| dc.contributor.author | Murad, Abdulmajid Abdullah Yahya | |
| dc.contributor.author | Kraemer, Frank Alexander | |
| dc.contributor.author | Bach, Kerstin | |
| dc.contributor.author | Taylor, Gavin | |
| dc.date.accessioned | 2019-11-07T12:51:56Z | |
| dc.date.available | 2019-11-07T12:51:56Z | |
| dc.date.created | 2019-10-09T10:35:01Z | |
| dc.date.issued | 2019 | |
| dc.identifier.isbn | 978-1-4503-7207-7 | |
| dc.identifier.uri | http://hdl.handle.net/11250/2627222 | |
| dc.description.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. | nb_NO |
| dc.language.iso | eng | nb_NO |
| dc.publisher | Association for Computing Machinery (ACM) | nb_NO |
| dc.relation.ispartof | 9th International Conference on the Internet of Things (IoT 2019), October 22--25, 2019, Bilbao, Spain | |
| dc.title | IoT Sensor Gym: Training Autonomous IoT Devices with Deep Reinforcement Learning | nb_NO |
| dc.type | Chapter | nb_NO |
| dc.description.version | acceptedVersion | nb_NO |
| dc.identifier.doi | 10.1145/3365871.3365911 | |
| dc.identifier.cristin | 1735322 | |
| dc.description.localcode | This article will not be available due to copyright restrictions (c) 2019 by ACM | nb_NO |
| cristin.unitcode | 194,63,30,0 | |
| cristin.unitcode | 194,63,10,0 | |
| cristin.unitname | Institutt for informasjonssikkerhet og kommunikasjonsteknologi | |
| cristin.unitname | Institutt for datateknologi og informatikk | |
| cristin.ispublished | false | |
| cristin.fulltext | postprint | |
| cristin.qualitycode | 1 | |
