| 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 | 30551 | 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 | 2614 | 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 | 1884 | 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 | 1656 | 2016 |
| Count-based exploration with neural density models G Ostrovski, MG Bellemare, A Oord, R Munos International conference on machine learning, 2721-2730, 2017 | 706 | 2017 |
| Implicit quantile networks for distributional reinforcement learning W Dabney, G Ostrovski, D Silver, R Munos International conference on machine learning, 1096-1105, 2018 | 571 | 2018 |
| Recurrent experience replay in distributed reinforcement learning S Kapturowski, G Ostrovski, J Quan, R Munos, W Dabney International conference on learning representations, 2018 | 535 | 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 | 170 | 2016 |
| Temporally-extended {\epsilon}-greedy exploration W Dabney, G Ostrovski, A Barreto arXiv preprint arXiv:2006.01782, 2020 | 97 | 2020 |
| Autoregressive quantile networks for generative modeling G Ostrovski, W Dabney, R Munos International Conference on Machine Learning, 3936-3945, 2018 | 86 | 2018 |
| 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 | 68 | 2021 |
| Human-level control through deep reinforcement learning., 2015, 518 V Mnih, K Kavukcuoglu, D Silver, AA Rusu, J Veness, MG Bellemare, ... DOI: https://doi. org/10.1038/nature14236. PMID: https://www. ncbi. nlm. nih …, 0 | 54 | |
| 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 | 48 | 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 | 43 | 2021 |
| When should agents explore? M Pislar, D Szepesvari, G Ostrovski, D Borsa, T Schaul arXiv preprint arXiv:2108.11811, 2021 | 26 | 2021 |
| Payoff performance of fictitious play G Ostrovski, S van Strien Journal of Dynamics and Games 1 (4), 621-638, 2014 | 25 | 2014 |
| Deep reinforcement learning with plasticity injection E Nikishin, J Oh, G Ostrovski, C Lyle, R Pascanu, W Dabney, A Barreto Advances in Neural Information Processing Systems 36, 2024 | 21 | 2024 |
| DQN Zoo: Reference implementations of DQN-based agents, 2020 J Quan, G Ostrovski URL http://github. com/deepmind/dqn_zoo, 44, 0 | 21 | |
| Human-level control through deep reinforcement learning. nature, 518 (7540): 529–533, 2015 V Mnih, K Kavukcuoglu, D Silver, AA Rusu, J Veness, MG Bellemare, ... Cited on 3 (4), 0 | 19 | |
| The phenomenon of policy churn T Schaul, A Barreto, J Quan, G Ostrovski Advances in Neural Information Processing Systems 35, 2537-2549, 2022 | 16 | 2022 |
