I might have missed it in the readme, but what hardware did the training take place on? This is really neat.
This is incredibly cool! Fascinating to see looped transformers be necessary for mini-Doom, but not for simpler games.
Is the actor trained on world model's mistakes as well? If so, it should learn to force hallucinations that force it into an easier state.
Very cool. I didn't know NNs were capable of such determinism, that's very interesting. Is this a similar effect to overfitting? Also, it's the same model for all the games, right?
> Also, it's the same model for all the games, right
It's a bit unclear but it looks like different models, since they all have different parameter and MAC counts. It would be much more impressive with a single world model that could maybe dream up new games inbetween the games in the training set.
It's a self contained net for each game but the data used for training doesn't overlap with the validation. I think it would be easy to merge them into a single model
Honestly fascinating. I almost feel like you'd be better off avoiding any discussion of the fact that this runs on a Raspberry Pi Pico 2 W because it takes away from the crazy world model capabilities (of course, the fact that this does run on a Pico 2 makes the whole thing much more impressive).
There's gotta be a good way to incorporate this logic into a larger, more "traditional" game in a meaningful way. Seems like an approach for procedural generation that could solve some of the issues usually found with actual procedural generation.
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For the world model training I've used an RTX 4060, but for the agent playing the game that was directly on the pico using stream-ac