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Joint Multi-Agent Reinforcement Learning and Message-Passing for Resilient Multi-UAV Networks
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- Authors
- Yeryeong Cho, Sungwon Yi, Soohyun Park
- Issue Date
- 2026-01
- Citation
- IEEE Transactions on Network and Service Management, v.권호미정, pp.1-13
- ISSN
- 1932-4537
- Publisher
- IEEE
- Language
- English
- Type
- Journal Article
- Abstract
- This paper introduces a novel resilient algorithm designed for distributed unmanned aerial vehicles (UAVs) in dynamic and unreliable network environments. Initially, the UAVs should be trained via multi-agent reinforcement learning (MARL) for autonomous mission-critical operations and are fundamentally grounded by centralized training and decentralized execution (CTDE) using a centralized MARL server. In this situation, it is crucial to consider the case where several UAVs cannot receive CTDE-based MARL learning parameters for resilient operations in unreliable network conditions. To tackle this issue, a communication graph is used where its edges are established when two UAVs/nodes are communicable. Then, the edge-connected UAVs can share their training data if one of the UAVs cannot be connected to the CTDE-based MARL server under unreliable network conditions. Additionally, the edge cost considers power efficiency. Based on this given communication graph, message-passing is used for electing the UAVs that can provide their MARL learning parameters to their edge-connected peers. Lastly, performance evaluations demonstrate the superiority of our proposed algorithm in terms of power efficiency and resilient UAV task management, outperforming existing benchmark algorithms.
- Keyword
- Multi-Agent System (MAS), Reinforcement Learning (RL), Communication Graph, Message Passing, Resilient Communication Network, Unmanned Aerial Vehicle (UAV), UAVs Networks
- KSP Keywords
- Critical operations, Learning parameters, Message Passing, Mission-critical, Multi-agent system(MAS), Performance evaluation, Power Efficiency, Reinforcement learning(RL), Task Management, UAV network, Unreliable network