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Decentralized policy network for time-optimal multi-drone flight using multi-agent reinforcement learning.

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KafuuChikai/Dashing-for-the-Golden-Snitch-Multi-Drone-RL

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Dashing for the Golden Snitch: Multi-Drone RL

Table of Contents

  1. Introduction
  2. Quick Installation
  3. Usage
  4. Citation
  5. License

Introduction

  • 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.

News

  • 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...

Demonstration Video

Demonstration

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.

Related Papers

Quick Installation

It's recommended to use a virtual environment, such as conda:

coming soon...

Usage

coming soon...

Citation

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}
}

License

This project is released under the MIT License. Please review the License file for more details.

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Decentralized policy network for time-optimal multi-drone flight using multi-agent reinforcement learning.

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