A Survey on Agentic AI-Assisted UAV Swarm Systems Using Multi-Agent Deep Reinforcement Learning

Currently, the use of UAV swarm systems is widespread for large-scale monitoring and surveillance purposes,

demanding effective coordination. While the Multi-Agent Deep Reinforcement Learning methods such as MADDPG leverage

decentralization by providing each agent with the ability to act individually depending on the condition of its local environment,

the learning-based approach is ineffective because of the redundant explorations and coordination that lack structure. In our

review, we focus on recent advancements in the domain of UAV swarm systems, with an accent on inefficiencies inherent in

MARL approaches, making it hard to achieve efficient coverage of the region of interest. The paper is devoted to hybrid

approaches, which combine MARL approaches and spatial planning approaches, specifically Voronoi partitioning