Tom Schaul

According to our database, Tom Schaul authored at least 84 papers between 2008 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Open-Endedness is Essential for Artificial Superhuman Intelligence.

Edward Hughes, Michael Dennis, Jack Parker-Holder, Feryal M. P. Behbahani, Aditi Mavalankar, Yuge Shi, Tom Schaul, Tim Rocktäschel

CoRR, 2024

Position: Open-Endedness is Essential for Artificial Superhuman Intelligence.

Edward Hughes, Michael D. Dennis, Jack Parker-Holder, Feryal M. P. Behbahani, Aditi Mavalankar, Yuge Shi, Tom Schaul, Tim Rocktäschel

Proceedings of the Forty-first International Conference on Machine Learning, 2024

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, 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

CoRR, 2023

Scaling Goal-based Exploration via Pruning Proto-goals.

Akhil Bagaria, Ray Jiang, Ramana Kumar, Tom Schaul

CoRR, 2023

Scaling Goal-based Exploration via Pruning Proto-goals.

Akhil Bagaria, Tom Schaul

Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 2023

Discovering Evolution Strategies via Meta-Black-Box Optimization.

Robert Tjarko Lange, Tom Schaul, Yutian Chen, Tom Zahavy, Valentin Dalibard, Chris Lu, Satinder Singh, Sebastian Flennerhag

Proceedings of the Companion Proceedings of the Conference on Genetic and Evolutionary Computation, 2023

Discovering Attention-Based Genetic Algorithms via Meta-Black-Box Optimization.

Robert Tjarko Lange, Tom Schaul, Yutian Chen, Chris Lu, Tom Zahavy, Valentin Dalibard, Sebastian Flennerhag

Proceedings of the Genetic and Evolutionary Computation Conference, 2023

2022

AI for the Social Good (Dagstuhl Seminar 22091).

Claudia Clopath, Ruben De Winne, Tom Schaul

Dagstuhl Reports, 2022

The Phenomenon of Policy Churn.

Tom Schaul, André Barreto, John Quan, Georg Ostrovski

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 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, Simon Osindero

Proceedings of the International Conference on Machine Learning, 2022

When should agents explore?

Miruna Pislar, David Szepesvari, Georg Ostrovski, Diana L. Borsa, Tom Schaul

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Return-based Scaling: Yet Another Normalisation Trick for Deep RL.

Tom Schaul, Georg Ostrovski, Iurii Kemaev, Diana Borsa

CoRR, 2021

2020

Policy Evaluation Networks.

Jean Harb, Tom Schaul, Doina Precup, Pierre-Luc Bacon

CoRR, 2020

Conditional Importance Sampling for Off-Policy Learning.

Mark Rowland, Anna Harutyunyan, Hado van Hasselt, Diana Borsa, Tom Schaul, Rémi Munos, Will Dabney

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

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, 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

Nat., 2019

Artificial and Computational Intelligence in Games: Revolutions in Computational Game AI (Dagstuhl Seminar 19511).

Jialin Liu, Tom Schaul, Pieter Spronck, Julian Togelius

Dagstuhl Reports, 2019

AI for the Social Good (Dagstuhl Seminar 19082).

Claudia Clopath, Ruben De Winne, Mohammad Emtiyaz Khan, Tom Schaul

Dagstuhl Reports, 2019

Adapting Behaviour for Learning Progress.

Tom Schaul, Diana Borsa, David Ding, David Szepesvari, Georg Ostrovski, Will Dabney, Simon Osindero

CoRR, 2019

Non-Differentiable Supervised Learning with Evolution Strategies and Hybrid Methods.

Karel Lenc, Erich Elsen, Tom Schaul, Karen Simonyan

CoRR, 2019

Ray Interference: a Source of Plateaus in Deep Reinforcement Learning.

Tom Schaul, Diana Borsa, Joseph Modayil, Razvan Pascanu

CoRR, 2019

Universal Successor Features Approximators.

