2 citations · 7 across the 11 of their papers we have counts for
4 papers · 1 filter
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…