Deep RL Quadcopter Controller

Udacity's Teach a Quadcopter How to Fly!

In this final project for Udacity, we designed an agent to fly a quadcopter, and then train it using a reinforcement learning algorithm. In this case DDPG or Deep Deterministic Policy Gragients.

Project Instructions

1. Clone the repository and navigate to the downloaded folder.

git clone https://github.com/jasensanders/DeepLearning_p5v2.git
cd DeepLearning_p5v2

2. Create and activate a new environment in anaconda.

conda create -n quadcop python=3.6 matplotlib numpy pandas
source activate quadcop

3. Create an IPython kernel for the quadcop environment.

python -m ipykernel install --user --name quadcop --display-name "quadcop"

4. Open the notebook.

jupyter notebook Quadcopter_Project.ipynb

5. 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.

6. 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."#DeepLearning_p5v2"