Merge branch 'dist' into derm
[pachi.git] / uct / dynkomi.c
blob1d3408a6ed9f49ea347d56588b3971037810ee34
1 #define DEBUG
2 #include <assert.h>
3 #include <math.h>
4 #include <stdio.h>
5 #include <stdlib.h>
6 #include <string.h>
8 #include "board.h"
9 #include "debug.h"
10 #include "tactics.h"
11 #include "uct/dynkomi.h"
12 #include "uct/internal.h"
13 #include "uct/tree.h"
16 static void
17 generic_done(struct uct_dynkomi *d)
19 if (d->data) free(d->data);
20 free(d);
24 /* NONE dynkomi strategy - never fiddle with komi values. */
26 struct uct_dynkomi *
27 uct_dynkomi_init_none(struct uct *u, char *arg, struct board *b)
29 struct uct_dynkomi *d = calloc(1, sizeof(*d));
30 d->uct = u;
31 d->permove = NULL;
32 d->persim = NULL;
33 d->done = generic_done;
34 d->data = NULL;
36 if (arg) {
37 fprintf(stderr, "uct: Dynkomi method none accepts no arguments\n");
38 exit(1);
41 return d;
45 /* LINEAR dynkomi strategy - Linearly Decreasing Handicap Compensation. */
46 /* At move 0, we impose extra komi of handicap_count*handicap_value, then
47 * we linearly decrease this extra komi throughout the game down to 0
48 * at @moves moves. */
50 struct dynkomi_linear {
51 int handicap_value;
52 int moves;
53 bool rootbased;
56 static float
57 linear_permove(struct uct_dynkomi *d, struct board *b, struct tree *tree)
59 struct dynkomi_linear *l = d->data;
60 if (b->moves >= l->moves)
61 return 0;
63 float base_komi = board_effective_handicap(b, l->handicap_value);
64 float extra_komi = base_komi * (l->moves - b->moves) / l->moves;
65 return extra_komi;
68 static float
69 linear_persim(struct uct_dynkomi *d, struct board *b, struct tree *tree, struct tree_node *node)
71 struct dynkomi_linear *l = d->data;
72 if (l->rootbased)
73 return tree->extra_komi;
74 /* We don't reuse computed value from tree->extra_komi,
75 * since we want to use value correct for this node depth.
76 * This also means the values will stay correct after
77 * node promotion. */
78 return linear_permove(d, b, tree);
81 struct uct_dynkomi *
82 uct_dynkomi_init_linear(struct uct *u, char *arg, struct board *b)
84 struct uct_dynkomi *d = calloc(1, sizeof(*d));
85 d->uct = u;
86 d->permove = linear_permove;
87 d->persim = linear_persim;
88 d->done = generic_done;
90 struct dynkomi_linear *l = calloc(1, sizeof(*l));
91 d->data = l;
93 if (board_size(b) - 2 >= 19)
94 l->moves = 200;
95 l->handicap_value = 7;
97 if (arg) {
98 char *optspec, *next = arg;
99 while (*next) {
100 optspec = next;
101 next += strcspn(next, ":");
102 if (*next) { *next++ = 0; } else { *next = 0; }
104 char *optname = optspec;
105 char *optval = strchr(optspec, '=');
106 if (optval) *optval++ = 0;
108 if (!strcasecmp(optname, "moves") && optval) {
109 /* Dynamic komi in handicap game; linearly
110 * decreases to basic settings until move
111 * #optval. */
112 l->moves = atoi(optval);
113 } else if (!strcasecmp(optname, "handicap_value") && optval) {
114 /* Point value of single handicap stone,
115 * for dynkomi computation. */
116 l->handicap_value = atoi(optval);
117 } else if (!strcasecmp(optname, "rootbased")) {
118 /* If set, the extra komi applied will be
119 * the same for all simulations within a move,
120 * instead of being same for all simulations
121 * within the tree node. */
122 l->rootbased = !optval || atoi(optval);
123 } else {
124 fprintf(stderr, "uct: Invalid dynkomi argument %s or missing value\n", optname);
125 exit(1);
130 return d;
134 /* ADAPTIVE dynkomi strategy - Adaptive Situational Compensation */
135 /* We adapt the komi based on current situation:
136 * (i) score-based: We maintain the average score outcome of our
137 * games and adjust the komi by a fractional step towards the expected
138 * score;
139 * (ii) value-based: While winrate is above given threshold, adjust
140 * the komi by a fixed step in the appropriate direction.
