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zolax

@ zolax @programming.dev

Posts
10
Comments
75
Joined
3 yr. ago

  • KDE on Debian 12 randomly gets frozen / stuck

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  • yeah with what other people have said it's most likely bad or unseated RAM

  • what's your current linux distro?

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  • Gentoo

  • Is there any closed source android app that you wish had a good open source alternative?

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  • +1

  • training a neural network to play a bullet hell game

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  • 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.)

  • training a neural network to play a bullet hell game

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  • 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.

  • training a neural network to play a bullet hell game

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  • 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

  • training a neural network to play a bullet hell game

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  • 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.

  • Triple Baka ringtone

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  • no problem!

  • Triple Baka ringtone

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  • 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

  • Music @lemmy.world
    zolax @programming.dev

    Triple Baka ringtone

  • training a neural network to play a bullet hell game

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  • 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.

  • training a neural network to play a bullet hell game

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  • I did create a music NN and started coding an UNO NN, but apart from that, no

  • training a neural network to play a bullet hell game

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  • yeah, the training environment was a basic bullet hell "game" (really just bullets being fired at the player and at random directions) to teach the neural network basic bullet dodging skills

    • the white dot with 2 surrounding squares is the player and the red dots are bullets
    • the data input from the environment is at the top-left and the confidence levels for each key (green = pressed) are at the bottom-left
    • the scoring system is basically the total of all bullet distances

    • this was one of the training sessions
    • the fitness does improve but stops improving pretty quickly
    • the increase in validation error (while training error decreased) is indicated overfitting
      • it's kinda hard to explain here but basically the neural network performs well with the training data it is trained with but doesn't perform well with training data it isn't (which it should also be good at)
  • Touhou Project @lemmy.world
    zolax @programming.dev

    training a neural network to play a bullet hell game

  • AI @lemmy.ml
    zolax @programming.dev

    training a neural network to play a bullet hell game

  • Programming @programming.dev
    zolax @programming.dev

    training a neural network to play a bullet hell game

  • Marisa

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  • h a t

  • A reasonable 21st Retaisai loot (not really)

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  • woah i want literally everything there

  • [OC] GS121: Eternal Night

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  • true true

  • [OC] GS121: Eternal Night

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  • woww not Youmu's fault

  • 【東方MV】Help me, ERINNNNNN!!【ビートまりお】

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  • beatmario at it again

  • Jellyfin | "We are pleased to announce the latest stable release of Jellyfin, version 10.9.0!"

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  • lol I understand the feeling

  • What does your todo list / project management / productivity setup look like?

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  • thanks for this!! there's so much info on this comment

    i'm currently using Logseq w/ Syncthing but i'll be looking at Org Mode and DokuWiki

  • Programming @programming.dev
    zolax @programming.dev

    browse (Old) Reddit with Lemmy style

  • Selfhosted @lemmy.world
    zolax @programming.dev
    IDEA

    self-hosted advertisements for personal sites