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arXiv · 2605.00751

NonZero: Interaction-Guided Exploration for Multi-Agent Monte Carlo Tree Search

Sizhe Tang·Zuyuan Zhang·Mahdi Imani·Tian Lan
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Abstract

Monte Carlo Tree Search (MCTS) scales poorly in cooperative multi-agent domains because expansion must consider an exponentially large set of joint actions, severely limiting exploration under realistic search budgets. We propose NonZero, which keeps multi-agent MCTS tractable by running surrogate-guided selection over a low-dimensional nonlinear representation using an interaction-guided proposal rule, instead of directly exploring the full joint-action space. Our exploration uses an interaction score: single-agent deviations are ranked by predicted gain, while two-agent deviations are scored by a mixed-difference measure that reveals coordination benefits even when no single agent can improve alone. We formalize candidate proposal as a bandit problem over local deviations and derive a proposal rule, NonZero, with a sublinear local-regret guarantee for reaching approximate graph-local optima without enumerating the joint-action space. Empirically, NonZero improves sample efficiency and final performance on MatGame, SMAC, and SMACv2 relative to strong model-based and model-free baselines under matched search budgets.

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Sizhe Tang, Zuyuan Zhang, Mahdi Imani, Tian Lan. 2026-05-01. NonZero: Interaction-Guided Exploration for Multi-Agent Monte Carlo Tree Search. https://arxiv.org/abs/2605.00751

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