Tom Schaul

Tom Schaul, Ph.D.

I am a senior research scientist at Google DeepMind in 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

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]
ICLR 2019 D. Borsa, A. Barreto, J. Quan, D. Mankowitz, H. van Hasselt, R. Munos, D. Silver and T. Schaul. Universal Successor Features Approximators.
International Conference on Learning Representations. [arXiv]
ICML 2017

D. Silver, H. van Hasselt, M. Hessel, T. Schaul, A. Guez et al. The Predictron: End-To-End Learning and Planning.
International Conference on Machine Learning. [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 50+ additional ones.

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