Communication-Aware Multi-Agent Reinforcement Learning for Decentralized Cooperative UAV Deployment

Abstract:Autonomous Unmanned Aerial Vehicle (UAV) swarms are increasingly used as rapidly deployable aerial relays and sensing platforms, yet practical deployments must operate under partial observability and intermittent peer-to-peer connectivity. We present a graph-based multi-agent reinforcement learning framework trained under centralized training with decentralized execution (CTDE): a centralized critic and global state are available only during training, while each UAV executes a shared policy using local observations and messages from nearby neighbors. Under restricted communication, neighbor relations are induced by an SNR-threshold connectivity graph. Our architecture encodes local agent state and nearby entities with an agent-entity attention module and aggregates inter-UAV messages with neighbor self-attention over a signal-quality-limited communication graph defined by a channel model. We evaluate the framework on a cooperative relay-deployment task, DroneConnect. Experimental results show that the proposed method achieves an approximately 12% increase in target coverage over MAPPO under restricted communication and partial observability, while remaining competitive with a mixed-integer linear programming (MILP)-based offline upper bound with full node observability.

Multiagent Systems (cs.MA); Machine Learning (cs.LG); Networking and Internet Architecture (cs.NI)

arXiv:2603.16141 [cs.MA]

(or arXiv:2603.16141v2 [cs.MA] for this version)

https://doi.org/10.48550/arXiv.2603.16141 Focus to learn more arXiv-issued DOI via DataCite

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