Distributed Deep Reinforcement Learning for Resource Allocation in Digital Twin Networks

Jie Luo; Jie Zeng; Ying Han; Xin Su

doi:10.1007/978-981-19-1607-6_69

ScienceGate Book Chapters

BOOK-CHAPTER

Distributed Deep Reinforcement Learning for Resource Allocation in Digital Twin Networks

Jie Luo Jie Zeng Ying Han Xin Su

Year: 2022 Lecture notes in networks and systems Pages: 771-781 Publisher: Springer International Publishing

DOI: 10.1007/978-981-19-1607-6_69

Get Full-Text PDF Get Analytical Report

Keywords:

Computer science Distributed computing Reinforcement learning Benchmark (surveying) Resource allocation Artificial intelligence Process (computing) Artificial neural network Generalization Wireless network Computer network Wireless

Metrics

Cited By

0.00

FWCI (Field Weighted Citation Impact)

Refs

0.25

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Topics

Digital Transformation in Industry

Physical Sciences → Engineering → Industrial and Manufacturing Engineering

IoT and Edge/Fog Computing

Physical Sciences → Computer Science → Computer Networks and Communications

Distributed Deep Reinforcement Learning for Resource Allocation in Digital Twin Networks

Metrics

Topics

Related Documents

Resource Allocation for Dynamic Platoon Digital Twin Networks: A Multi-Agent Deep Reinforcement Learning Method

Collective reinforcement learning based resource allocation for digital twin service in 6G networks

Digital Twin-Enhanced Deep Reinforcement Learning for Resource Management in Networks Slicing

Multi-Agent Deep Reinforcement Learning Based Distributed Resource Allocation

Digital-Twin-Assisted Resource Allocation for Network Slicing in Industry 4.0 and Beyond Using Distributed Deep Reinforcement Learning