Diana Borsa, André Barreto, John Quan, Daniel J. Mankowitz, Hado van Hasselt, Rémi Munos, David Silver, Tom Schaul

Proceedings of the 7th International Conference on Learning Representations, 2019

2018

The Barbados 2018 List of Open Issues in Continual Learning.

Tom Schaul, Hado van Hasselt, Joseph Modayil, Martha White, Adam White, Pierre-Luc Bacon, Jean Harb, Shibl Mourad, Marc G. Bellemare, Doina Precup

CoRR, 2018

Unicorn: Continual Learning with a Universal, Off-policy Agent.

Daniel J. Mankowitz, Augustin Zídek, André Barreto, Dan Horgan, Matteo Hessel, John Quan, Junhyuk Oh, Hado van Hasselt, David Silver, Tom Schaul

CoRR, 2018

Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy Improvement.

André Barreto, Diana Borsa, John Quan, Tom Schaul, David Silver, Matteo Hessel, Daniel J. Mankowitz, Augustin Zídek, Rémi Munos

Proceedings of the 35th International Conference on Machine Learning, 2018

Meta-learning by the baldwin effect.

Chrisantha Fernando, Jakub Sygnowski, Simon Osindero, Jane Wang, Tom Schaul, Denis Teplyashin, Pablo Sprechmann, Alexander Pritzel, Andrei A. Rusu

Proceedings of the Genetic and Evolutionary Computation Conference Companion, 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, Joel Z. Leibo, Audrunas Gruslys

Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

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

Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

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, Tom Schaul, Christopher Summerfield, Greg Wayne, Theophane Weber, Daan Wierstra, Shane Legg, Demis Hassabis

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, 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

CoRR, 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

Natural Value Approximators: Learning when to Trust Past Estimates.

Zhongwen Xu, Joseph Modayil, Hado van Hasselt, André Barreto, David Silver, Tom Schaul

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Successor Features for Transfer in Reinforcement Learning.

André Barreto, Will Dabney, Rémi Munos, Jonathan J. Hunt, Tom Schaul, David Silver, Hado van Hasselt

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

FeUdal Networks for Hierarchical Reinforcement Learning.

Alexander Sasha Vezhnevets, Simon Osindero, Tom Schaul, Nicolas Heess, Max Jaderberg, David Silver, Koray Kavukcuoglu

Proceedings of the 34th International Conference on Machine Learning, 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, Thomas Degris

Proceedings of the 34th International Conference on Machine Learning, 2017

Reinforcement Learning with Unsupervised Auxiliary Tasks.

Max Jaderberg, Volodymyr Mnih, Wojciech Marian Czarnecki, Tom Schaul, Joel Z. Leibo, David Silver, Koray Kavukcuoglu

Proceedings of the 5th International Conference on Learning Representations, 2017

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

Prioritized Experience Replay.

Tom Schaul, John Quan, Ioannis Antonoglou, David Silver

Proceedings of the 4th International Conference on Learning Representations, 2016

Successor Features for Transfer in Reinforcement Learning.

André Barreto, Rémi Munos, Tom Schaul, David Silver

CoRR, 2016

Unifying Count-Based Exploration and Intrinsic Motivation.

Marc G. Bellemare, Sriram Srinivasan, Georg Ostrovski, Tom Schaul, David Saxton, Rémi Munos

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 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

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Dueling Network Architectures for Deep Reinforcement Learning.

Ziyu Wang, Tom Schaul, Matteo Hessel, Hado van Hasselt, Marc Lanctot, Nando de Freitas

Proceedings of the 33nd International Conference on Machine Learning, 2016

Analyzing the robustness of general video game playing agents.

Diego Perez Liebana, Spyridon Samothrakis, Julian Togelius, Tom Schaul, Simon M. Lucas

Proceedings of the IEEE Conference on Computational Intelligence and Games, 2016

General Video Game AI: Competition, Challenges and Opportunities.

Diego Perez Liebana, Spyridon Samothrakis, Julian Togelius, Tom Schaul, Simon M. Lucas

Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 2016

2015

Universal Value Function Approximators.

