用多智能体强化学习优化车联网元宇宙的资源分配与孪生体迁移。
A Multi-Agent DRL-Based Framework for Optimal Resource Allocation and Twin Migration in the Multi-Tier Vehicular Metaverse
- 融合图神经网络与博弈论，实现车路协同动态决策。
- 降低12.8%延迟、提升16.1%用户体验，迁移成本下降14.2%。
- 适合研究智能交通与元宇宙系统集成的学者与工程师。
多层车联网元宇宙虽有望将车辆转化为数字生态中的关键节点，通过高效的资源分配与无缝的车辆孪生体（VT）迁移实现互联，但现有技术在高度动态的车联网环境中难以平衡延迟降低、资源利用率和用户体验（UX）等多目标优化问题。为此，本文提出一种新型多层资源分配与VT迁移框架，融合图卷积网络（GCN）、基于斯塔克尔伯格博弈的激励机制与多智能体深度强化学习（MADRL）。GCN模型捕捉车联网中时空依赖关系；斯塔克尔伯格博弈机制促进车与基础设施间的协作；MADRL算法实时联合优化资源分配与VT迁移。通过将动态多层车联网元宇宙建模为马尔可夫决策过程（MDP），设计出多目标多智能体深度确定性策略梯度（MO-MADDPG）算法，有效平衡多项冲突目标。大量仿真验证表明，该算法显著提升可扩展性、可靠性与效率，使延迟降低12.8%、资源利用率提升9.7%、迁移成本下降14.2%、整体用户体验提升16.1%。
原文摘要 · Abstract (English)
Although multi-tier vehicular Metaverse promises to transform vehicles into essential nodes -- within an interconnected digital ecosystem -- using efficient resource allocation and seamless vehicular twin (VT) migration, this can hardly be achieved by the existing techniques operating in a highly dynamic vehicular environment, since they can hardly balance multi-objective optimization problems such as latency reduction, resource utilization, and user experience (UX). To address these challenges, we introduce a novel multi-tier resource allocation and VT migration framework that integrates Graph Convolutional Networks (GCNs), a hierarchical Stackelberg game-based incentive mechanism, and Multi-Agent Deep Reinforcement Learning (MADRL). The GCN-based model captures both spatial and temporal dependencies within the vehicular network; the Stackelberg game-based incentive mechanism fosters cooperation between vehicles and infrastructure; and the MADRL algorithm jointly optimizes resource allocation and VT migration in real time. By modeling this dynamic and multi-tier vehicular Metaverse as a Markov Decision Process (MDP), we develop a MADRL-based algorithm dubbed the Multi-Objective Multi-Agent Deep Deterministic Policy Gradient (MO-MADDPG), which can effectively balances the various conflicting objectives. Extensive simulations validate the effectiveness of this algorithm that is demonstrated to enhance scalability, reliability, and efficiency while considerably improving latency, resource utilization, migration cost, and overall UX by 12.8%, 9.7%, 14.2%, and 16.1%, respectively.
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