Search Results for author: Nino Vieillard

Found 11 papers, 2 papers with code

Offline Reinforcement Learning with Pseudometric Learning

no code implementations ICLR Workshop SSL-RL 2021 Robert Dadashi, Shideh Rezaeifar, Nino Vieillard, Léonard Hussenot, Olivier Pietquin, Matthieu Geist

In the presence of function approximation, and under the assumption of limited coverage of the state-action space of the environment, it is necessary to enforce the policy to visit state-action pairs close to the support of logged transitions.

reinforcement-learning

Leverage the Average: an Analysis of KL Regularization in Reinforcement Learning

no code implementations NeurIPS 2020 Nino Vieillard, Tadashi Kozuno, Bruno Scherrer, Olivier Pietquin, Remi Munos, Matthieu Geist

Recent Reinforcement Learning (RL) algorithms making use of Kullback-Leibler (KL) regularization as a core component have shown outstanding performance.

reinforcement-learning

Leverage the Average: an Analysis of KL Regularization in RL

no code implementations31 Mar 2020 Nino Vieillard, Tadashi Kozuno, Bruno Scherrer, Olivier Pietquin, Rémi Munos, Matthieu Geist

Recent Reinforcement Learning (RL) algorithms making use of Kullback-Leibler (KL) regularization as a core component have shown outstanding performance.

Deep Conservative Policy Iteration

no code implementations24 Jun 2019 Nino Vieillard, Olivier Pietquin, Matthieu Geist

Conservative Policy Iteration (CPI) is a founding algorithm of Approximate Dynamic Programming (ADP).

Atari Games reinforcement-learning

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