Masters Thesis: A Practical Exploration of Deep Reinforcement Learning

As part of my Masters thesis, I chose a practical way to explore how Deep Reinforcement Learning (DRL) works by building a game and training a neural net how to play...and eventually beat me.

To add to the fun I made it so that a human player could race against the AI!

Highlights on training:

• The agent was trained with over 7 million attempts made.

• The agent was rewarded for how far it traveled through the course and how fast.

• The agent has basic obstacle and ground detection using physics raycasts.

This project made me fall in love with the possibilities of using machine learning in games ❤