Tom Schaul

ORCID

Publication Activity (10 Years)

Years Active: 2008-2024
Publications (10 Years): 59

Top Venues

Technical University Munich (PHD)

Education History

Technical University Munich

PHD

Publications

Edward Hughes, Michael Dennis, Jack Parker-Holder, Feryal M. P. Behbahani, Aditi Mavalankar, Yuge Shi, Tom Schaul, Tim Rocktäschel
Open-Endedness is Essential for Artificial Superhuman Intelligence. CoRR (2024)
Robert Tjarko Lange, Tom Schaul, Yutian Chen, Tom Zahavy, Valentin Dalibard, Chris Lu, Satinder Singh, Sebastian Flennerhag
Discovering Evolution Strategies via Meta-Black-Box Optimization. GECCO Companion (2023)
Robert Tjarko Lange, Tom Schaul, Yutian Chen, Tom Zahavy, Valentin Dalibard, Chris Lu, Satinder Singh, Sebastian Flennerhag
Discovering Evolution Strategies via Meta-Black-Box Optimization. ICLR (2023)
Robert Tjarko Lange, Tom Schaul, Yutian Chen, Chris Lu, Tom Zahavy, Valentin Dalibard, Sebastian Flennerhag
Discovering Attention-Based Genetic Algorithms via Meta-Black-Box Optimization. GECCO (2023)
Akhil Bagaria, Ray Jiang, Ramana Kumar, Tom Schaul
Scaling Goal-based Exploration via Pruning Proto-goals. CoRR (2023)
Akhil Bagaria, Tom Schaul
Scaling Goal-based Exploration via Pruning Proto-goals. IJCAI (2023)
Robert Tjarko Lange, Tom Schaul, Yutian Chen, Chris Lu, Tom Zahavy, Valentin Dalibard, Sebastian Flennerhag
Discovering Attention-Based Genetic Algorithms via Meta-Black-Box Optimization. CoRR (2023)
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, Kay McKinney, Volodymyr Mnih, Alexander Neitz, Fabio Pardo, Jack Parker-Holder, John Quan, Tim Rocktäschel, Himanshu Sahni, Tom Schaul, Yannick Schroecker, Stephen Spencer, Richie Steigerwald, Luyu Wang, Lei Zhang
Vision-Language Models as a Source of Rewards. CoRR (2023)
Claudia Clopath, Ruben De Winne, Tom Schaul
AI for the Social Good (Dagstuhl Seminar 22091). Dagstuhl Reports 12 (2) (2022)
Robert Tjarko Lange, Tom Schaul, Yutian Chen, Tom Zahavy, Valentin Dallibard, Chris Lu, Satinder Singh, Sebastian Flennerhag
Discovering Evolution Strategies via Meta-Black-Box Optimization. CoRR (2022)
Tom Schaul, André Barreto, John Quan, Georg Ostrovski
The Phenomenon of Policy Churn. CoRR (2022)
Angelos Filos, Eszter Vértes, Zita Marinho, Gregory Farquhar, Diana Borsa, Abram L. Friesen, Feryal Behbahani, Tom Schaul, André Barreto, Simon Osindero
Model-Value Inconsistency as a Signal for Epistemic Uncertainty. ICML (2022)
Tom Schaul, André Barreto, John Quan, Georg Ostrovski
The Phenomenon of Policy Churn. NeurIPS (2022)
Miruna Pislar, David Szepesvari, Georg Ostrovski, Diana L. Borsa, Tom Schaul
When should agents explore? ICLR (2022)
Tom Schaul, Georg Ostrovski, Iurii Kemaev, Diana Borsa
Return-based Scaling: Yet Another Normalisation Trick for Deep RL. CoRR (2021)
Miruna Pislar, David Szepesvari, Georg Ostrovski, Diana Borsa, Tom Schaul
When should agents explore? CoRR (2021)
Angelos Filos, Eszter Vértes, Zita Marinho, Gregory Farquhar, Diana Borsa, Abram L. Friesen, Feryal Behbahani, Tom Schaul, André Barreto, Simon Osindero
Model-Value Inconsistency as a Signal for Epistemic Uncertainty. CoRR (2021)
Jean Harb, Tom Schaul, Doina Precup, Pierre-Luc Bacon
Policy Evaluation Networks. CoRR (2020)
Mark Rowland, Anna Harutyunyan, Hado van Hasselt, Diana Borsa, Tom Schaul, Rémi Munos, Will Dabney
