flycraft 0.0.10

A fixed-wing UAV environment based on gymnasium.

fly-craft

An efficient goal-conditioned reinforcement learning environment for fixed-wing UAV velocity vector control based on Gymnasium.

Demos

The policies are trained by "Iterative Regularized Policy Optimization with Imperfect Demonstrations (ICML2024)". Code

Target velocity vector (v, $\mu$, $\chi$) from (200, 0, 0) to (140, -40, -165)

Target velocity vector (v, $\mu$, $\chi$) from (200, 0, 0) to (120, 50, 170)

Installation

Using PyPI

pip install flycraft

From source

git clone https://github.com/GongXudong/fly-craft.git
pip install -e fly-craft

Usage

import gymnasium as gym
import flycraft

env = gym.make('FlyCraft-v0')

observation, info = env.reset()

for _ in range(500):
    action = env.action_space.sample() # random action
    observation, reward, terminated, truncated, info = env.step(action)

    if terminated or truncated:
        observation, info = env.reset()

env.close()

Applications

Examples

1. Examples based on StableBaselines3 and Imitation: https://github.com/GongXudong/fly-craft-examples

Researches on FlyCraft

1. Xudong, Gong, et al. "Iterative Regularized Policy Optimization with Imperfect Demonstrations." Forty-first International Conference on Machine Learning. 2024.

Citation

Cite as

@article{gong2024flycraft,
  title        = {FlyCraft: An Efficient Goal-Conditioned Reinforcement Learning Environment for Fixed-Wing UAV Velocity Vector Control},
  author       = {Gong, Xudong and Wang, Hao and Feng, Dawei and Wang, Weijia},
  year         = 2024,
  journal      = {},
}