| Human-level control through deep reinforcement learning V Mnih, K Kavukcuoglu, D Silver, AA Rusu, J Veness, MG Bellemare, ... nature 518 (7540), 529-533, 2015 | 26920 | 2015 |
| Rainbow: Combining improvements in deep reinforcement learning M Hessel, J Modayil, H Van Hasselt, T Schaul, G Ostrovski, W Dabney, ... Proceedings of the AAAI conference on artificial intelligence 32 (1), 2018 | 2276 | 2018 |
| Hybrid computing using a neural network with dynamic external memory A Graves, G Wayne, M Reynolds, T Harley, I Danihelka, ... Nature 538 (7626), 471-476, 2016 | 1770 | 2016 |
| Unifying count-based exploration and intrinsic motivation M Bellemare, S Srinivasan, G Ostrovski, T Schaul, D Saxton, R Munos Advances in neural information processing systems 29, 2016 | 1496 | 2016 |
| Count-based exploration with neural density models G Ostrovski, MG Bellemare, A Oord, R Munos International conference on machine learning, 2721-2730, 2017 | 646 | 2017 |
| Implicit quantile networks for distributional reinforcement learning W Dabney, G Ostrovski, D Silver, R Munos International conference on machine learning, 1096-1105, 2018 | 488 | 2018 |
| Recurrent experience replay in distributed reinforcement learning S Kapturowski, G Ostrovski, J Quan, R Munos, W Dabney International conference on learning representations, 2018 | 461 | 2018 |
| Increasing the action gap: New operators for reinforcement learning MG Bellemare, G Ostrovski, A Guez, P Thomas, R Munos Proceedings of the AAAI Conference on Artificial Intelligence 30 (1), 2016 | 158 | 2016 |
| Autoregressive quantile networks for generative modeling G Ostrovski, W Dabney, R Munos International Conference on Machine Learning, 3936-3945, 2018 | 83 | 2018 |
| Temporally-extended {\epsilon}-greedy exploration W Dabney, G Ostrovski, A Barreto arXiv preprint arXiv:2006.01782, 2020 | 72 | 2020 |
| On the effect of auxiliary tasks on representation dynamics C Lyle, M Rowland, G Ostrovski, W Dabney International Conference on Artificial Intelligence and Statistics, 1-9, 2021 | 55 | 2021 |
| Symmetric decomposition of asymmetric games K Tuyls, J Pérolat, M Lanctot, G Ostrovski, R Savani, JZ Leibo, T Ord, ... Scientific reports 8 (1), 1015, 2018 | 44 | 2018 |
| The difficulty of passive learning in deep reinforcement learning G Ostrovski, PS Castro, W Dabney Advances in Neural Information Processing Systems 34, 23283-23295, 2021 | 31 | 2021 |
| others (2015). Human-level control through deep reinforcement learning V Mnih, K Kavukcuoglu, D Silver, AA Rusu, J Veness, MG Bellemare, ... Nature 518 (7540), 529, 0 | 31 | |
| Grabska-Barwi nska A Graves, G Wayne, M Reynolds, T Harley, I Danihelka A., Colmenarejo, SG, Grefenstette, E., Ramalho, T., Agapiou, J., et al, 471-476, 2016 | 30* | 2016 |
| Payoff performance of fictitious play G Ostrovski, S van Strien Journal of Dynamics and Games 1 (4), 621-638, 2014 | 23 | 2014 |
| Payoff performance of fictitious play G Ostrovski, S van Strien arXiv preprint arXiv:1308.4049, 2013 | 23 | 2013 |
| When should agents explore? M Pislar, D Szepesvari, G Ostrovski, D Borsa, T Schaul arXiv preprint arXiv:2108.11811, 2021 | 19 | 2021 |
| Adapting behaviour for learning progress T Schaul, D Borsa, D Ding, D Szepesvari, G Ostrovski, W Dabney, ... arXiv preprint arXiv:1912.06910, 2019 | 13 | 2019 |
| Piecewise linear Hamiltonian flows associated to zero-sum games: transition combinatorics and questions on ergodicity G Ostrovski, S van Strien Regular and Chaotic Dynamics 16, 128-153, 2011 | 13 | 2011 |
