| 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 | 2584 | 2020 |
| Rainbow: Combining improvements in deep reinforcement learning M Hessel, J Modayil, H Van Hasselt, T Schaul, G Ostrovski, W Dabney, ... Thirty-second AAAI conference on artificial intelligence, 2018 | 1778 | 2018 |
| Deep q-learning from demonstrations T Hester, M Vecerik, O Pietquin, M Lanctot, T Schaul, B Piot, D Horgan, ... Proceedings of the AAAI Conference on Artificial Intelligence 32 (1), 2018 | 838 | 2018 |
| Leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards M Vecerik, T Hester, J Scholz, F Wang, O Pietquin, B Piot, N Heess, ... arXiv preprint arXiv:1707.08817, 2017 | 529 | 2017 |
| Noisy networks for exploration M Fortunato, MG Azar, B Piot, J Menick, I Osband, A Graves, V Mnih, ... arXiv preprint arXiv:1706.10295, 2017 | 487 | 2017 |
| Agent57: Outperforming the atari human benchmark AP Badia, B Piot, S Kapturowski, P Sprechmann, A Vitvitskyi, ZD Guo, ... International Conference on Machine Learning, 507-517, 2020 | 347 | 2020 |
| Never give up: Learning directed exploration strategies AP Badia, P Sprechmann, A Vitvitskyi, D Guo, B Piot, S Kapturowski, ... arXiv preprint arXiv:2002.06038, 2020 | 159 | 2020 |
| Learning from demonstrations for real world reinforcement learning T Hester, M Vecerik, O Pietquin, M Lanctot, T Schaul, B Piot, A Sendonaris, ... | 154 | 2017 |
| Acme: A research framework for distributed reinforcement learning M Hoffman, B Shahriari, J Aslanides, G Barth-Maron, F Behbahani, ... arXiv preprint arXiv:2006.00979, 2020 | 131 | 2020 |
| Inverse reinforcement learning through structured classification E Klein, M Geist, B Piot, O Pietquin Advances in neural information processing systems 25, 2012 | 99 | 2012 |
| Approximate dynamic programming for two-player zero-sum markov games J Perolat, B Scherrer, B Piot, O Pietquin International Conference on Machine Learning, 1321-1329, 2015 | 89 | 2015 |
| Bridging the gap between imitation learning and inverse reinforcement learning B Piot, M Geist, O Pietquin IEEE transactions on neural networks and learning systems 28 (8), 1814-1826, 2016 | 84 | 2016 |
| Bootstrap latent-predictive representations for multitask reinforcement learning ZD Guo, BA Pires, B Piot, JB Grill, F Altché, R Munos, MG Azar International Conference on Machine Learning, 3875-3886, 2020 | 83 | 2020 |
| 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 | 82 | 2018 |
| 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 | 75 | 2017 |
| End-to-end optimization of goal-driven and visually grounded dialogue systems F Strub, H De Vries, J Mary, B Piot, A Courville, O Pietquin arXiv preprint arXiv:1703.05423, 2017 | 74 | 2017 |
| Laugh-aware virtual agent and its impact on user amusement R Niewiadomski, J Hofmann, J Urbain, T Platt, J Wagner, P Bilal, T Ito, ... University of Zurich, 2013 | 69 | 2013 |
| Boosted bellman residual minimization handling expert demonstrations B Piot, M Geist, O Pietquin Joint European Conference on machine learning and knowledge discovery in …, 2014 | 68 | 2014 |
| Neural predictive belief representations ZD Guo, MG Azar, B Piot, BA Pires, R Munos arXiv preprint arXiv:1811.06407, 2018 | 57 | 2018 |
| A cascaded supervised learning approach to inverse reinforcement learning E Klein, B Piot, M Geist, O Pietquin Joint European conference on machine learning and knowledge discovery in …, 2013 | 57 | 2013 |
Olivier PietquinGoogle Brain (On leave of Professor at University of Lille - CRIStAL - SequeL team)Verified email at univ-lille.fr
