ctak, branch mainAn implementation of Tak and a computer opponent in Chttps://git.l-3.space/ctak/atom/?h=main2026-09-16T20:02:53Zfix tt issue for non-self-play2026-09-16T20:02:53Ztsliltslil@posteo.de2026-09-16T19:10:33Zurn:sha1:1898add0142b70b3fef5af78a6f3d7399a3f2cd5
A little subtle, but in non-self-play games sharing tt entries is incorrect. We must therefore key additionally on the evaulation function itself.
move data generation into the epoch loop2026-09-13T19:15:31Ztsliltslil@posteo.de2026-09-12T18:37:56Zurn:sha1:7f1566fb21dfe8c99bc770885cc2cc4e478e16b4
Previously we would spend all of the training epochs on making the network a better predictor of the *initial* TD-leaf values that we computed before the start of the first epoch. There's definitely something stale about this, given that the TD-leaf training targets involved the net evaluation at PVs. I'm not overly confident that this makes sense, but as a kind of compromise between fully on-line TD-leaf and the fully stale version we had before, we now recompute the TD-leaf values in each epoch with the fine-tuned net instead of the epoch 0 net.
Perhaps we were just lucky, but this seems to train a strong network under the default setting.
More experimentation is required.
still observing non-monotonic improvements between training generations2026-09-12T14:54:20Ztsliltslil@posteo.de2026-09-12T14:51:16Zurn:sha1:f3ed463e8a215b0ca3219aebbb14c4a7073a0f9fflood fill for roads, fix dragon, memoise leaf eval, enable diagonal in nn_train.py2026-09-12T14:47:15Ztsliltslil@posteo.de2026-09-11T17:36:10Zurn:sha1:a361728f39812e25599323c52510c0eb3131bd90move from single candidate td-leaf with gating to tournament of a population2026-09-11T16:00:18Ztsliltslil@posteo.de2026-09-11T11:56:12Zurn:sha1:6e90f1145b2c5c0f2dcc82d21ec45432274b8cad
The goal in this change was to overcome the rock-paper-scissors phenomenon that was happening in capability space by having on a single candidate learn to beat the most recent version of itself. In some sense this lead to rotating around in capability space instead of chasing monotonic improvements.
I hope that flipping to a system in which many (N=5) candidates play each other and learn from their own set of diverse games in this pool, combined with --replay-iters, will lead to a given network having to have a much more comprehensive understanding of the space of tactics. I haven't yet had time to train this thorougly, will run those experiments on my desktop computer.
credit Mitchell Riley: we can drop 65 parameters for about the same strength; also train for longer2026-09-10T20:04:40Ztsliltslil@posteo.de2026-09-10T13:12:41Zurn:sha1:220a4b79e4b6d742a9ca7c278a1962b870500fbc
The idea is that the current model is M_theta(board, ply) = "score for player 0", but we can instead create a different model M'_theta(board) = "score for player 0 assuming that it's player 0's move" and for a certain pair of functions sigma, normalise, recover M_theta(board, ply) = sigma(ply) M'_theta(normalise(board, ply)).
This pair of functions is sigma(ply) = +/- 1 (parity), and normalise being a conditional interchange of pieces on the board depending on the parity of the ply. The only things stopping this from being completely straightforward is that nn_train.py evaluates M'_theta itself so we need to pass sigma(ply) to the training loop.
6x6 is still untrained.
use silu instead of relu with the intuition being that we avoid dead neurons2026-09-10T12:54:52Ztsliltslil@posteo.de2026-09-10T11:31:51Zurn:sha1:710d5a5225edb44d171ca9ac6345e9a61aa4f125
In a head-to-head this is +187 elo against the previous weights, although it trained more.
~25% speedup in selfplay due to: incremental zobrist hashing, smarter road checking, re-using the input computed for the NN2026-09-09T20:45:17Ztsliltslil@posteo.de2026-09-08T10:55:28Zurn:sha1:1c9e266ae25f7f6067806d139e5971ce8513d2f6cleanup, match now plays pairs of symmetric games2026-09-07T18:58:03Ztsliltslil@posteo.de2026-09-06T16:24:44Zurn:sha1:7ef68374e8a58684b299b0f46f7ed850d5a87190adding more thoroughly trained 6x6 weights, but it's clear that we're not scaling well to this size2026-09-05T18:06:27Ztsliltslil@posteo.de2026-09-05T17:41:27Zurn:sha1:f9f8f650dc557ea9dbb4652e48c5cdca7ea3c62c