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!
- Clone the repository and navigate to the downloaded folder.
git clone https://github.com/udacity/RL-Quadcopter-2.git cd RL-Quadcopter-2
- Create and activate a new environment.
conda create -n quadcop python=3.6 matplotlib numpy pandas source activate quadcop
- Create an IPython kernel for the
python -m ipykernel install --user --name quadcop --display-name "quadcop"
- Open the notebook.
jupyter notebook Quadcopter_Project.ipynb
Before running code, change the kernel to match the
quadcopenvironment by using the drop-down menu (Kernel > Change kernel > quadcop). Then, follow the instructions in the notebook.
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.txtfile in the repository.