Search-and-rescue (SAR) operations in disaster environments require drone swarms to coordinate efficiently despite incomplete information and potential communication failures. Existing stigmergy-based approaches provide low-bandwidth coordination but rely on fixed rules, while multi-agent reinforcement learning (MARL) offers adaptive behavior but struggles with scalability and coordination. This paper proposes a novel approach combining directional pheromone gradient observations with decentralized MARL for swarm drone SAR missions.
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