AI RESEARCH
NonZero: Interaction-Guided Exploration for Multi-Agent Monte Carlo Tree Search
arXiv CS.LG
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ArXi:2605.00751v1 Announce Type: new 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.