| 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 | 807 | 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 | 403 | 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 | 394 | 2017 |
| 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 | 223 | 2017 |
| Learning from demonstrations for real world reinforcement learning T Hester, M Vecerik, O Pietquin, M Lanctot, T Schaul, B Piot, A Sendonaris, ... | 127 | 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 | 85 | 2020 |
| 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 | 85 | 2017 |
| Inverse reinforcement learning through structured classification E Klein, M Geist, B Piot, O Pietquin Advances in neural information processing systems 25, 1007-1015, 2012 | 80 | 2012 |
| 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 | 61 | 2013 |
| Agent57: Outperforming the atari human benchmark AP Badia, B Piot, S Kapturowski, P Sprechmann, A Vitvitskyi, D Guo, ... arXiv preprint arXiv:2003.13350, 2020 | 59 | 2020 |
| Boosted bellman residual minimization handling expert demonstrations B Piot, M Geist, O Pietquin Joint European Conference on Machine Learning and Knowledge Discovery in …, 2014 | 46 | 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 | 45 | 2018 |
| 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 | 43 | 2015 |
| 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 | 41 | 2013 |
| 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 | 39 | 2016 |
| 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 | 37 | 2017 |
| Score-based inverse reinforcement learning L El Asri, B Piot, M Geist, R Laroche, O Pietquin | 28 | 2016 |
| Learning from demonstrations: Is it worth estimating a reward function? B Piot, M Geist, O Pietquin Joint European Conference on Machine Learning and Knowledge Discovery in …, 2013 | 28 | 2013 |
| Hybrid collaborative filtering with autoencoders F Strub, J Mary, R Gaudel arXiv preprint arXiv:1603.00806, 2016 | 26 | 2016 |
| 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 | 24* | 2020 |
Olivier PietquinGoogle Brain (On leave of Professor at University of Lille - CRIStAL - SequeL team)Verified email at univ-lille.fr
