Natural Evolution Strategies. - Daan Wierstra; Tom Schaul; Jan Peters 001; Jürgen Schmidhuber (IEEE Congress on Evolutionary Computation, 2008)

A scalable neural network architecture for board games. - Tom Schaul; Jürgen Schmidhuber (CIG, 2008)

Episodic Reinforcement Learning by Logistic Reward-Weighted Regression. - Daan Wierstra; Tom Schaul; Jan Peters 001; Jürgen Schmidhuber (ICANN (1), 2008)

Countering Poisonous Inputs with Memetic Neuroevolution. - Julian Togelius; Tom Schaul; Jürgen Schmidhuber; Faustino J. Gomez (PPSN, 2008)

Fitness Expectation Maximization. - Daan Wierstra; Tom Schaul; Jan Peters 001; Jürgen Schmidhuber (PPSN, 2008)

Efficient natural evolution strategies. - Yi Sun 003; Daan Wierstra; Tom Schaul; Jürgen Schmidhuber (GECCO, 2009)

Scalable Neural Networks for Board Games. - Tom Schaul; Jürgen Schmidhuber (ICANN (1), 2009)

Stochastic search using the natural gradient. - Yi Sun 003; Daan Wierstra; Tom Schaul; Jürgen Schmidhuber (ICML, 2009)

Ontogenetic and Phylogenetic Reinforcement Learning. - Tom Schaul; Daan Wierstra; Faustino J. Gomez; Jürgen Schmidhuber; Christian Igel; Julian Togelius (Künstliche Intell., 2009)

Exponential natural evolution strategies. - Tobias Glasmachers; Tom Schaul; Yi Sun 003; Daan Wierstra; Jürgen Schmidhuber (GECCO, 2010)

Multi-Dimensional Deep Memory Atari-Go Players for Parameter Exploring Policy Gradients. - Mandy Grüttner; Frank Sehnke; Tom Schaul; Jürgen Schmidhuber (ICANN (2), 2010)

A Natural Evolution Strategy for Multi-objective Optimization. - Tobias Glasmachers; Tom Schaul; Jürgen Schmidhuber (PPSN (1), 2010)

PyBrain. - Tom Schaul; Justin Bayer; Daan Wierstra; Yi Sun 003; Martin Felder; Frank Sehnke; Thomas Rückstieß; Jürgen Schmidhuber (J. Mach. Learn. Res., 2010)

Exploring parameter space in reinforcement learning. - Thomas Rückstieß; Frank Sehnke; Tom Schaul; Daan Wierstra; Yi Sun 003; Jürgen Schmidhuber (Paladyn J. Behav. Robotics, 2010)

Metalearning. - Tom Schaul; Jürgen Schmidhuber (Scholarpedia, 2010)

Coherence Progress: A Measure of Interestingness Based on Fixed Compressors. - Tom Schaul; Leo Pape; Tobias Glasmachers; Vincent Graziano; Jürgen Schmidhuber (AGI, 2011)

Curiosity-driven optimization. - Tom Schaul; Yi Sun 003; Daan Wierstra; Faustino J. Gomez; Jürgen Schmidhuber (IEEE Congress on Evolutionary Computation, 2011)

High dimensions and heavy tails for natural evolution strategies. - Tom Schaul; Tobias Glasmachers; Jürgen Schmidhuber (GECCO, 2011)

The two-dimensional organization of behavior. - Mark B. Ring; Tom Schaul; Jürgen Schmidhuber (ICDL-EPIROB, 2011)

Q-Error as a Selection Mechanism in Modular Reinforcement-Learning Systems. - Mark B. Ring; Tom Schaul (IJCAI, 2011)

Natural Evolution Strategies - Daan Wierstra; Tom Schaul; Tobias Glasmachers; Yi Sun 003; Jürgen Schmidhuber (CoRR, 2011)

Measuring Intelligence through Games - Tom Schaul; Julian Togelius; Jürgen Schmidhuber (CoRR, 2011)

Studies in Continuous Black-box Optimization. - Tom Schaul (2011)

Natural evolution strategies converge on sphere functions. - Tom Schaul (GECCO, 2012)

Benchmarking separable natural evolution strategies on the noiseless and noisy black-box optimization testbeds. - Tom Schaul (GECCO (Companion), 2012)

Benchmarking exponential natural evolution strategies on the noiseless and noisy black-box optimization testbeds. - Tom Schaul (GECCO (Companion), 2012)

Investigating the impact of adaptation sampling in natural evolution strategies on black-box optimization testbeds. - Tom Schaul (GECCO (Companion), 2012)

Benchmarking natural evolution strategies with adaptation sampling on the noiseless and noisy black-box optimization testbeds. - Tom Schaul (GECCO (Companion), 2012)

