Reinforcement Learning (RL) differs from supervised and unsupervised learning primarily in its approach to learning and the types of problems it is designed to solve. In supervised learning, a model is trained on a labeled dataset, meaning each training example is paired with an expected output. The model learns by comparing its predictions with these labels and adjusting its parameters to minimize the difference. For example, in a spam detection system, the model learns to classify emails based on labeled examples of spam and non-spam emails.
In contrast, unsupervised learning deals with datasets that do not have labeled outputs. The goal here is to find hidden patterns or structures in the data. For example, clustering algorithms in unsupervised learning, such as K-means or hierarchical clustering, group similar data points together without any prior knowledge of the categories. In both supervised and unsupervised learning, the focus is on a static dataset where the relationship between input and output is clear from the start.
Reinforcement Learning, however, involves an agent that interacts with an environment to learn how to achieve a specific goal through trial and error. Instead of learning from a fixed dataset, the agent receives feedback in the form of rewards or penalties based on its actions. For instance, in a game-playing context, the agent learns which actions lead to winning (positive reward) or losing (negative reward). Over time, the agent develops a strategy to maximize its total reward, adapting its approach based on the outcomes of its previous actions. This dynamic learning process makes RL suitable for tasks where the optimal behavior is not known in advance, such as robotic control or game AI.