🧑‍🔬 Tom Schaul

🏛️ Affiliations

🏢Google DeepMindLondon · United Kingdom2008–2025 · 33 paperscurrent🔬Dalle Molle Institute for Artificial Intelligence ResearchLugano · Switzerland2008–2011 · 21 papers🎓New York UniversityNew York · United States2012–2015 · 17 papers🏢GoogleMountain View · United States2015–2020 · 14 papers🎓Università della Svizzera italianaLugano · Switzerland2010–2011 · 9 papers🎓Courant Institute of Mathematical SciencesNew York · United States2012–2014 · 8 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 2 more institutions on single bylines are not listed.

🧭 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📅 Works by year

2020s

2010s

📄 Papers

The most cited first (OpenAlex and our library).

1. Reinforcement Learning with Unsupervised Auxiliary Tasks (2016 · 319 citations)
   Deep RL agents usually learn only from extrinsic reward, which is often sparse. That leaves most of the information in an agent's observation stream unused. Jaderberg et al. (DeepMind) propose UNREAL (UNsupervised REinforcement and Auxiliary Learning), which extends the A3C actor-critic with extra objectives that…
2. Open-Endedness is Essential for Artificial Superhuman Intelligence (2024 · 14 citations)
   This position paper from Google DeepMind (ICML 2024) asks what an AI system would need in order to keep generating new knowledge by itself.

All works

8 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

1. StarCraft II: A New Challenge for Reinforcement Learning ↗arXiv (Cornell University) · no summary yetOriol Vinyals et al.2017 · 680 citations
2. Reinforcement Learning with Unsupervised Auxiliary TasksarXiv:1611.05397Max Jaderberg, Volodymyr Mnih, Wojciech Marian Czarnecki, et al.2016 · 319 citations
3. Unifying Count-Based Exploration and Intrinsic Motivation ↗arXiv:1606.01868 · no summary yetMarc G. Bellemare et al.2016 · 311 citations
4. FeUdal Networks for Hierarchical Reinforcement Learning ↗arXiv (Cornell University) · no summary yetAlexander Vezhnevets et al.2017 · 272 citations
5. General Video Game AI: Competition, Challenges and Opportunities ↗Proceedings of the AAAI Conference on Artificial Intelligence · no summary yetDiego Pérez-Liébana et al.2016 · 178 citations
6. Stochastic search using the natural gradient ↗International Conference on Machine Learning · no summary yetYi Sun et al.2009 · 81 citations
7. Open-Endedness is Essential for Artificial Superhuman IntelligencearXiv:2406.04268Edward Hughes, Michael Dennis, Jack Parker-Holder, et al.2024 · 14 citations
8. DataRater: Meta-Learned Dataset Curation ↗neural information processing systems · no summary yetDan A. Calian et al.2025 · 4 citations

🤝 Co-authors here

9 co-authors with a page, the most papers together first.

• David Silver
• Edward Hughes
• Feryal Behbahani
• Joel Z Leibo
• Koray Kavukcuoglu
• Max Jaderberg
• Tim Rocktaschel
• Volodymyr Mnih
• Wojciech Marian Czarnecki

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

• International Conference on Machine Learning
• Proceedings of the AAAI Conference on Artificial Intelligence
• neural information processing systems

🔗 Cited by

Papers of our library cite their works 239 times (of the 149,439 papers whose bibliographies we read); 35 citing papers with a page here →.

Updated 3 November 2026

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