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Over the past decade, Unmanned Aerial Vehicles (UAVs) have attracted significant attention due to their potential applications in emergency-response applications, including wireless power transfer (WPT) and data collection from Internet of Things (IoT) devices in disaster-affected areas. UAVs are more attractive than traditional techniques due to their maneuverability, flexibility, and low deployment costs. However, using UAVs for such critical tasks comes with challenges, including limited resources, energy constraints, and the need to complete missions within strict time frames. IoT devices in disaster areas have limited resources (e.g., computation, energy), so they depend on the UAVs’ resources to accomplish vital missions. To address these resource problems in a disaster scenario, we propose a meta-reinforcement learning (RL)-based energy harvesting (EH) framework. Our system model considers a swarm of …