# Attention-based Reinforcement Learning for Real-Time UAV Semantic Communication

arXiv: https://arxiv.org/abs/2105.10716 · HTML: https://mlsanity.com/paper/2105.10716/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 20 references that are in the map.
- First posted on arXiv 22 May 2021.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1803.11485/) (2018)
- [Learning to Communicate with Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1605.06676/) (2016)
- [Learning Multiagent Communication with Backpropagation](https://mlsanity.com/paper/1605.07736/) (2016)
- [Gate-Variants of Gated Recurrent Unit (GRU) Neural Networks](https://mlsanity.com/paper/1701.05923/) (2017)
- [Multiagent Cooperation and Competition with Deep Reinforcement Learning](https://mlsanity.com/paper/1511.08779/) (2015)
- [Wireless Network Intelligence at the Edge](https://mlsanity.com/paper/1812.02858/) (2018)
- [Multi-Agent Game Abstraction via Graph Attention Neural Network](https://mlsanity.com/paper/1911.10715/) (2019)
- [Extreme URLLC: Vision, Challenges, and Key Enablers](https://mlsanity.com/paper/2001.09683/) (2020)
- [Multi-Agent Deep Reinforcement Learning using Attentive Graph Neural Architectures for Real-Time Strategy Games](https://mlsanity.com/paper/2105.10211/) (2021)
- [Massive UAV-to-Ground Communication and its Stable Movement Control: A Mean-Field Approach](https://mlsanity.com/paper/1803.03285/) (2018)

## Built on by (most cited first, 14 of 14)

- [A Survey of Multi-Agent Deep Reinforcement Learning with Communication](https://mlsanity.com/paper/2203.08975/) (2022)
- [Scalable Multi-Agent Reinforcement Learning through Intelligent Information Aggregation](https://mlsanity.com/paper/2211.02127/) (2022)
- [Large Language Models for UAVs: Current State and Pathways to the Future](https://mlsanity.com/paper/2405.01745/) (2024)
- [Semantics-Native Communication with Contextual Reasoning](https://mlsanity.com/paper/2108.05681/) (2021)
- [Reinforcement Learning-powered Semantic Communication via Semantic Similarity](https://mlsanity.com/paper/2108.12121/) (2021)
- [Curriculum Learning for Goal-Oriented Semantic Communications with a Common Language](https://mlsanity.com/paper/2204.10429/) (2022)
- [Common Language for Goal-Oriented Semantic Communications: A Curriculum Learning Framework](https://mlsanity.com/paper/2111.08051/) (2021)
- [Semantics-Empowered Communication: A Tutorial-cum-Survey](https://mlsanity.com/paper/2212.08487/) (2022)
- [Goal-Oriented Quantization: Analysis, Design, and Application to Resource Allocation](https://mlsanity.com/paper/2209.15347/) (2022)
- [Distributed Machine Learning for UAV Swarms: Computing, Sensing, and Semantics](https://mlsanity.com/paper/2301.00912/) (2023)
- [Economics of Semantic Communication System: An Auction Approach](https://mlsanity.com/paper/2208.05040/) (2022)
- [Seq2Seq-SC: End-to-End Semantic Communication Systems with Pre-trained Language Model](https://mlsanity.com/paper/2210.15237/) (2022)
- [Scalable Safe Multi-Agent Reinforcement Learning for Multi-Agent System](https://mlsanity.com/paper/2501.13727/) (2025)
- [QMNet: Importance-Aware Message Exchange for Decentralized Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2307.09051/) (2023)

## Abstract (arXiv)

In this article, we study the problem of air-to-ground ultra-reliable and low-latency communication (URLLC) for a moving ground user. This is done by controlling multiple unmanned aerial vehicles (UAVs) in real time while avoiding inter-UAV collisions. To this end, we propose a novel multi-agent deep reinforcement learning (MADRL) framework, coined a graph attention exchange network (GAXNet). In GAXNet, each UAV constructs an attention graph locally measuring the level of attention to its neighboring UAVs, while exchanging the attention weights with other UAVs so as to reduce the attention mismatch between them. Simulation results corroborates that GAXNet achieves up to 4.5x higher rewards during training. At execution, without incurring inter-UAV collisions, GAXNet achieves 6.5x lower latency with the target 0.0000001 error rate, compared to a state-of-the-art baseline framework.