PANAMA让多智能体在数字孪生中更智能地规划路径，兼顾网络状态和任务优先级。
PANAMA: A Network-Aware MARL Framework for Multi-Agent Path Finding in Digital Twin Ecosystems
- 基于中心化训练、去中心化执行框架，引入网络感知的异步学习机制。
- 相比基准方法，路径规划在准确率、速度和可扩展性上均显著提升。
- 适合研究数字孪生、智能交通或自动化系统的开发者使用。
数字孪生（DT）正通过先进数据处理与分析推动产业变革，成为下一代技术如具身人工智能的核心。随着机器人与自动化系统规模扩大，高效的数据共享框架与稳健算法愈发关键。本文聚焦于数字孪生生态系统中应用与网络提供商（AP/NP）之间的动态交互，提出PANAMA——一种面向网络感知的多智能体强化学习（MARL）路径规划新算法。该算法采用中央训练、分布式执行（CTDE）框架与异步演员-学习者架构，加速训练过程并支持具身AI自主任务执行。实验表明，其在路径规划的准确性、速度与可扩展性方面均优于现有基准。仿真结果揭示了适用于大规模自动化系统的优化数据共享策略，保障复杂真实环境下的系统鲁棒性。PANAMA实现了网络感知决策与多智能体协同的融合，推动数字孪生、无线网络与人工智能自动化之间的协同发展。
原文摘要 · Abstract (English)
Digital Twins (DTs) are transforming industries through advanced data processing and analysis, positioning the world of DTs, Digital World, as a cornerstone of nextgeneration technologies including embodied AI. As robotics and automated systems scale, efficient data-sharing frameworks and robust algorithms become critical. We explore the pivotal role of data handling in next-gen networks, focusing on dynamics between application and network providers (AP/NP) in DT ecosystems. We introduce PANAMA, a novel algorithm with Priority Asymmetry for Network Aware Multi-agent Reinforcement Learning (MARL) based multi-agent path finding (MAPF). By adopting a Centralized Training with Decentralized Execution (CTDE) framework and asynchronous actor-learner architectures, PANAMA accelerates training while enabling autonomous task execution by embodied AI. Our approach demonstrates superior pathfinding performance in accuracy, speed, and scalability compared to existing benchmarks. Through simulations, we highlight optimized data-sharing strategies for scalable, automated systems, ensuring resilience in complex, real-world environments. PANAMA bridges the gap between network-aware decision-making and robust multi-agent coordination, advancing the synergy between DTs, wireless networks, and AI-driven automation.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。