Name
Abstract | ||
|---|---|---|
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.
|
Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3365871.3365911 | Proceedings of the 9th International Conference on the Internet of Things |
Keywords | Field | DocType |
Deep Reinforcement Learning, Embedded Systems, Energy Management, Internet of Things, IoT | Computer science,Internet of Things,Computer network,Multimedia,Reinforcement learning | Conference |
ISBN | Citations | PageRank |
978-1-4503-7207-7 | 1 | 0.35 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Abdulmajid Murad | 1 | 3 | 0.74 |
| Kerstin Bach | 2 | 1 | 0.35 |
| Frank Alexander Kraemer | 3 | 262 | 21.13 |
| Gavin Taylor | 4 | 249 | 15.48 |