Léonard Hussenot
Léonard Hussenot
PhD student, Google Research & INRIA SequeL
Verified email at google.com
Title
Cited by
Cited by
Year
What matters for on-policy deep actor-critic methods? a largescale study
M Andrychowicz, A Raichuk, P Stanczyk, M Orsini, S Girgin, R Marinier, ...
International Conference on Learning Representations (ICLR), 2021, 2021
45*2021
Primal wasserstein imitation learning
R Dadashi, L Hussenot, M Geist, O Pietquin
International Conference on Learning Representations (ICLR), 2021, 2020
152020
CopyCAT: Taking Control of Neural Policies with Constant Attacks
L Hussenot, M Geist, O Pietquin
International Conference on Autonomous Agents and Multiagent Systems (AAMAS …, 2020
14*2020
What Matters for Adversarial Imitation Learning?
M Orsini*, A Raichuk*, L Hussenot*, D Vincent, R Dadashi, S Girgin, ...
arXiv preprint arXiv:2106.00672, 2021
32021
Hyperparameter Selection for Imitation Learning
L Hussenot, M Andrychowicz, D Vincent, R Dadashi, A Raichuk, ...
International Conference on Machine Learning (ICML), 2021, 2021
32021
Show me the Way: Intrinsic Motivation from Demonstrations
L Hussenot, R Dadashi, M Geist, O Pietquin
International Conference on Autonomous Agents and Multiagent Systems (AAMAS …, 2020
32020
Offline Reinforcement Learning as Anti-Exploration
S Rezaeifar, R Dadashi, N Vieillard, L Hussenot, O Bachem, O Pietquin, ...
arXiv preprint arXiv:2106.06431, 2021
12021
Offline Reinforcement Learning with Pseudometric Learning
R Dadashi, S Rezaeifar, N Vieillard, L Hussenot, O Pietquin, M Geist
International Conference on Machine Learning (ICML), 2021, 2021
12021
RLDS: an Ecosystem to Generate, Share and Use Datasets in Reinforcement Learning
S Ramos, S Girgin, L Hussenot, D Vincent, H Yakubovich, DK Toyama, ...
2021
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Articles 1–9