This document summarizes a study analyzing different types of games using convolutional neural networks (CNNs). It describes initial attempts to develop a Pong game that failed to perform well due to lack of data and human elements. The study then discovered that runner games, which have a defined move for every environment instance, are better suited to CNNs. Testing a Dino runner game achieved 90% accuracy, showing that simple CNNs can be effective for certain game classes. The author proposes using advanced techniques like reinforcement learning in future work.