# Multi-attribute Auction-based Resource Allocation for Twins Migration in Vehicular Metaverses: A GPT-based DRL Approach

arXiv: https://arxiv.org/abs/2406.05418 · HTML: https://mlsanity.com/paper/2406.05418/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 50 references that are in the map.
- First posted on arXiv 8 Jun 2024.

## Builds on (most cited first, 19 of 19)

- [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)
- [Decision Transformer: Reinforcement Learning via Sequence Modeling](https://mlsanity.com/paper/2106.01345/) (2021)
- [A Full Dive into Realizing the Edge-enabled Metaverse: Visions, Enabling Technologies,and Challenges](https://mlsanity.com/paper/2203.05471/) (2022)
- [The Stability-Efficiency Dilemma: Investigating Sequence Length Warmup for Training GPT Models](https://mlsanity.com/paper/2108.06084/) (2021)
- [Generative AI-empowered Simulation for Autonomous Driving in Vehicular Mixed Reality Metaverses](https://mlsanity.com/paper/2302.08418/) (2023)
- [A Survey on Digital Twins: Architecture, Enabling Technologies, Security and Privacy, and Future Prospects](https://mlsanity.com/paper/2301.13350/) (2023)
- [Wireless Edge-Empowered Metaverse: A Learning-Based Incentive Mechanism for Virtual Reality](https://mlsanity.com/paper/2111.03776/) (2021)
- [Reliable Distributed Computing for Metaverse: A Hierarchical Game-Theoretic Approach](https://mlsanity.com/paper/2111.10548/) (2021)
- [Multi-Agent Deep Reinforcement Learning for Dynamic Avatar Migration in AIoT-enabled Vehicular Metaverses with Trajectory Prediction](https://mlsanity.com/paper/2306.14683/) (2023)
- [Blockchain-assisted Twin Migration for Vehicular Metaverses: A Game Theory Approach](https://mlsanity.com/paper/2309.02173/) (2023)
- [Task Freshness-aware Incentive Mechanism for Vehicle Twin Migration in Vehicular Metaverses](https://mlsanity.com/paper/2309.04703/) (2023)
- [Semantic Information Market For The Metaverse: An Auction Based Approach](https://mlsanity.com/paper/2204.04878/) (2022)
- [Resource Allocation for Augmented Reality Empowered Vehicular Edge Metaverse](https://mlsanity.com/paper/2212.01325/) (2022)
- [Transferable Deep Reinforcement Learning Framework for Autonomous Vehicles with Joint Radar-Data Communications](https://mlsanity.com/paper/2105.13670/) (2021)
- [Reconfiguring Wireless Environment via Intelligent Surfaces for 6G: Reflection, Modulation, and Security](https://mlsanity.com/paper/2208.10931/) (2022)
- [A Survey on Multi-AP Coordination Approaches over Emerging WLANs: Future Directions and Open Challenges](https://mlsanity.com/paper/2306.04164/) (2023)
- [Decentralized Multimedia Data Sharing in IoV: A Learning-based Equilibrium of Supply and Demand](https://mlsanity.com/paper/2403.20218/) (2024)
- [Generative AI-Based Text Generation Methods Using Pre-Trained GPT-2 Model](https://mlsanity.com/paper/2404.01786/) (2024)

## Built on by (most cited first, 5 of 5)

- [A Survey on Large Language Models for Communication, Network, and Service Management: Application Insights, Challenges, and Future Directions](https://mlsanity.com/paper/2412.19823/) (2024)
- [MetaTrading: An Immersion-Aware Model Trading Framework for Vehicular Metaverse Services](https://mlsanity.com/paper/2410.19665/) (2024)
- [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)
- [Hybrid Stackelberg Game and Diffusion-based Auction for Two-tier Agentic AI Task Offloading in Internet of Agents](https://mlsanity.com/paper/2511.22076/) (2025)
- [Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks](https://mlsanity.com/paper/2606.05208/) (2026)

## Abstract (arXiv)

Vehicular Metaverses are developed to enhance the modern automotive industry with an immersive and safe experience among connected vehicles and roadside infrastructures, e.g., RoadSide Units (RSUs). For seamless synchronization with virtual spaces, Vehicle Twins (VTs) are constructed as digital representations of physical entities. However, resource-intensive VTs updating and high mobility of vehicles require intensive computation, communication, and storage resources, especially for their migration among RSUs with limited coverages. To address these issues, we propose an attribute-aware auction-based mechanism to optimize resource allocation during VTs migration by considering both price and non-monetary attributes, e.g., location and reputation. In this mechanism, we propose a two-stage matching for vehicular users and Metaverse service providers in multi-attribute resource markets. First, the resource attributes matching algorithm obtains the resource attributes perfect matching, namely, buyers and sellers can participate in a double Dutch auction (DDA). Then, we train a DDA auctioneer using a generative pre-trained transformer (GPT)-based deep reinforcement learning (DRL) algorithm to adjust the auction clocks efficiently during the auction process. We compare the performance of social welfare and auction information exchange costs with state-of-the-art baselines under different settings. Simulation results show that our proposed GPT-based DRL auction schemes have better performance than others.