Augmented Lagrangian-Based Reinforcement Learning for Network Slicing in IIoT

Network slicing enables the multiplexing of independent logical networks on the same physical network infrastructure to provide different network services for different applications. The resource allocation problem involved in network slicing is typically a decision-making problem, falling within th...

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Veröffentlicht in:Electronics (Basel) Jg. 11; H. 20; S. 3385
Hauptverfasser: Qi, Qi, Lin, Wenbin, Guo, Boyang, Chen, Jinshan, Deng, Chaoping, Lin, Guodong, Sun, Xin, Chen, Youjia
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 01.10.2022
Schlagworte:
Algorithms
Bandwidths
Computer networks
Constraints
Decision making
Factories
Genetic algorithms
Information networks
Lagrange multiplier
Learning
Methods
Mixed integer
Multiplexing
Network slicing
Neural networks
Nonlinear programming
Optimization
Problem solving
Reinforcement learning (Machine learning)
Resource allocation
Wireless networks
China
ISSN:2079-9292, 2079-9292
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Zusammenfassung:Network slicing enables the multiplexing of independent logical networks on the same physical network infrastructure to provide different network services for different applications. The resource allocation problem involved in network slicing is typically a decision-making problem, falling within the scope of reinforcement learning. The advantage of adapting to dynamic wireless environments makes reinforcement learning a good candidate for problem solving. In this paper, to tackle the constrained mixed integer nonlinear programming problem in network slicing, we propose an augmented Lagrangian-based soft actor–critic (AL-SAC) algorithm. In this algorithm, a hierarchical action selection network is designed to handle the hybrid action space. More importantly, inspired by the augmented Lagrangian method, both neural networks for Lagrange multipliers and a penalty item are introduced to deal with the constraints. Experiment results show that the proposed AL-SAC algorithm can strictly satisfy the constraints, and achieve better performance than other benchmark algorithms.
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ISSN:2079-9292
2079-9292
DOI:10.3390/electronics11203385