Comment by emporas
2 years ago
>The entire point of this is to not encode the board state!
I am not sure about this. From the article "The 50M parameter model played at 1300 ELO with 99.8% of its moves being legal within one day of training."
I thought that the experiment was how well the model will perform, given that it's reward function is to predict text, rather than checkmate. Leela, Alpha0 their reward function is to win the game, checkmate or capture pieces. Also it goes without saying that Leela, Alpha0 cannot make illegal moves.
The experiment does not need to include the whole board position if that's a problem, if that's an important point of interest. It could encode more information about squares covered by each side for example. See for example this training experiment for Trackmania [1]. There are techniques that the ML algorithm will *never* figure out by itself if this information is not encoded in it's training data.
The point still stands. PGN notation certainly is not a good format if the goal (or one of the goals) of the experiment is to be a good chess player.
That just shows that it worked in some sense. If it didn't reach any ELO, clearly the results would be uninformative: maybe it's impossible to learn chess from PGN, or maybe you just screwed up. He's clear that the point is to interrogate what it learns:
"This model is only trained to predict the next character in PGN strings (1.e4 e5 2.Nf3 …) and is never explicitly given the state of the board or the rules of chess. Despite this, in order to better predict the next character, it learns to compute the state of the board at any point of the game, and learns a diverse set of rules, including check, checkmate, castling, en passant, promotion, pinned pieces, etc. In addition, to better predict the next character it also learns to estimate latent variables such as the ELO rating of the players in the game."