141 * These adjustments can be
142 * (a) Move-stepped, new extra komi value is always set only at the
143 * beginning of the tree search for next move;
144 * (b) Continuous, new extra komi value is periodically re-determined
145 * and adjusted throughout a single tree search. */
147 struct dynkomi_adaptive {
148 /* Do not take measured average score into regard for
149 * first @lead_moves - the variance is just too much.
150 * (Instead, we consider the handicap-based komi provided
151 * by linear dynkomi.) */
152 int lead_moves;
153 /* Maximum komi to pretend the opponent to give. */
154 float max_losing_komi;
155 float (*indicator)(struct uct_dynkomi *d, struct board *b, struct tree *tree);
157 /* Value-based adaptation. */
158 float zone_red, zone_green;
159 int score_step;
160 bool use_komi_latch;
161 float komi_latch; // runtime, not configuration
163 /* Score-based adaptation. */
164 float (*adapter)(struct uct_dynkomi *d, struct board *b);
165 float adapt_base; // [0,1)
166 /* Sigmoid adaptation rate parameter; see below for details. */
167 float adapt_phase; // [0,1]
168 float adapt_rate; // [1,infty)
169 bool adapt_aport; // alternative game portion determination
170 /* Linear adaptation rate parameter. */
171 int adapt_moves;
172 float adapt_dir; // [-1,1]
174 #define TRUSTWORTHY_KOMI_PLAYOUTS 200
176 static float
177 adapter_sigmoid(struct uct_dynkomi *d, struct board *b)
179 struct dynkomi_adaptive *a = d->data;
180 /* Figure out how much to adjust the komi based on the game
181 * stage. The adaptation rate is 0 at the beginning,
182 * at game stage a->adapt_phase crosses though 0.5 and
183 * approaches 1 at the game end; the slope is controlled
184 * by a->adapt_rate. */
185 float game_portion;
186 if (!a->adapt_aport) {
187 int total_moves = b->moves + 2 * board_estimated_moves_left(b);
188 game_portion = (float) b->moves / total_moves;
189 } else {
190 int brsize = board_size(b) - 2;
191 game_portion = 1.0 - (float) b->flen / (brsize * brsize);
193 float l = game_portion - a->adapt_phase;
194 return 1.0 / (1.0 + exp(-a->adapt_rate * l));
197 static float
198 adapter_linear(struct uct_dynkomi *d, struct board *b)
200 struct dynkomi_adaptive *a = d->data;
201 /* Figure out how much to adjust the komi based on the game
202 * stage. We just linearly increase/decrease the adaptation
203 * rate for first N moves. */
204 if (b->moves > a->adapt_moves)
205 return 0;
206 if (a->adapt_dir < 0)
207 return 1 - (- a->adapt_dir) * b->moves / a->adapt_moves;
208 else
209 return a->adapt_dir * b->moves / a->adapt_moves;
212 static float
213 komi_by_score(struct uct_dynkomi *d, struct board *b, struct tree *tree)
215 struct dynkomi_adaptive *a = d->data;
216 if (d->score.playouts < TRUSTWORTHY_KOMI_PLAYOUTS)
217 return tree->extra_komi;
219 struct move_stats score = d->score;
220 /* Almost-reset tree->score to gather fresh stats. */
221 d->score.playouts = 1;
223 /* Look at average score and push extra_komi in that direction. */
224 float p = a->adapter(d, b);
225 p = a->adapt_base + p * (1 - a->adapt_base);
226 if (p > 0.9) p = 0.9; // don't get too eager!
