| Prioritized Experience Replay T Schaul, J Quan, I Antonoglou, D Silver International Conference on Learning Representations (ICLR), 2016 | 977 | 2016 |
| Dueling Network Architectures for Deep Reinforcement Learning Z Wang, T Schaul, M Hessel, H van Hasselt, M Lanctot, N de Freitas International Conference on Machine Learning (ICML), 2016 | 871 | 2016 |
| Learning to learn by gradient descent by gradient descent M Andrychowicz, M Denil, S Gomez, MW Hoffman, D Pfau, T Schaul, ... Neural Information Processing Systems (NIPS), 2016 | 613 | 2016 |
| Reinforcement learning with unsupervised auxiliary tasks M Jaderberg, V Mnih, WM Czarnecki, T Schaul, JZ Leibo, D Silver, ... International Conference on Learning Representations (ICLR), 2017 | 448 | 2017 |
| Unifying Count-Based Exploration and Intrinsic Motivation MG Bellemare, S Srinivasan, G Ostrovski, T Schaul, D Saxton, R Munos Neural Information Processing Systems (NIPS), 2016 | 423 | 2016 |
| PyBrain T Schaul, J Bayer, D Wierstra, Y Sun, M Felder, F Sehnke, T Rückstieß, ... Journal of Machine Learning Research 11 (Feb), 743-746, 2010 | 383 | 2010 |
| Rainbow: Combining Improvements in Deep Reinforcement Learning M Hessel, J Modayil, H Van Hasselt, T Schaul, G Ostrovski, W Dabney, ... AAAI Conference on Artificial Intelligence, 2018 | 381 | 2018 |
| No more pesky learning rates T Schaul, S Zhang, Y LeCun International Conference on Machine Learning (ICML'13), 2013 | 307 | 2013 |
| Feudal networks for hierarchical reinforcement learning AS Vezhnevets, S Osindero, T Schaul, N Heess, M Jaderberg, D Silver, ... International Conference on Machine Learning (ICML), 2017 | 265 | 2017 |
| Universal value function approximators T Schaul, D Horgan, K Gregor, D Silver International Conference on Machine Learning (ICML-15), 1312-1320, 2015 | 263 | 2015 |
| Deep Q-learning from Demonstrations T Hester, M Vecerik, O Pietquin, M Lanctot, T Schaul, B Piot, D Horgan, ... AAAI Conference on Artificial Intelligence, 2018 | 260 | 2018 |
| StarCraft II: A New Challenge for Reinforcement Learning O Vinyals, T Ewalds, S Bartunov, P Georgiev, AS Vezhnevets, M Yeo, ... arXiv preprint arXiv:1708.04782, 2017 | 258 | 2017 |
| Natural evolution strategies D Wierstra, T Schaul, J Peters, J Schmidhuber IEEE Congress on Evolutionary Computation (IEEE World Congress on …, 2008 | 219 | 2008 |
| The 2014 General Video Game Playing Competition D Perez, S Samothrakis, J Togelius, T Schaul, S Lucas, A Couëtoux, ... Computational Intelligence and AI in Games, 2015 | 175* | 2015 |
| Natural evolution strategies D Wierstra, T Schaul, T Glasmachers, Y Sun, J Peters, J Schmidhuber The Journal of Machine Learning Research 15 (1), 949-980, 2014 | 163 | 2014 |
| A video game description language for model-based or interactive learning T Schaul Conference on Computational Intelligence in Games (IEEE-CIG), 1-8, 2013 | 158 | 2013 |
| Successor features for transfer in reinforcement learning A Barreto, W Dabney, R Munos, J Hunt, T Schaul, D Silver, H van Hasselt Neural Information Processing Systems (NIPS), 2017 | 136 | 2017 |
| Exponential natural evolution strategies T Glasmachers, T Schaul, S Yi, D Wierstra, J Schmidhuber Genetic and evolutionary computation conference (GECCO), 393-400, 2010 | 128 | 2010 |
| The predictron: End-to-end learning and planning D Silver*, H van Hasselt*, M Hessel*, T Schaul*, A Guez*, T Harley, ... International Conference on Machine Learning (ICML), 2017 | 121 | 2017 |
| General Video Game AI: Competition, Challenges and Opportunities D Perez-Liebana, S Samothrakis, J Togelius, T Schaul, SM Lucas AAAI What's Hot, 2016 | 115 | 2016 |
Juergen SchmidhuberThe Swiss AI Lab IDSIA / USI & SUPSIVerified email at idsia.ch
