From 220a4b79e4b6d742a9ca7c278a1962b870500fbc Mon Sep 17 00:00:00 2001 From: tslil Date: Thu, 10 Sep 2026 14:12:41 +0100 Subject: credit Mitchell Riley: we can drop 65 parameters for about the same strength; also train for longer 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. --- src/selfplay.c | 13 +++++++------ 1 file changed, 7 insertions(+), 6 deletions(-) (limited to 'src/selfplay.c') diff --git a/src/selfplay.c b/src/selfplay.c index 5ffa6f6..42d82d2 100644 --- a/src/selfplay.c +++ b/src/selfplay.c @@ -38,10 +38,11 @@ static tak_state_p state; -static float row_head[MAX_PLY][3]; +static float row_head[MAX_PLY][2]; static uint8_t row_cells[MAX_PLY][MAX_CELLS]; static uint8_t row_term[MAX_PLY]; static float row_terminal_value[MAX_PLY]; +static float row_parity_sign[MAX_PLY]; static uint32_t row_game_id[MAX_PLY]; static uint32_t n_rows; @@ -90,6 +91,7 @@ static int play_game(const uint8_t min_ply) { memcpy(row_cells[n_rows], leaf->cells, sizeof(row_cells[n_rows])); row_term[n_rows] = leaf->terminal; row_terminal_value[n_rows] = leaf->terminal_value; + row_parity_sign[n_rows] = leaf->parity_sign; row_game_id[n_rows] = state->current_colour == C_BLACK ? game_id_black : game_id_white; n_rows++; } @@ -114,12 +116,11 @@ static int play_game(const uint8_t min_ply) { const float R = win_value(state->won); for (uint32_t k = 0; k < n_rows; k++) { - fprintf(stdout, "%.8f,%.8f,%.8f,", row_head[k][0], row_head[k][1], - row_head[k][2]); + fprintf(stdout, "%.8f,%.8f,", row_head[k][0], row_head[k][1]); for (uint8_t c = 0; c < n_cells; c++) fprintf(stdout, "%u,", (unsigned)row_cells[k][c]); - fprintf(stdout, "%u,%.0f,%u,%.0f\n", row_game_id[k], R, - (unsigned)row_term[k], row_terminal_value[k]); + fprintf(stdout, "%u,%.0f,%u,%.0f,%.0f\n", row_game_id[k], R, + (unsigned)row_term[k], row_terminal_value[k], row_parity_sign[k]); } return (int)n_rows; @@ -129,7 +130,7 @@ const char *usage = "selfplay, one game of asymmetric self-play training data.\n\n\ Usage: selfplay \n\n\ Plays a single game of x self-play, with moves made randomly. Only the deterministic plies are written to stdout, one CSV row per recorded search, from ply onwards:\n\n\ -\tparity,white_flats,black_flats,,game_id,R,terminal,leaf_R\n\n\ +\tmover_flats,opponent_flats,,game_id,R,terminal,leaf_R,parity_sign\n\n\ nn_train.py runs many of these in parallel to generate training data.\n\n\ Copyright (C) 2026, tslil clingman\n\n\ This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version."; -- cgit v1.3.1