227 float extra_komi = tree->extra_komi + p * score.value;
228 if (DEBUGL(3))
229 fprintf(stderr, "mC += %f * %f\n", p, score.value);
230 return extra_komi;
233 static float
234 komi_by_value(struct uct_dynkomi *d, struct board *b, struct tree *tree)
236 struct dynkomi_adaptive *a = d->data;
237 if (d->value.playouts < TRUSTWORTHY_KOMI_PLAYOUTS)
238 return tree->extra_komi;
240 struct move_stats value = d->value;
241 /* Almost-reset tree->value to gather fresh stats. */
242 d->value.playouts = 1;
244 /* We have three "value zones":
245 * red zone | yellow zone | green zone
246 * ~45% ~60%
247 * red zone: reduce komi
248 * yellow zone: do not touch komi
249 * green zone: enlage komi.
251 * Also, at some point komi will be tuned in such way
252 * that it will be in green zone but increasing it will
253 * be unfeasible. Thus, we have a _latch_ - we will
254 * remember the last komi that has put us into the
255 * red zone, and not use it or go over it. We use the
256 * latch only when giving extra komi, we always want
257 * to try to reduce extra komi we take.
259 * TODO: Make the latch expire after a while. */
260 float extra_komi = tree->extra_komi;
262 if (value.value < a->zone_red) {
263 /* Red zone. Take extra komi. */
264 if (DEBUGL(3))
265 fprintf(stderr, "[red] %f, komi latch %f -> %f\n",
266 value.value, a->komi_latch, extra_komi);
267 if (extra_komi > 0) a->komi_latch = extra_komi;
268 extra_komi -= a->score_step; // XXX: we depend on being black
269 return extra_komi;
271 } else if (value.value < a->zone_green) {
272 /* Yellow zone, do nothing. */
273 return extra_komi;
275 } else {
276 /* Green zone. Give extra komi. */
277 extra_komi += a->score_step; // XXX: we depend on being black
278 if (DEBUGL(3))
279 fprintf(stderr, "[green] %f, += %d | komi latch %f\n",
280 value.value, a->score_step, a->komi_latch);
281 return !a->use_komi_latch || extra_komi < a->komi_latch ? extra_komi : a->komi_latch - 1;
285 static float
286 adaptive_permove(struct uct_dynkomi *d, struct board *b, struct tree *tree)
288 struct dynkomi_adaptive *a = d->data;
289 if (DEBUGL(3))
290 fprintf(stderr, "m %d/%d ekomi %f permove %f/%d\n",
291 b->moves, a->lead_moves, tree->extra_komi,
292 d->score.value, d->score.playouts);
293 if (b->moves <= a->lead_moves)
294 return board_effective_handicap(b, 7 /* XXX */);
296 /* Get lower bound on komi value so that we don't underperform
297 * too much. XXX: We rely on the fact that we don't use dynkomi
298 * as white for now. */
299 float min_komi = - a->max_losing_komi;
301 float komi = a->indicator(d, b, tree);
302 if (DEBUGL(3))
303 fprintf(stderr, "dynkomi: %f -> %f\n", tree->extra_komi, komi);
304 return komi > min_komi ? komi : min_komi;
307 static float
308 adaptive_persim(struct uct_dynkomi *d, struct board *b, struct tree *tree, struct tree_node *node)
310 return tree->extra_komi;
313 struct uct_dynkomi *
314 uct_dynkomi_init_adaptive(struct uct *u, char *arg, struct board *b)
316 struct uct_dynkomi *d = calloc(1, sizeof(*d));
317 d->uct = u;
318 d->permove = adaptive_permove;
319 d->persim = adaptive_persim;
320 d->done = generic_done;
322 struct dynkomi_adaptive *a = calloc(1, sizeof(*a));