Tom Schaul, Daniel Horgan, Karol Gregor, David Silver

Proceedings of the 32nd International Conference on Machine Learning, 2015

2014

An Extensible Description Language for Video Games.

Tom Schaul

IEEE Trans. Comput. Intell. AI Games, 2014

Natural evolution strategies.

Daan Wierstra, Tom Schaul, Tobias Glasmachers, Yi Sun, Jan Peters, Jürgen Schmidhuber

J. Mach. Learn. Res., 2014

Unit Tests for Stochastic Optimization.

Tom Schaul, Ioannis Antonoglou, David Silver

Proceedings of the 2nd International Conference on Learning Representations, 2014

2013

Adaptive learning rates and parallelization for stochastic, sparse, non-smooth gradients

Tom Schaul, Yann LeCun

Proceedings of the 1st International Conference on Learning Representations, 2013

Better Generalization with Forecasts.

Tom Schaul, Mark B. Ring

Proceedings of the IJCAI 2013, 2013

No more pesky learning rates.

Tom Schaul, Sixin Zhang, Yann LeCun

Proceedings of the 30th International Conference on Machine Learning, 2013

A linear time natural evolution strategy for non-separable functions.

Yi Sun, Tom Schaul, Faustino J. Gomez, Jürgen Schmidhuber

Proceedings of the Genetic and Evolutionary Computation Conference, 2013

General Video Game Playing.

John Levine, Clare Bates Congdon, Marc Ebner, Graham Kendall, Simon M. Lucas, Risto Miikkulainen, Tom Schaul, Tommy Thompson

Proceedings of the Artificial and Computational Intelligence in Games, 2013

Towards a Video Game Description Language.

Marc Ebner, John Levine, Simon M. Lucas, Tom Schaul, Tommy Thompson, Julian Togelius

Proceedings of the Artificial and Computational Intelligence in Games, 2013

A video game description language for model-based or interactive learning.

Tom Schaul

Proceedings of the 2013 IEEE Conference on Computational Inteligence in Games (CIG), 2013

2012

The organization of behavior into temporal and spatial neighborhoods.

Mark B. Ring, Tom Schaul

Proceedings of the 2012 IEEE International Conference on Development and Learning and Epigenetic Robotics, 2012

Comparing natural evolution strategies to BIPOP-CMA-ES on noiseless and noisy black-box optimization testbeds.

Tom Schaul

Proceedings of the Genetic and Evolutionary Computation Conference, 2012

Benchmarking natural evolution strategies with adaptation sampling on the noiseless and noisy black-box optimization testbeds.

Tom Schaul

Proceedings of the Genetic and Evolutionary Computation Conference, 2012

Investigating the impact of adaptation sampling in natural evolution strategies on black-box optimization testbeds.

Tom Schaul

Proceedings of the Genetic and Evolutionary Computation Conference, 2012

Benchmarking exponential natural evolution strategies on the noiseless and noisy black-box optimization testbeds.

Tom Schaul

Proceedings of the Genetic and Evolutionary Computation Conference, 2012

Benchmarking separable natural evolution strategies on the noiseless and noisy black-box optimization testbeds.

Tom Schaul

Proceedings of the Genetic and Evolutionary Computation Conference, 2012

Natural evolution strategies converge on sphere functions.

Tom Schaul

Proceedings of the Genetic and Evolutionary Computation Conference, 2012

2011

Studies in Continuous Black-box Optimization.

Tom Schaul

PhD thesis, 2011

Measuring Intelligence through Games

Tom Schaul, Julian Togelius, Jürgen Schmidhuber

CoRR, 2011

Natural Evolution Strategies

Daan Wierstra, Tom Schaul, Tobias Glasmachers, Yi Sun, Jürgen Schmidhuber

CoRR, 2011

Q-Error as a Selection Mechanism in Modular Reinforcement-Learning Systems.

Mark B. Ring, Tom Schaul

Proceedings of the IJCAI 2011, 2011

The two-dimensional organization of behavior.

