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ORCIDTom Schaul
Person information
- affiliation: Google DeepMind, London, UK
- affiliation: New York University, Courant Institute of Mathematical Sciences, NY, USA
- affiliation: IDSIA, Manno-Lugano, Switzerland
- affiliation (PhD 2011): TU Munich, Germany
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2020 – today
- 2024
[c56]Edward Hughes, Michael D. Dennis, Jack Parker-Holder, Feryal M. P. Behbahani, Aditi Mavalankar, Yuge Shi, Tom Schaul, Tim Rocktäschel:
Position: Open-Endedness is Essential for Artificial Superhuman Intelligence. ICML 2024- [i37]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 abs/2406.04268 (2024) - 2023
- [c55]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: 29-30 - [c54]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: 929-937 - [c53]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 - [c52]Akhil Bagaria, Tom Schaul:
Scaling Goal-based Exploration via Pruning Proto-goals. IJCAI 2023: 3451-3460 - [i36]Akhil Bagaria, Ray Jiang, Ramana Kumar, Tom Schaul:
Scaling Goal-based Exploration via Pruning Proto-goals. CoRR abs/2302.04693 (2023) - [i35]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 abs/2304.03995 (2023) - [i34]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 abs/2312.09187 (2023) - 2022
- [c51]Miruna Pislar, David Szepesvari, Georg Ostrovski, Diana L. Borsa, Tom Schaul:
When should agents explore? ICLR 2022 - [c50]Angelos Filos, Eszter Vértes, Zita Marinho, Gregory Farquhar, Diana Borsa, Abram L. Friesen, Feryal M. P. Behbahani, Tom Schaul, André Barreto, Simon Osindero:
Model-Value Inconsistency as a Signal for Epistemic Uncertainty. ICML 2022: 6474-6498 - [c49]Tom Schaul, André Barreto, John Quan, Georg Ostrovski:
The Phenomenon of Policy Churn. NeurIPS 2022 - [i33]Tom Schaul, André Barreto, John Quan, Georg Ostrovski:
The Phenomenon of Policy Churn. CoRR abs/2206.00730 (2022) - [i32]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 abs/2211.11260 (2022) - [i31]Claudia Clopath, Ruben De Winne, Tom Schaul:
AI for the Social Good (Dagstuhl Seminar 22091). Dagstuhl Reports 12(2): 134-142 (2022) - 2021
- [i30]Tom Schaul, Georg Ostrovski, Iurii Kemaev, Diana Borsa:
Return-based Scaling: Yet Another Normalisation Trick for Deep RL. CoRR abs/2105.05347 (2021) - [i29]Miruna Pislar, David Szepesvari, Georg Ostrovski, Diana Borsa, Tom Schaul:
When should agents explore? CoRR abs/2108.11811 (2021) - [i28]Angelos Filos, Eszter Vértes, Zita Marinho, Gregory Farquhar, Diana Borsa, Abram L. Friesen, Feryal M. P. Behbahani, Tom Schaul, André Barreto, Simon Osindero:
Model-Value Inconsistency as a Signal for Epistemic Uncertainty. CoRR abs/2112.04153 (2021) - 2020
- [c48]Mark Rowland, Anna Harutyunyan, Hado van Hasselt, Diana Borsa, Tom Schaul, Rémi Munos, Will Dabney:
Conditional Importance Sampling for Off-Policy Learning. AISTATS 2020: 45-55 - [i27]Jean Harb, Tom Schaul, Doina Precup, Pierre-Luc Bacon:
Policy Evaluation Networks. CoRR abs/2002.11833 (2020)
2010 – 2019
- 2019
- [j8]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): 350-354 (2019) - [c47]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 - [i26]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 abs/1901.10964 (2019) - [i25]Tom Schaul, Diana Borsa, Joseph Modayil, Razvan Pascanu:
Ray Interference: a Source of Plateaus in Deep Reinforcement Learning. CoRR abs/1904.11455 (2019) - [i24]Karel Lenc, Erich Elsen, Tom Schaul, Karen Simonyan:
Non-Differentiable Supervised Learning with Evolution Strategies and Hybrid Methods. CoRR abs/1906.03139 (2019) - [i23]Mark Rowland, Anna Harutyunyan, Hado van Hasselt, Diana Borsa, Tom Schaul, Rémi Munos, Will Dabney:
