Teach a Quadcopter How to Fly!
In this Udacity machine learning 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 ipykernel keras
additional comments for activation
-
on e.g. DOS shell:
- To activate this environment, use:
activate quadcop
- To activate this environment, use:
-
for power-users using bash, you must source
- Create an IPython kernel for the
quadcopenvironment. Be sure ipykernel is installed.
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.
This project coding is released under the MIT Licence.
For the given project as reinforcement learning concept the actor-critic method is chosen. Its architecture together with general Reinforcement Learning and domain information is explained more detailed here for this project topic.