In this project, we train a snake AI using Q-learning by updating Q-values based on predefined rewards and actions. The environment is modeled with two danger stages, four possible movement directions, and eight food directions, resulting in a total of 256 states—the minimal number of states required for this setup. After sufficient training, the snake demonstrates improved performance and decision-making capabilities.