So, I had this idea from a heartfelt meme that I saw online many years ago about someone who saved their XBOX because it had a save file from their dead brother. The save file was a replay of their last run around a particular track in the racing game. I was really moved by the meme and wanted to create a game that sort of "immortalizes" you like that. When I learned about machine learning, I identified it as a way to make that happen. To make the current game, I started with a car driving around a track. The input data for the neural network was the distance from the central spline, tangent angles, and the preceding and the following curvature of the spline. It worked well but I wanted to add different gameplay than just driving around a track. In the current iteration, the players are chasing a rabbit around the park and the input data is much simpler than in the first iteration - just the distance and angles. I'm only collecting 15000 data points right now, 5 minutes of game time at Unity's 50 physics per second. I originally wanted the player to be able to play alongside multiple other data models that may choose to do different activities in the park. So, there was a track, a rabbit, and a butterfly - for each target, I collected different data points. But I wasn't able to make a network that accurately determined what they originally chose to do - i.e. which target they preferred - drive around the track or chase the rabbit or butterfly. The goal was to truly capture what a player preferred to do in the game space rather than just how effectively they were able to chase one goal. I'm still working on that.

This is what Drivatar tech does in the Forza games on Xbox, it learns your playstyle so you can be part of other players sessions while you're offline.