Maximizing The Promptness Of Metaverse Systems Using Edge Computing By Deep Reinforcement Learning

Abstract

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.

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keythithanh2025maximizing

Related papers

• Digital Twin-assisted Efficient Reinforcement Learning For Edge Task Scheduling (2022)9.23
• Control-optimized Deep Reinforcement Learning For Artificially Intelligent Autonomous Systems (2025)0.00
• Edgerl: Reinforcement Learning-driven Deep Learning Model Inference Optimization At Edge (2024)0.00
• To Train Or Not To Train: Balancing Efficiency And Training Cost In Deep Reinforcement Learning For Mobile Edge Computing (2024)0.00
• Provable Performance Bounds For Digital Twin-driven Deep Reinforcement Learning In Wireless Networks: A Novel Digital-twin Bisimulation Metric (2025)0.00
• Ubiquitous Distributed Deep Reinforcement Learning At The Edge: Analyzing Byzantine Agents In Discrete Action Spaces (2020)0.00
• The Cost Of Learning: Efficiency Vs. Efficacy Of Learning-based RRM For 6G (2022)0.00
• Learning To Reinforcement Learn (2016)0.00