So this library is intended to just deal with different neural network architectures. You could implement various architectures to sort of intake gamestate and output controller output (so like what direction a stick is facing, whether buttons are pressed). A system like this can be trained to play a game, given the right training data. For melee, there is a pre-existing method of gathering large amounts of training data (slp files, which act as ways to replay a game after the fact). So if people create a similar replay system for PM or RoA, this library could be used for training models to interface with those games. I don’t know if other problems need to be solved to actually hook up the inputs you’d get from a model to the game itself. Probably.
The gold standard that I’m sort of hoping to replicate in elixir for the exphil library is GitHub - vladfi1/slippi-ai: Imitation learning from slippi replays. · GitHub And then the philip v2 bot you can just connect to online and play against via ! commands on twitch and Slippi Direct ( Twitch ). I think that is awesome, and if I can just build that capability in Elixir, the exphil library will be a roaring success.
Edifice exists because there’s a huge amount of variety in how to actually implement a neural net, and for elixir, there doesn’t appear to be a library that just implements many different kind of architectures. Or there are ones that sort of give you the building blocks where you can make those architectures (axon, nx, polaris, etc), but the math and theory of how neural nets actually work, and the incredible variation within types of them sort of warrants its own little scaffolding library.
It’s open source, so take a look if you’re trying to use Nx and Axon to do any of these architectures, or think the elixir ecosystem might give you benefits over Python.