Comparing natural evolution strategies to BIPOP-CMA-ES on noiseless and noisy black-box optimization testbeds. - Tom Schaul (GECCO (Companion), 2012)

The organization of behavior into temporal and spatial neighborhoods. - Mark B. Ring; Tom Schaul (ICDL-EPIROB, 2012)

A video game description language for model-based or interactive learning. - Tom Schaul (CIG, 2013)

Towards a Video Game Description Language. - Marc Ebner; John Levine; Simon M. Lucas; Tom Schaul; Tommy Thompson; Julian Togelius (Artificial and Computational Intelligence in Games, 2013)

General Video Game Playing. - John Levine; Clare Bates Congdon; Marc Ebner; Graham Kendall; Simon M. Lucas; Risto Miikkulainen; Tom Schaul; Tommy Thompson (Artificial and Computational Intelligence in Games, 2013)

A linear time natural evolution strategy for non-separable functions. - Yi Sun 003; Tom Schaul; Faustino J. Gomez; Jürgen Schmidhuber (GECCO (Companion), 2013)

No more pesky learning rates. - Tom Schaul; Sixin Zhang; Yann LeCun (ICML (3), 2013)

Better Generalization with Forecasts. - Tom Schaul; Mark B. Ring (IJCAI, 2013)

Adaptive learning rates and parallelization for stochastic, sparse, non-smooth gradients - Tom Schaul; Yann LeCun (ICLR (Poster), 2013)

Unit Tests for Stochastic Optimization. - Tom Schaul; Ioannis Antonoglou; David Silver (ICLR, 2014)

Natural evolution strategies. - Daan Wierstra; Tom Schaul; Tobias Glasmachers; Yi Sun 003; Jan Peters 001; Jürgen Schmidhuber (J. Mach. Learn. Res., 2014)

An Extensible Description Language for Video Games. - Tom Schaul (IEEE Trans. Comput. Intell. AI Games, 2014)

Universal Value Function Approximators. - Tom Schaul; Daniel Horgan; Karol Gregor; David Silver (ICML, 2015)

General Video Game AI: Competition, Challenges and Opportunities. - Diego Perez Liebana; Spyridon Samothrakis; Julian Togelius; Tom Schaul; Simon M. Lucas (AAAI, 2016)

Analyzing the robustness of general video game playing agents. - Diego Perez Liebana; Spyridon Samothrakis; Julian Togelius; Tom Schaul; Simon M. Lucas (CIG, 2016)

Dueling Network Architectures for Deep Reinforcement Learning. - Ziyu Wang 001; Tom Schaul; Matteo Hessel; Hado van Hasselt; Marc Lanctot; Nando de Freitas (ICML, 2016)

Learning to learn by gradient descent by gradient descent. - Marcin Andrychowicz; Misha Denil; Sergio Gomez Colmenarejo; Matthew W. Hoffman; David Pfau; Tom Schaul; Nando de Freitas (NIPS, 2016)

Unifying Count-Based Exploration and Intrinsic Motivation. - Marc G. Bellemare; Sriram Srinivasan 005; Georg Ostrovski; Tom Schaul; David Saxton; Rémi Munos (NIPS, 2016)

Successor Features for Transfer in Reinforcement Learning. - André Barreto 001; Rémi Munos; Tom Schaul; David Silver (CoRR, 2016)

Prioritized Experience Replay. - Tom Schaul; John Quan; Ioannis Antonoglou; David Silver (ICLR (Poster), 2016)

The 2014 General Video Game Playing Competition. - Diego Perez Liebana; Spyridon Samothrakis; Julian Togelius; Tom Schaul; Simon M. Lucas; Adrien Couëtoux; Jerry Lee; Chong-U Lim; Tommy Thompson (IEEE Trans. Comput. Intell. AI Games, 2016)

Reinforcement Learning with Unsupervised Auxiliary Tasks. - Max Jaderberg; Volodymyr Mnih; Wojciech Marian Czarnecki; Tom Schaul; Joel Z. Leibo; David Silver; Koray Kavukcuoglu (ICLR, 2017)

The Predictron: End-To-End Learning and Planning. - David Silver; Hado van Hasselt; Matteo Hessel; Tom Schaul; Arthur Guez; Tim Harley; Gabriel Dulac-Arnold; David P. Reichert; Neil C. Rabinowitz; André Barreto 001; Thomas Degris (ICML, 2017)

FeUdal Networks for Hierarchical Reinforcement Learning. - Alexander Sasha Vezhnevets; Simon Osindero; Tom Schaul; Nicolas Heess; Max Jaderberg; David Silver; Koray Kavukcuoglu (ICML, 2017)

Successor Features for Transfer in Reinforcement Learning. - André Barreto 001; Will Dabney; Rémi Munos; Jonathan J. Hunt; Tom Schaul; David Silver; Hado van Hasselt (NIPS, 2017)

