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[pachi/json.git] / uct / dynkomi.c
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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 dynkomi_adaptive *a, 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 dynkomi_adaptive *a, 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 dynkomi_adaptive *a, struct board *b)
179 /* Figure out how much to adjust the komi based on the game
180 * stage. The adaptation rate is 0 at the beginning,
181 * at game stage a->adapt_phase crosses though 0.5 and
182 * approaches 1 at the game end; the slope is controlled
183 * by a->adapt_rate. */
184 float game_portion;
185 if (!a->adapt_aport) {
186 int total_moves = b->moves + 2 * board_estimated_moves_left(b);
187 game_portion = (float) b->moves / total_moves;
188 } else {
189 int brsize = board_size(b) - 2;
190 game_portion = 1.0 - (float) b->flen / (brsize * brsize);
192 float l = game_portion - a->adapt_phase;
193 return 1.0 / (1.0 + exp(-a->adapt_rate * l));
196 static float
197 adapter_linear(struct dynkomi_adaptive *a, struct board *b)
199 /* Figure out how much to adjust the komi based on the game
200 * stage. We just linearly increase/decrease the adaptation
201 * rate for first N moves. */
202 if (b->moves > a->adapt_moves)
203 return 0;
204 if (a->adapt_dir < 0)
205 return 1 - (- a->adapt_dir) * b->moves / a->adapt_moves;
206 else
207 return a->adapt_dir * b->moves / a->adapt_moves;
210 static float
211 komi_by_score(struct dynkomi_adaptive *a, struct board *b, struct tree *tree)
213 if (tree->score.playouts < TRUSTWORTHY_KOMI_PLAYOUTS)
214 return tree->extra_komi;
216 struct move_stats score = tree->score;
217 /* Almost-reset tree->score to gather fresh stats. */
218 tree->score.playouts = 1;
220 /* Look at average score and push extra_komi in that direction. */
221 float p = a->adapter(a, b);
222 p = a->adapt_base + p * (1 - a->adapt_base);
223 if (p > 0.9) p = 0.9; // don't get too eager!
224 float extra_komi = tree->extra_komi + p * score.value;
225 if (DEBUGL(3))
226 fprintf(stderr, "mC += %f * %f\n", p, score.value);
227 return extra_komi;
230 static float
231 komi_by_value(struct dynkomi_adaptive *a, struct board *b, struct tree *tree)
233 if (tree->value.playouts < TRUSTWORTHY_KOMI_PLAYOUTS)
234 return tree->extra_komi;
236 struct move_stats value = tree->value;
237 /* Almost-reset tree->value to gather fresh stats. */
238 tree->value.playouts = 1;
240 /* We have three "value zones":
241 * red zone | yellow zone | green zone
242 * ~45% ~60%
243 * red zone: reduce komi
244 * yellow zone: do not touch komi
245 * green zone: enlage komi.
247 * Also, at some point komi will be tuned in such way
248 * that it will be in green zone but increasing it will
249 * be unfeasible. Thus, we have a _latch_ - we will
250 * remember the last komi that has put us into the
251 * red zone, and not use it or go over it. We use the
252 * latch only when giving extra komi, we always want
253 * to try to reduce extra komi we take.
255 * TODO: Make the latch expire after a while. */
256 float extra_komi = tree->extra_komi;
258 if (value.value < a->zone_red) {
259 /* Red zone. Take extra komi. */
260 if (DEBUGL(3))
261 fprintf(stderr, "[red] %f, komi latch %f -> %f\n",
262 value.value, a->komi_latch, extra_komi);
263 if (extra_komi > 0) a->komi_latch = extra_komi;
264 extra_komi -= a->score_step; // XXX: we depend on being black
265 return extra_komi;
267 } else if (value.value < a->zone_green) {
268 /* Yellow zone, do nothing. */
269 return extra_komi;
271 } else {
272 /* Green zone. Give extra komi. */
273 extra_komi += a->score_step; // XXX: we depend on being black
274 if (DEBUGL(3))
275 fprintf(stderr, "[green] %f, += %d | komi latch %f\n",
276 value.value, a->score_step, a->komi_latch);
277 return !a->use_komi_latch || extra_komi < a->komi_latch ? extra_komi : a->komi_latch - 1;
281 static float
282 adaptive_permove(struct uct_dynkomi *d, struct board *b, struct tree *tree)
284 struct dynkomi_adaptive *a = d->data;
285 if (DEBUGL(3))
286 fprintf(stderr, "m %d/%d ekomi %f permove %f/%d\n",
287 b->moves, a->lead_moves, tree->extra_komi,
