timefly-1989 / gym_multirotor

Gym environments used in the paper: "Developmental Reinforcement Learning of Control Policy of a Quadcopter UAV with Thrust Vectoring Rotors"

gym_multirotor

Gym to train reinforcement learning agents on UAV platforms

Quadrotor	Tiltrotor

Requirements

This package has been tested on Ubuntu 18.04 with python 3.6.

python=3.6
numpy
scipy
gym
mujoco_py

• To install gym refer this link.
• To install MuJoCo refer this link.

Installation

To install, you will have to clone this repository on your personal machine. Follow the below commands:

$ git clone https://github.com/adipandas/gym_multirotor.git
$ cd gym_multirotor
$ pip install -e .

Environments

List of environments available in this repository include:

Environment-ID	Description
QuadrotorPlusHoverEnv-v0	Quadrotor with + configuration with task to hover.
TiltrotorPlus8DofHoverEnv-v0	Tiltrotor with + configuration.
QuadrotorXHoverEnv-v0	Quadrotor with x configuration with a task to hover.

References

REFERENCES.md

Citation

If you find this work useful, please cite our work:

@inproceedings{deshpande2020developmental,
  title={Developmental reinforcement learning of control policy of a quadcopter UAV with thrust vectoring rotors},
  author={Deshpande, Aditya M and Kumar, Rumit and Minai, Ali A and Kumar, Manish},
  booktitle={Dynamic Systems and Control Conference},
  volume={84287},
  pages={V002T36A011},
  year={2020},
  organization={American Society of Mechanical Engineers}
}

Notes:

• Some of the environment parameters have been updated but the task of these drone environments still remains the same as what was discussed in the paper.
• I will keep on updating these codes as I make further progress in my work.