# Attention Based Communication and Control for Multi-UAV Path Planning

arXiv: https://arxiv.org/abs/2112.12584 · HTML: https://mlsanity.com/paper/2112.12584/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 21 references that are in the map.
- First posted on arXiv 20 Dec 2021.

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Soft Actor-Critic Algorithms and Applications](https://mlsanity.com/paper/1812.05905/) (2018)
- [Learning to Communicate with Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1605.06676/) (2016)
- [Actor-Attention-Critic for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1810.02912/) (2018)
- [Off-Belief Learning](https://mlsanity.com/paper/2103.04000/) (2021)
- [An Introductory Survey on Attention Mechanisms in NLP Problems](https://mlsanity.com/paper/1811.05544/) (2018)
- [UAV Communications for 5G and Beyond: Recent Advances and Future Trends](https://mlsanity.com/paper/1901.06637/) (2019)
- [Massive Autonomous UAV Path Planning: A Neural Network Based Mean-Field Game Theoretic Approach](https://mlsanity.com/paper/1905.04152/) (2019)
- [Cellular UAV-to-Device Communications: Trajectory Design and Mode Selection by Multi-agent Deep Reinforcement Learning](https://mlsanity.com/paper/2002.08040/) (2020)
- [Attention-based Fault-tolerant Approach for Multi-agent Reinforcement Learning Systems](https://mlsanity.com/paper/1910.02240/) (2019)
- [Remote UAV Online Path Planning via Neural Network Based Opportunistic Control](https://mlsanity.com/paper/1910.04969/) (2019)

## Built on by (most cited first, 2 of 2)

- [Towards Semantic Communication Protocols for 6G: From Protocol Learning to Language-Oriented Approaches](https://mlsanity.com/paper/2310.09506/) (2023)
- [Bayesian Inverse Contextual Reasoning for Heterogeneous Semantics-Native Communication](https://mlsanity.com/paper/2306.06403/) (2023)

## Abstract (arXiv)

Inspired by the multi-head attention (MHA) mechanism in natural language processing, this letter proposes an iterative single-head attention (ISHA) mechanism for multi-UAV path planning. The ISHA mechanism is run by a communication helper collecting the state embeddings of UAVs and distributing an attention score vector to each UAV. The attention scores computed by ISHA identify how many interactions with other UAVs should be considered in each UAV's control decision-making. Simulation results corroborate that the ISHA-based communication and control framework achieves faster travel with lower inter-UAV collision risks than an MHA-aided baseline, particularly under limited communication resources.