mlsanityData to 14 Oct 2026

Maximizing the Promptness of Metaverse Systems using Edge Computing by Deep Reinforcement Learning

arXiv 2506.02657 ↗First posted 3 Jun 2025cs.IT cs.LG math.ITcited by 0

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 2 of its 12 references that are in the map. First posted on arXiv 3 Jun 2025.

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2 of 12 references.

References · 2

  1. 2015Deep Reinforcement Learning with Double Q-learningcited by 1,745
  2. 2016Optimizing Communication and Computation for Multi-UAV Information Gathering Applicationscited by 3

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  2. 2026Toward Efficient Deployment and Synchronization in Digital Twins-Empowered Networks
  3. 2024Digital Twin-assisted Reinforcement Learning for Resource-aware Microservice Offloading in Edge Computing
  4. 2025A Multi-Agent DRL-Based Framework for Optimal Resource Allocation and Twin Migration in the Multi-Tier Vehicular Metaverse
  5. 2023Digital Twin-Enhanced Deep Reinforcement Learning for Resource Management in Networks Slicing
  6. 2024Hybrid-Generative Diffusion Models for Attack-Oriented Twin Migration in Vehicular Metaverses
  7. 2024Tiny Multi-Agent DRL for Twins Migration in UAV Metaverses: A Multi-Leader Multi-Follower Stackelberg Game Approach
  8. 2025A Novel Deep Reinforcement Learning Method for Computation Offloading in Multi-User Mobile Edge Computing with Decentralization