SREC：基于深度强化学习的能量受限无人机网络主动自我修复
SREC: Proactive Self-Remedy of Energy-Constrained UAV-Based Networks via Deep Reinforcement Learning
摘要：
多无人机能量感知控制是无人机组网的主要研究方向之一。然而，很少有现有的工作集中在网络应该如何反应时，无人机阵容改变的时间。在这项工作中，我们研究了主动自我补救的能量受限的无人机网络时，一个或多个无人机的能量不足，即将退出充电。我们的目标是一个能量感知的最优无人机控制策略，主动重新定位无人机时，任何无人机即将退出网络，而不是被动地派遣剩余的无人机退出后。具体而言，提出了一种基于深度强化学习（DRL）的自我补救方法，称为SREC-DRL，以最大化在一定时间内至少有一个UAV将退出网络的累积用户满意度得分。为了处理问题中的连续状态和动作空间，最先进的行动者-评论者DRL算法，即，深度确定性策略梯度（DDPG）算法具有更好的收敛稳定性。数值结果表明，与被动反应方法相比，所提出的SREC-DRL方法显示了$12.12\%$增益的累积用户满意度得分在补救期间。
英文摘要：
Energy-aware control for multiple unmanned aerial vehicles (UAVs) is one of the major research interests in UAV based networking. Yet few existing works have focused on how the network should react around the timing when the UAV lineup is changed. In this work, we study proactive self-remedy of energy-constrained UAV networks when one or more UAVs are short of energy and about to quit for charging. We target at an energy-aware optimal UAV control policy which proactively relocates the UAVs when any UAV is about to quit the network, rather than passively dispatches the remaining UAVs after the quit. Specifically, a deep reinforcement learning (DRL)-based self remedy approach, named SREC-DRL, is proposed to maximize the accumulated user satisfaction scores for a certain period within which at least one UAV will quit the network. To handle the continuous state and action space in the problem, the state-of-the-art algorithm of the actor-critic DRL, i.e., deep deterministic policy gradient (DDPG), is applied with better convergence stability. Numerical results demonstrate that compared with the passive reaction method, the proposed SREC-DRL approach shows a $12.12\%$ gain in accumulative user satisfaction score during the remedy period.
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