activity
20172024
most citedFast active learning for pure exploration in reinforcement learning

31 citations · 104 across the 21 of their papers we have counts for

collaborators
Showing 2022Show all

2 papers · 1 filter

stat.ML2022★ 1 cited

Adapting to game trees in zero-sum imperfect information games

Côme Fiegel, Pierre Ménard, Tadashi Kozuno +3

Imperfect information games (IIG) are games in which each player only partially observes the current game state. We study how to learn -optimal strategies in a zero-sum IIG thro…

cs.LG2022

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…