This paper introduces the Sensed Information Value Realization Multi-Agent Reinforcement Learning (SVR-MARL) framework, designed for collaborative missions involving multiple autonomous underwater vehicles (AUVs). The framework addresses the challenges of limited perception and communication risks in underwater environments, where active sensing and acoustic communications can increase exposure. SVR-MARL aims to optimize cooperative task efficiency by learning distributed policies that consider realistic communication constraints and the utility of sensed information, as demonstrated in a case study of cooperative localization and tracking. AI
IMPACT This research could improve the efficiency and stealth of coordinated operations for autonomous underwater vehicles.
RANK_REASON The cluster contains a research paper detailing a new framework for multi-agent reinforcement learning.
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