Conditional Importance Sampling for Off-Policy Learning. AISTATS (2020)
Mark Rowland, Anna Harutyunyan, Hado van Hasselt, Diana Borsa, Tom Schaul, Rémi Munos, Will Dabney
Conditional Importance Sampling for Off-Policy Learning. CoRR (2019)
Karel Lenc, Erich Elsen, Tom Schaul, Karen Simonyan
Non-Differentiable Supervised Learning with Evolution Strategies and Hybrid Methods. CoRR (2019)
Claudia Clopath, Ruben De Winne, Mohammad Emtiyaz Khan, Tom Schaul
AI for the Social Good (Dagstuhl Seminar 19082). Dagstuhl Reports 9 (2) (2019)
Jialin Liu, Tom Schaul, Pieter Spronck, Julian Togelius
Artificial and Computational Intelligence in Games: Revolutions in Computational Game AI (Dagstuhl Seminar 19511). Dagstuhl Reports 9 (12) (2019)
Tom Schaul, Diana Borsa, David Ding, David Szepesvari, Georg Ostrovski, Will Dabney, Simon Osindero
Adapting Behaviour for Learning Progress. CoRR (2019)
Tom Schaul, Diana Borsa, Joseph Modayil, Razvan Pascanu
Ray Interference: a Source of Plateaus in Deep Reinforcement Learning. CoRR (2019)
André Barreto, Diana Borsa, John Quan, Tom Schaul, David Silver, Matteo Hessel, Daniel J. Mankowitz, Augustin Zídek, Rémi Munos
Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy Improvement. CoRR (2019)
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, Manuel Kroiss, Ivo Danihelka, Aja Huang, Laurent Sifre, Trevor Cai, John P. Agapiou, Max Jaderberg, Alexander Sasha Vezhnevets, Rémi Leblond, Tobias Pohlen, Valentin Dalibard, David Budden, Yury Sulsky, James Molloy, Tom Le Paine, Çaglar Gülçehre, Ziyu Wang, Tobias Pfaff, Yuhuai Wu, Roman Ring, Dani Yogatama, Dario Wünsch, Katrina McKinney, Oliver Smith, Tom Schaul, Timothy P. Lillicrap, Koray Kavukcuoglu, Demis Hassabis, Chris Apps, David Silver
Grandmaster level in StarCraft II using multi-agent reinforcement learning. Nat. 575 (7782) (2019)
Diana Borsa, André Barreto, John Quan, Daniel J. Mankowitz, Hado van Hasselt, Rémi Munos, David Silver, Tom Schaul
Universal Successor Features Approximators. ICLR (Poster) (2019)
Diana Borsa, André Barreto, John Quan, Daniel J. Mankowitz, Rémi Munos, Hado van Hasselt, David Silver, Tom Schaul
Universal Successor Features Approximators. CoRR (2018)
Daniel J. Mankowitz, Augustin Zídek, André Barreto, Dan Horgan, Matteo Hessel, John Quan, Junhyuk Oh, Hado van Hasselt, David Silver, Tom Schaul
Unicorn: Continual Learning with a Universal, Off-policy Agent. CoRR (2018)
Chrisantha Thomas Fernando, Jakub Sygnowski, Simon Osindero, Jane Wang, Tom Schaul, Denis Teplyashin, Pablo Sprechmann, Alexander Pritzel, Andrei A. Rusu
Meta-Learning by the Baldwin Effect. CoRR (2018)
Tom Schaul, Hado van Hasselt, Joseph Modayil, Martha White, Adam White, Pierre-Luc Bacon, Jean Harb, Shibl Mourad, Marc G. Bellemare, Doina Precup
The Barbados 2018 List of Open Issues in Continual Learning. CoRR (2018)
Chrisantha Fernando, Jakub Sygnowski, Simon Osindero, Jane Wang, Tom Schaul, Denis Teplyashin, Pablo Sprechmann, Alexander Pritzel, Andrei A. Rusu
Meta-learning by the baldwin effect. GECCO (Companion) (2018)
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, Joel Z. Leibo, Audrunas Gruslys
Deep Q-learning From Demonstrations. AAAI (2018)
Chrisantha Fernando, Jakub Sygnowski, Simon Osindero, Jane Wang, Tom Schaul, Denis Teplyashin, Pablo Sprechmann, Alexander Pritzel, Andrei A. Rusu
