{"paper":{"title":"Task-Semantic Graph-Driven Distributed Agent Networking for Underwater Target Tracking","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"An open MARL platform with a semantic task graph lets AUV swarms track moving targets under acoustic constraints and limited observations.","cross_cats":["cs.MA"],"primary_cat":"cs.RO","authors_text":"Chuan Lin, Guangjie Han, Shengchao Zhu, Yu He","submitted_at":"2026-05-15T01:55:47Z","abstract_excerpt":"Autonomous underwater vehicle (AUV) swarms are emerging as intelligent underwater networks, where each node must sense, communicate, process local data, and make decisions under severe acoustic constraints. Persistent underwater target tracking is a typical task with moving targets, changing communication topology, intermittent acoustic links, and limited observation for each AUV. Multi-agent reinforcement learning (MARL) is a natural candidate for distributed tracking, yet existing studies still lack a unified open-source platform for evaluating different MARL algorithms under six-degree-of-f"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"To the best of our knowledge, it is the first open platform that connects a public MARL training framework with physically modeled AUV swarm-based tasks, and provides a unified experimental protocol for fair training, testing, and comparison of representative RL and MARL algorithms. 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