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IronRabbit69 t1_j84njph wrote

Tabular CFR can be approximated with a neural network, as Noam Brown (1st author of Pluribus) and co-authors show in follow-up work: https://arxiv.org/abs/1811.00164

But you're comparing apples to oranges a bit asking if transformers can replace CFR. Transformers are a neural net architecture. You could of course encode poker stuff in text and feed that to a transformer which predicts the right move to play. But how do you train that network? CFR is a self-play learning algorithm (sort of like Alphago's MCTS) which learns good policies.

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lmtog OP t1_j84uk0n wrote

I think the training part is what I was missing.

I thought you would train a transformer like a normal neural net in the sense that you tell it what output you like and what is wrong.

Looking into it a bit more I assume you could get an output but nothing close to the nash equilibrium.

Thank you for the feedback.

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