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Unmanned aerial vehicles (UAVs) have attracted much attention lately and are being used in a multitude of applications. But the duration of being in the sky remains to be an issue due to their energy limitation. In particular, this represents a major challenge when UAVs are used as base stations (BSs) to complement the wireless network. Therefore, as UAVs execute their missions in the sky, it becomes beneficial to wirelessly harvest energy from external and adjustable flying energy sources (FESs) to power their onboard batteries and avoid disrupting their trajectories. For this purpose, wireless power transfer (WPT) is seen as a promising charging technology to keep UAVs in flight and allow them to complete their missions. In this work, we leverage a multiagent deep reinforcement learning (MADRL) method to optimize the task of energy transfer between FESs and UAVs. The optimization is performed by carrying …