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/*
selfplay: generate training data
Copyright (C) 2026, tslil clingman
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.
This program is distributed in the hope that it will be useful, but
WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
General Public License for more details.
You should have received a copy of the GNU General Public License
along with ct. If not, see <https://www.gnu.org/licenses/>.
*/
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <time.h>
#include <unistd.h>
#include "actions.h"
#include "negamax.h"
#include "nn.h"
#include "tak.h"
#include "xorshift64.h"
#define MAX_PLY 256
#define MAX_INP (3 + 6 * 6)
static tak_state_p state;
static uint32_t min_ply;
// Positions buffered for the game (the net input only)
static float rows[MAX_PLY][MAX_INP];
static uint32_t row_ply[MAX_PLY];
static uint32_t n_rows;
static uint32_t game_id; // PID, tags every row so games can be grouped
void negamax_display_progress(const uint8_t cur_depth,
const uint8_t init_depth,
const uint32_t length) {
(void)(cur_depth);
(void)(init_depth);
(void)(length);
}
static int play_game(const uint32_t eps10000) {
reset_state(state, state->board_size);
n_rows = 0;
char ptn[16];
for (uint32_t ply = 0; ply < MAX_PLY; ply++) {
// The engine's best move for this position (sets negamax_ptn)
negamax_generate(state);
if (rand32() % 10000 < eps10000) {
// Epsilon: uniform random legal move
uint32_t n_actions;
action_t *actions = actions_generate(state, &n_actions);
action_to_ptn(actions[rand32() % n_actions], ptn);
} else {
// Greedy: the engine's best move
strcpy(ptn, negamax_ptn);
}
// Buffer this position, before the move changes the board
if (ply >= min_ply && n_rows < MAX_PLY) {
nn_prepare_input(rows[n_rows], state);
row_ply[n_rows] = ply;
n_rows++;
}
const enum ACT_RESULT r = do_ptn(state, ptn);
if (r == GAME_END)
break;
if (r != ACT_OK) {
fprintf(stderr, "selfplay: unexpected result %d for ptn %s\n", r,
ptn);
return 0;
}
}
if (n_rows == 0)
return 0;
const uint8_t n_cells = state->board_size * state->board_size;
// final outcome from black's perspective
float R;
switch (state->won) {
case WIN_FLAT_BLACK:
case WIN_ROAD_BLACK:
R = 1.0f;
break;
case WIN_FLAT_WHITE:
case WIN_ROAD_WHITE:
R = -1.0f;
break;
default:
R = 0.0f;
break;
}
for (uint32_t k = 0; k < n_rows; k++) {
fprintf(stdout, "%d,%.8f,%.8f,", (int)rows[k][0], rows[k][1],
rows[k][2]);
for (uint8_t c = 0; c < n_cells; c++)
fprintf(stdout, "%.2f,", rows[k][3 + c]);
fprintf(stdout, "%u,%u,%.0f\n", game_id, row_ply[k], R);
}
return (int)n_rows;
}
const char *license =
"selfplay, one game of self-play training data for the ct net\n\
Usage: selfplay <size> <eps> <depth> <min_ply>\n\
Plays a single game of <size>x<size>. On each ply the engine's best move is played with probability 1-eps, and a uniform random legal move with probability eps. Every position from ply <min_ply> onwards is written to stdout as one CSV row:\n\
\n\
parity,white_flats,black_flats,<board values...>,game_id,ply,R\n\
\n\
The first 3 + size*size columns are the training input, game_id identifies the game, ply is the move number the position precedes, and R is the game's final outcome from black's perspective (+1 black won, -1 white won, 0 draw). nn_train.py runs many of these in parallel, pools the rows, and derives TD(lambda) targets from them.\n\
\n\
Copyright (C) 2026, tslil clingman\n\
\n\
This program is free software: you can redistribute it and/or modify\n \
it under the terms of the GNU General Public License as published by\n \
the Free Software Foundation, either version 3 of the License, or\n\
(at your option) any later version.";
int main(int argc, char **argv) {
if (argc != 5) {
puts(license);
return EXIT_FAILURE;
}
const uint8_t size = (uint8_t)atoi(argv[1]);
const uint32_t eps10000 = (uint32_t)(atof(argv[2]) * 10000.0 + 0.5);
negamax_search_depth = (uint8_t)atoi(argv[3]);
min_ply = (uint32_t)atoi(argv[4]);
if (size != 5 && size != 6) {
fprintf(stderr, "selfplay: size must be 5 or 6\n");
return EXIT_FAILURE;
}
if (eps10000 >= 10000) {
fprintf(stderr, "selfplay: eps must be < 1\n");
return EXIT_FAILURE;
}
// Processes launched in the same second must not share a seed
game_id = (uint32_t)getpid();
xors = ((uint64_t)time(NULL) ^ ((uint64_t)getpid() << 32)) *
0x9E3779B97F4A7C15ULL +
0xC0FFEEEE;
state = new_tak_state(size);
negamax_init(size);
play_game(eps10000);
negamax_free();
free_tak_state(state);
return EXIT_SUCCESS;
}
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