2. Michael Janner
3. Lukas Brunke
4. Daniel Jaymin Mankowitz
5. Sergey Levine
6. Yash Chandak
7. Arthur Argenson
8. Caglar Gulcehre
9. Christian Schröder de Witt
10. Guy Tennenholtz

Papers from Gabriel Dulac-Arnold's perspective

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

Their papers

• The Predictron: End-To-End Learning and Planning (2016) — 310 citations
• Deep Q-learning From Demonstrations (2017) — 1,289 citations
• Learning from Demonstrations for Real World Reinforcement Learning (2017) — 160 citations
• Challenges of Real-World Reinforcement Learning (2019) — 673 citations
• RL Unplugged: Benchmarks for Offline Reinforcement Learning (2020) — 74 citations
• Deep Reinforcement Learning in Large Discrete Action Spaces (2015) — 640 citations
• Challenges of real-world reinforcement learning: definitions, benchmarks and analysis (2021) — 780 citations
• Model-Based Offline Planning (2020) — 172 citations
• Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities (2025) — 3,609 citations
• RL Unplugged: A Suite of Benchmarks for Offline Reinforcement Learning (2020)
• An empirical investigation of the challenges of real-world reinforcement learning (2020) — 140 citations