Mark B. Ring, Tom Schaul, Jürgen Schmidhuber

Proceedings of the 1st International Conference on Development and Learning and on Epigenetic Robotics, 2011

High dimensions and heavy tails for natural evolution strategies.

Tom Schaul, Tobias Glasmachers, Jürgen Schmidhuber

Proceedings of the 13th Annual Genetic and Evolutionary Computation Conference, 2011

Curiosity-driven optimization.

Tom Schaul, Yi Sun, Daan Wierstra, Faustino J. Gomez, Jürgen Schmidhuber

Proceedings of the IEEE Congress on Evolutionary Computation, 2011

Coherence Progress: A Measure of Interestingness Based on Fixed Compressors.

Tom Schaul, Leo Pape, Tobias Glasmachers, Vincent Graziano, Jürgen Schmidhuber

Proceedings of the Artificial General Intelligence - 4th International Conference, 2011

2010

Metalearning.

Tom Schaul, Jürgen Schmidhuber

Scholarpedia, 2010

Exploring parameter space in reinforcement learning.

Thomas Rückstieß, Frank Sehnke, Tom Schaul, Daan Wierstra, Yi Sun, Jürgen Schmidhuber

Paladyn J. Behav. Robotics, 2010

PyBrain.

Tom Schaul, Justin Bayer, Daan Wierstra, Yi Sun, Martin Felder, Frank Sehnke, Thomas Rückstieß, Jürgen Schmidhuber

J. Mach. Learn. Res., 2010

A Natural Evolution Strategy for Multi-objective Optimization.

Tobias Glasmachers, Tom Schaul, Jürgen Schmidhuber

Proceedings of the Parallel Problem Solving from Nature, 2010

Multi-Dimensional Deep Memory Atari-Go Players for Parameter Exploring Policy Gradients.

Mandy Grüttner, Frank Sehnke, Tom Schaul, Jürgen Schmidhuber

Proceedings of the Artificial Neural Networks, 2010

Exponential natural evolution strategies.

Tobias Glasmachers, Tom Schaul, Yi Sun, Daan Wierstra, Jürgen Schmidhuber

Proceedings of the Genetic and Evolutionary Computation Conference, 2010

2009

Ontogenetic and Phylogenetic Reinforcement Learning.

Tom Schaul, Daan Wierstra, Faustino J. Gomez, Jürgen Schmidhuber, Christian Igel, Julian Togelius

Künstliche Intell., 2009

Stochastic search using the natural gradient.

Yi Sun, Daan Wierstra, Tom Schaul, Jürgen Schmidhuber

Proceedings of the 26th Annual International Conference on Machine Learning, 2009

Scalable Neural Networks for Board Games.

Tom Schaul, Jürgen Schmidhuber

Proceedings of the Artificial Neural Networks, 2009

Efficient natural evolution strategies.

Yi Sun, Daan Wierstra, Tom Schaul, Jürgen Schmidhuber

Proceedings of the Genetic and Evolutionary Computation Conference, 2009

2008

Fitness Expectation Maximization.

Daan Wierstra, Tom Schaul, Jan Peters, Jürgen Schmidhuber

Proceedings of the Parallel Problem Solving from Nature, 2008

Countering Poisonous Inputs with Memetic Neuroevolution.

Julian Togelius, Tom Schaul, Jürgen Schmidhuber, Faustino J. Gomez

Proceedings of the Parallel Problem Solving from Nature, 2008

Episodic Reinforcement Learning by Logistic Reward-Weighted Regression.

Daan Wierstra, Tom Schaul, Jan Peters, Jürgen Schmidhuber

Proceedings of the Artificial Neural Networks, 2008

A scalable neural network architecture for board games.

Tom Schaul, Jürgen Schmidhuber

Proceedings of the 2008 IEEE Symposium on Computational Intelligence and Games, 2008

Natural Evolution Strategies.

Daan Wierstra, Tom Schaul, Jan Peters, Jürgen Schmidhuber

Proceedings of the IEEE Congress on Evolutionary Computation, 2008