Comes with Gymnasium and PettingZoo environments built in!
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 wheel numpy
pip3 install pyflyt
numpyandwheelmust be installed prior topyflytsuch thatpybulletis built withnumpysupport.
Usage is similar to any other Gymnasium and PettingZoo environment:
import gymnasium
import PyFlyt.gym_envs # noqa
env = gymnasium.make("PyFlyt/QuadX-Hover-v1", 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.
from PyFlyt.pz_envs import MAFixedwingDogfightEnv
env = MAFixedwingDogfightEnv(render_mode="human")
observations, infos = env.reset()
while env.agents:
# this is where you would insert your policy
actions = {agent: env.action_space(agent).sample() for agent in env.agents}
observations, rewards, terminations, truncations, infos = env.step(actions)
env.close()View the official documentation for pettingzoo 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}
}