Policy iteration is a fancy way of saying "try and try again, but with a fancy algorithm". It's a method used in reinforcement learning to find the optimal policy for an MDP (Markov Decision Process). In simpler terms, it's a way to teach a computer how to play games without actually playing them.
Think of it like teaching a toddler how to ride a bike. You start with a bunch of different policies (or rules), like "look both ways" and "hold the saddle", and see which one gets the best results. But instead of actually riding a bike, you just simulate it in a fancy virtual world.
Because humans are terrible at math and computers are really good at it, but not good enough to actually play games. It's a way to make computers better at playing games without actually having to, you know, play games.
See examples of Policy Iteration Algorithms in action Learn about use cases for Policy Iteration Algorithms