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Multi-Agent Deep Reinforcement Learning-Based Trajectory Planning for Multi-UAV Assisted Mobile Edge Computing

机译:基于多功能的深度加强学习基于学习的多UAV辅助移动边缘计算的轨迹规划

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An unmanned aerial vehicle (UAV)-aided mobile edge computing (MEC) framework is proposed, where several UAVs having different trajectories fly over the target area and support the user equipments (UEs) on the ground. We aim to jointly optimize the geographical fairness among all the UEs, the fairness of each UAV' UE-load and the overall energy consumption of UEs. The above optimization problem includes both integer and continues variables and it is challenging to solve. To address the above problem, a multi-agent deep reinforcement learning based trajectory control algorithm is proposed for managing the trajectory of each UAV independently, where the popular Multi-Agent Deep Deterministic Policy Gradient (MADDPG) method is applied. Given the UAVs' trajectories, a low-complexity approach is introduced for optimizing the offloading decisions of UEs. We show that our proposed solution has considerable performance over other traditional algorithms, both in terms of the fairness for serving UEs, fairness of UE-load at each UAV and energy consumption for all the UEs.
机译:提出了一种无人驾驶飞行器(UAV)的移动边缘计算(MEC)框架,其中几个具有不同轨迹的无人机在目标区域上飞行并支持地面上的用户设备(UE)。我们的目标是共同优化所有UE中的地理公平,每个无人机的UE负荷和UE的整体能量消耗的公平性。上述优化问题包括整数并继续变量,解决问题是具有挑战性的。为了解决上述问题,提出了一种基于多代理深度加强学习学习的轨迹控制算法,用于独立地管理每个无人机的轨迹,其中应用了流行的多代理深度确定性政策梯度(MADDPG)方法。鉴于无人机的轨迹,介绍了一种低复杂性的方法,以优化UE的卸载决策。我们展示我们所提出的解决方案对其他传统算法具有相当大的性能,无论是为服务UE的公平,每个UE负载的公平性和所有UE的能耗。

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