PANAMA: A Network-Aware MARL Framework for Multi-Agent Path Finding in Digital Twin Ecosystems
- URL: http://arxiv.org/abs/2508.06767v1
- Date: Sat, 09 Aug 2025 00:59:55 GMT
- Title: PANAMA: A Network-Aware MARL Framework for Multi-Agent Path Finding in Digital Twin Ecosystems
- Authors: Arman Dogru, R. Irem Bor-Yaliniz, Nimal Gamini Senarath,
- Abstract summary: We introduce PANAMA, a novel algorithm with Priority Asymmetry for Network Multi-agent Reinforcement Learning (MARL) based multi-agent path finding (MAPF)<n>Our approach demonstrates superior pathfinding performance in accuracy, speed, and scalability compared to existing benchmarks.<n>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.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
Related papers
- Federated Agentic AI for Wireless Networks: Fundamentals, Approaches, and Applications [60.721304295812445]
Federated learning (FL) has the potential to improve the overall loop of agentic AI.<n>We first summarize fundamentals of agentic AI and mainstream FL types. Then, we illustrate how each FL type can strengthen a specific component of agentic AI's loop.<n>We conduct a case study on using FRL to improve the performance of agentic AI's action decision in low-altitude wireless networks.
arXiv Detail & Related papers (2026-03-02T11:26:56Z) - Digital Twins & ZeroConf AI: Structuring Automated Intelligent Pipelines for Industrial Applications [3.534869097377701]
This work proposes a modular and interoperable solution that enables seamless AI pipeline integration into Cyber-Physical Systems.<n>We introduce the concept of Zero configuration (ZeroConf) AI pipelines, where DTs orchestrate data management and intelligent augmentation.<n>The approach is demonstrated in a MicroFactory scenario, showing support for concurrent ML models and dynamic data processing.
arXiv Detail & Related papers (2026-02-04T10:11:06Z) - ComAgent: Multi-LLM based Agentic AI Empowered Intelligent Wireless Networks [62.031889234230725]
6G networks rely on complex cross-layer optimization.<n> manually translating high-level intents into mathematical formulations remains a bottleneck.<n>We present ComAgent, a multi-LLM agentic AI framework.
arXiv Detail & Related papers (2026-01-27T13:43:59Z) - Multi-Agent Collaborative Intrusion Detection for Low-Altitude Economy IoT: An LLM-Enhanced Agentic AI Framework [60.72591149679355]
The rapid expansion of low-altitude economy Internet of Things (LAE-IoT) networks has created unprecedented security challenges.<n>Traditional intrusion detection systems fail to tackle the unique characteristics of aerial IoT environments.<n>We introduce a large language model (LLM)-enabled agentic AI framework for enhancing intrusion detection in LAE-IoT networks.
arXiv Detail & Related papers (2026-01-25T12:47:25Z) - Towards 6G Native-AI Edge Networks: A Semantic-Aware and Agentic Intelligence Paradigm [85.7583231789615]
6G positions intelligence as a native network capability, transforming the design of radio access networks (RANs)<n>Within this vision, Semantic-native communication and agentic intelligence are expected to play central roles.<n>Agentic intelligence endows distributed RAN entities with goal-driven autonomy, reasoning, planning, and multi-agent collaboration.
arXiv Detail & Related papers (2025-12-04T03:09:33Z) - Twill: Scheduling Compound AI Systems on Heterogeneous Mobile Edge Platforms [1.7835990287552501]
Compound AI (cAI) systems chain multiple AI models to solve complex problems.<n>Existing mobile edge AI inference strategies manage multi-DNN or transformer-only workloads.<n>We present Twill, a run-time framework to handle concurrent inference requests of cAI workloads.
arXiv Detail & Related papers (2025-07-01T07:06:45Z) - INSIGHT: A Survey of In-Network Systems for Intelligent, High-Efficiency AI and Topology Optimization [43.37351326629751]
In-network AI is a transformative approach to addressing the escalating demands of Artificial Intelligence (AI) on network infrastructure.<n>This paper provides a comprehensive analysis of optimizing in-network computation for AI.<n>It examines methodologies for mapping AI models onto resource-constrained network devices, addressing challenges like limited memory and computational capabilities.
arXiv Detail & Related papers (2025-05-30T06:47:55Z) - Future-Proofing Mobile Networks: A Digital Twin Approach to Multi-Signal Management [2.5341871361006456]
Digital Twins (DTs) are set to become a key enabling technology in future wireless networks.
Our framework integrates diverse data sources to provide real-time, holistic insights into network performance.
Traditional analytics will evolve to rely on emerging AI models, such as Generative AI (GenAI)
arXiv Detail & Related papers (2024-07-22T10:13:46Z) - Automatic AI Model Selection for Wireless Systems: Online Learning via Digital Twinning [50.332027356848094]
AI-based applications are deployed at intelligent controllers to carry out functionalities like scheduling or power control.
The mapping between context and AI model parameters is ideally done in a zero-shot fashion.
This paper introduces a general methodology for the online optimization of AMS mappings.
arXiv Detail & Related papers (2024-06-22T11:17:50Z) - An Interactive Agent Foundation Model [49.77861810045509]
We propose an Interactive Agent Foundation Model that uses a novel multi-task agent training paradigm for training AI agents.
Our training paradigm unifies diverse pre-training strategies, including visual masked auto-encoders, language modeling, and next-action prediction.
We demonstrate the performance of our framework across three separate domains -- Robotics, Gaming AI, and Healthcare.
arXiv Detail & Related papers (2024-02-08T18:58:02Z) - Intelligent Trajectory Design for RIS-NOMA aided Multi-robot
Communications [59.34642007625687]
The goal is to maximize the sum-rate of whole trajectories for multi-robot system by jointly optimizing trajectories and NOMA decoding orders of robots.
An integrated machine learning (ML) scheme is proposed, which combines long short-term memory (LSTM)-autoregressive integrated moving average (ARIMA) model and dueling double deep Q-network (D$3$QN) algorithm.
arXiv Detail & Related papers (2022-05-03T17:14:47Z) - Hierarchical Multi-Agent DRL-Based Framework for Joint Multi-RAT
Assignment and Dynamic Resource Allocation in Next-Generation HetNets [21.637440368520487]
This paper considers the problem of cost-aware downlink sum-rate via joint optimal radio access technologies (RATs) assignment and power allocation in next-generation wireless networks (HetNets)
We propose a hierarchical multi-agent deep reinforcement learning (DRL) framework, called DeepRAT, to solve it efficiently and learn system dynamics.
In particular, the DeepRAT framework decomposes the problem into two main stages; the RATs-EDs assignment stage, which implements a single-agent Deep Q Network algorithm, and the power allocation stage, which utilizes a multi-agent Deep Deterministic Policy Gradient
arXiv Detail & Related papers (2022-02-28T09:49:44Z) - Graph Neural Networks for Decentralized Multi-Robot Submodular Action
Selection [101.38634057635373]
We focus on applications where robots are required to jointly select actions to maximize team submodular objectives.
We propose a general-purpose learning architecture towards submodular at scale, with decentralized communications.
We demonstrate the performance of our GNN-based learning approach in a scenario of active target coverage with large networks of robots.
arXiv Detail & Related papers (2021-05-18T15:32:07Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.