| 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 | 898 | 2018 |
| Noisy networks for exploration M Fortunato, MG Azar, B Piot, J Menick, I Osband, A Graves, V Mnih, ... arXiv preprint arXiv:1706.10295, 2017 | 411 | 2017 |
| Minimax regret bounds for reinforcement learning MG Azar, I Osband, R Munos International Conference on Machine Learning, 263-272, 2017 | 266 | 2017 |
| Bootstrap your own latent: A new approach to self-supervised learning JB Grill, F Strub, F Altché, C Tallec, PH Richemond, E Buchatskaya, ... arXiv preprint arXiv:2006.07733, 2020 | 192 | 2020 |
| Speedy Q-Learning MG Azar, M Ghavamzadeh, HJ Kappen, R Munos Advances in Neural Information Processing Systems, 2411-2419, 2011 | 122* | 2011 |
| Dynamic Policy Programming M Gheshlaghi Azar, V Gomez, HJ Kappen Journal of Machine Learning Research 13, 3207-3245, 2012 | 100 | 2012 |
| Minimax PAC bounds on the sample complexity of reinforcement learning with a generative model MG Azar, R Munos, HJ Kappen Machine learning 91 (3), 325-349, 2013 | 98 | 2013 |
| The reactor: A fast and sample-efficient actor-critic agent for reinforcement learning A Gruslys, W Dabney, MG Azar, B Piot, M Bellemare, R Munos arXiv preprint arXiv:1704.04651, 2017 | 97* | 2017 |
| Sequential transfer in multi-armed bandit with finite set of models MG Azar, A Lazaric, E Brunskill Advances in Neural Information Processing Systems, 2220-2228, 2013 | 65 | 2013 |
| On the sample complexity of reinforcement learning with a generative model MG Azar, R Munos, B Kappen arXiv preprint arXiv:1206.6461, 2012 | 53 | 2012 |
| Stochastic optimization of a locally smooth function under correlated bandit feedback MG Azar, A Lazaric, E Brunskill arXiv preprint arXiv:1402.0562, 2014 | 50* | 2014 |
| Observe and look further: Achieving consistent performance on atari T Pohlen, B Piot, T Hester, MG Azar, D Horgan, D Budden, G Barth-Maron, ... arXiv preprint arXiv:1805.11593, 2018 | 43 | 2018 |
| Dynamic policy programming with function approximation MG Azar, V Gómez, B Kappen Proceedings of the Fourteenth International Conference on Artificial …, 2011 | 40 | 2011 |
| Regret bounds for reinforcement learning with policy advice MG Azar, A Lazaric, E Brunskill Joint European Conference on Machine Learning and Knowledge Discovery in …, 2013 | 31 | 2013 |
| Meta-learning of sequential strategies PA Ortega, JX Wang, M Rowland, T Genewein, Z Kurth-Nelson, ... arXiv preprint arXiv:1905.03030, 2019 | 29 | 2019 |
| Neural predictive belief representations ZD Guo, MG Azar, B Piot, BA Pires, R Munos arXiv preprint arXiv:1811.06407, 2018 | 26 | 2018 |
| Reinforcement learning with a near optimal rate of convergence MG Azar, R Munos, M Ghavamzadeh, H Kappen | 24 | 2011 |
| A cryptography-based approach for movement decoding EL Dyer, MG Azar, MG Perich, HL Fernandes, S Naufel, LE Miller, ... Nature biomedical engineering 1 (12), 967-976, 2017 | 22 | 2017 |
| Mel Vecerík, et al. Observe and look further: Achieving consistent performance on atari T Pohlen, B Piot, T Hester, MG Azar, D Horgan, D Budden, G Barth-Maron, ... arXiv preprint arXiv:1805.11593, 2018 | 20 | 2018 |
| Hindsight credit assignment A Harutyunyan, W Dabney, T Mesnard, M Azar, B Piot, N Heess, ... arXiv preprint arXiv:1912.02503, 2019 | 16 | 2019 |
Rémi MunosDeepMindVerified email at inria.fr
