Distributed Deep Reinforcement Learning Resource Allocation Scheme For Industry 4.0 Device-To-Device Scenarios

Jesus Burgueno Romero, Ramoni Ojekunle Adeogun, Rasmus Bruun, Santiago Morejon, Isabel de-la-Bandera, Raquel Barco

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Abstrakt

This paper proposes a distributed deep reinforcement learning (DRL) methodology for autonomous mobile robots (AMRs) to manage radio resources in an indoor factory with no network infrastructure. Hence, deep neural networks (DNN) are used to optimize the decision policy of the robots, which will make decisions in a distributed manner without signalling exchange. To speed up the learning phase, a centralized training is adopted in which a single DNN is trained using the experience from all robots. Once completed, the pre-trained DNN is deployed at all robots for distributed selection of resources. The performance of this approach is evaluated and compared to 5G NR sidelink mode 2 via simulations. The results show that the proposed method achieves up to 5\% higher probability of successful reception when the density of robots in the scenario is high.
OriginalsprogEngelsk
Titel2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall)
Antal sider7
ForlagIEEE
Publikationsdatodec. 2021
Sider1-7
Artikelnummer9625582
ISBN (Trykt)978-1-6654-1368-8
ISBN (Elektronisk)978-1-6654-1369-5
DOI
StatusUdgivet - dec. 2021
Begivenhed2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall) - Norman, USA
Varighed: 27 sep. 202130 sep. 2021

Konference

Konference2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall)
Land/OmrådeUSA
ByNorman
Periode27/09/202130/09/2021
NavnIEEE Vehicular Technology Conference. Proceedings
ISSN1090-3038

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