Conditional Importance Sampling for Off-Policy Learning. CoRR abs/1910.07479 (2019) - [i22]Tom Schaul, Diana Borsa, David Ding, David Szepesvari, Georg Ostrovski, Will Dabney, Simon Osindero:
Adapting Behaviour for Learning Progress. CoRR abs/1912.06910 (2019) - [i21]Claudia Clopath, Ruben De Winne, Mohammad Emtiyaz Khan, Tom Schaul:
AI for the Social Good (Dagstuhl Seminar 19082). Dagstuhl Reports 9(2): 111-122 (2019) - [i20]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): 67-114 (2019) - 2018
- [c46]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: 3215-3222 - [c45]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: 3223-3230 - [c44]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: 109-110 - [c43]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: 1313-1320 - [c42]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: 510-519 - [i19]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 abs/1802.08294 (2018) - [i18]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 abs/1806.07917 (2018) - [i17]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 abs/1811.07004 (2018) - [i16]Diana Borsa, André Barreto, John Quan, Daniel J. Mankowitz, Rémi Munos, Hado van Hasselt, David Silver, Tom Schaul:
Universal Successor Features Approximators. CoRR abs/1812.07626 (2018) - 2017
- [c41]Max Jaderberg, Volodymyr Mnih, Wojciech Marian Czarnecki, Tom Schaul, Joel Z. Leibo, David Silver, Koray Kavukcuoglu:
Reinforcement Learning with Unsupervised Auxiliary Tasks. ICLR 2017 - [c40]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: 3191-3199 - [c39]Alexander Sasha Vezhnevets, Simon Osindero, Tom Schaul, Nicolas Heess, Max Jaderberg, David Silver, Koray Kavukcuoglu:
FeUdal Networks for Hierarchical Reinforcement Learning. ICML 2017: 3540-3549 - [c38]Zhongwen Xu, Joseph Modayil, Hado van Hasselt, André Barreto, David Silver, Tom Schaul:
Natural Value Approximators: Learning when to Trust Past Estimates. NIPS 2017: 2120-2128 - [c37]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: 4055-4065 - [i15]Alexander Sasha Vezhnevets, Simon Osindero, Tom Schaul, Nicolas Heess, Max Jaderberg, David Silver, Koray Kavukcuoglu:
FeUdal Networks for Hierarchical Reinforcement Learning. CoRR abs/1703.01161 (2017) - [i14]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 abs/1704.03732 (2017) - [i13]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 abs/1708.04782 (2017) - [i12]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 abs/1710.02298 (2017) - [i11]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:
Building Machines that Learn and Think for Themselves: Commentary on Lake et al., Behavioral and Brain Sciences, 2017. CoRR abs/1711.08378 (2017) - 2016
- [j7]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): 229-243 (2016) - [c36]Diego Perez Liebana, Spyridon Samothrakis, Julian Togelius, Tom Schaul, Simon M. Lucas:
General Video Game AI: Competition, Challenges and Opportunities. AAAI 2016: 4335-4337 - [c35]Diego Perez Liebana, Spyridon Samothrakis, Julian Togelius, Tom Schaul, Simon M. Lucas:
Analyzing the robustness of general video game playing agents. CIG 2016: 1-8 - [c34]Ziyu Wang, Tom Schaul, Matteo Hessel, Hado van Hasselt, Marc Lanctot, Nando de Freitas:
Dueling Network Architectures for Deep Reinforcement Learning. ICML 2016: 1995-2003 - [c33]Marc G. Bellemare, Sriram Srinivasan, Georg Ostrovski, Tom Schaul, David Saxton, Rémi Munos:
