PANAMA: A Network-Aware MARL Framework for Multi-Agent Path Finding in Digital Twin Ecosystems

TL;DR

This paper introduces PANAMA, a network-aware MARL framework that enhances multi-agent pathfinding in Digital Twin ecosystems, improving accuracy, speed, and scalability through innovative data-sharing and coordination strategies.

Contribution

The paper presents PANAMA, a novel algorithm with Priority Asymmetry for Network Aware MARL, integrating centralized training and decentralized execution for improved multi-agent pathfinding.

Findings

Superior pathfinding accuracy and speed compared to benchmarks

Enhanced scalability and resilience in complex environments

Optimized data-sharing strategies for network-aware multi-agent systems

Abstract

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.