Tuan Quang Dam — accepted AI-conference papers

2025

Monte-Carlo Tree Search with Uncertainty Propagation via Optimal Transport

Tuan Quang Dam, Pascal Stenger, Lukas Schneider, Joni Pajarinen, Carlo D'Eramo, Odalric-Ambrym Maillard

ICML 2025spotlight

This paper introduces a novel backup strategy for Monte-Carlo Tree Search (MCTS) tailored for highly stochastic and partially observable Markov decision processes. We adopt a probabilistic approach, modeling both value and action-value nodes as Gaussian distributions, to introduce a novel backup ope…

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2025

Online Robust Reinforcement Learning Through Monte-Carlo Planning

Tuan Quang Dam, Kishan Panaganti, Brahim Driss, Adam Wierman

ICML 2025poster

Monte Carlo Tree Search (MCTS) is a powerful framework for solving complex decision-making problems, yet it often relies on the assumption that the simulator and the real-world dynamics are identical. Although this assumption helps achieve the success of MCTS in games like Chess, Go, and Shogi, the…

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2025

Optimal Regret Bounds via Low-Rank Structured Variation in Non-Stationary Reinforcement Learning

Tuan Quang Dam

NeurIPS 2025poster

We study reinforcement learning in non-stationary communicating MDPs whose transition drift admits a low-rank plus sparse structure. We propose SVUCRL (Structured Variation UCRL) and prove the dynamic-regret bound $ \widetilde{\mathcal O}\bigl( D_{\max}S\sqrt{A T} +D_{\max}\sqrt{(B_r…

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2025

Power Mean Estimation in Stochastic Continuous Monte-Carlo Tree Search

Tuan Quang Dam

ICML 2025poster

Monte-Carlo Tree Search (MCTS) has demonstrated success in online planning for deterministic environments, yet significant challenges remain in adapting it to stochastic Markov Decision Processes (MDPs), particularly in continuous state-action spaces. Existing methods, such as HOOT, which combines M…

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