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iiMonteCarlo.cpp
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iiMonteCarlo.cpp
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#include "Player.h"
#include "iiMonteCarlo.h"
#include <deque>
#include <pthread.h>
#ifndef __MAC__
#else
#include <CoreServices/CoreServices.h>
#endif
#include <assert.h>
#include "iiGameState.h"
#include "fpUtil.h"
#ifdef __MAC__
//#include "Diagnostics.h"
#endif
namespace hearts {
#define MAXMOVES 52
#define _PRINT_ 0
//#define min(x, y) ((x)<(y)?(x):(y))
#if GCC_VERSION < 4
//extern "C" int isnan(double);
#endif
iiMonteCarlo::iiMonteCarlo(Algorithm *a, int _numModels, int _numChoices)
{
dr = kMaxWeighted;
this->numModels = _numModels;
if (_numChoices == -1)
numChoices = numModels;
else
numChoices = _numChoices;
algorithm = a;
player = 0;
}
iiMonteCarlo::iiMonteCarlo(Player *_player, int _numModels)
{
dr = kMaxWeighted;
this->numModels = _numModels;
algorithm = 0;
player = _player;
}
iiMonteCarlo::~iiMonteCarlo()
{
// delete algorithm;
delete player;
}
const char *iiMonteCarlo::getName()
{
static char name[1024];
if (algorithm)
sprintf(name, "MC_D-%s_M-%d__%s", getDecisionName(), numModels, algorithm->getName());
else
sprintf(name, "MC_D-%s_m-%d__%s", getDecisionName(), numModels, player->getName());
return name;
}
const char *iiMonteCarlo::getDecisionName()
{
switch (dr)
{
case kMaxWeighted: return "MaxWgt";
case kMaxAverage: return "MaxAvg";
case kMaxAvgVar: return "MaxAvgVar";
case kMaxMinScore: return "MaxMin";
}
return "?";
}
returnValue *iiMonteCarlo::Play(GameState *g, Player *p)
{
std::vector<returnValue *> v;
std::vector<double> probs;
Move *best;
// 1. procure and analyze each model
if (usingThreads() && (algorithm) && (algorithm->getSearchTimeLimit() == kMaxTimeLimit))
doThreadedModels(g, p, v, probs);
else
doModels(g, p, v, probs);
// 2. combine the results - only the algorithm knows how to do this.
// 3. get the move with the highest expected results
// printf("Analyzing results\n");
best = Combine(g, v, g->getPlayerNum(p), probs);
// best->Print(1);
// 4. clean up memory
for (int x = 0; x < numModels; x++)
delete v[x];
//delete [] v;
// printf("Returning result\n");
// 5. return result
return new returnValue(best);
}
void iiMonteCarlo::doModels(GameState *g, Player *p, std::vector<returnValue *> &v, std::vector<double> &probs)
{
//returnValue **v;
v.resize(numModels);
//v = new returnValue *[numModels];
// if (strstr(p->getName(), "UCT250") != 0)
// {
// printf("%s not using advanced modeling\n", p->getName());
// ((iiSheepsheadState*)iiState)->advancedModeling = false;
// }
std::vector<GameState *> toAnalyze;
GetGameStates(g, p, toAnalyze, probs);
assert((int)toAnalyze.size() == numModels);
for (int x = 0; x < numModels; x++)
{
v[x] = 0;
if (!toAnalyze[x])
continue;
assert(toAnalyze[x] != 0);
#if _PRINT_
printf("Getting model %d (prob: %f) for player %d\n", x, probs[x], g->getPlayerNum(p));
toAnalyze[x]->Print(1);
// printf("Searching model %d for %d\n", x, toAnalyze->getPlayerNum(toAnalyze->getNextPlayer()));
#endif
algorithm->resetCounters(toAnalyze[x]);
toAnalyze[x]->copyMoveList(g);
v[x] = algorithm->Analyze(toAnalyze[x], toAnalyze[x]->getNextPlayer());
g->copyMoveList(toAnalyze[x]);
assert(v[x] != 0);
#if _PRINT_
printf("Results:\n");
for (returnValue *tmp = v[x]; tmp; tmp = tmp->next)
{
printf("(%1.3f)\t", tmp->getValue(g->getPlayerNum(p)));
tmp->m->Print(1);
//tmp->Print(0);
printf("\n");
}
#endif
// toAnalyze[x]->deletePlayers();
