LAUSR

To overcome the problems caused by the limited battery lifetime in multiple-unmanned aerial vehicle (UAV) wireless networks, we propose a hierarchical multi-agent reinforcement learning (RL) framework to maximize the energy efficiency (EE) of UAVs by finding the optimal frequency reuse factor and transmit power. The proposed algorithm consists of distributed inner-loop RL for transmit power control of the UAV terminal (UT) and centralized outer-loop RL for finding the optimal frequency reuse factor. Specifically, the proposed algorithm adjusts these two factors jointly to effectively mitigate intercell interference and reduce undesired transmit power consumption in multi-UAV wireless networks. We show that, for this reason, the proposed algorithm outperforms conventional algorithms, such as a random action algorithm with a fixed frequency reuse factor and a hierarchical multi-agent Q-learning algorithm with binary transmit power controls. Furthermore, even in the environment where UTs are continuously moving based on the mixed mobility model, we show that the proposed algorithm can find the best reward when compared to conventional algorithms.