SpecMAGIC: Specification-Guided Multi-Task Generalization in Multi-Agent Reinforcement Learning

arXiv cs.AIOctober 23, 2026en

arXiv cs.AI

AI Global Wire

arXiv:2610.30443v1 Announce Type: new Abstract: Multi-task generalization is an important yet underexplored problem in multi-agent reinforcement learning (MARL). Many real-world applications require multi-agent systems to generalize across complex, temporally extended tasks with diverse inter-agent coordination requirements. We introduce a specification language that combines temporal-logic task specifications with operators for expressing such requirements. We propose SpecMAGIC, a hierarchical framework that leverages the automaton structure of task specifications for subtask decomposition. A centralized high-level planner identifies and allocates compatible subtask sequences across agents,

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