This is a library for testing reinforcement learning algorithms on UAVs. This repo is still under development. We are also actively looking for users and developers, if this sounds like you, don't hesitate to get in touch!
pip3 install pyflyt
Usage is similar to any other Gymnasium and (soon) PettingZoo environment:
import gymnasium
import PyFlyt.gym_envs # noqa
env = gymnasium.make("PyFlyt/QuadX-Hover-v0", render_mode="human")
obs = env.reset()
termination = False
truncation = False
while not termination or truncation:
observation, reward, termination, truncation, info = env.step(env.action_space.sample())
View the official documentation for gymnasium environments here.
If you use our work in your research and would like to cite it, please use the following bibtex entry:
@article{tai2023pyflyt,
title={PyFlyt--UAV Simulation Environments for Reinforcement Learning Research},
author={Tai, Jun Jet and Wong, Jim and Innocente, Mauro and Horri, Nadjim and Brusey, James and Phang, Swee King},
journal={arXiv preprint arXiv:2304.01305},
year={2023}
}