# Human-Centric Resource Allocation for the Metaverse With Multiaccess Edge Computing

arXiv: https://arxiv.org/abs/2312.15313 · HTML: https://mlsanity.com/paper/2312.15313/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 42 references that are in the map.
- First posted on arXiv 23 Dec 2023.

## Builds on (most cited first, 23 of 23)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games](https://mlsanity.com/paper/2103.01955/) (2021)
- [MineRL: A Large-Scale Dataset of Minecraft Demonstrations](https://mlsanity.com/paper/1907.13440/) (2019)
- [Mean Field Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1802.05438/) (2018)
- [Graph Convolutional Reinforcement Learning](https://mlsanity.com/paper/1810.09202/) (2018)
- [A Survey on Metaverse: Fundamentals, Security, and Privacy](https://mlsanity.com/paper/2203.02662/) (2022)
- [Horizon: Facebook's Open Source Applied Reinforcement Learning Platform](https://mlsanity.com/paper/1811.00260/) (2018)
- [Online Planning Algorithms for POMDPs](https://mlsanity.com/paper/1401.3436/) (2014)
- [A Full Dive into Realizing the Edge-enabled Metaverse: Visions, Enabling Technologies,and Challenges](https://mlsanity.com/paper/2203.05471/) (2022)
- [HoloLens 2 Research Mode as a Tool for Computer Vision Research](https://mlsanity.com/paper/2008.11239/) (2020)
- [Towards Low-Latency and Ultra-Reliable Virtual Reality](https://mlsanity.com/paper/1801.07587/) (2018)
- [Resource Scheduling in Edge Computing: A Survey](https://mlsanity.com/paper/2108.08059/) (2021)
- [Attention-aware Resource Allocation and QoE Analysis for Metaverse xURLLC Services](https://mlsanity.com/paper/2208.05438/) (2022)
- [Multi-agent Reinforcement Learning for Resource Allocation in IoT networks with Edge Computing](https://mlsanity.com/paper/2004.02315/) (2020)
- [Wireless Edge-Empowered Metaverse: A Learning-Based Incentive Mechanism for Virtual Reality](https://mlsanity.com/paper/2111.03776/) (2021)
- [Compute- and Data-Intensive Networks: The Key to the Metaverse](https://mlsanity.com/paper/2204.02001/) (2022)
- [Interacting with New York City Data by HoloLens through Remote Rendering](https://mlsanity.com/paper/2212.10295/) (2022)
- [The Metaverse from a Multimedia Communications Perspective](https://mlsanity.com/paper/2301.07740/) (2023)
- [ECHO: An Adaptive Orchestration Platform for Hybrid Dataflows across Cloud and Edge](https://mlsanity.com/paper/1707.00889/) (2017)

## Built on by (most cited first, 3 of 3)

- [A Multi-Agent DRL-Based Framework for Optimal Resource Allocation and Twin Migration in the Multi-Tier Vehicular Metaverse](https://mlsanity.com/paper/2502.19004/) (2025)
- [Immersive Multimedia Communication: State-of-the-Art on eXtended Reality Streaming](https://mlsanity.com/paper/2506.10004/) (2025)
- [Adaptive Social Metaverse Streaming based on Federated Multi-Agent Deep Reinforcement Learning](https://mlsanity.com/paper/2506.17342/) (2025)

## Abstract (arXiv)

Multi-access edge computing (MEC) is a promising solution to the computation-intensive, low-latency rendering tasks of the metaverse. However, how to optimally allocate limited communication and computation resources at the edge to a large number of users in the metaverse is quite challenging. In this paper, we propose an adaptive edge resource allocation method based on multi-agent soft actor-critic with graph convolutional networks (SAC-GCN). Specifically, SAC-GCN models the multi-user metaverse environment as a graph where each agent is denoted by a node. Each agent learns the interplay between agents by graph convolutional networks with self-attention mechanism to further determine the resource usage for one user in the metaverse. The effectiveness of SAC-GCN is demonstrated through the analysis of user experience, balance of resource allocation, and resource utilization rate by taking a virtual city park metaverse as an example. Experimental results indicate that SAC-GCN outperforms other resource allocation methods in improving overall user experience, balancing resource allocation, and increasing resource utilization rate by at least 27%, 11%, and 8%, respectively.