Unifying Count-Based Exploration and Intrinsic Motivation. NIPS 2016: 1471-1479 - [c32]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: 3981-3989 - [c31]Tom Schaul, John Quan, Ioannis Antonoglou, David Silver:
Prioritized Experience Replay. ICLR (Poster) 2016 - [i10]Marc G. Bellemare, Sriram Srinivasan, Georg Ostrovski, Tom Schaul, David Saxton, Rémi Munos:
Unifying Count-Based Exploration and Intrinsic Motivation. CoRR abs/1606.01868 (2016) - [i9]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 abs/1606.04474 (2016) - [i8]André Barreto, Rémi Munos, Tom Schaul, David Silver:
Successor Features for Transfer in Reinforcement Learning. CoRR abs/1606.05312 (2016) - [i7]Max Jaderberg, Volodymyr Mnih, Wojciech Marian Czarnecki, Tom Schaul, Joel Z. Leibo, David Silver, Koray Kavukcuoglu:
Reinforcement Learning with Unsupervised Auxiliary Tasks. CoRR abs/1611.05397 (2016) - [i6]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 abs/1612.08810 (2016) - 2015
- [c30]Tom Schaul, Daniel Horgan, Karol Gregor, David Silver:
Universal Value Function Approximators. ICML 2015: 1312-1320 - 2014
- [j6]Daan Wierstra, Tom Schaul, Tobias Glasmachers, Yi Sun, Jan Peters, Jürgen Schmidhuber:
Natural evolution strategies. J. Mach. Learn. Res. 15(1): 949-980 (2014) - [j5]Tom Schaul:
An Extensible Description Language for Video Games. IEEE Trans. Comput. Intell. AI Games 6(4): 325-331 (2014) - [c29]Tom Schaul, Ioannis Antonoglou, David Silver:
Unit Tests for Stochastic Optimization. ICLR 2014 - 2013
- [c28]Tom Schaul:
A video game description language for model-based or interactive learning. CIG 2013: 1-8 - [c27]Yi Sun, Tom Schaul, Faustino J. Gomez, Jürgen Schmidhuber:
A linear time natural evolution strategy for non-separable functions. GECCO (Companion) 2013: 61-62 - [c26]Tom Schaul, Sixin Zhang, Yann LeCun:
No more pesky learning rates. ICML (3) 2013: 343-351 - [c25]Tom Schaul, Mark B. Ring:
Better Generalization with Forecasts. IJCAI 2013: 1656-1662 - [c24]Tom Schaul, Yann LeCun:
Adaptive learning rates and parallelization for stochastic, sparse, non-smooth gradients. ICLR (Poster) 2013 - [p2]John Levine, Clare Bates Congdon, Marc Ebner, Graham Kendall, Simon M. Lucas, Risto Miikkulainen, Tom Schaul, Tommy Thompson:
General Video Game Playing. Artificial and Computational Intelligence in Games 2013: 77-83 - [p1]Marc Ebner, John Levine, Simon M. Lucas, Tom Schaul, Tommy Thompson, Julian Togelius:
Towards a Video Game Description Language. Artificial and Computational Intelligence in Games 2013: 85-100 - 2012
- [c23]Tom Schaul:
Benchmarking separable natural evolution strategies on the noiseless and noisy black-box optimization testbeds. GECCO (Companion) 2012: 205-212 - [c22]Tom Schaul:
Benchmarking exponential natural evolution strategies on the noiseless and noisy black-box optimization testbeds. GECCO (Companion) 2012: 213-220 - [c21]Tom Schaul:
Investigating the impact of adaptation sampling in natural evolution strategies on black-box optimization testbeds. GECCO (Companion) 2012: 221-228 - [c20]Tom Schaul:
Benchmarking natural evolution strategies with adaptation sampling on the noiseless and noisy black-box optimization testbeds. GECCO (Companion) 2012: 229-236 - [c19]Tom Schaul:
Comparing natural evolution strategies to BIPOP-CMA-ES on noiseless and noisy black-box optimization testbeds. GECCO (Companion) 2012: 237-244 - [c18]Tom Schaul:
Natural evolution strategies converge on sphere functions. GECCO 2012: 329-336 - [c17]Mark B. Ring, Tom Schaul:
The organization of behavior into temporal and spatial neighborhoods. ICDL-EPIROB 2012: 1-6 - [i5]Tom Schaul, Sixin Zhang, Yann LeCun:
