Maximizing the Promptness of Metaverse Systems using Edge Computing by Deep Reinforcement Learning
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866916775319306240 |
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| author | Thi-Thanh, Tam Ninh Van Chien, Trinh Tran, Hung Son, Nguyen Hoai Vo, Van Nhan |
| author_facet | Thi-Thanh, Tam Ninh Van Chien, Trinh Tran, Hung Son, Nguyen Hoai Vo, Van Nhan |
| contents | Metaverse and Digital Twin (DT) have attracted much academic and industrial attraction to approach the future digital world. This paper introduces the advantages of deep reinforcement learning (DRL) in assisting Metaverse system-based Digital Twin. In this system, we assume that it includes several Metaverse User devices collecting data from the real world to transfer it into the virtual world, a Metaverse Virtual Access Point (MVAP) undertaking the processing of data, and an edge computing server that receives the offloading data from the MVAP. The proposed model works under a dynamic environment with various parameters changing over time. The experiment results show that our proposed DRL algorithm is suitable for offloading tasks to ensure the promptness of DT in a dynamic environment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_02657 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Maximizing the Promptness of Metaverse Systems using Edge Computing by Deep Reinforcement Learning Thi-Thanh, Tam Ninh Van Chien, Trinh Tran, Hung Son, Nguyen Hoai Vo, Van Nhan Information Theory Machine Learning Metaverse and Digital Twin (DT) have attracted much academic and industrial attraction to approach the future digital world. This paper introduces the advantages of deep reinforcement learning (DRL) in assisting Metaverse system-based Digital Twin. In this system, we assume that it includes several Metaverse User devices collecting data from the real world to transfer it into the virtual world, a Metaverse Virtual Access Point (MVAP) undertaking the processing of data, and an edge computing server that receives the offloading data from the MVAP. The proposed model works under a dynamic environment with various parameters changing over time. The experiment results show that our proposed DRL algorithm is suitable for offloading tasks to ensure the promptness of DT in a dynamic environment. |
| title | Maximizing the Promptness of Metaverse Systems using Edge Computing by Deep Reinforcement Learning |
| topic | Information Theory Machine Learning |
| url | https://arxiv.org/abs/2506.02657 |