I wondered if this was using a machine-learning style AI and it was, among other things, learning the state of the random number generator so it could predict p

I wondered if this was using a machine-learning style AI and it was, among other things, learning the state of the random number generator so it could predict pieces accurately more than one turn out? (And if not, how would you prove that it wasn't? Perhaps tweak the game RNG and see if the AI performs badly?) Looking at the github repo, it looks like it's actually more of a classical AI doing traditional game tree search. There is some interesting code around the RNG, though: apparently the RNG does make certain piece sequences more likely than others, and there's a lookup table for the probability of the next piece given the current piece: https://github.com/GregoryCannon/StackRabbit/blob/master/src/server/piece_rng.ts https://github.com/GregoryCannon/StackRabbit/blob/master/src... I suppose one could extend this to be a 3-dimensional lookup table with the probabilities of the next pieces given the last two pieces, or to extend it to 4 or 5 or (if you had infinite resources) 100. At some point you'd know enough to be able to predict the next piece with 100% accuracy.