Re: The status of gnubg?

[Isaac Keslassy]

Hi,

It would be great to renew the effort on gnubg!

I have a question regarding the fundamental NN weight improvement 
technique. If I understand correctly, to improve the NN weights, you are 
trying the supervised-learning approach of picking tough positions, 
determining the best move using rollouts, then gradually optimizing the 
NN weights. However, as Joseph mentioned, this may affect the NN play in 
positions arising in regular games.

However, there are other techniques that have proved more efficient at 
games like chess. They avoid the long rollouts and work on positions of 
regular games. For instance:

1. SPSA: This is an obvious approach. Let the NN play against a very 
slightly modified version of it, pick the winner, and using a random 
walk, gradually converge to better parameters; or:

2. Logistic regression: Instead of teaching the best move, teach the 
position equity (as also mentioned by Aaron). Specifically, we could try 
to minimize the equity error associated to each position. Assume DMP for 
simplicity. Run a million games through self-play, and associate all the 
obtained positions to the final game result (-1 for loss, +1 for win). 
Then tune all the NN weights through gradient descent to minimize the 
difference between the position estimate and the final game result.

(see https://www.chessprogramming.org/Automated_Tuning, Texel's tuning, 
SPSA etc. for more details)

Has anybody tried such alternative methods?

Thanks,
Isaac