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In this project, we investigate the application of PID control and Q-learning algorithms to the Ball Balancing Table (BBT) in order to solve a maze. An open-source tool called the BBT makes it possible to experiment with control systems directly. The device can guide a ball through a maze by adjusting the tilt of the table, imparting important knowledge about reinforcement learning, control theory, and feedback systems. The table's orientation is adjusted by using a PID controller in response to real-time feedback from the ball's position. A matrix of 0s and 1s with open paths and walls, respectively, is used to depict the maze. The system associates movements with the Q-learning reinforcement learning algorithm to learn how to move the ball through the maze.