论文标题
多-UAV辅助移动边缘计算的两个跳跃信息时间安排：FRL与MADDPG
Two-Hop Age of Information Scheduling for Multi-UAV Assisted Mobile Edge Computing: FRL vs MADDPG
论文作者
论文摘要
在这项工作中，我们在无人驾驶汽车（UAV）中采用移动边缘计算的新兴技术（MEC），以进行通信计算系统，以优化网络中的信息时代（AOI）。我们假设任务是在无人机和BS上共同处理的，以通过有限的连接性和计算来增强边缘性能。与MEC共同使用无人机和BS可以减少网络上的AOI。为了保持任务的新鲜感，我们在两跳沟通框架中制定了AOI最小化，第一个在UAVS和BS上的跳跃。为了应对挑战，我们使用深厚的加固学习（DRL）框架（称为联合加固学习（FRL））来优化问题。在我们的网络中，我们有两种具有不同状态和行动的代理，但具有相同的政策。我们的FRL使我们能够处理两步的AOI最小化和无人机轨迹问题。此外，我们比较了我们提出的算法，该算法具有集中的处理单元来更新权重，并与完全分散的多代理深层确定性策略梯度（MADDPG）进行了比较，从而提高了代理商的性能。结果，建议的算法的表现优于MADDPG约38 \％
In this work, we adopt the emerging technology of mobile edge computing (MEC) in the Unmanned aerial vehicles (UAVs) for communication-computing systems, to optimize the age of information (AoI) in the network. We assume that tasks are processed jointly on UAVs and BS to enhance edge performance with limited connectivity and computing. Using UAVs and BS jointly with MEC can reduce AoI on the network. To maintain the freshness of the tasks, we formulate the AoI minimization in two-hop communication framework, the first hop at the UAVs and the second hop at the BS. To approach the challenge, we optimize the problem using a deep reinforcement learning (DRL) framework, called federated reinforcement learning (FRL). In our network we have two types of agents with different states and actions but with the same policy. Our FRL enables us to handle the two-step AoI minimization and UAV trajectory problems. In addition, we compare our proposed algorithm, which has a centralized processing unit to update the weights, with fully decentralized multi-agent deep deterministic policy gradient (MADDPG), which enhances the agent's performance. As a result, the suggested algorithm outperforms the MADDPG by about 38\%
