AirGym - GitLite

KO | EN

gitlite — search

#python #react #typescript #android #rust #deep-learning #docker #nodejs #awesome-list #vue #pytorch #mcp #openai #open-source

AirGym

(★ 115)

AirSim with openAI gym and keras-rl integration for autonomous copter RL

파일 탐색기

최종 버전 다운로드 (.zip)

- drone_stress_test_kjell.py
- First_Train.gif
- - __init__.cpython-35.pyc
- - __init__.py
  - AirGym.py
  - myAirSimClient.py
- __init__.py
- dqn_AirSimEnv-v41_weights.h5f
- dqn_AirSimEnv-v42_weights.h5f
- README.md
AirSimClient.py
callbacks.py
DQN-Train.py
LICENSE
PythonClient.pyproj
README.md
setup.py

# 설치 가이드

1. 코드 내려받기

git clone https://github.com/Kjell-K/AirGym

깃허브에서 프로젝트 코드 전체를 내 컴퓨터로 내려받습니다.

cd AirGym

방금 내려받은 프로젝트 폴더 안으로 이동합니다.

2. Python

쉬움 추천

사전 준비물

Git GitHub에서 프로젝트 코드를 내려받으려면 필요합니다.
Python 3 설치 시 'Add Python to PATH' 옵션을 꼭 체크하세요 (Windows).

pip install .

PyPI에 배포된 패키지를 바로 설치합니다. 소스 클론이 필요 없습니다.

python <실행할 파일명>.py # README에서 정확한 실행 파일명을 확인하세요

파이썬 스크립트(또는 모듈)를 실행합니다.

✅ 에러 메시지 없이 실행되고 터미널에 안내 문구가 출력되면 정상입니다.

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

Note: This repository is not maintained any longer and is very likely that new versions of AirSim will not be compatible (e.g. methods of AirSim now require different amount of inputs etc.).

However, from this project you can still retrace how to expose AirSim as an OpenAI Gym environment.

If somebody updates for the newest AirSim, I would be happy for a pull request.

___________________________________________

# AirGym
This repository integrates AirSim with openAI gym and keras-rl for autonomous vehicles through reinforcement learning. AirSim allows you to easly create your own environment in Unreal Editor and keras-rl let gives your the RL tools to solve the task.

Requirements:

[AirSim](https://github.com/Microsoft/AirSim)

[keras-rl](https://github.com/matthiasplappert/keras-rl)

[openAI gym](https://github.com/openai/gym)

My test environment binaries for Win10 can be downlaoded [here](https://drive.google.com/open?id=1iNeK47r9e54Ba554rHY8o5ADeiDVyPLz).

Click here for a old (the performance is much better now) demo video:

[![Youtube Video here](https://img.youtube.com/vi/ZE5hPHqJC64/0.jpg)](https://youtu.be/ZE5hPHqJC64)

#### How to use:
You can either train yourself or load the exciting weights by setting Train to True or False. 
CAREFUL: When you cancel the training with STRG + C, weights are saved and will overright the already trained weights.

#### State:
We are taking as state input a depth image extended by the encoded information of the relative goal direction. Take a look at it by uncommenting [here](https://github.com/Kjell-K/AirGym/blob/master/gym_airsim/envs/myAirSimClient.py#L155).

#### Action:
For this environment, we force the quadcopter to move in a fix plane and therefore confront the obstacles. The action space consist of three discrete actions and are available at any state:
- straight: Move in direction of current heading with 4m/s for 1s
- right yaw: Rotate right with 26°/s for 1s
- left yaw: Rotate left with 30°/s for 1s