Wireless charging has been demonstrated as a promising technology for prolonging device operational lifetimes in Wireless Rechargeable Networks ( WRNs ). To schedule a mobile charger to move along a predesigned trajectory to charge devices, most existing studies assume that the precise location information of devices is already known. Unfortunately, this assumption does not always hold in real mobile application, because the activities of the vast majority of mobile devices carried by mobile agents appear dynamic and random. To the best of our knowledge, this is the first work to study how to wirelessly charge mobile devices with non-deterministic mobility. We aim to provide effective charging service to them, subject to the energy capacity of the mobile charger. We formalize the effective charging problem as a charging reward maximization problem ( CRMP ), where the amount of reward obtained by charging a device is inversely proportional to the residual lifetime of the device. Then, we prove that CRMP is NP-hard. To derive an effective charging heuristic, an algorithm based on Reinforcement Learning ( RL ) is proposed. The evaluation results show that the RL-based charging algorithm achieves excellent charging effectiveness. We further interpret the learned heuristic to gain deep and valuable insights into the design options.

中文翻译：

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RLC：基于强化学习的移动设备充电算法

无线充电已被证明是一种很有前途的技术，可以延长设备的使用寿命无线充电网络(警告）。为了安排移动充电器沿着预先设计的轨迹移动给设备充电，大多数现有研究假设设备的精确位置信息是已知的。不幸的是，这种假设在实际的移动应用程序中并不总是成立，因为移动代理携带的绝大多数移动设备的活动都是动态和随机的。据我们所知，这是第一项研究如何为具有非确定性移动性的移动设备进行无线充电的工作。我们的目标是根据移动充电器的能量容量为他们提供有效的充电服务。我们将有效充电问题形式化为充电奖励最大化问题(客户关系管理计划)，其中通过为设备充电获得的奖励量与设备的剩余寿命成反比。然后，我们证明 CRMP 是 NP 难的。为了推导出有效的充电启发式，一种基于强化学习(强化学习) 建议。评估结果表明，基于RL的充电算法取得了优异的充电效果。我们进一步解释学习启发式，以获得对设计选项的深刻和有价值的见解。