Here’s an example scenario: let’s say you want your computer to write a short story in the style of Ernest Hemingway. You give it some prompts and instructions, but instead of writing something that sounds like this: “The sun shone brightly over the rolling hills as the farmer tended to his crops,” it writes something more like this: “Farmer worked on fields under hot sun.”

Now, you could just manually edit the computer’s output and correct its mistakes. But that would take forever! Instead, we can use reinforcement learning to teach the computer how to write better by giving it rewards for writing sentences that sound more like Hemingway and punishing it for writing sentences that don’t.

Here’s how it works: first, you define a set of rules or guidelines for what makes good Hemingway-style writing (e.g., short, simple sentences with minimal adjectives). Then, you create a reward function based on those guidelines for example, giving the computer points for using fewer than 10 words in a sentence and deducting points if it uses more than 20.

Next, you train your computer to write by feeding it prompts and instructions (like “Write a short story about a farmer’s day”) and letting it generate its own output based on what it has learned from previous examples. As the computer writes, you apply the reward function to each sentence if it meets the guidelines for good Hemingway-style writing, you give it points; if not, you deduct points.

Over time, your computer will learn which sentences are more likely to earn rewards and which ones aren’t. It will start to write better and better, until eventually it can produce output that sounds like a real Hemingway story! And the best part is, once it has learned how to do this for one style of writing (like Hemingway), you can use the same techniques to teach it other styles as well like Shakespeare or Jane Austen.

It’s a powerful tool that can help computers learn how to write better and more accurately, without requiring any manual editing or correction from humans.