NonZero introduces an interaction score and bandit-formalized proposal rule for local agent deviations in multi-agent MCTS, delivering a sublinear local-regret guarantee and improved sample efficiency on game benchmarks without full joint-action enumeration.
arXiv preprint arXiv:1910.05366 , year=
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MASK schedules top-K agents via semantic gating and a global encoder to achieve risk-aware multi-robot coordination that matches unconstrained baselines under bandwidth caps.
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NonZero: Interaction-Guided Exploration for Multi-Agent Monte Carlo Tree Search
NonZero introduces an interaction score and bandit-formalized proposal rule for local agent deviations in multi-agent MCTS, delivering a sublinear local-regret guarantee and improved sample efficiency on game benchmarks without full joint-action enumeration.
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MASK: Multi-Agent Semantic K-Scheduling for Risk-Sensitive 6G Robotics
MASK schedules top-K agents via semantic gating and a global encoder to achieve risk-aware multi-robot coordination that matches unconstrained baselines under bandwidth caps.