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bubudumbdumb t1_j84mygn wrote

The strength of transformers lies in the transfer of representations learned over large corpuses of text or images. Those are less likely to bring capabilities that generalise to pocker so traditional RL and Monte Carlo approaches are likely to have the upper hand. Pocker's challenges are not linguistic or visual perspective challenges.

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lmtog OP t1_j84uw2j wrote

But technically it should be possible to train the model on hands, in the mentioned representation, and get an output that would be a valid poker play?

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bubudumbdumb t1_j84w7r2 wrote

Correct but the goal is not to train but to infer. I am not saying it wouldn't work just that I don't see why the priors of a transformer model would work better than RNNs or LSTMs in modeling the rewards of each play. Maybe there is something that I don't get about pocker that maps the game to graphs that can be learned through self attention.

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