In the last two posts, we’ve looked at using machine learning for playing iterated Roshambo. Specifically, we saw how to use Bayes’ theorem to try to detect and exploit patterns, and then saw how Fourier transforms can give us a concrete measurement of the randomness (and non-randomness) in our opponent’s play. Today’s post is about how we can use representation theory to improve our chances of finding interesting patterns.

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