Virtual Reality in Metaverse over Wireless Networks with User-centered Deep Reinforcement Learning
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Abstract (arXiv)
The Metaverse and its promises are fast becoming reality as maturing technologies are empowering the different facets. One of the highlights of the Metaverse is that it offers the possibility for highly immersive and interactive socialization. Virtual reality (VR) technologies are the backbone for the virtual universe within the Metaverse as they enable a hyper-realistic and immersive experience, and especially so in the context of socialization. As the virtual world 3D scenes to be rendered are of high resolution and frame rate, these scenes will be offloaded to an edge server for computation. Besides, the metaverse is user-center by design, and human users are always the core. In this work, we introduce a multi-user VR computation offloading over wireless communication scenario. In addition, we devised a novel user-centered deep reinforcement learning approach to find a near-optimal solution. Extensive experiments demonstrate that our approach can lead to remarkable results under various requirements and constraints.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 9 of its 20 references that are in the map. First posted on arXiv 8 Mar 2023.
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9 of 20 references.
References · 9
- 2017Proximal Policy Optimization Algorithmscited by 14,441
- 2015High-Dimensional Continuous Control Using Generalized Advantage Estimationcited by 1,916
- 2018Applications of Deep Reinforcement Learning in Communications and Networking: A Surveycited by 163
- 2019The Roadmap to 6G -- AI Empowered Wireless Networkscited by 150
- 2021All One Needs to Know about Metaverse: A Complete Survey on Technological Singularity, Virtual Ecosystem, and Research Agendacited by 67
- 2017Hybrid Reward Architecture for Reinforcement Learningcited by 57
- 2022A Survey on Metaverse: Fundamentals, Security, and Privacycited by 51
- 2021Survey of Recent Multi-Agent Reinforcement Learning Algorithms Utilizing Centralized Trainingcited by 16
- 2022Sampling, Communication, and Prediction Co-Design for Synchronizing the Real-World Device and Digital Model in Metaversecited by 7
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