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

arXiv 2603.16141 ↗First posted 17 Mar 2026cs.MA cs.LG cs.NIcited by 0

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Abstract (arXiv)

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.

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References · 17

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5 more
  1. 2018Distributed Wildfire Surveillance with Autonomous Aircraft using Deep Reinforcement Learningcited by 16
  2. 2018Reinforcement Learning for Decentralized Trajectory Design in Cellular UAV Networks with Sense-and-Send Protocolcited by 8
  3. 2021DeepCQ+: Robust and Scalable Routing with Multi-Agent Deep Reinforcement Learning for Highly Dynamic Networkscited by 6
  4. 2020An Experimental mmWave Channel Model for UAV-to-UAV Communicationscited by 4
  5. 2023Model-aided Federated Reinforcement Learning for Multi-UAV Trajectory Planning in IoT Networkscited by 3

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