Reinforcement learning vs Unsupervised 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.

Unsupervised learning

Machine learning basics

Machine learning from examples with no answers attached, where the model finds groups, patterns or odd cases on its own.

Formal

A form of machine learning that works on training data without labels, finding structure in it, for example grouping similar items or flagging items that differ from the rest.

In plain English

Like sorting a box of mixed buttons into piles by colour and size without anyone telling you what the piles should be.

In practice

A monitoring tool at a hospital learns what normal logins look like and raises an alert when a nurse's account suddenly logs in at 3 a.m. from another country.

Why it matters

It can spot new, unknown threats nobody has labelled yet, but it also flags harmless unusual behaviour, so people must still judge the alerts.

Shared connections

Machine learning Supervised learning