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Deep RL Quadcopter Controller

In this project, I have designed an agent to take off a quadcopter, and then train it using a Deep Deterministic Policy Gradients reinforcement learning algorithm based on this paper.

Project Instructions

  1. Clone the repository and navigate to the downloaded folder.
git clone https://github.com/jcarlosgm30/RL_Quadcopter.git
cd RL_Quadcopter
  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. You will need the next packages:

matplotlib==2.0.0
numpy==1.14.1
pandas==0.19.2