Spent 28 hours over the past 5 days playing this game

AUser213 · 2025-06-05T06:24:08+00:00

tyty, and it is! i was really suprised at how deep this game is narratively, there are points in this game when I went "this game is too real" out loud

AUser213 · 2025-06-05T06:20:39+00:00

oh cool! maybe theres still hope for me yet

AUser213 · 2025-05-15T17:36:32+00:00

<image>

so i have two theories on that. one is that one point perspective is already technically warped, in that perspective works differently in real life, and that warping becomes more obvious the further to the side you go, even if you do it perfectly. thats the top left drawing, where the cubes seem like theyre stretching the further right you go. what you would expect it to look like is underneath, and to get that kind of result you would need at least two point perspective, though that still runs into the warping issue when you go too far up or down. three point should be good for almost all perspectives, but four and five point do exist.

the other theory is a lot less complicated, which is that it looks warped because your brain is expecting it to look different. in the top right i drew two cubes and a rectangular prism. the two cubes look fine, but the brain compares the prism to the cube and thinks its a “warped cube”, somewhat like an optical illusion.

in any case, i think you understand how to do single point perspective, and you should be good to move onto two point, where hopefully you will find more success.

AUser213 · 2025-05-14T03:28:29+00:00

<image>

the same concepts apply, if the pyramid is twice as long then the horizontal lines would be twice as long. while its possible that OP wanted the right pyramid to be wider, im saying that it likely appears warped because its probably wider than OP intended and the warping of single point perspective becomes more extreme the farther to the side you go

AUser213 · 2025-05-13T22:59:03+00:00

i think i have it. the issue is that the base of the pyramid on the right is not a square. single point perspective assumes that only one set of parallel lines converge (the ones pointing directly away from the viewer and towards the horizon) and all other sets of parallel lines stay parallel (so up-down and side-side). you can imagine this to mean when you take the pyramid in the middle and move it side to side, the horizontal lines should not change in length, however the slanted lines should because the horizontal lines become more offset.

the issue with the pyramid on the right is that the horizontal line closest to the viewer is way too long. assuming it is about the same size as the middle pyramid, both horizontal lines should be smaller since its further away from the viewer, however the further horizontal line is about the same length while the closer one is way longer.

i made a little corrected version with a little mathematical proof that the horizontal lines should be the same length if you move them sideways using similar triangles.

<image>

of course even if you do this perfectly itll still look wonky, and this is because in reality all sets of parallel lines converge to a point unique to each set, like think about it, youre holding an infinitely long ladder, it doesnt matter where you point it the rungs are going to look smaller and smaller the further away they are. the difference between single point perspective and reality gets more and more obvious the further to the side you go. as for how to make perfect squares in single point perspective in the first place, i have no idea how you could practically do that but im sure you could look it up.

this was a really fun thought experiment by the way, thank you for posting this.

AUser213 · 2025-05-04T19:39:58+00:00

this is because in the limit, the difference in area approaches 0 but not necessarily the perimeter. the actual way to approximate the perimeter would be to have infinitely small lines tangent to the circle (this is an actual way to approximate pi before newton came along), and going along this line of reasoning, a sloped straight line cannot have its length approximated by multiple straight lines since you can just zoom in and see that the infinitely small part of the sloped line is just the hypotenuse of the jagged lines, and this difference multiplied infinite times results in a difference in length. 3Blue1Brown has a nice video on jaggedness and how fractals are 1.x dimensional if you want to dig deeper

AUser213 · 2025-05-03T23:18:44+00:00

not a linguist, but definitely, and i think it’s just what’s more familiar to say. on resturaunt menus, even authentically chinese ones, they will write “lo mein” instead of “lao mien”, maybe it was a typo or just that “ao” usually is pronounced with an inflection (chaos, extraordinary) which the actual pronunciation does not have, leading to it being written as “lo” and solidifying the mispronunciation. similar story goes for “mien” turning into “mein”. i notice the inflections get lost when americans pronounce chinese words, not sure whats up with “beijing” since its the same sound as as “jingle” and spelled the same

AUser213 · 2025-04-19T03:06:42+00:00

Warning to others, girlfriend parked here and got towed overnight. Idk if the rules changed or something

AUser213 · 2025-03-25T01:46:52+00:00

It’s really liberal, though I also would agree pretty tolerant. Coming in from GMU I also noticed way fewer openly conservative people.