delete toAnalyze[x];
}
//return v;
}
//pthread_mutex_t mutex;
//pthread_mutex_init(&mutex, 0);
//pthread_mutex_destroy(&mutex);
//pthread_mutex_lock(&mutex);
//pthread_mutex_unlock(&mutex);
void iiMonteCarlo::doThreadedModels(GameState *g, Player *p, std::vector<returnValue*> &v, std::vector<double> &probs)
{
iiGameState *iiState;
Algorithm **algs;
threadModel **tm;
//returnValue **v;
GameState **gameStates;
#ifndef __MAC__
pthread_t *threads;
threads = new pthread_t[numModels];
#else
MPQueueID returnQ;
MPTaskID *threads;
threads = new MPTaskID[numModels];
MPCreateQueue(&returnQ);
#endif
v.resize(numModels);
//v = new returnValue *[numModels];
algs = new Algorithm *[numModels];
tm = new threadModel *[numModels];
gameStates = new GameState *[numModels];
std::vector<int> modelQ;
for (int x = 0; x < numModels; x++)
modelQ.push_back(x);
iiState = g->getiiGameState(true, g->getPlayerNum(p), player);
if (!algorithm)
{
fprintf(stderr, "Error, algorithm is null, can't do models!\n");
exit(0);
}
double probSum = 0;
for (int x = 0; x < numModels; x++)
{
threads[x] = 0;
v[x] = 0;
double prob;
gameStates[x] = iiState->getGameState(prob);
probs.push_back(prob);
probSum += prob;
algs[x] = algorithm->clone();
algs[x]->resetCounters(gameStates[x]);
tm[x] = new threadModel();
tm[x]->alg = algs[x];
tm[x]->gs = gameStates[x];
//tm[x]->v = &v[x];
#ifdef __MAC__
tm[x]->returnQ = returnQ;
#endif
}
for (int x = 0; x < numModels; x++)
probs[x]/=probSum;
int numCPU = 1;
#ifdef __APPLE__
// numCPU = MPProcessors();
#endif
int numRunning = 0;
std::deque<int> running;
while ((modelQ.size() > 0) || (numRunning > 0))
{
while ((numRunning < numCPU) && (modelQ.size() > 0))
{
int next = modelQ.back();
modelQ.pop_back();
running.push_back(next);
numRunning++;
#if _PRINT_
printf("Starting up %d, %d now running\n", next, numRunning);
#endif
#ifndef __MAC__
pthread_create(&threads[next], NULL, doThreadedModel, (void**)tm[next]);
#else
MPCreateTask(doThreadedModel, (void*)tm[next], 512*1024, 0, NULL, NULL, 0, &threads[next]);
#endif
}
int res;
int waitFor = running.front();
running.pop_front();
#ifndef __MAC__
res = pthread_join(threads[waitFor], (void **)&v[waitFor]);
#else
res = MPWaitOnQueue(returnQ, (void **)&v[waitFor], 0, 0, kDurationForever);
if (v[waitFor] == 0)
{ printf("Got NIL return\n"); }
#endif
#if _PRINT_
printf("Got result from %d\n", waitFor);
v[waitFor]->Print();
#endif
numRunning--;
if (res != 0)
{
printf("Error %d joining with %d\n", res, waitFor);
exit(1);
}
}
for (int x = 0; x < numModels; x++)
{
//printf("deleting alg %d: %p\n", x, algs[x]); fflush(stdout);
delete algs[x];
algs[x] = 0;
//printf("deleting tm %d\n", x); fflush(stdout);
delete tm[x];
tm[x] = 0;
//printf("deleting gamestate players\n"); fflush(stdout);
//gameStates[x]->deletePlayers();
//printf("deleting gamestate %d\n", x); fflush(stdout);
delete gameStates[x];
gameStates[x] = 0;
}
#ifdef __MAC__
MPDeleteQueue(returnQ);
#endif
delete [] tm;
delete [] algs;
delete [] gameStates;
delete [] threads;
delete iiState;
//return v;
}
#ifdef __MAC__
OSStatus doThreadedModel(void *data)
#else
void *doThreadedModel(void *data)
#endif
{
threadModel *m = (threadModel *)data;
returnValue *val = m->alg->Analyze(m->gs, m->gs->getNextPlayer());
//printf("Writing to: %p (%p)\n", (m->v), m);
#ifndef __MAC__
pthread_exit((void *)val);
return 0;
#else
MPNotifyQueue(m->returnQ, (void *)val, 0, 0);
MPExit(0);
return 0;
#endif
}
// this function is required of other algorithms for the sake of monte-carlo
// experiments.
