- A multi-agent environment for time-optimal motion planning. This repository uses multi-agent reinforcement learning to present a decentralized policy network for time-optimal multi-drone flight.
- This project is a reimplementation of gym-pybullet-drones optimized for multi-agent scenarios. We have adjusted the code to make it more suitable for handling many agents simultaneously.
- We customize PPO in a centralized training, decentralized execution (CTDE) fashion, based on stable-baselines3 and inspired by the on-policy(MAPPO) repository.
- March 5, 2025: The camera-ready version of our paper has been updated on arXiv.
- January 27, 2025: Our paper has been accepted to ICRA 2025!
- September 25, 2024: Paper preprint available on arXiv.
- Coming Soon: The public and release version is coming soon...
Real-world experiments with two quadrotors using the same network achieve a maximum speed of 13.65 m/s and a maximum body rate of 13.4 rad/s in a 5.5 m x 5.5 m x 2.0 m space across various tracks, relying entirely on onboard computation.
- Dashing for the Golden Snitch: Multi-Drone Time-Optimal Motion Planning with Multi-Agent Reinforcement Learning, Wang, X., Zhou, J., Feng, Y., Mei, J., Chen, J., & Li, S. (2024), arXiv preprint arXiv:2409.16720. Accepted at ICRA 2025.
It's recommended to use a virtual environment, such as conda:
coming soon...
coming soon...
If you use this repository in your research, please consider citing:
@article{Wang2024Dashing,
author = {Wang, X. and Zhou, J. and Feng, Y. and Mei, J. and Chen, J. and Li, S.},
title = {Dashing for the Golden Snitch: Multi-Drone Time-Optimal Motion Planning with Multi-Agent Reinforcement Learning},
journal = {arXiv preprint arXiv:2409.16720},
year = {2024},
url = {https://arxiv.org/abs/2409.16720}
}This project is released under the MIT License. Please review the License file for more details.