Reinforcement learning vs Supervised learning

Why they differ

Supervised learning is shown the right answer for each example; reinforcement learning is only told afterwards how good its choice turned out.

Reinforcement learning

Machine learning basics

Machine learning by trial and error, where a system acts, gets a reward or a penalty, and slowly learns which actions pay off.

Formal

A form of machine learning in which an agent acts in a setting, receives a reward signal for the results, and learns a way of choosing actions that brings the most reward over time, without being shown the right action.

In plain English

Like teaching a dog a trick with treats; nobody explains the trick, the dog just learns which moves earn a treat.

In practice

A region's energy manager tests a system that adjusts the ventilation in a hospital wing; it earns a reward for using less power and a penalty each time a ward gets too warm or too cold.

Why it matters

The system learns exactly what the reward measures, not what you meant, so a badly chosen reward can teach it to cheat or to please instead of to be right.

Supervised learning

Machine learning basics

Machine learning from examples that come with the right answer attached, such as emails already marked spam or not spam.

Formal

A form of machine learning in which each training example is paired with a label giving the wanted output, and the model learns to map new inputs to the right label.

In plain English

Like learning with an answer key. You try each exercise, check the key, and correct yourself.

In practice

A region's security team labels thousands of past alerts as real attacks or false alarms, and a model trained on them learns to sort new alerts the same way.

Why it matters

The model can only be as good as its labels; labels are often made by people paid per item, and their mistakes and biases become the model's.

Shared connections

Machine learning Unsupervised learning