// but if we write it...will it allow recursive monte-carlo experiments(?)
returnValue *iiMonteCarlo::Analyze(GameState *g, Player *p)
{
std::vector<returnValue *> v;
std::vector<double> probs;
returnValue *best;
// 1. procure and analyze each model
if (usingThreads() && (algorithm->getSearchTimeLimit() == kMaxTimeLimit))
doThreadedModels(g, p, v, probs);
else
doModels(g, p, v, probs);
// 2. combine the results - only the algorithm knows how to do this.
// 3. get the move with the highest expected results
// printf("Analyzing results\n");
best = CombinedAnalyze(g, v, g->getPlayerNum(p), probs);
// best->Print(1);
// 4. clean up memory
for (int x = 0; x < numModels; x++)
delete v[x];
//delete [] v;
return best;
}
returnValue *iiMonteCarlo::DispatchSearch(unsigned int depth, int cp, GameState *g)
{
return 0;
}
//this code is currently not used and is probably buggy!
returnValue *iiMonteCarlo::CombinedAnalyze(GameState *g, std::vector<returnValue *> &v, int whichPlayer, std::vector<double> &probs)
{
assert(false);
Move *val[MAXMOVES];
double valSum[MAXMOVES];
int valCnt[MAXMOVES];
int maxIndex = 0;
double sum = 0;
for (unsigned int x = 0; x < probs.size(); x++)
sum += probs[x];
if (!fequal(sum, 0))
{
for (unsigned int x = 0; x < probs.size(); x++)
probs[x]/=sum;
printf("Normalizing probabilities!\n");
}
sum = 0;
for (int x = 0; x < MAXMOVES; x++)
{
valSum[x] = 0;
valCnt[x] = 0;
val[x] = 0;
}
for (int x = 0; x < numModels; x++)
{
#if _PRINT_
// printf("Trying results from %d\n", x);
#endif
returnValue *iter = v[x];
// each of these is a list of possible moves...
while (iter)
{
// find value in list so far, and add in current result...
for (int y = 0; y < MAXMOVES; y++)
{
if (val[y] == 0)
{
#if _PRINT_
//printf("Putting in move loc %d\n", y);
#endif
if (iter->m == 0)
printf("Hey; we didn't get a move with our return value!\n");
val[y] = iter->m->clone(g);
if (val[y] == 0)
printf("Hey; clone returned 0!\n");
valCnt[y] = 1;
valSum[y] = probs[x]*iter->getValue(whichPlayer);
if (isnan(iter->getValue(whichPlayer)))
{ printf("We got NAN!\n"); exit(1); }
break;
}
if (val[y]->equals(iter->m))
{
valCnt[y]++;
valSum[y] += probs[x]*iter->getValue(whichPlayer);
if (isnan(iter->getValue(whichPlayer)))
{ printf("We got NAN!\n"); iter->Print(0); exit(1); }
break;
}
}
iter = iter->next;
}
}
returnValue *answer = 0;
// for (int x = 0; x < MAXMOVES; x++)
// {
// if (val[x])
// sum+=valSum[x]/valCnt[x];
// }
// now choose which move to make
for (int x = 0; x < MAXMOVES; x++)
{
if (val[x])
{
#if _PRINT_
printf("===***creating combined value %1.2f for \n", valSum[x]/*/(double)valCnt[x]*/);
val[x]->Print(0);
#endif
returnValue *tmp = new minimaxval(1-(double)valSum[x]/*/(double)valCnt[x]*/, val[x]->clone(g));
tmp->next = answer;
answer = tmp;
}
}
#if _PRINT_
printf("index\tcount\tsum\tmove\n");
for (int x = 0; x < MAXMOVES; x++)
{
if (val[x])
{
printf("%d\t%d\t%1.2f\t", x, valCnt[x], valSum[x]);
val[x]->Print(0);
printf("\n");
}
}
#endif
for (int x = 0; x < MAXMOVES; x++)
{
if (val[x] && (x != maxIndex))
delete val[x];
}
return answer;
}
Move *iiMonteCarlo::Combine(GameState *g, std::vector<returnValue *> &v, int whichPlayer, std::vector<double> &probs)
{
Move *val[MAXMOVES];
double valSum[MAXMOVES];
double valWeighted[MAXMOVES];
double valMin[MAXMOVES];
double S[MAXMOVES];
double mean[MAXMOVES];
int valCnt[MAXMOVES];
int maxIndex = 0;
double sum = 0;
for (unsigned int x = 0; x < probs.size(); x++)
sum += probs[x];
if (!fequal(sum, 0))
{
for (unsigned int x = 0; x < probs.size(); x++)
probs[x]/=sum;
printf("Normalizing probabilities!\n");
}
for (int x = 0; x < MAXMOVES; x++)
{
mean[x] = 0;
S[x] = 0;
valWeighted[x] = 0;
valSum[x] = 0;
valMin[x] = 0;
valCnt[x] = 0;
val[x] = 0;
}
for (int x = 0; x < numModels; x++)
{
#if _PRINT_
// printf("Trying results from %d\n", x);
#endif
returnValue *iter = v[x];
if (!iter)
break;
// each of these is a list of possible moves...
