This is a submission for DEV Computer Science Challenge v24.06.12: One Byte Explainer.

Explainer

In Reinforcement Learning (RL), we train models to make decisions based on actions, mistakes, and feedback, like humans learn from experience. In RL, model generate outputs on it's own, store feedback from incorrect outputs to improve future responses.

Additional Context

When I learned about Reinforcement Learning in a Machine Learning course, I found it to be an interesting topic to explain. It is hard to explain briefly, but I still tried to do my best.