Computer scientists view reinforcement learning as a mathematical way to solve decision-making problems. It uses a framework called the Markov Decision Process. This framework breaks down the world into states, actions, and rewards. By mapping these out, scientists can turn the messy process of trial-and-error into clean mathematical equations. This lets the computer systematically calculate the absolute best path to success, even in highly unpredictable environments.