An invulnerable leader–follower collision-free unmanned aerial vehicle flocking system with attention-based Multi-Agent Reinforcement Learning

This is the Attention Based Cucker-Smale Flocking algorithm (A Multi-Agent Reinforcement Learning Algorithm for UAV/Drone Flocking) implementation on Multi-Agent Particle Environment(MPE), the corresponding paper is An invulnerable leader–follower collision-free unmanned aerial vehicle flocking system with attention-based Multi-Agent Reinforcement Learning

The MADDPG part is come from: MADDPG

Requirements

Key Requirements

• python>=3.6.5
• Multi-Agent Particle Environment(MPE)
• torch=1.1.0

The full requirements can be installed by:

pip install -r requirements.txt

If you are troubled in installation of the requirements. We provide the Anaconda-python environment that you can download directly: LG-CS.zip

Extract code: MARL

Complie Cython Code

Before running the code, please complie the environment code (Or download the complied version (.pyd file) from: release, and put .pyd in ./envs):

cd ./envs
python setup.py build_ext --inplace --force

Training Agents

Running the main.py, the agents will learn from the flocking scenario：

python main.py --n-agents=5 --evaluate-episodes=256

If you want to adjust the parameters, please see the ./common/arguments.py for more details.

Testing Agents

python main.py --n-agents=5 --evaluate-episodes=10 --evaluate=True

Display Results

After data collection:

python display.py

Citation

If you use this code, please cite our paper:

[1] Yunxiao Guo, Dan Xu, Chang Wang, Jinxi Li, Han Long, An invulnerable leader–follower collision-free unmanned aerial vehicle flocking system with attention-based Multi-Agent Reinforcement Learning,Engineering Applications of Artificial Intelligence,
2025,160, Part C,111797,doi: 10.1016/j.engappai.2025.111797. 

Bibtex form:

@article{GUO2025111797,
author = {Yunxiao Guo and Dan Xu and Chang Wang and Jinxi Li and Han Long},
title = {An invulnerable leader–follower collision-free unmanned aerial vehicle flocking system with attention-based Multi-Agent Reinforcement Learning},
journal = {Engineering Applications of Artificial Intelligence},
volume = {160},
pages = {111797},
year = {2025},
issn = {0952-1976},
doi = {https://doi.org/10.1016/j.engappai.2025.111797},
url = {https://www.sciencedirect.com/science/article/pii/S0952197625017993}
}