Machine Learning Final Project - Optimizing Predator-Prey Behavior Through Q-Learning

For our final project we designed a game in a discrete 2D world in which a predator is attempting to catch a prey. To do this the predator was taught an optimal strategy using Q-Learning. The most simple scenario for this was one predator chasing one prey; however, multiple agents were added for increased complexity. See the final report for more details.