No More Pesky Learning Rates. CoRR abs/1206.1106 (2012) - [i4]Yi Sun, Daan Wierstra, Tom Schaul, Jürgen Schmidhuber:
Efficient Natural Evolution Strategies. CoRR abs/1209.5853 (2012) - 2011
- [b1]Tom Schaul:
Studies in Continuous Black-box Optimization. Technical University Munich, 2011 - [c16]Tom Schaul, Leo Pape, Tobias Glasmachers, Vincent Graziano, Jürgen Schmidhuber:
Coherence Progress: A Measure of Interestingness Based on Fixed Compressors. AGI 2011: 21-30 - [c15]Tom Schaul, Yi Sun, Daan Wierstra, Faustino J. Gomez, Jürgen Schmidhuber:
Curiosity-driven optimization. IEEE Congress on Evolutionary Computation 2011: 1343-1349 - [c14]Tom Schaul, Tobias Glasmachers, Jürgen Schmidhuber:
High dimensions and heavy tails for natural evolution strategies. GECCO 2011: 845-852 - [c13]Mark B. Ring, Tom Schaul, Jürgen Schmidhuber:
The two-dimensional organization of behavior. ICDL-EPIROB 2011: 1-8 - [c12]Mark B. Ring, Tom Schaul:
Q-Error as a Selection Mechanism in Modular Reinforcement-Learning Systems. IJCAI 2011: 1452-1457 - [i3]Yi Sun, Faustino J. Gomez, Tom Schaul, Jürgen Schmidhuber:
A Linear Time Natural Evolution Strategy for Non-Separable Functions. CoRR abs/1106.1998 (2011) - [i2]Daan Wierstra, Tom Schaul, Tobias Glasmachers, Yi Sun, Jürgen Schmidhuber:
Natural Evolution Strategies. CoRR abs/1106.4487 (2011) - [i1]Tom Schaul, Julian Togelius, Jürgen Schmidhuber:
Measuring Intelligence through Games. CoRR abs/1109.1314 (2011) - 2010
- [j4]Tom Schaul, Justin Bayer, Daan Wierstra, Yi Sun, Martin Felder, Frank Sehnke, Thomas Rückstieß, Jürgen Schmidhuber:
PyBrain. J. Mach. Learn. Res. 11: 743-746 (2010) - [j3]Thomas Rückstieß, Frank Sehnke, Tom Schaul, Daan Wierstra, Yi Sun, Jürgen Schmidhuber:
Exploring parameter space in reinforcement learning. Paladyn J. Behav. Robotics 1(1): 14-24 (2010) - [j2]Tom Schaul, Jürgen Schmidhuber:
Metalearning. Scholarpedia 5(6): 4650 (2010) - [c11]Tobias Glasmachers, Tom Schaul, Yi Sun, Daan Wierstra, Jürgen Schmidhuber:
Exponential natural evolution strategies. GECCO 2010: 393-400 - [c10]Mandy Grüttner, Frank Sehnke, Tom Schaul, Jürgen Schmidhuber:
Multi-Dimensional Deep Memory Atari-Go Players for Parameter Exploring Policy Gradients. ICANN (2) 2010: 114-123 - [c9]Tobias Glasmachers, Tom Schaul, Jürgen Schmidhuber:
A Natural Evolution Strategy for Multi-objective Optimization. PPSN (1) 2010: 627-636
2000 – 2009
- 2009
- [j1]Tom Schaul, Daan Wierstra, Faustino J. Gomez, Jürgen Schmidhuber, Christian Igel, Julian Togelius:
Ontogenetic and Phylogenetic Reinforcement Learning. Künstliche Intell. 23(3): 30-33 (2009) - [c8]Yi Sun, Daan Wierstra, Tom Schaul, Jürgen Schmidhuber:
Efficient natural evolution strategies. GECCO 2009: 539-546 - [c7]Tom Schaul, Jürgen Schmidhuber:
Scalable Neural Networks for Board Games. ICANN (1) 2009: 1005-1014 - [c6]Yi Sun, Daan Wierstra, Tom Schaul, Jürgen Schmidhuber:
Stochastic search using the natural gradient. ICML 2009: 1161-1168 - 2008
- [c5]Daan Wierstra, Tom Schaul, Jan Peters, Jürgen Schmidhuber:
Natural Evolution Strategies. IEEE Congress on Evolutionary Computation 2008: 3381-3387 - [c4]Tom Schaul, Jürgen Schmidhuber:
A scalable neural network architecture for board games. CIG 2008: 357-364 - [c3]Daan Wierstra, Tom Schaul, Jan Peters, Jürgen Schmidhuber:
Episodic Reinforcement Learning by Logistic Reward-Weighted Regression. ICANN (1) 2008: 407-416 - [c2]Daan Wierstra, Tom Schaul, Jan Peters, Jürgen Schmidhuber:
Fitness Expectation Maximization. PPSN 2008: 337-346 - [c1]Julian Togelius, Tom Schaul, Jürgen Schmidhuber, Faustino J. Gomez:
Countering Poisonous Inputs with Memetic Neuroevolution. PPSN 2008: 610-619