288 tree->score.value, tree->score.playouts);
289 if (b->moves <= a->lead_moves)
290 return board_effective_handicap(b, 7 /* XXX */);
292 /* Get lower bound on komi value so that we don't underperform
293 * too much. XXX: We rely on the fact that we don't use dynkomi
294 * as white for now. */
295 float min_komi = - a->max_losing_komi;
297 float komi = a->indicator(a, b, tree);
298 if (DEBUGL(3))
299 fprintf(stderr, "dynkomi: %f -> %f\n", tree->extra_komi, komi);
300 return komi > min_komi ? komi : min_komi;
303 static float
304 adaptive_persim(struct uct_dynkomi *d, struct board *b, struct tree *tree, struct tree_node *node)
306 return tree->extra_komi;
309 struct uct_dynkomi *
310 uct_dynkomi_init_adaptive(struct uct *u, char *arg, struct board *b)
312 struct uct_dynkomi *d = calloc(1, sizeof(*d));
313 d->uct = u;
314 d->permove = adaptive_permove;
315 d->persim = adaptive_persim;
316 d->done = generic_done;
318 struct dynkomi_adaptive *a = calloc(1, sizeof(*a));
319 d->data = a;
321 if (board_size(b) - 2 >= 19)
322 a->lead_moves = 20;
323 else
324 a->lead_moves = 4; // XXX
325 a->max_losing_komi = 10;
326 a->indicator = komi_by_score;
328 a->adapter = adapter_sigmoid;
329 a->adapt_rate = 20;
330 a->adapt_phase = 0.5;
331 a->adapt_moves = 200;
332 a->adapt_dir = -0.5;
334 a->zone_red = 0.45;
335 a->zone_green = 0.6;
336 a->score_step = 2;
337 a->use_komi_latch = true;
338 a->komi_latch = 1000;
340 if (arg) {
341 char *optspec, *next = arg;
342 while (*next) {
343 optspec = next;
344 next += strcspn(next, ":");
345 if (*next) { *next++ = 0; } else { *next = 0; }
347 char *optname = optspec;
348 char *optval = strchr(optspec, '=');
349 if (optval) *optval++ = 0;
351 if (!strcasecmp(optname, "lead_moves") && optval) {
352 /* Do not adjust komi adaptively for first
353 * N moves. */
354 a->lead_moves = atoi(optval);
355 } else if (!strcasecmp(optname, "max_losing_komi") && optval) {
356 a->max_losing_komi = atof(optval);
357 } else if (!strcasecmp(optname, "indicator")) {
358 /* Adaptatation indicator - how to decide
359 * the adaptation rate and direction. */
360 if (!strcasecmp(optval, "value")) {
361 /* Winrate w/ komi so far. */
362 a->indicator = komi_by_value;
363 } else if (!strcasecmp(optval, "score")) {
364 /* Expected score w/ current komi. */
365 a->indicator = komi_by_score;
366 } else {
367 fprintf(stderr, "UCT: Invalid indicator %s\n", optval);
368 exit(1);
371 /* value indicator settings */
372 } else if (!strcasecmp(optname, "zone_red") && optval) {
373 a->zone_red = atof(optval);
374 } else if (!strcasecmp(optname, "zone_green") && optval) {
375 a->zone_green = atof(optval);
376 } else if (!strcasecmp(optname, "score_step") && optval) {
377 a->score_step = atoi(optval);
378 } else if (!strcasecmp(optname, "use_komi_latch")) {
379 a->use_komi_latch = !optval || atoi(optval);
381 /* score indicator settings */
382 } else if (!strcasecmp(optname, "adapter") && optval) {
383 /* Adaptatation method. */
384 if (!strcasecmp(optval, "sigmoid")) {
385 a->adapter = adapter_sigmoid;
386 } else if (!strcasecmp(optval, "linear")) {
387 a->adapter = adapter_linear;
388 } else {
389 fprintf(stderr, "UCT: Invalid adapter %s\n", optval);
390 exit(1);
392 } else if (!strcasecmp(optname, "adapt_base") && optval) {
393 /* Adaptation base rate; see above. */
394 a->adapt_base = atof(optval);
395 } else if (!strcasecmp(optname, "adapt_rate") && optval) {
396 /* Adaptation slope; see above. */
397 a->adapt_rate = atof(optval);
398 } else if (!strcasecmp(optname, "adapt_phase") && optval) {
399 /* Adaptation phase shift; see above. */
400 a->adapt_phase = atof(optval);
401 } else if (!strcasecmp(optname, "adapt_moves") && optval) {
402 /* Adaptation move amount; see above. */
403 a->adapt_moves = atoi(optval);
404 } else if (!strcasecmp(optname, "adapt_aport")) {
405 a->adapt_aport = !optval || atoi(optval);
406 } else if (!strcasecmp(optname, "adapt_dir") && optval) {
407 /* Adaptation direction vector; see above. */
408 a->adapt_dir = atof(optval);
410 } else {
411 fprintf(stderr, "uct: Invalid dynkomi argument %s or missing value\n", optname);
412 exit(1);
417 return d;