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

arXiv: https://arxiv.org/abs/2603.16141 · HTML: https://mlsanity.com/paper/2603.16141/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 23 references that are in the map.
- First posted on arXiv 17 Mar 2026.

## Builds on (most cited first, 17 of 17)

- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [Counterfactual Multi-Agent Policy Gradients](https://mlsanity.com/paper/1705.08926/) (2017)
- [Learning Multiagent Communication with Backpropagation](https://mlsanity.com/paper/1605.07736/) (2016)
- [Emergent Tool Use From Multi-Agent Autocurricula](https://mlsanity.com/paper/1909.07528/) (2019)
- [TarMAC: Targeted Multi-Agent Communication](https://mlsanity.com/paper/1810.11187/) (2018)
- [Multiagent Cooperation and Competition with Deep Reinforcement Learning](https://mlsanity.com/paper/1511.08779/) (2015)
- [Graph Convolutional Reinforcement Learning](https://mlsanity.com/paper/1810.09202/) (2018)
- [Multiagent Bidirectionally-Coordinated Nets: Emergence of Human-level Coordination in Learning to Play StarCraft Combat Games](https://mlsanity.com/paper/1703.10069/) (2017)
- [VAIN: Attentional Multi-agent Predictive Modeling](https://mlsanity.com/paper/1706.06122/) (2017)
- [Learning Transferable Cooperative Behavior in Multi-Agent Teams](https://mlsanity.com/paper/1906.01202/) (2019)
- [Distributed Wildfire Surveillance with Autonomous Aircraft using Deep Reinforcement Learning](https://mlsanity.com/paper/1810.04244/) (2018)
- [Reinforcement Learning for Decentralized Trajectory Design in Cellular UAV Networks with Sense-and-Send Protocol](https://mlsanity.com/paper/1809.02934/) (2018)
- [DeepCQ+: Robust and Scalable Routing with Multi-Agent Deep Reinforcement Learning for Highly Dynamic Networks](https://mlsanity.com/paper/2111.15013/) (2021)
- [An Experimental mmWave Channel Model for UAV-to-UAV Communications](https://mlsanity.com/paper/2007.11869/) (2020)
- [Model-aided Federated Reinforcement Learning for Multi-UAV Trajectory Planning in IoT Networks](https://mlsanity.com/paper/2306.02029/) (2023)

## 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.