7 citations · 7 across the 3 of their papers we have counts for
8 papers
KL-Entropy-Regularized RL with a Generative Model is Minimax Optimal
Tadashi Kozuno, Wenhao Yang, Nino Vieillard +10
In this work, we consider and analyze the sample complexity of model-free reinforcement learning with a generative model. Particularly, we analyze mirror descent value iteration (M…
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…
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…
Munchausen Reinforcement Learning
Nino Vieillard, Olivier Pietquin, Matthieu Geist
Bootstrapping is a core mechanism in Reinforcement Learning (RL). Most algorithms, based on temporal differences, replace the true value of a transiting state by their current esti…
Leverage the Average: an Analysis of KL Regularization in RL
Nino Vieillard, Tadashi Kozuno, Bruno Scherrer +3
Recent Reinforcement Learning (RL) algorithms making use of Kullback-Leibler (KL) regularization as a core component have shown outstanding performance. Yet, only little is underst…
On Connections between Constrained Optimization and Reinforcement Learning
Nino Vieillard, Olivier Pietquin, Matthieu Geist
Dynamic Programming (DP) provides standard algorithms to solve Markov Decision Processes. However, these algorithms generally do not optimize a scalar objective function. In this p…