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arXiv 2103.15374cs.NI
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终身学习以最小化物联网网络中的信息年龄

Lifelong Learning for Minimizing Age of Information in Internet of Things Networks

Zhenzhen Gong, Qimei Cui, Christina Chaccour, Bo Zhou, Mingzhe Chen, Walid Saad

摘要:

在本文中,终身学习问题的物联网(IoT)系统进行了研究。在所考虑的模型中,每个IoT设备旨在通过在动态环境下控制其在每个时隙的计算资源分配来平衡其信息新鲜度和能量消耗折衷。无人机(UAV)被部署为飞行基站,以便使物联网设备能够适应新的环境。为此,提出了一种由UAV使用的新的终身强化学习算法,以便在UAV每次访问时适应设备的操作。通过使用来自先前访问过的设备和环境的经验,UAV可以帮助设备更快地适应其环境的未来状态。为此,在UAV处维护由所有设备共享的知识库。仿真结果表明,该算法可以收敛$25\%$到$50\%$速度比策略梯度基线算法,优化每个设备的决策问题在隔离。

英文摘要:

In this paper, a lifelong learning problem is studied for an Internet of Things (IoT) system. In the considered model, each IoT device aims to balance its information freshness and energy consumption tradeoff by controlling its computational resource allocation at each time slot under dynamic environments. An unmanned aerial vehicle (UAV) is deployed as a flying base station so as to enable the IoT devices to adapt to novel environments. To this end, a new lifelong reinforcement learning algorithm, used by the UAV, is proposed in order to adapt the operation of the devices at each visit by the UAV. By using the experience from previously visited devices and environments, the UAV can help devices adapt faster to future states of their environment. To do so, a knowledge base shared by all devices is maintained at the UAV. Simulation results show that the proposed algorithm can converge $25\%$ to $50\%$ faster than a policy gradient baseline algorithm that optimizes each device's decision making problem in isolation.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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