What is the primary goal of reinforcement learning in machine learning?

The primary goal of reinforcement learning is for the agent to learn how to make decisions based on maximizing rewards and minimizing penalties. In this learning paradigm, the agent interacts with an environment and takes actions that lead to certain outcomes. The agent receives feedback in the form of rewards (positive reinforcement) or penalties (negative reinforcement) based on the actions it takes. This feedback helps the agent to learn optimal strategies over time, essentially allowing it to improve its decision-making process.

Reinforcement learning differs fundamentally from other types of machine learning such as supervised learning, where the main objective is to classify or predict based on labeled data. The focus in reinforcement learning is not just on classifying or fitting models, but rather on learning a policy that defines the best action to take in various situations to achieve the highest cumulative reward. This makes it highly suitable for tasks such as game playing, robotics, and autonomous systems where ongoing decision-making is essential.