107 citations · 136 across the 6 of their papers we have counts for
10 papers
vec2text with Round-Trip Translations
Geoffrey Cideron, Sertan Girgin, Anton Raichuk +3
We investigate models that can generate arbitrary natural language text (e.g. all English sentences) from a bounded, convex and well-behaved control space. We call them universal v…
RLDS: an Ecosystem to Generate, Share and Use Datasets in Reinforcement Learning
Sabela Ramos, Sertan Girgin, Léonard Hussenot +9
We introduce RLDS (Reinforcement Learning Datasets), an ecosystem for recording, replaying, manipulating, annotating and sharing data in the context of Sequential Decision Making (…
Offline Reinforcement Learning as Anti-Exploration
Shideh Rezaeifar, Robert Dadashi, Nino Vieillard +4
Offline Reinforcement Learning (RL) aims at learning an optimal control from a fixed dataset, without interactions with the system. An agent in this setting should avoid selecting…
What Matters for Adversarial Imitation Learning?
Manu Orsini, Anton Raichuk, Léonard Hussenot +7
Adversarial imitation learning has become a popular framework for imitation in continuous control. Over the years, several variations of its components were proposed to enhance the…
Hyperparameter Selection for Imitation Learning
Leonard Hussenot, Marcin Andrychowicz, Damien Vincent +11
We address the issue of tuning hyperparameters (HPs) for imitation learning algorithms in the context of continuous-control, when the underlying reward function of the demonstratin…
Offline Reinforcement Learning with Pseudometric Learning
Robert Dadashi, Shideh Rezaeifar, Nino Vieillard +3
Offline Reinforcement Learning methods seek to learn a policy from logged transitions of an environment, without any interaction. In the presence of function approximation, and und…