| 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 | 6499 | 2016 |
| Prioritized Experience Replay T Schaul, J Quan, I Antonoglou, D Silver International Conference on Learning Representations (ICLR), 2016 | 6379 | 2016 |
| Grandmaster level in StarCraft II using multi-agent reinforcement learning O Vinyals, I Babuschkin, WM Czarnecki, M Mathieu, A Dudzik, J Chung, ... Nature 575 (7782), 350–354, 2019 | 6131 | 2019 |
| 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 | 3560 | 2018 |
| 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 (NeurIPS), 2016 | 2871 | 2016 |
| Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities G Team https://storage.googleapis.com/deepmind-media/gemini/gemini_v2_5_report.pdf, 2025 | 2259* | 2025 |
| Unifying Count-Based Exploration and Intrinsic Motivation MG Bellemare, S Srinivasan, G Ostrovski, T Schaul, D Saxton, R Munos Neural Information Processing Systems (NeurIPS), 2016 | 2069 | 2016 |
| 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 | 1632 | 2018 |
| 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 | 1617 | 2017 |
| Universal value function approximators T Schaul, D Horgan, K Gregor, D Silver International Conference on Machine Learning (ICML-15), 1312-1320, 2015 | 1503 | 2015 |
| 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 | 1376 | 2017 |
| 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 | 1346 | 2017 |
| 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 | 1253 | 2014 |
| Successor features for transfer in reinforcement learning A Barreto, W Dabney, R Munos, JJ Hunt, T Schaul, D Silver, ... Neural Information Processing Systems (NeurIPS), 2017 | 843 | 2017 |
| AlphaStar: Mastering the Real-Time Strategy Game StarCraft II O Vinyals, I Babuschkin, J Chung, M Mathieu, M Jaderberg, W Czarnecki, ... (DeepMind blog), 2019 | 694* | 2019 |
| No more pesky learning rates T Schaul, S Zhang, Y LeCun International Conference on Machine Learning (ICML'13), 2013 | 643 | 2013 |
| 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 | 498 | 2010 |
| AI for social good: unlocking the opportunity for positive impact N Tomašev, J Cornebise, F Hutter, S Mohamed, A Picciariello, B Connelly, ... Nature Communications 11 (2468), 1-6, 2020 | 441 | 2020 |
| 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 | 391* | 2017 |
| A video game description language for model-based or interactive learning T Schaul Conference on Computational Intelligence in Games (IEEE-CIG), 1-8, 2013 | 318 | 2013 |
David SilverDeepMind, UCLVerified email at google.com