Natural Value Approximators: Learning when to Trust Past Estimates. - Zhongwen Xu; Joseph Modayil; Hado van Hasselt; André Barreto 001; David Silver; Tom Schaul (NIPS, 2017)

Learning from Demonstrations for Real World Reinforcement Learning. - Todd Hester; Matej Vecerík; Olivier Pietquin; Marc Lanctot; Tom Schaul; Bilal Piot; Andrew Sendonaris; Gabriel Dulac-Arnold; Ian Osband; John P. Agapiou; Joel Z. Leibo; Audrunas Gruslys (CoRR, 2017)

StarCraft II: A New Challenge for Reinforcement Learning. - Oriol Vinyals; Timo Ewalds; Sergey Bartunov; Petko Georgiev; Alexander Sasha Vezhnevets; Michelle Yeo; Alireza Makhzani; Heinrich Küttler; John P. Agapiou; Julian Schrittwieser; John Quan; Stephen Gaffney; et al. (CoRR, 2017)

Building Machines that Learn and Think for Themselves: Commentary on Lake et al., Behavioral and Brain Sciences, 2017. - Matthew M. Botvinick; David G. T. Barrett; Peter W. Battaglia; Nando de Freitas; Dharshan Kumaran; Joel Z. Leibo; Tim Lillicrap; Joseph Modayil; S. Mohamed; Neil C. Rabinowitz; Danilo Jimenez Rezende; Adam Santoro; et al. (CoRR, 2017)

Rainbow: Combining Improvements in Deep Reinforcement Learning. - Matteo Hessel; Joseph Modayil; Hado van Hasselt; Tom Schaul; Georg Ostrovski; Will Dabney; Dan Horgan; Bilal Piot; Mohammad Gheshlaghi Azar; David Silver (AAAI, 2018)

Deep Q-learning From Demonstrations. - Todd Hester; Matej Vecerík; Olivier Pietquin; Marc Lanctot; Tom Schaul; Bilal Piot; Dan Horgan; John Quan; Andrew Sendonaris; Ian Osband; Gabriel Dulac-Arnold; John P. Agapiou; et al. (AAAI, 2018)

Meta-learning by the baldwin effect. - Chrisantha Fernando; Jakub Sygnowski; Simon Osindero; Jane Wang 001; Tom Schaul; Denis Teplyashin; Pablo Sprechmann; Alexander Pritzel; Andrei A. Rusu (GECCO (Companion), 2018)

Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy Improvement. - André Barreto 001; Diana Borsa; John Quan; Tom Schaul; David Silver; Matteo Hessel; Daniel J. Mankowitz; Augustin Zídek; Rémi Munos (ICML, 2018)

Unicorn: Continual Learning with a Universal, Off-policy Agent. - Daniel J. Mankowitz; Augustin Zídek; André Barreto 001; Dan Horgan; Matteo Hessel; John Quan; Junhyuk Oh; Hado van Hasselt; David Silver; Tom Schaul (CoRR, 2018)

The Barbados 2018 List of Open Issues in Continual Learning. - Tom Schaul; Hado van Hasselt; Joseph Modayil; Martha White; Adam White 001; Pierre-Luc Bacon; Jean Harb; Shibl Mourad; Marc G. Bellemare; Doina Precup (CoRR, 2018)

Universal Successor Features Approximators. - Diana Borsa; André Barreto 001; John Quan; Daniel J. Mankowitz; Hado van Hasselt; Rémi Munos; David Silver; Tom Schaul (ICLR (Poster), 2019)

Ray Interference: a Source of Plateaus in Deep Reinforcement Learning. - Tom Schaul; Diana Borsa; Joseph Modayil; Razvan Pascanu (CoRR, 2019)

Non-Differentiable Supervised Learning with Evolution Strategies and Hybrid Methods. - Karel Lenc; Erich Elsen; Tom Schaul; Karen Simonyan (CoRR, 2019)

Adapting Behaviour for Learning Progress. - Tom Schaul; Diana Borsa; David Ding; David Szepesvari; Georg Ostrovski; Will Dabney; Simon Osindero (CoRR, 2019)

AI for the Social Good (Dagstuhl Seminar 19082). - Claudia Clopath; Ruben De Winne; Mohammad Emtiyaz Khan; Tom Schaul (Dagstuhl Reports, 2019)

Artificial and Computational Intelligence in Games: Revolutions in Computational Game AI (Dagstuhl Seminar 19511). - Jialin Liu 001; Tom Schaul; Pieter Spronck; Julian Togelius (Dagstuhl Reports, 2019)