323 d->data = a;
325 if (board_size(b) - 2 >= 19)
326 a->lead_moves = 20;
327 else
328 a->lead_moves = 4; // XXX
329 a->max_losing_komi = 10;
330 a->indicator = komi_by_score;
332 a->adapter = adapter_sigmoid;
333 a->adapt_rate = 20;
334 a->adapt_phase = 0.5;
335 a->adapt_moves = 200;
336 a->adapt_dir = -0.5;
338 a->zone_red = 0.45;
339 a->zone_green = 0.6;
340 a->score_step = 2;
341 a->use_komi_latch = true;
342 a->komi_latch = 1000;
344 if (arg) {
345 char *optspec, *next = arg;
346 while (*next) {
347 optspec = next;
348 next += strcspn(next, ":");
349 if (*next) { *next++ = 0; } else { *next = 0; }
351 char *optname = optspec;
352 char *optval = strchr(optspec, '=');
353 if (optval) *optval++ = 0;
355 if (!strcasecmp(optname, "lead_moves") && optval) {
356 /* Do not adjust komi adaptively for first
357 * N moves. */
358 a->lead_moves = atoi(optval);
359 } else if (!strcasecmp(optname, "max_losing_komi") && optval) {
360 a->max_losing_komi = atof(optval);
361 } else if (!strcasecmp(optname, "indicator")) {
362 /* Adaptatation indicator - how to decide
363 * the adaptation rate and direction. */
364 if (!strcasecmp(optval, "value")) {
365 /* Winrate w/ komi so far. */
366 a->indicator = komi_by_value;
367 } else if (!strcasecmp(optval, "score")) {
368 /* Expected score w/ current komi. */
369 a->indicator = komi_by_score;
370 } else {
371 fprintf(stderr, "UCT: Invalid indicator %s\n", optval);
372 exit(1);
375 /* value indicator settings */
376 } else if (!strcasecmp(optname, "zone_red") && optval) {
377 a->zone_red = atof(optval);
378 } else if (!strcasecmp(optname, "zone_green") && optval) {
379 a->zone_green = atof(optval);
380 } else if (!strcasecmp(optname, "score_step") && optval) {
381 a->score_step = atoi(optval);
382 } else if (!strcasecmp(optname, "use_komi_latch")) {
383 a->use_komi_latch = !optval || atoi(optval);
385 /* score indicator settings */
386 } else if (!strcasecmp(optname, "adapter") && optval) {
387 /* Adaptatation method. */
388 if (!strcasecmp(optval, "sigmoid")) {
389 a->adapter = adapter_sigmoid;
390 } else if (!strcasecmp(optval, "linear")) {
391 a->adapter = adapter_linear;
392 } else {
393 fprintf(stderr, "UCT: Invalid adapter %s\n", optval);
394 exit(1);
396 } else if (!strcasecmp(optname, "adapt_base") && optval) {
397 /* Adaptation base rate; see above. */
398 a->adapt_base = atof(optval);
399 } else if (!strcasecmp(optname, "adapt_rate") && optval) {
400 /* Adaptation slope; see above. */
401 a->adapt_rate = atof(optval);
402 } else if (!strcasecmp(optname, "adapt_phase") && optval) {
403 /* Adaptation phase shift; see above. */
404 a->adapt_phase = atof(optval);
405 } else if (!strcasecmp(optname, "adapt_moves") && optval) {
406 /* Adaptation move amount; see above. */
407 a->adapt_moves = atoi(optval);
408 } else if (!strcasecmp(optname, "adapt_aport")) {
409 a->adapt_aport = !optval || atoi(optval);
410 } else if (!strcasecmp(optname, "adapt_dir") && optval) {
411 /* Adaptation direction vector; see above. */
412 a->adapt_dir = atof(optval);
414 } else {
415 fprintf(stderr, "uct: Invalid dynkomi argument %s or missing value\n", optname);
416 exit(1);
421 return d;