2 citations · 7 across the 11 of their papers we have counts for
13 papers
TreeFlex: Agents Navigating Their Own Search Trees
Dongzhuo Li, Fangzhou Cheng, Weiwei Li +8
A ReAct-style agent accumulates observations, intermediate results, and failed attempts in a single context. As this context grows, stale and off-path information consumes its limi…
EgoReveal: Learning Active Vision and Object Manipulation under Partial Occlusions from Egocentric Demonstrations
Hiromu Kurihara, Kazutoshi Tanaka, Masashi Hatano +3
Learning from Demonstrations (LfD) enables robots to learn object manipulation directly from human demonstrations. In contrast to resource-intensive and labor-intensive teleoperati…
The Harder Path: Last Iterate Convergence for Uncoupled Learning in Zero-Sum Games with Bandit Feedback
Côme Fiegel, Pierre Ménard, Tadashi Kozuno +2
We study the problem of learning in zero-sum matrix games with repeated play and bandit feedback. Specifically, we focus on developing uncoupled algorithms that guarantee, without…
Optimal last-iterate convergence in matrix games with bandit feedback using the log-barrier
Come Fiegel, Pierre Menard, Tadashi Kozuno +2
We study the problem of learning minimax policies in zero-sum matrix games. Fiegel et al. (2025) recently showed that achieving last-iterate convergence in this setting is harder w…
Local and adaptive mirror descents in extensive-form games
Côme Fiegel, Pierre Ménard, Tadashi Kozuno +3
We study how to learn -optimal strategies in zero-sum imperfect information games (IIG) with trajectory feedback. In this setting, players update their policies sequentially bas…
DoMo-AC: Doubly Multi-step Off-policy Actor-Critic Algorithm
Yunhao Tang, Tadashi Kozuno, Mark Rowland +4
Multi-step learning applies lookahead over multiple time steps and has proved valuable in policy evaluation settings. However, in the optimal control case, the impact of multi-step…
Regularization and Variance-Weighted Regression Achieves Minimax Optimality in Linear MDPs: Theory and Practice
Toshinori Kitamura, Tadashi Kozuno, Yunhao Tang +12
Mirror descent value iteration (MDVI), an abstraction of Kullback-Leibler (KL) and entropy-regularized reinforcement learning (RL), has served as the basis for recent high-performi…
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…
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…
Model-Free Learning for Two-Player Zero-Sum Partially Observable Markov Games with Perfect Recall
Tadashi Kozuno, Pierre Ménard, Rémi Munos +1
We study the problem of learning a Nash equilibrium (NE) in an imperfect information game (IIG) through self-play. Precisely, we focus on two-player, zero-sum, episodic, tabular II…
Unifying Gradient Estimators for Meta-Reinforcement Learning via Off-Policy Evaluation
Yunhao Tang, Tadashi Kozuno, Mark Rowland +2
Model-agnostic meta-reinforcement learning requires estimating the Hessian matrix of value functions. This is challenging from an implementation perspective, as repeatedly differen…
Revisiting Peng's Q() for Modern Reinforcement Learning
Tadashi Kozuno, Yunhao Tang, Mark Rowland +5
Off-policy multi-step reinforcement learning algorithms consist of conservative and non-conservative algorithms: the former actively cut traces, whereas the latter do not. Recently…
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…