Reinforcement Learning

Reinforcement
Learning

Reinforcement learning is a way that computers can learn to make decisions by trying different actions and seeing what works best. It’s called “reinforcement” because the computer is reinforced or rewarded for making good decisions.

For example, let’s say we want to teach a computer to play a game. We can set up a reinforcement learning program so that the computer can try different actions in the game, such as moving left or right, and see how well it does.

If the computer does well, it gets a reward, like points or a virtual prize. If it does poorly, it doesn’t get a reward. Over time, the computer learns to make decisions that lead to the best rewards, because it has learned that these decisions are the most successful.

Reinforcement learning is used in many different applications, such as self-driving cars and robots, because it allows the computer to learn and improve its performance on its own.

One important thing to remember about reinforcement learning is that it’s important to set up the rewards correctly. If the rewards are too big or too small, the computer might not learn as well. It’s like giving a child a toy every time they eat their vegetables – if the toy is too small, they might not be motivated to eat their vegetables. But if the toy is too big, they might start to expect a big reward every time they do something good, which isn’t realistic.