/Deep_RL_Quadcopter_Controller

Teach a Quadcopter How to Fly

Primary LanguageHTML

Deep RL Quadcopter Controller

Teach a Quadcopter How to Fly!

In this project, you will design an agent to fly a quadcopter, and then train it using a reinforcement learning algorithm of your choice!

Project Instructions

  1. Clone the repository and navigate to the downloaded folder.
git clone https://github.com/stoneboy100200/Deep_RL_Quadcopter_Controller.git
cd Deep_RL_Quadcopter_Controller
  1. Create and activate a new environment.
conda create -n quadcop python=3.6 matplotlib numpy pandas
source activate quadcop
  1. Create an IPython kernel for the quadcop environment.
python -m ipykernel install --user --name quadcop --display-name "quadcop"
  1. Open the notebook.
jupyter notebook Quadcopter_Project.ipynb

  1. Before running code, change the kernel to match the quadcop environment by using the drop-down menu (Kernel > Change kernel > quadcop). Then, follow the instructions in the notebook.

  2. You will likely need to install more pip packages to complete this project. Please curate the list of packages needed to run your project in the requirements.txt file in the repository.