drone

drone is a reinforcement learning based drone controller and simulator tool for the Warwick AI drone project.

This project was presented at the 2025 Warwick Artificial Intelligence summit. The slides for this talk are available here.

Installation

Install the required packages - which are outlined in pyproject.toml. They can be installed with pip or a Python package management tool such as uv.

git clone https://github.com/stmio/drone.git
cd drone
uv sync

Usage

First, the C code needs to be compiled through Cython (this must be re-run each time the C is edited):

# If installed with pip
python setup.py build_ext --inplace
mv cy_env.cpython-312-darwin.so ./simulator/.

# If installed with uv
uv run setup.py build_ext --inplace
mv cy_env.cpython-312-darwin.so ./simulator/.

To open the matplotlib visualisation tool, run the following command:

# If installed with pip
python drone.py

# If installed with uv
uv run drone.py

To train the model, run the following command:

python puffer.py --env drone --mode train

# If installed with uv
uv run puffer.py --env drone --mode train

You can also use wandb to visualise the training process (requires an account):

python puffer.py --env drone --mode train --track --wandb-project drone

# If installed with uv
uv run puffer.py --env drone --mode train --track --wandb-project drone

Demo

Screen.Recording.2025-02-08.at.2.29.46.pm.mov

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

License

MIT License

Unless you explicitly state otherwise, any contribution intentionally submitted for inclusion in the work by you, shall be licensed as above, without any additional terms or conditions.