What is the difference between supervised learning and agent-based learning?

Supervised learning and agent-based learning are distinct approaches in machine learning, each suited for different types of problems. Supervised learning relies on labeled datasets to train models, where each input example is paired with a known output. The goal is to learn a mapping from inputs to outputs, enabling the model to make predictions on new, unseen data. For example, a supervised learning model could be trained to classify images of cats and dogs using a dataset where each image is explicitly tagged as “cat” or “dog.” The model adjusts its parameters by minimizing prediction errors (via loss functions) using techniques like gradient descent.

Agent-based learning, in contrast, focuses on training autonomous agents to make decisions by interacting with an environment. Instead of learning from static labeled data, agents learn through trial and error, often guided by rewards or penalties. This approach is common in reinforcement learning, where an agent learns a policy to maximize cumulative rewards over time. For instance, a robot navigating a maze learns by attempting movements and receiving positive rewards for reaching the goal or negative rewards for hitting obstacles. Unlike supervised learning, agent-based methods don’t require pre-labeled data; instead, the agent discovers strategies through exploration and feedback from the environment.

The key differences lie in data requirements and problem structure. Supervised learning demands a fixed, curated dataset with clear input-output pairs, making it effective for tasks like regression, classification, or object detection. Agent-based learning is better suited for dynamic, sequential decision-making problems, such as game-playing AI (e.g., AlphaGo) or autonomous vehicle control, where actions influence future states. While supervised learning optimizes for accuracy on predefined labels, agent-based systems optimize for long-term goals, often requiring simulations or real-world interactions. Developers choose between them based on whether the problem involves structured prediction (supervised) or adaptive, interactive behavior (agent-based).