Bilal Piot
Bilal Piot
DeepMind
Verified email at univ-lille1.fr - Homepage
Title
Cited by
Cited by
Year
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
5252018
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
2672018
Noisy networks for exploration
M Fortunato, MG Azar, B Piot, J Menick, I Osband, A Graves, V Mnih, ...
arXiv preprint arXiv:1706.10295, 2017
2562017
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
1542017
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
942017
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
762017
Inverse reinforcement learning through structured classification
E Klein, M Geist, B Piot, O Pietquin
Advances in neural information processing systems, 1007-1015, 2012
692012
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
562013
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
372018
Boosted bellman residual minimization handling expert demonstrations
B Piot, M Geist, O Pietquin
Joint European Conference on Machine Learning and Knowledge Discovery in …, 2014
372014
Approximate dynamic programming for two-player zero-sum Markov games
B Scherrer
342015
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
342013
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
282017
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
272016
Score-based inverse reinforcement learning
L El Asri, B Piot, M Geist, R Laroche, O Pietquin
232016
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
232013
Difference of convex functions programming for reinforcement learning
B Piot, M Geist, O Pietquin
Advances in Neural Information Processing Systems, 2519-2527, 2014
202014
Actor-critic fictitious play in simultaneous move multistage games
J Perolat, B Piot, O Pietquin
192018
Hybrid collaborative filtering with autoencoders
F Strub, J Mary, R Gaudel
arXiv preprint arXiv:1603.00806, 2016
192016
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
172019
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