| 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 | 525 | 2018 |
| Deep q-learning from demonstrations T Hester, M Vecerik, O Pietquin, M Lanctot, T Schaul, B Piot, D Horgan, ... Thirty-Second AAAI Conference on Artificial Intelligence, 2018 | 267 | 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 | 256 | 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 | 154 | 2017 |
| Learning from demonstrations for real world reinforcement learning T Hester, M Vecerik, O Pietquin, M Lanctot, T Schaul, B Piot, A Sendonaris, ... arXiv preprint arXiv:1704.03732, 4565-4573, 2017 | 94 | 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 | 76 | 2017 |
| Inverse reinforcement learning through structured classification E Klein, M Geist, B Piot, O Pietquin Advances in neural information processing systems, 1007-1015, 2012 | 69 | 2012 |
| Laugh-aware virtual agent and its impact on user amusement R Niewiadomski, J Hofmann, J Urbain, T Platt, J Wagner, B Piot, ... Proceedings of the 2013 international conference on Autonomous agents and …, 2013 | 56 | 2013 |
| 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 | 37 | 2018 |
| Boosted bellman residual minimization handling expert demonstrations B Piot, M Geist, O Pietquin Joint European Conference on Machine Learning and Knowledge Discovery in …, 2014 | 37 | 2014 |
| Approximate dynamic programming for two-player zero-sum Markov games B Scherrer | 34 | 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 | 34 | 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 | 28 | 2017 |
| 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 | 27 | 2016 |
| Score-based inverse reinforcement learning L El Asri, B Piot, M Geist, R Laroche, O Pietquin | 23 | 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 | 23 | 2013 |
| Difference of convex functions programming for reinforcement learning B Piot, M Geist, O Pietquin Advances in Neural Information Processing Systems, 2519-2527, 2014 | 20 | 2014 |
| Actor-critic fictitious play in simultaneous move multistage games J Perolat, B Piot, O Pietquin | 19 | 2018 |
| Hybrid collaborative filtering with autoencoders F Strub, J Mary, R Gaudel arXiv preprint arXiv:1603.00806, 2016 | 19 | 2016 |
| Observational learning by reinforcement learning D Borsa, N Heess, B Piot, S Liu, L Hasenclever, R Munos, O Pietquin Proceedings of the 18th International Conference on Autonomous Agents and …, 2019 | 17 | 2019 |
Olivier PietquinGoogle Brain (On leave of Professor at University of Lille - CRIStAL - SequeL team)Verified email at univ-lille.fr
