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

• URL: http://arxiv.org/abs/2603.16141v1
• Date: Tue, 17 Mar 2026 05:48:51 GMT
• Title: Communication-Aware Multi-Agent Reinforcement Learning for Decentralized Cooperative UAV Deployment
• Abstract summary: We present a graph-based multi-agent reinforcement learning framework trained under centralized training with decentralized execution (CTDE)<n>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.<n>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 distance-limited communication graph.
• Score: 5.204648436671471
• License: http://creativecommons.org/licenses/by/4.0/
• 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 links. 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. 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 distance-limited communication graph. We evaluate primarily on a cooperative relay deployment task (DroneConnect) and secondarily on an adversarial engagement task (DroneCombat). In DroneConnect, the proposed method achieves high coverage under restricted communication and partial observation (e.g. 74% coverage with M = 5 UAVs and N = 10 nodes) while remaining competitive with a mixed-integer linear programming (MILP) optimization-based offline upper bound, and it generalizes to unseen team sizes without fine-tuning. In the adversarial setting, the same framework transfers without architectural changes and improves win rate over non-communicating baselines.

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