Meta-learning by the Baldwin effect. GECCO (Companion) (2018)
Matteo Hessel, Joseph Modayil, Hado van Hasselt, Tom Schaul, Georg Ostrovski, Will Dabney, Dan Horgan, Bilal Piot, Mohammad Gheshlaghi Azar, David Silver
Rainbow: Combining Improvements in Deep Reinforcement Learning. AAAI (2018)
André Barreto, Diana Borsa, John Quan, Tom Schaul, David Silver, Matteo Hessel, Daniel J. Mankowitz, Augustin Zídek, Rémi Munos
Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy Improvement. ICML (2018)
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, Stig Petersen, Karen Simonyan, Tom Schaul, Hado van Hasselt, David Silver, Timothy P. Lillicrap, Kevin Calderone, Paul Keet, Anthony Brunasso, David Lawrence, Anders Ekermo, Jacob Repp, Rodney Tsing
StarCraft II: A New Challenge for Reinforcement Learning. CoRR (2017)
André Barreto, Will Dabney, Rémi Munos, Jonathan J. Hunt, Tom Schaul, David Silver, Hado van Hasselt
Successor Features for Transfer in Reinforcement Learning. NIPS (2017)
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
Learning from Demonstrations for Real World Reinforcement Learning. CoRR (2017)
Alexander Sasha Vezhnevets, Simon Osindero, Tom Schaul, Nicolas Heess, Max Jaderberg, David Silver, Koray Kavukcuoglu
FeUdal Networks for Hierarchical Reinforcement Learning. ICML (2017)
Zhongwen Xu, Joseph Modayil, Hado van Hasselt, André Barreto, David Silver, Tom Schaul
Natural Value Approximators: Learning when to Trust Past Estimates. NIPS (2017)
Matthew 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, Tom Schaul, Christopher Summerfield, Greg Wayne, Theophane Weber, Daan Wierstra, Shane Legg, Demis Hassabis
Building Machines that Learn and Think for Themselves: Commentary on Lake et al., Behavioral and Brain Sciences, 2017. CoRR (2017)
David Silver, Hado van Hasselt, Matteo Hessel, Tom Schaul, Arthur Guez, Tim Harley, Gabriel Dulac-Arnold, David P. Reichert, Neil C. Rabinowitz, André Barreto, Thomas Degris
The Predictron: End-To-End Learning and Planning. ICML (2017)
Max Jaderberg, Volodymyr Mnih, Wojciech Marian Czarnecki, Tom Schaul, Joel Z. Leibo, David Silver, Koray Kavukcuoglu
Reinforcement Learning with Unsupervised Auxiliary Tasks. ICLR (2017)
Alexander Sasha Vezhnevets, Simon Osindero, Tom Schaul, Nicolas Heess, Max Jaderberg, David Silver, Koray Kavukcuoglu
FeUdal Networks for Hierarchical Reinforcement Learning. CoRR (2017)
Matteo Hessel, Joseph Modayil, Hado van Hasselt, Tom Schaul, Georg Ostrovski, Will Dabney, Daniel Horgan, Bilal Piot, Mohammad Gheshlaghi Azar, David Silver
Rainbow: Combining Improvements in Deep Reinforcement Learning. CoRR (2017)
Diego Perez Liebana, Spyridon Samothrakis, Julian Togelius, Tom Schaul, Simon M. Lucas
General Video Game AI: Competition, Challenges and Opportunities. AAAI (2016)
Ziyu Wang, Tom Schaul, Matteo Hessel, Hado van Hasselt, Marc Lanctot, Nando de Freitas
Dueling Network Architectures for Deep Reinforcement Learning. ICML (2016)
Marcin Andrychowicz, Misha Denil, Sergio Gomez Colmenarejo, Matthew W. Hoffman, David Pfau, Tom Schaul, Nando de Freitas
Learning to learn by gradient descent by gradient descent. CoRR (2016)
Diego Perez Liebana, Spyridon Samothrakis, Julian Togelius, Tom Schaul, Simon M. Lucas, Adrien Couëtoux, Jerry Lee, Chong-U Lim, Tommy Thompson
