A recent paper called ‘world models’ has gotten really popular in the machine learning community. They trained an AI to play a racing game by having it learn inside of its own simulated dream environment. Meaning, the AI learned a model of what the game world was like, then was able to generate a game world that was roughly similar to what it learned and train inside of that. A simulation inside of a simulation. I’ll explain how their model was structured both theoretically and programmatically in this video.
Code for this video:
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