TS
Tom Schaul
cs.LGcs.AIstat.MLcs.NEcs.CLcs.SELearningReinforcement, PsychologyThinkingVideo Games
On Valency
published · living versionsW_mhugx9qb·v1 · currentpublished
W_z8p2yx7z·v1published
Preprints & journals
46 papers in the corpus · 2011–2025DataRater: Meta-Learned Dataset Curation2505.17895v2 · Dan A. Calian, Gregory Farquhar, Iurii Kemaev et al.2025 · 1 citationarXiv
Plasticity as the Mirror of Empowerment2505.10361v2 · David Abel, Michael Bowling, Andr'e Barreto et al.2025 · 0 citationsarXiv
AuPair: Golden Example Pairs for Code Repair2502.18487v1 · Aditi Mavalankar, Hassan Mansoor, Zita Marinho et al.2025 · 0 citationsarXiv
Agency Is Frame-Dependent2502.04403v1 · David Abel, Andr'e Barreto, Michael Bowling et al.2025 · 0 citationsarXiv
Boundless Socratic Learning with Language Games2411.16905v1 · Tom Schaul2024 · 1 citationarXiv
Open-Endedness is Essential for Artificial Superhuman Intelligence2406.04268v1 · Edward Hughes, Michael Dennis, Jack Parker-Holder et al.2024 · 4 citationsarXiv
Vision-Language Models as a Source of Rewards2312.09187v3 · Kate Baumli, Satinder Baveja, Feryal Behbahani et al.2023 · 4 citationsarXiv
Discovering Attention-Based Genetic Algorithms via Meta-Black-Box Optimization2304.03995v1 · Robert Tjarko Lange, Tom Schaul, Yutian Chen et al.2023 · 23 citationsarXiv
Scaling Goal-based Exploration via Pruning Proto-goals2302.04693v1 · Akhil Bagaria, Ray Jiang, Ramana Kumar et al.2023 · 0 citationsarXiv
Discovering Evolution Strategies via Meta-Black-Box Optimization2211.11260v3 · Robert Tjarko Lange, Tom Schaul, Yutian Chen et al.2022 · 31 citations11th International Conference on Learning Representations, ICLR 2023
The Phenomenon of Policy Churn2206.00730v3 · Tom Schaul, Andr'e Barreto, John Quan et al.2022 · 2 citationsarXiv
Model-Value Inconsistency as a Signal for Epistemic Uncertainty2112.04153v3 · Angelos Filos, Eszter V'ertes, Zita Marinho et al.2021 · 1 citationarXiv
When should agents explore?2108.11811v2 · Miruna Pislar, David Szepesvari, Georg Ostrovski et al.2021 · 11 citationsarXiv
Return-based Scaling: Yet Another Normalisation Trick for Deep RL2105.05347v1 · Tom Schaul, Georg Ostrovski, Iurii Kemaev et al.2021 · 7 citationsarXiv
AI for social good: unlocking the opportunity for positive impact.32424119 · Tomašev, Nenad, Cornebise, Julien, Hutter, Frank et al.2020 · 356 citationsNature communications. 2020;11(1):2468
Policy Evaluation Networks2002.11833v1 · Jean Harb, Tom Schaul, Doina Precup et al.2020 · 14 citationsarXiv
Adapting Behaviour for Learning Progress1912.06910v1 · Tom Schaul, Diana Borsa, David Ding et al.2019 · 10 citationsarXiv
Grandmaster level in StarCraft II using multi-agent reinforcement learning.31666705 · Vinyals, Oriol, Babuschkin, Igor, Czarnecki, Wojciech M et al.2020 · 3,448 citationsNature. 2019;575(7782):350-354
Conditional Importance Sampling for Off-Policy Learning1910.07479v2 · Mark Rowland, Anna Harutyunyan, Hado van Hasselt et al.2019 · 3 citationsarXiv
Non-Differentiable Supervised Learning with Evolution Strategies and Hybrid Methods1906.03139v1 · Karel Lenc, Erich Elsen, Tom Schaul et al.2019 · 4 citationsarXiv
Ray Interference: a Source of Plateaus in Deep Reinforcement Learning1904.11455v1 · Tom Schaul, Diana Borsa, Joseph Modayil et al.2019 · 33 citationsarXiv
Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy Improvement1901.10964v1 · Andr'e Barreto, Diana Borsa, John Quan et al.2019 · 79 citationsarXiv
Universal Successor Features Approximators1812.07626v1 · Diana Borsa, Andr'e Barreto, John Quan et al.2018 · 41 citationsarXiv
