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david_a

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About david_a

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  1. david_a

    Has PBR for Doom been a disappointment so far?

    Someone on a different forum pointed out that the first video is pretty close to the aesthetics of the fake screenshots on the D!Zone box: Thinking about it like that somehow makes it more lovable.
  2. david_a

    EDGE 2.1.0 Impending Release

    I thought that the name 3DGE was adopted when the software renderer was dropped. Now that I look at it I'm not even sure how it's supposed to be pronounced. 3D Edge? Thredge?
  3. Did Unreal expose that in the menu? I know it can do stuff under-the-hood like Quake can. EDIT: Unreal was 1998 anyway
  4. david_a

    Papa John is suing Papa John's Pizza

    For real. With the thin crust it's palatable, but the normal crust is trash. It's above garbage-tier stuff like Little Ceasars but not by much.
  5. The Gizmodo article (along with the BBC one) don’t accurately convey the point of the paper. You can read it yourself or look through the thread for details; the word “fun” does not appear in the paper.
  6. Everyone at work keeps sharing it with me since they know I've been working on something similar, so I feel like I need to share the pain
  7. Gizmodo posted another terrible article on the paper: An AI Created New Doom Levels That Are as Fun as the Game's Original Ones [citation needed] It also includes this zinger: "Few gamers are still playing Doom these days [...]". Time to pack it in, everyone!
  8. Descent supported user-made levels out of the box and shipped with a level editor. The level format is a bit weird - it's a bunch of "cubes" (hexahedrons, technically) linked together. It's not nearly as flexible as something like Quake, and I suspect that simple things are easier but complicated geometry is much harder to express. Descent used a portal renderer so it also (unintentionally) supports overlapping level geometry. There have been a few proof-of-concept maps showing this, but it's impossible to reason about in a level editor so it's a fairly unexplored area. Let's try the screenshot again:
  9. No, but that might be interesting, although the Cube 2 engine probably has infinitely less limitations than Descent does.
  10. I was planning on messing around with octrees since they're practically the same as the level format 🙂. That wouldn't get you any interesting curves or anything, though. I think the cube-based level format would make some things easier and some things much harder; you can't design one side/wall of a room without taking the other side into account, for example.
  11. The talk will not be recorded, but I will submit it again next year to the Indy.Code() conference (they didn't pick it this year, which I'm a little miffed about considering some of the slop they did pick, but I wouldn't have been done in time anyway 🙂). The talk is more of a general overview of deep learning. I chose Wolf 3D as my subject since the maps are close but not identical to the types of data you normally see in examples (and it's a good hook since people my age have nostalgia for it). I figured I would learn more by going a little bit beyond what all the samples do. I worked on and off with the non-ML portions of this project for about 2 years, mainly at weekend Hackathon events at my company. I haven't thought much about what happens when the ML thing is "done" since it's been looming over me for a while, but there's a ton of stuff there that it would be a shame to abandon. The .NET portion of this ("Tiledriver") can convert binary GAMEMAPS files to ECWolf's UWMF (pretty sure this is the only tool out there that can do this). I'm sure I'm missing some edge cases but it's probably 95% there. There's a WPF GUI for it that could probably be turned into a level editor (I've attached a screenshot; not sure how to inline it and resize it). I think generating maps is a fascinating topic too, but what I've found out is that I don't have much intrinsic motivation to work on Wolf 3D. I... don't really like the game, and I can only play it for 10-15 minutes at a time before getting bored. I would love it if ECWolf supported RoTT or Blake Stone properly since those are at least a little more interesting. Still, I think Wolf 3D is a great subject for level generation since a straightforward map is conceptually dead simple, but they can have more advanced elements too (keys, patrolling enemies, push walls) that would be more challenging to integrate. I had planned to look into messing with Descent maps next, since A) nobody to my knowledge has ever programatically generated a Descent level, B) it's 3D so I have to level up with all that math, and C) Descent allows for overlapping geometry, but humans quickly can't reason about it in a level editor. Doom is an option too, but I feel intimidated by all the great things that have already happened in the Doom community (Oblige, WadC, etc) so I'm not sure what I could do that would be novel. I think it's likely that I would like to tinker with the Wolf 3D stuff some more, though. My ML attempts weren't terribly satisfying and I would at least like to get something that vaguely looks like a level out of it. I would love to cooperate on something, and even having someone to bounce ideas off of would be great!
  12. I didn’t even read it as snarky; I really don’t know the backgrounds of anybody else here. I knew absolutely nothing about machine learning before I set off on my Wolf 3D odyssey maybe 6 months ago. As it turns out, trying to go from “nothing” to Generative Models (the fringes of what’s possible in ML today) needs more than 6 months to fully comprehend 🙂. I have a deadline of Thursday this week for a talk I have to do at a community meetup about it. For the talk it’s enough that I understand it at a high level even though none of my attempts worked, but it might be fun to spend more time afterwards improving it with the help of the community. What I’ve tried so far for making Wolf 3D levels: * Variational Autoencoders: Not sure this is at all appropriate for a Wolf 3D level. It seems more useful for continuous data like colors; Wolf 3D maps are very discrete (what’s halfway between a door and a wall? It doesn’t really make sense). * Generative Adversarial Networks: There’s about a billion ways to tweak these and I just made one attempt adapted from some sample code. I suspect the discriminator is vastly better than the generator so it’s not figuring out how to move towards a real-looking map. Technique has some potential I think. * Long Short-Term Memory / Recurrent Neural Network: I think this is pretty promising but it needs to be fed 2D context, not just 1D. One issue common to all these that I’ve found is that my data had some REALLY terrible maps in them. I had already removed a lot of trash, but there were still stuff like 2-tile placeholder maps in my data set. I don’t have time to re-run everything again before the talk with that stuff removed.
  13. Yes. Edit: I mean, at a high level. The one I made didn’t produce any useful output. They are by all accounts extremely difficult to train, and my understanding of deep learning isn’t deep enough to intuitively know what to tweak to improve it.
  14. The awful BBC article? I didn't even bother reading it. I found the github page earlier in the thread.
  15. You guys might have missed their github repo: https://github.com/DanieleLoiacono/DoomGAN There's a level there along with a link to a YouTube video of it. The overall shape certainly seems to be from their output but the mechanics of how the level was created are unclear.
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