IoT Sensor Gym: Training Autonomous IoT Devices with Deep Reinforcement Learning

被引：3

作者：

Murad, Abdulmajid ^{[1
]}

Kraemer, Frank Alexander ^{[1
]}

Bach, Kerstin ^{[1
]}

Taylor, Gavin ^{[2
]}

机构：

[1] Norwegian Univ Sci & Technol, NTNU, Trondheim, Norway

[2] US Naval Acad, Annapolis, MD 21402 USA

来源：

PROCEEDINGS OF THE 9TH INTERNATIONAL CONFERENCE ON THE INTERNET OF THINGS ( IOT 2019) | 2019年

关键词：

Deep Reinforcement Learning; Internet of Things; IoT; Embedded Systems; Energy Management;

D O I：

10.1145/3365871.3365911

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

We describe IoT Sensor Gym, a framework to train the behavior of constrained IoT devices using deep reinforcement learning. We focus on the main architectural choices to align problems from the IoT domain with cutting-edge reinforcement learning algorithms and exemplify our results with the autonomous control of a solar-powered IoT device.

引用

页数：4

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