The Barbados 2018 List of Open Issues in Continual Learning1811.07004v1 · Tom Schaul, Hado van Hasselt, Joseph Modayil et al.2018 · 4 citationsarXiv
Meta-Learning by the Baldwin Effect1806.07917v2 · Chrisantha Thomas Fernando, Jakub Sygnowski, Simon Osindero et al.2018 · 25 citationsarXiv
Unicorn: Continual Learning with a Universal, Off-policy Agent1802.08294v2 · Daniel J. Mankowitz, Augustin Z'idek, Andr'e Barreto et al.2018 · 35 citationsarXiv
Building Machines that Learn and Think for Themselves: Commentary on Lake et al., Behavioral and Brain Sciences, 20171711.08378v1 · M. Botvinick, D.G.T. Barrett, P. Battaglia et al.2017 · 5 citationsarXiv
Rainbow: Combining Improvements in Deep Reinforcement Learning1710.02298v1 · Matteo Hessel, Joseph Modayil, Hado van Hasselt et al.2017 · 2,050 citationsarXiv
StarCraft II: A New Challenge for Reinforcement Learning1708.04782v1 · Oriol Vinyals, Timo Ewalds, Sergey Bartunov et al.2017 · 678 citationsarXiv
Deep Q-learning from Demonstrations1704.03732v4 · Todd Hester, Matej Vecerik, Olivier Pietquin et al.2017 · 1,021 citationsarXiv
FeUdal Networks for Hierarchical Reinforcement Learning1703.01161v2 · Alexander Sasha Vezhnevets, Simon Osindero, Tom Schaul et al.2017 · 608 citationsarXiv
Building machines that learn and think for themselves.29342685 · Botvinick, Matthew, Barrett, David G T, Battaglia, Peter et al.2019 · 54 citationsThe Behavioral and brain sciences. 2017;40:e255
The Predictron: End-To-End Learning and Planning1612.08810v3 · David Silver, Hado van Hasselt, Matteo Hessel et al.2016 · 169 citationsarXiv
Reinforcement Learning with Unsupervised Auxiliary Tasks1611.05397v1 · Max Jaderberg, Volodymyr Mnih, Wojciech Marian Czarnecki et al.2016 · 824 citationsarXiv
Successor Features for Transfer in Reinforcement Learning1606.05312v2 · Andr'e Barreto, Will Dabney, R'emi Munos et al.2016 · 331 citationsarXiv
Learning to learn by gradient descent by gradient descent1606.04474v2 · Marcin Andrychowicz, Misha Denil, Sergio Gomez et al.2016 · 1,420 citationsarXiv
Unifying Count-Based Exploration and Intrinsic Motivation1606.01868v2 · Marc G. Bellemare, Sriram Srinivasan, Georg Ostrovski et al.2016 · 838 citationsarXiv
Dueling Network Architectures for Deep Reinforcement Learning1511.06581v3 · Ziyu Wang, Tom Schaul, Matteo Hessel et al.2015 · 3,300 citationsarXivon Valency
Prioritized Experience Replay1511.05952v4 · Tom Schaul, John Quan, Ioannis Antonoglou et al.2015 · 3,003 citationsarXivon Valency
Unit Tests for Stochastic Optimization1312.6055v3 · Tom Schaul, Ioannis Antonoglou, David Silver2013 · 54 citationsarXiv
Adaptive learning rates and parallelization for stochastic, sparse, non-smooth gradients1301.3764v2 · Tom Schaul, Yann LeCun2013 · 25 citationsarXiv
Efficient Natural Evolution Strategies1209.5853v1 · Yi Sun, Daan Wierstra, Tom Schaul et al.2012 · 87 citationsarXiv
No More Pesky Learning Rates1206.1106v2 · Tom Schaul, Sixin Zhang, Yann LeCun2012 · 331 citationsarXiv
Measuring Intelligence through Games1109.1314v1 · Tom Schaul, Julian Togelius, Jurgen Schmidhuber2011 · 43 citationsarXiv
Natural Evolution Strategies1106.4487v1 · Daan Wierstra, Tom Schaul, Tobias Glasmachers et al.2011 · 257 citationsarXiv
A Linear Time Natural Evolution Strategy for Non-Separable Functions1106.1998v2 · Yi Sun, Faustino Gomez, Tom Schaul et al.2011 · 33 citationsarXiv
Career total: 133 works. 46 are in this corpus.
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