Reinforcement Learning is a type of machine learning where a computer learns to make decisions by trying things out and learning from its experiences.
You can think of it like a game where the computer selects an action and receives a reward or punishment for it.
The goal is to find out through many trials which actions are best to receive as many rewards as possible.
For example, a robot can learn how to move around a room by trying out what works and what doesn't.
This way, the system gets better and better at making good decisions, even if it didn't know exactly what was right before.