Gymnasium Environments

Natively, PyFlyt provides various default Gymnasium environments for testing reinforcement learning algorithms. Usage is no different to how Gymnasium environments are initialized:

import gymnasium
import PyFlyt.gym_envs # noqa

env = gymnasium.make("PyFlyt/QuadX-Hover-v2", 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())