The 2014 General Video Game Playing Competition. IEEE Trans. Comput. Intell. AI Games 8 (3) (2016)
Marc G. Bellemare, Sriram Srinivasan, Georg Ostrovski, Tom Schaul, David Saxton, Rémi Munos
Unifying Count-Based Exploration and Intrinsic Motivation. CoRR (2016)
André Barreto, Rémi Munos, Tom Schaul, David Silver
Successor Features for Transfer in Reinforcement Learning. CoRR (2016)
David Silver, Hado van Hasselt, Matteo Hessel, Tom Schaul, Arthur Guez, Tim Harley, Gabriel Dulac-Arnold, David P. Reichert, Neil C. Rabinowitz, André Barreto, Thomas Degris
The Predictron: End-To-End Learning and Planning. CoRR (2016)
Max Jaderberg, Volodymyr Mnih, Wojciech Marian Czarnecki, Tom Schaul, Joel Z. Leibo, David Silver, Koray Kavukcuoglu
Reinforcement Learning with Unsupervised Auxiliary Tasks. CoRR (2016)
Diego Perez Liebana, Spyridon Samothrakis, Julian Togelius, Tom Schaul, Simon M. Lucas
Analyzing the robustness of general video game playing agents. CIG (2016)
Marc G. Bellemare, Sriram Srinivasan, Georg Ostrovski, Tom Schaul, David Saxton, Rémi Munos
Unifying Count-Based Exploration and Intrinsic Motivation. NIPS (2016)
Tom Schaul, John Quan, Ioannis Antonoglou, David Silver
Prioritized Experience Replay. ICLR (Poster) (2016)
Marcin Andrychowicz, Misha Denil, Sergio Gomez Colmenarejo, Matthew W. Hoffman, David Pfau, Tom Schaul, Nando de Freitas
Learning to learn by gradient descent by gradient descent. NIPS (2016)
Tom Schaul, Daniel Horgan, Karol Gregor, David Silver
Universal Value Function Approximators. ICML (2015)
Daan Wierstra, Tom Schaul, Tobias Glasmachers, Yi Sun, Jan Peters, Jürgen Schmidhuber
Natural evolution strategies. J. Mach. Learn. Res. 15 (1) (2014)
Tom Schaul
An Extensible Description Language for Video Games. IEEE Trans. Comput. Intell. AI Games 6 (4) (2014)
Tom Schaul, Ioannis Antonoglou, David Silver
Unit Tests for Stochastic Optimization. ICLR (2014)
Tom Schaul, Mark B. Ring
Better Generalization with Forecasts. IJCAI (2013)
Tom Schaul, Yann LeCun
Adaptive learning rates and parallelization for stochastic, sparse, non-smooth gradients ICLR (Poster) (2013)
Tom Schaul
A video game description language for model-based or interactive learning. CIG (2013)
Yi Sun, Tom Schaul, Faustino J. Gomez, Jürgen Schmidhuber
A linear time natural evolution strategy for non-separable functions. GECCO (Companion) (2013)
Tom Schaul, Sixin Zhang, Yann LeCun
No more pesky learning rates. ICML (3) (2013)
Tom Schaul
Comparing natural evolution strategies to BIPOP-CMA-ES on noiseless and noisy black-box optimization testbeds. GECCO (Companion) (2012)
Yi Sun, Daan Wierstra, Tom Schaul, Jürgen Schmidhuber
Efficient Natural Evolution Strategies CoRR (2012)
Tom Schaul, Sixin Zhang, Yann LeCun
No More Pesky Learning Rates CoRR (2012)
Tom Schaul
Natural evolution strategies converge on sphere functions. GECCO (2012)
Tom Schaul
Benchmarking natural evolution strategies with adaptation sampling on the noiseless and noisy black-box optimization testbeds. GECCO (Companion) (2012)
Tom Schaul
Investigating the impact of adaptation sampling in natural evolution strategies on black-box optimization testbeds. GECCO (Companion) (2012)
Tom Schaul
Benchmarking exponential natural evolution strategies on the noiseless and noisy black-box optimization testbeds. GECCO (Companion) (2012)
Tom Schaul
