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
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)
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!
lol I understand the feeling
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
this is the ad-free version, which is available with the exact same (if I'm correct) features on F-Droid for free, along with the source code on GitHub.
the versions on the Play Store (paid version and free version with ads) likely just help pay the developer for their work
and as others have said already, free software is free as in freedom, not free beer.
oh, I forgot about the API not being freely available; so an alternate frontend wouldn't be a proper solution?
going by the other comments, though, there are client-side options that can avoid API issues entirely by just re-styling the webpage. thanks for the info, though!
yeah, that was the main reason I wanted to apply it to old Reddit specifically, because it would have been easier with simpler theming and old Reddit is close to Lemmy's style too
I installed RES beforehand, but haven't used any of its features. I'll try this out first and maybe Stylish if that doesn't work. thanks!
okay thanks for the tip! I'm already using Stylish but I couldn't find a pre-made style for Lemmy.
I figured I could make my own but I didn't want to waste time doing something that could have been done already or could be done faster. at least I know I'm on the right track!
don't know if this is the correct community to post this on, but is there a way to style Reddit (most likely old Reddit but it doesn't matter) with a similar style to Lemmy? there are a few small subreddits I still browse (because there is no Lemmy equivalent) and it would be cool if I could style it like the regular Lemmy style (if you need an example: lemmy.ml)
I know there are frontends for viewing Lemmy instances like Reddit, but I haven't heard of any way to do the reverse.
it could be through a stylesheet, an alternative frontend, even just a pointer on how you could style a website into a different style. \ thanks!
yeah, my bad. edited the comment with more accurate info
and this does apply to creative writing, not knowledgeable stuff like coding
in terms of the quality of writing you can get models from 20GB at a similar level to GPT-4 (good for creative writing but much worse if knowledge of something is required)
the model I use (~20GB) would know what rclone is but would most likely not know how to use it
EDIT: now that I think about it is was based off of some benchmark. personally I wouldn't say it performs at GPT-4 but maybe GPT-3.5
maybe it's just me, but isn't it quite hard (at least for people not confident doing technical stuff) to root a phone?
like a decade ago the bootloader may have been unlocked by default and for many phones there were exploits so that they could be rooted with an app, but nowadays you would have to:
- unlock the bootloader by installing ADB and fastboot drivers, booting into download mode and run terminal commands that would reset your phone in the process; and for some phones, you would also need to shorten a test point and for quite a few of them nowadays, unlocking the bootloader is impossible
- boot into download mode and flash a custom recovery with fastboot or potentially with Odin or some other proprietary software (or sometimes you can root from download mode)
- for some newer (including Samsung) phones, you also need to disable dm-verity otherwise your phone wouldn't be able to boot into Android
- boot into recovery mode and finally flash (probably Magisk) an image to root the system
I guess there are usually detailed instructions for this, but I doubt that most people rooting their phones now would be non-techie people who are just watching generic online tutorials. they would most likely stumble upon XDA or other forums that would have proper instructions. and even then, they are not very beginners friendly as they aren't usually supposed to be followed by people with little to no experience with using the command-line, drivers, how Android phones work internally, etc.
to clarify:
The developers of the Alpine Linux-based postmarketOS mobile distribution today that they’re now supporting the systemd init system alongside OpenRC and other alternative init systems.
and:
postmarketOS currently supports the Sxmo, Phosh, GNOME Shell on Mobile, and KDE Plasma Mobile UIs. While the Sxmo images will stay with OpenRC, the GNOME and KDE Plasma Mobile images will be built on top of systemd
so I was looking at someone's personal website from Mastodon, and noticed that they had banners to advertise other people's servers. while server lists like fediring exist, I was thinking of a more automatic method of advertisement within someone's website.
the concept is this: people could store advertisements (small banners, gifs) on their websites with a server and people willing to embed them could use an API to retrieve a random ad onto their website.
people would self-host their ads and "federate" with other websites to embed other ads on their website. not sure if this would scale up as well, though.
what do you think? just curious on lemmy's POV
edit: going by the comments, this idea is quite flawed and webrings (in small sizes) are a better approach.
thanks for the help