Grandmaster level in StarCraft II using multi-agent reinforcement learning. - Oriol Vinyals; Igor Babuschkin; Wojciech M. Czarnecki; Michaël Mathieu; Andrew Dudzik; Junyoung Chung; David H. Choi; Richard Powell; Timo Ewalds; Petko Georgiev; Junhyuk Oh; Dan Horgan; et al. (Nat., 2019)

Conditional Importance Sampling for Off-Policy Learning. - Mark Rowland 001; Anna Harutyunyan; Hado van Hasselt; Diana Borsa; Tom Schaul; Rémi Munos; Will Dabney (AISTATS, 2020)

Policy Evaluation Networks. - Jean Harb; Tom Schaul; Doina Precup; Pierre-Luc Bacon (CoRR, 2020)

Return-based Scaling: Yet Another Normalisation Trick for Deep RL. - Tom Schaul; Georg Ostrovski; Iurii Kemaev; Diana Borsa (CoRR, 2021)

When should agents explore? - Miruna Pislar; David Szepesvari; Georg Ostrovski; Diana L. Borsa; Tom Schaul (ICLR, 2022)

Model-Value Inconsistency as a Signal for Epistemic Uncertainty. - Angelos Filos; Eszter Vértes; Zita Marinho; Gregory Farquhar; Diana Borsa; Abram L. Friesen; Feryal M. P. Behbahani; Tom Schaul; André Barreto 001; Simon Osindero (ICML, 2022)

The Phenomenon of Policy Churn. - Tom Schaul; André Barreto 001; John Quan; Georg Ostrovski (NeurIPS, 2022)

AI for the Social Good (Dagstuhl Seminar 22091). - Claudia Clopath; Ruben De Winne; Tom Schaul (Dagstuhl Reports, 2022)

Discovering Attention-Based Genetic Algorithms via Meta-Black-Box Optimization. - Robert Tjarko Lange; Tom Schaul; Yutian Chen 001; Chris Lu 001; Tom Zahavy; Valentin Dalibard; Sebastian Flennerhag (GECCO, 2023)

Discovering Evolution Strategies via Meta-Black-Box Optimization. - Robert Tjarko Lange; Tom Schaul; Yutian Chen 001; Tom Zahavy; Valentin Dalibard; Chris Lu 001; Satinder Singh 001; Sebastian Flennerhag (GECCO Companion, 2023)

Scaling Goal-based Exploration via Pruning Proto-goals. - Akhil Bagaria; Tom Schaul (IJCAI, 2023)

Scaling Goal-based Exploration via Pruning Proto-goals. - Akhil Bagaria; Ray Jiang; Ramana Kumar; Tom Schaul (CoRR, 2023)

Vision-Language Models as a Source of Rewards. - Kate Baumli; Satinder Baveja; Feryal M. P. Behbahani; Harris Chan; Gheorghe Comanici; Sebastian Flennerhag; Maxime Gazeau; Kristian Holsheimer; Dan Horgan; Michael Laskin; Clare Lyle; Hussain Masoom; et al. (CoRR, 2023)

Position: Open-Endedness is Essential for Artificial Superhuman Intelligence. - Edward Hughes 001; Michael D. Dennis; Jack Parker-Holder; Feryal M. P. Behbahani; Aditi Mavalankar; Yuge Shi; Tom Schaul; Tim Rocktäschel (ICML, 2024)

Open-Endedness is Essential for Artificial Superhuman Intelligence. - Edward Hughes 001; Michael Dennis 001; Jack Parker-Holder; Feryal M. P. Behbahani; Aditi Mavalankar; Yuge Shi; Tom Schaul; Tim Rocktäschel (CoRR, 2024)

Boundless Socratic Learning with Language Games. - Tom Schaul (CoRR, 2024)

Agency Is Frame-Dependent. - David Abel; André Barreto 001; Michael Bowling; Will Dabney; Shi Dong; Steven Hansen 001; Anna Harutyunyan; Khimya Khetarpal; Clare Lyle; Razvan Pascanu; Georgios Piliouras; Doina Precup; et al. (CoRR, 2025)

AuPair: Golden Example Pairs for Code Repair. - Aditi Mavalankar; Hassan Mansoor; Zita Marinho; Masha Samsikova; Tom Schaul (CoRR, 2025)

Plasticity as the Mirror of Empowerment. - David Abel; Michael Bowling; André Barreto 001; Will Dabney; Shi Dong; Steven Hansen 001; Anna Harutyunyan; Khimya Khetarpal; Clare Lyle; Razvan Pascanu; Georgios Piliouras; Doina Precup; et al. (CoRR, 2025)

DataRater: Meta-Learned Dataset Curation. - Dan A. Calian; Gregory Farquhar; Iurii Kemaev; Luisa M. Zintgraf; Matteo Hessel; Jeremy Shar; Junhyuk Oh; András György 001; Tom Schaul; Jeffrey Dean; Hado van Hasselt; David Silver (CoRR, 2025)