jdatpnw / qplane

Fixed Wing Flight Simulation Environment for Reinforcement Learning

QPlane

Fixed Wing Flight Simulation Environment for Reinforcement Learning

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Table of Contents

1. About The Project
   • Built With
2. Getting Started
   • Prerequisites
   • Installation
3. Usage
4. Roadmap
5. License
6. Contact
7. Acknowledgements

About The Project

This repository is being written as part of my masters thesis. I am trying to develop a fixed wing attitude control system using Reinforcement Learning algorithms. As of right now this code works with XPlane 11 and QLearning as well as Deep QLearning.

Built With

This project is built with these frameworks, libraries, repositories and software:

• tensorflow
• XPlaneConnect
• XPlane 11
• JSBSim
• Flightgear

Getting Started

Simple clone this repository to your local filesystem:

git clone https://github.com/JDatPNW/QPlane

Prerequisites

Tested and running with:

Software	Version
XPlane11 Version:	11.50r3 (build 115033 64-bit, OpenGL)
JSBSim Version:	1.1.5 (GitHub build 277)
Flightgear Version:	2020.3.6
XPlaneConnect Version:	1.3-rc.2
Python Version:	3.8.2
numpy Version:	1.19.4
tensorflow Version:	2.3.0
Anaconda Version:	4.9.2
Windows Version:	1909

Installation

1. Clone the repo

   git clone https://github.com/JDatPNW/QPlane

2. Install the above listed software (other versions might work)
   • For JSBSim clone the JSBsim repo into src/environments/jsbsim
   • For visualizing JSBSim download the c172r plane model in the Flightgear Menu

Usage

Once downloaded and installed, simply execute the QPlane.py file to run and test the code.

• For the XPlane Environment, XPlane (the game) needs to run.
• For JSBSim with rendering, Flightgear needs to run with the following flags --fdm=null --native-fdm=socket,in,60,localhost,5550,udp --aircraft=c172r --airport=RKJJ

Proof

This gif shows an attitude agent (using Q-Learning) in action and compares it to the baseline random agent.

Full Video in HD

Roadmap

Planned future features are:

• Double Deep Q Learning

Contributing

Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are greatly appreciated.

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

License

Distributed under the MIT License. See misc/LICENSE for more information.

Contact

Github Pages: JDatPNW

Publications

• ACM MMSys'21 - QPlane: An Open-Source Reinforcement Learning Toolkit for Autonomous Fixed Wing Aircraft Simulation
  • ACM MMSys'21 Presentation

Citation

Please cite QPlane if you use it in your research.

@inproceedings{richter2021qplane,
  title={QPlane: An Open-Source Reinforcement Learning Toolkit for Autonomous Fixed Wing Aircraft Simulation},
  author={Richter, David J and Calix, Ricardo A},
  booktitle={Proceedings of the 12th ACM Multimedia Systems Conference},
  pages={261--266},
  year={2021}
}

or

Richter, D. J., & Calix, R. A. (2021, June). QPlane: An Open-Source Reinforcement Learning Toolkit for Autonomous Fixed Wing Aircraft Simulation. In Proceedings of the 12th ACM Multimedia Systems Conference (pp. 261-266).

Acknowledgements

• Readme Template
• Python Programming - DeepRL DQN
• Deeplizard - DeepRL DQN
• NeuralNetAI - DDQN (Video found on the linked YouTube, not on the site)
• Python Lessons - DeepRL PPO
• adderbyte
• XPlane Forum
• JSBSim