This project demonstrates machine learning by building a neural network controlled agent that learns to navigate Chrome’s dinosaur game. It reads pixel data from the game screen (cactus distance, size, and speed) as inputs and outputs decisions to jump or duck via keyboard simulation. A genetic algorithm evolves 12 neural network genomes per generation, evaluating fitness by counting jumped cacti and using selection, crossover, and mutation to breed increasingly capable agents.