Data Science Fundamentals

Reinforcement learning trains an agent by having it interact with an environment and receive feedback in the form of rewards (positive reinforcement) or punishments (negative reinforcement/penalties) based on the actions it takes. The algorithm's goal is to maximize cumulative reward over time, effectively adding points for desirable outcomes and subtracting points for undesirable ones, which distinguishes it from supervised learning (which relies on labeled data and error minimization) and unsupervised learning (which identifies patterns in unlabeled data without any reward signal).