yeah with what other people have said it's most likely bad or unseated RAM
zolax
joined 1 year ago
currently, yes, but this is more an investigation into how well a neural network could play a bullet hell game
very few bullet hell AI programs rely on machine learning and virtually all of the popular ones use algorithms.
but it is interesting to see how it mimics human behaviour, skills and strategies and how different methods of machine learning perform and why
(plus I understand machine learning more than the theory behind those bullet hell bots.)
the training environment is pretty basic right now so all bullets shoot from the top of the screen with no enemy to destroy.
additionally, the program I'm using to get player and bullet data (twinject) doesn't support enemy detection so the neural network wouldn't be able to see enemies in an existing bullet hell game. the character used has a wide bullet spread and honing bullets so the neural network inadvertently destroys the enemies on screen.
the time spent in each training session is constant rather than dependent on survival time because the scoring system is based on the total bullet distance only.
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
one problem ive seen with these game ai projects is that you have to constantly tweak it and reset training because it eventually ends up in a loop of bad habits and doesnt progress
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).
so is it even possible to complete such a project with this kind of approach as it seems to take too much time to get anywhere without insane server farms?
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.
no problem!
the body of the post has the ringtone attached. I might need to edit it to make it viewable through Photon but you can also view it on a browser
a gapless, ringtone version of the song "Triple Baka" from the first few seconds.
reply or DM if you want a loseless download because this upload is compressed.
I always find it interesting to see how optimization algorithms play games and to see how their habits can change how we would approach the game.
me too! there aren't many attempts at machine learning in this type of game so I wasn't really sure what to expect.
Humans would usually try to find the safest area on the screen and leave generous amounts of space in their dodges, whereas the AI here seems happy to make minimal motions and cut dodges as closely as possible.
yeah, the NN did this as well in the training environment. most likely it just doesn't understand these tactics as well as it could so it's less aware of (and therefore more comfortable) to make smaller, more riskier dodges.
I also wonder if the AI has any concept of time or ability to predict the future.
this was one of its main weaknesses. the timespan of the input and output data are both 0.1 seconds - meaning it sees 0.1 seconds into the past to perform moves for 0.1 seconds into the future - and that amount of time is only really suitable for quick, last-minute dodges, not complex sequences of moves to dodge several bullets at a time.
If not, I imagine it could get cornered easily if it dodges into an area where all of its escape routes are about to get closed off.
the method used to input data meant it couldn't see the bounds of the game window so it does frequently corner itself. I am working on a different method that prevents this issue, luckily.
I did create a music NN and started coding an UNO NN, but apart from that, no
cross-posted from: https://programming.dev/post/14979173
- 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!
- 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!
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that's good then! i had this same issue (randomly freezing after turning it om for some time) though new RAM ended up fixing it