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HTTP/1.1 200 OK Content-Type: text/html Last-Modified: Fri, 26 Sep 2025 21:22:51 GMT Date: Thu, 25 Dec 2025 17:55:47 GMT Keep-Alive: timeout=5 Vary: Accept-Encoding Content-Encoding: gzip ETag: W/"11bb4c929696a1a9b7080fb54ee11d5a" X-Varnish: 14054530 14854811 Age: 355132 Via: 1.1 varnish-v4 Access-Control-Allow-Origin: * Cache-Control: public, max-age=86400 Content-Length: 8311 Connection: keep-alive Accept-Ranges: bytes Tom Schaul, PhD - Google DeepMind

Tom Schaul, Ph.D.

I am a senior staff research scientist at DeepMind London.

My research interests include (modular/hierarchical) reinforcement learning, (stochastic/black-box) optimization with minimal hyperparameter tuning, and (deep/recurrent) neural networks. My favorite application domain are games.

I grew up in Luxembourg and studied computer science in Switzerland (with exchanges at Waterloo and Columbia), where I obtained an MSc from the EPFL in 2005. I hold a PhD from TU Munich (2011), which I did under the supervision of Jürgen Schmidhuber at the Swiss AI Lab IDSIA. From 2011 to 2013 I was a postdoc at the Courant Institute of NYU, in the lab of Yann LeCun.

Selected papers

NeurIPS 2022	T. Schaul, A. Barreto, J. Quan and G. Ostrovski. The Phenomenon of Policy Churn. Advances in Neural Information Processing Systems. [arXiv]
Nature Comm. 2020	N. Tomašev, J. Cornebise, F. Hutter et al. AI for Social Good: Unlocking the Opportunity for Positive Impact. Nature Communications 11 (2468). [Link]
Nature 2019	O. Vinyals, I. Babuschkin, W. Czarnecki et al. Grandmaster level in StarCraft II using multi-agent reinforcement learning. Nature 574 (7780). [Link] [Preprint] [Blog] [Video]
RLDM 2019	T. Schaul, D. Borsa, J. Modayil and R. Pascanu. Ray Interference: a Source of Plateaus in Deep Reinforcement Learning. Multidisciplinary Conference on Reinforcement Learning and Decision Making . [arXiv]
ICLR 2016	T. Schaul, J. Quan, I. Antonoglou and D. Silver. Prioritized Experience Replay. International Conference on Learning Representations. [arXiv][BibTeX]
ICML 2015	T. Schaul, D. Horgan, K. Gregor and D. Silver. Universal Value Function Approximators. International Conference on Machine Learning. [Pdf] [BibTeX]

See the full list of publications for 60+ additional ones.

News

2025-26: program chair for RLC 2026.
12/2025: two papers at NeurIPS, on how to meta-learn training data curation[69] and on the symmetry of plasticity and empowerment and what that means for agents[70] (spotlight).
08/2025: tech report for Gemini 2.5[T8] released.
2024-25: organizing committee for RLC 2025, keynote chair and SAC.
07/2025: AuPair paper on inference-time scaling of code repair with complementary examples[68] at ICML 2025 in Vancouver.
06/2025: frame-dependent agency paper[W24] at RLDM 2025 in Dublin (oral).
12/2024: invited talk on language games at NeurIPS 2024 workshop on Language Gamification, presenting Boundless Socratic Learning[W23].
07/2024: position paper on open-endedness[67] at ICML 2024 in Vienna: oral presentation.
06-09/2024: area chair for NeurIPS 2024.
02/2024: attended Dagstuhl workshop on AI for Social Good.
02-05/2024: area chair for ICML 2024.
08/2023: paper on discovery via pruning proto-goals[66] at IJCAI 2023 in Macao.
05-07/2023: papers on transformer-based meta-learning of evolution strategies[64], or genetic algorithms[65], presented at ICLR in Kigali and GECCO in Lisbon.
02/2023: attending Barbados workshop on Lifelong Reinforcement Learning and presenting [W21].
11-12/2022: attended NeurIPS 2022 in New Orleans, presenting "Policy Churn"[63].
04/2022: spotlight talk on "When to Explore"[61] at ICLR 2022.
02-03/2022: co-organizing Dagstuhl workshop on AI for the Social Good [T7].
07/2021: teaching at the International Summer School on Artificial Intelligence and Games.
11/2020: Keynote talk at the BNAIC/Benelearn conference.
05/2020: Perspective paper on guidelines around AI for Social Good published in Nature Communications [60].
10/2019: Our work on StarCraft made it into Nature [58]

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