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Bilal Piot
Bilal Piot
Google Deepmind
Dirección de correo verificada de google.com
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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
66232020
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
27542018
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
12812018
Noisy Networks for Exploration
M Fortunato, MG Azar, B Piot, J Menick, I Osband, A Graves, V Mnih, ...
arXiv preprint arXiv:1706.10295 2018, 2017
1211*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
8192017
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
6812020
k. kavukcuoglu, R
JB Grill, F Strub, F Altché, C Tallec, P Richemond, E Buchatskaya, ...
Munos, and M. Valko,“Bootstrap your own latent-a new approach to self …, 2020
480*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
3572020
Acme: A research framework for distributed reinforcement learning
MW Hoffman, B Shahriari, J Aslanides, G Barth-Maron, N Momchev, ...
arXiv preprint arXiv:2006.00979, 2020
2532020
A general theoretical paradigm to understand learning from human preferences
MG Azar, ZD Guo, B Piot, R Munos, M Rowland, M Valko, D Calandriello
International Conference on Artificial Intelligence and Statistics, 4447-4455, 2024
2212024
Mastering the game of Stratego with model-free multiagent reinforcement learning
J Perolat, B De Vylder, D Hennes, E Tarassov, F Strub, V de Boer, ...
Science 378 (6623), 990-996, 2022
2002022
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, 2017
1812017
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
1542020
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
1402018
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
1242015
Inverse reinforcement learning through structured classification
E Klein, M Geist, B Piot, O Pietquin
Advances in neural information processing systems 25, 2012
1242012
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
1102016
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
1062017
Byol works even without batch statistics
PH Richemond, JB Grill, F Altché, C Tallec, F Strub, A Brock, S Smith, ...
arXiv preprint arXiv:2010.10241, 2020
1032020
Hindsight credit assignment
A Harutyunyan, W Dabney, T Mesnard, M Gheshlaghi Azar, B Piot, ...
Advances in neural information processing systems 32, 2019
972019
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Artículos 1–20