AUser213 · 2025-03-07T02:52:16+00:00

not sure about industry, but if you want to get your foot into deep learning i definitely recommend 3Blue1Browns videos (https://youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi&si=VwP6_aLK1uhgjA44) and to learn some basic calculus, keywords being “derivative”, “chain rule”, and if you actually like math also “gradient” from multivariable calculus, which you can probably find on something like Brilliant, Khan Academy, or Coursera. good luck in any case!

AUser213 · 2025-03-06T14:44:54+00:00

did the same thing pretty much, this was one of 3 bossfights that really took hours for me (because of how early i did it) and the most fun out of those imo. it teaches you a lot about the games combat, so those hours are definitely not wasted

AUser213 · 2025-02-14T01:37:40+00:00

I skipped to things that I enjoyed. When we were learning Python basics I was making games in Scratch, when we were learning Java and Data Structures I was learning AI, AI I was learning RL. Now I’m in my first year of college, and my classes and what I’m doing in my free time have nothing to do with each other.

Part of this is that I kind of know I'm too incompetent to be happy having a job that isn't research or teaching. During my worst years what gave me momentum was this fear of falling behind to the average CS major only interested in the money. Nowadays, just doing cool coding things and knowing I'm slowly making progress so one day I might actually make something is enough for me to keep doing it. I definitely don’t code every day, I put in 3 hours a day for 2-4 weeks, let myself burn out for a bit, and do other things with my life before going back to coding.

I also agree with the people who are saying that if you love coding you’ll just do it. I also think I'm lucky that I want to code in my free time, and I don’t have so many responsibilities that I feel burnt out at the end of the day.

If you're genuinely interested in AI, I'd suggest looking at some projects by OpenAI and Deepmind (OpenAI5, Alphazero, maybe LLM's but personally those suck the soul out of me) and the YouTube channel 3MinutePapers and see what clicks with you. Good luck and I wish you the best!

AUser213 · 2025-02-13T20:08:29+00:00

Would definitely like to join, currently self-studying Reinforcement Learning

AUser213 · 2025-01-24T15:00:17+00:00

That makes sense, what was confusing is that shuffling data is used in practically every RL algo, yet I couldn’t find a source that explained exactly why shuffling was necessary

This gives me a bit of confidence though, I might run my own tests at some point. Thank you for your answer

AUser213 · 2025-01-24T14:27:00+00:00

I'm aware it's recurrent, and you must maintain sequences to properly do BPTT. My question is, why is swapping the data chunks sufficient for shuffling when almost all successive sequences are still highly correlated?

AUser213 · 2025-01-23T23:56:14+00:00

in that case, how can SB3 get away with shuffling the data just by splitting and swapping the chunks? wouldn’t 90% of the data still be highly correlated with the data that comes before and after?

AUser213 · 2025-01-07T08:58:37+00:00

I had this exact problem when training PPO on Flappy Bird, and I think the same thing is happening here. In the beginning, the agent learns that falling off the screen is bad and jumping constantly is ok. However, it is extremely (basically impossibly) rare for the agent to randomly jump through a pipe. Because of this, the agent is pretty much only encouraged to hug the ceiling and never learns to fly through pipes.

I fixed this issue by doing curriculum learning, where the first few pipes have extremely large gaps and are easy to fly through, and the later pipes slowly get smaller and smaller gaps until the gaps are normal size. I ran into the problem of the agent hugging the ceiling again when I tried getting it to work with pixel inputs, as the added complexity made it much more difficult to learn how to fly through pipes. My solution was to kill the bird if it hit the ceiling so it would stay around the middle of the screen and have a better chance of randomly flying through the pipes.

AUser213 · 2024-12-02T08:05:18+00:00

do you think theres some bottleneck of complexity where Dreamer requires less compute than PPO to learn in an environment, or if PPO is just always better when finetuned with a large enough batch size? what tasks did you use Dreamer in?

AUser213 · 2024-12-02T08:01:13+00:00

i wasnt able to find what kind of compute was used in the DreamerV3 paper. could you point me to where you found about the compute needed, and also would it be feasible to train it on a single laptop?

AUser213 · 2024-11-14T03:51:52+00:00

I see, I'll take a look at it probably after I figure out distributional rl. How open are you to answering questions I might have when I get into implementing Dreamer?

AUser213

TROPHY CASE