Rendered at 22:57:35 GMT+0000 (Coordinated Universal Time) with Cloudflare Workers.
ainch 3 hours ago [-]
Very cool work, the learned world state is a smart way of getting consistent generation across all the views (and not having the map vanish when you 180 like some other models). Multi-agent is such an interesting field, because it's clear that humanity benefits from distributed intelligence, but I don't think MARL has really had a big breakthrough like AlphaGo or RLVR for single-agent RL.
Two thoughts about where this could go: first, the internal world state would need to be learned to transfer to real-life robotics, since you can't query the internals of a game engine in training. Second, an enormous challenge for many of these world models is going to be truly unbounded environmental interactivity - Agora is still mostly about a few agents interacting in a static environment. Learning interaction will be hard, because the interactions in games are intentionally added in, by hand. But we (human learners) acquire a strong model for environental interaction very efficiently, which is part of what helps us generalise so effectively.
iugtmkbdfil834 57 minutes ago [-]
Hmm. Neat ( especially prowl -- as an idea ). But.. I don't see anything beyond the game. I might be a little cautious, but there is no way for me test any of it ( and I was actually setting up an old mmo this weekend to see how well agent can survive within a rigid ecosystem ). Is it just intended for pure researchers or something?
hydra-f 3 hours ago [-]
Unlike LLMs which made it into the public view, I have a hard time seeing these world simulation models doing the same
I'm not sure how to imagine their use in education or gaming, but it's clear that they have a real potential for being used in military programs
It's nightmarish to think these could be trained on shooting game footage and thrown into real life scenarios in some form or another
aspenmartin 3 hours ago [-]
World models are there for planning capabilities and data efficiency in training, they are an old and general idea (model based RL). You just see them in video games etc because these are easier cases.
Aboutplants 2 hours ago [-]
So much of tech progress links back to gaming, it’s astounding
ianbutler 1 hours ago [-]
gaming is all about simulation after all
empath75 51 minutes ago [-]
Yeah, it's obvious that this will be used to pilot drones.
MASNeo 3 hours ago [-]
Is there a little bit more on this in terms of evaluation or is this rather a Show-HN post?
graphememes 41 minutes ago [-]
good step in a direction
syntex 3 hours ago [-]
Be careful when transposing game-learned behaviors into real life.
Stevvo 3 hours ago [-]
Underwhelming demo. Also the controls are terrible, but, the real Goldeneye was also underwhelming with bad controls if you had played Quake II.
gamer20123123 2 hours ago [-]
I played the game - the inputs feel like trash... I'm not convinced this is the correct direction to generate games. We should probably only be generating scripts and assets to plug into game engines, rather than relying on GenAI for the actual engine.
Two thoughts about where this could go: first, the internal world state would need to be learned to transfer to real-life robotics, since you can't query the internals of a game engine in training. Second, an enormous challenge for many of these world models is going to be truly unbounded environmental interactivity - Agora is still mostly about a few agents interacting in a static environment. Learning interaction will be hard, because the interactions in games are intentionally added in, by hand. But we (human learners) acquire a strong model for environental interaction very efficiently, which is part of what helps us generalise so effectively.
I'm not sure how to imagine their use in education or gaming, but it's clear that they have a real potential for being used in military programs
It's nightmarish to think these could be trained on shooting game footage and thrown into real life scenarios in some form or another