- neural network is trained with deep Q-learning in its own training environment
- controls the game with twinject
demonstration video of the neural network playing Touhou (Imperishable Night):
it actually makes progress up to the stage boss which is fairly impressive. it performs okay in its training environment but performs poorly in an existing bullet hell game and makes a lot of mistakes.
let me know your thoughts and any questions you have!
you’re correct that this is a recurring problem with a lot of machine learning projects, but this is more a problem with some evolutionary algorithms (simulating evolution to create better-performing neural networks) where the randomness of evolution usually leads to unintended behaviour and an eventual lack of progression, while this project instead uses deep Q-learning.
the neural network is scored based on its total distance between every bullet. so while the neural network doesn’t perform well in-game, it does actually score very good (better than me in most attempts).
the vast majority of these kind of projects - including mine - aren’t created to solve a problem. they just investigate the potential of such an algorithm as a learning experience and for others to learn off of.
the only practical applications for this project would be to replace the “CPU” in 2 player bullet hell games and maybe to automatically gauge a game’s difficulty and programs already exist to play bullet hell games automatically so the application is quite limited.
i mean if you could in the future make an ai play long games from start to finish, it would be very useful to test games with thousands running at once
definitely. usually algorithms are used to calculate the difficulty of a game (eg. in osu!, a rhythm game) so there’s definitely a practical application there