Reinforcement Learning is a type of computer learning where a program, called an agent, learns to make decisions by trying different actions and getting feedback from its environment. It’s like how we learn through trial and error by doing things and seeing what happens.

Here’s how it works: Imagine you have a robot that needs to learn how to navigate a maze. The robot starts at the beginning and has to figure out which way to go to reach the goal. At first, the robot doesn’t know which path is the right one, so it tries different paths and sees what happens. If it makes progress and gets closer to the goal, it receives a reward, like a point. But if it goes in the wrong direction, it gets a penalty, like losing a point.

As the robot keeps exploring and taking actions, it learns which paths are better and which ones are worse based on the rewards and penalties it receives. Over time, it figures out the best sequence of actions to take to reach the goal with the highest score. It’s like playing a game and trying to get the highest score possible.

Reinforcement learning is used in many real-life applications. For example, it can be used to teach self-driving cars how to navigate on their own or to train robots to perform tasks like picking up objects. It’s a way for computers to learn to make smart decisions in different situations without being explicitly programmed for every possible scenario.

So, in simple terms, reinforcement learning is about a computer program learning by trying different actions, getting feedback, and adjusting its behavior to get the best possible outcome. It’s like teaching a robot to make good choices by learning from its mistakes and successes.