Benchmarking separable natural evolution strategies on the noiseless and noisy black-box optimization testbeds. GECCO (Companion) (2012)
Mark B. Ring, Tom Schaul
The organization of behavior into temporal and spatial neighborhoods. ICDL-EPIROB (2012)
Daan Wierstra, Tom Schaul, Tobias Glasmachers, Yi Sun, Jürgen Schmidhuber
Natural Evolution Strategies CoRR (2011)
Mark B. Ring, Tom Schaul
Q-Error as a Selection Mechanism in Modular Reinforcement-Learning Systems. IJCAI (2011)
Tom Schaul, Yi Sun, Daan Wierstra, Faustino J. Gomez, Jürgen Schmidhuber
Curiosity-driven optimization. IEEE Congress on Evolutionary Computation (2011)
Tom Schaul, Leo Pape, Tobias Glasmachers, Vincent Graziano, Jürgen Schmidhuber
Coherence Progress: A Measure of Interestingness Based on Fixed Compressors. AGI (2011)
Mark B. Ring, Tom Schaul, Jürgen Schmidhuber
The two-dimensional organization of behavior. ICDL-EPIROB (2011)
Yi Sun, Faustino J. Gomez, Tom Schaul, Jürgen Schmidhuber
A Linear Time Natural Evolution Strategy for Non-Separable Functions CoRR (2011)
Tom Schaul, Julian Togelius, Jürgen Schmidhuber
Measuring Intelligence through Games CoRR (2011)
Tom Schaul, Tobias Glasmachers, Jürgen Schmidhuber
High dimensions and heavy tails for natural evolution strategies. GECCO (2011)
Tom Schaul, Justin Bayer, Daan Wierstra, Yi Sun, Martin Felder, Frank Sehnke, Thomas Rückstieß, Jürgen Schmidhuber
PyBrain. J. Mach. Learn. Res. 11 (2010)
Tom Schaul, Jürgen Schmidhuber
Metalearning. Scholarpedia 5 (6) (2010)
Tobias Glasmachers, Tom Schaul, Jürgen Schmidhuber
A Natural Evolution Strategy for Multi-objective Optimization. PPSN (1) (2010)
Thomas Rückstieß, Frank Sehnke, Tom Schaul, Daan Wierstra, Yi Sun, Jürgen Schmidhuber
Exploring parameter space in reinforcement learning. Paladyn J. Behav. Robotics 1 (1) (2010)
Tobias Glasmachers, Tom Schaul, Yi Sun, Daan Wierstra, Jürgen Schmidhuber
Exponential natural evolution strategies. GECCO (2010)
Mandy Grüttner, Frank Sehnke, Tom Schaul, Jürgen Schmidhuber
Multi-Dimensional Deep Memory Atari-Go Players for Parameter Exploring Policy Gradients. ICANN (2) (2010)
Yi Sun, Daan Wierstra, Tom Schaul, Jürgen Schmidhuber
Stochastic search using the natural gradient. ICML (2009)
Tom Schaul, Daan Wierstra, Faustino J. Gomez, Jürgen Schmidhuber, Christian Igel, Julian Togelius
Ontogenetic and Phylogenetic Reinforcement Learning. Künstliche Intell. 23 (3) (2009)
Yi Sun, Daan Wierstra, Tom Schaul, Jürgen Schmidhuber
Efficient natural evolution strategies. GECCO (2009)
Tom Schaul, Jürgen Schmidhuber
Scalable Neural Networks for Board Games. ICANN (1) (2009)
Daan Wierstra, Tom Schaul, Jan Peters, Jürgen Schmidhuber
Natural Evolution Strategies. IEEE Congress on Evolutionary Computation (2008)
Daan Wierstra, Tom Schaul, Jan Peters, Jürgen Schmidhuber
Episodic Reinforcement Learning by Logistic Reward-Weighted Regression. ICANN (1) (2008)
Daan Wierstra, Tom Schaul, Jan Peters, Jürgen Schmidhuber
Fitness Expectation Maximization. PPSN (2008)
Tom Schaul, Jürgen Schmidhuber
A scalable neural network architecture for board games. CIG (2008)
Julian Togelius, Tom Schaul, Jürgen Schmidhuber, Faustino J. Gomez
Countering Poisonous Inputs with Memetic Neuroevolution. PPSN (2008)

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Publication Activity (10 Years)

Top Topics

Top Venues

Tom Schaul