minimal-gym-rotor

Minimal OpenAI Gym-based environments for a quadrotor UAV

Learn by Doing

This repository contains OpenAI Gym-based environments for low-level control of quadrotor unmanned aerial vehicles. PyTorch implementations of DDPG and TD3 with gym-rotor can be found in this repo. To better understand What Deep RL Do, see OpenAI Spinning UP. Please feel free to create new issues or pull requests for any suggestions and corrections.

Installation

It is recommended to create Anaconda environment with Python 3. The official installation guide is available here. Visual Studio Code in Anaconda Navigator is highly recommended.

1. Open your Anaconda Prompt and install major packages.

conda install -c conda-forge gym 
conda install pytorch torchvision torchaudio cudatoolkit=11.3 -c pytorch 
conda install -c conda-forge vpython

Check out Gym, Pytorch, and Vpython.

2. Clone the repositroy.

git clone https://github.com/BeomyeolYu/minimal-gym-rotor.git

TODO:

• Update README.md
• Tensorboard
• Gym Wrappers
• Evaluate un/pre-trained policy
• Test trained policy
• Plot graphs from saved data
• Resume training

Reference:

• https://github.com/openai/gym
• https://github.com/ethz-asl/reinmav-gym