This is part of an ongoing series in which we experiment with different methods of AI. We’ll look at the state of the art and the out of fashion; the practical and the (seemingly) impractical; to find what works and what doesn’t. You can read the first tutorial in the series for more information on what we are trying to accomplish.
Before we can implement our neural network for the UFOs we need to know what inputs we are going to feed into the network and how we are going to use the network’s output. It’s important to get these right otherwise our neural network will perform very poorly; for example, we could provide each UFOs neural network with the position of every other UFO but it would take a lot of adjusting before a relationship between them can be established, if it can establish one at all. For that reason, we want to keep the inputs to a minimum but also ensure that they provide all the information our UFO will need. To understand what input our UFOs need we need to go the goal we outlined in the first part of the series:
Have 60+ UFOs onscreen that have taught themselves to avoid each other and the sides of their environment.