while (iter)
{
// find value in list so far, and add in current result...
for (int y = 0; y < MAXMOVES; y++)
{
if (val[y] == 0)
{
#if _PRINT_
// printf("%d Putting in move loc %d (%1.2f)\n", whichPlayer, y, iter->getValue(whichPlayer));
#endif
if (iter->m == 0)
printf("Hey; we didn't get a move with our return value!\n");
val[y] = iter->m->clone(g);
if (val[y] == 0)
printf("Hey; clone returned 0!\n");
valCnt[y] = 1;
valSum[y] = iter->getValue(whichPlayer);
valMin[y] = iter->getValue(whichPlayer);
valWeighted[y] = probs[x]*iter->getValue(whichPlayer);
double delta = iter->getValue(whichPlayer) - mean[y];
mean[y] = mean[y] + delta/valCnt[y];
S[y] = S[y] + delta*(iter->getValue(whichPlayer) - mean[y]);
if (isnan(iter->getValue(whichPlayer)))
{ printf("We got NAN!\n"); exit(1); }
break;
}
if (val[y]->equals(iter->m))
{
valCnt[y]++;
valSum[y] += iter->getValue(whichPlayer);
valMin[y] = std::min(valMin[y], iter->getValue(whichPlayer));
valWeighted[y] += probs[x]*iter->getValue(whichPlayer);
double delta = iter->getValue(whichPlayer) - mean[y];
mean[y] = mean[y] + delta/valCnt[y];
S[y] = S[y] + delta*(iter->getValue(whichPlayer) - mean[y]);
if (isnan(iter->getValue(whichPlayer)))
{ printf("We got NAN!\n"); iter->Print(0); exit(1); }
break;
}
}
iter = iter->next;
}
}
mt_random r;
//r.srand(time(0));
// make best move
int ties = 1;
// now choose which move to make
if (dr == kMaxWeighted)
printf("Using kMaxWeighted\n");
for (int x = 1; x < MAXMOVES; x++)
{
if (val[x])
{
if (((dr == kMaxWeighted) && (valWeighted[x] > valWeighted[maxIndex])) ||
((dr == kMaxAverage) && (mean[x] > mean[maxIndex])) ||
((dr == kMaxAvgVar) && (mean[x]-sqrt(S[x]/(valCnt[x]-1)) > mean[maxIndex]-sqrt(S[maxIndex]/(valCnt[maxIndex]-1)))) ||
((dr == kMaxMinScore) && (valMin[x] > valMin[maxIndex])))
{
if (dr == kMaxWeighted)
assert(valWeighted[x] > valWeighted[maxIndex]);
ties = 1;
maxIndex = x;
}
// else if (valSum[x]/valCnt[x] == valSum[maxIndex]/valCnt[maxIndex])
// //else if (valMin[x] == valMin[maxIndex])
// {
// ties++;
// if (r.rand_double() < (double)1/ties)
// maxIndex = x;
// }
}
}
#if _PRINT_
//ai_debug("-=-=->\tindex\tcount\tsum\tmove");
printf("index\tcount\tsum\tmin\tavg\tavg-s\tstd\twgt\n");
for (int x = 0; x < MAXMOVES; x++)
{
if (val[x])
{
//char msg[255];
//sprintf(msg, "-=-=->\t%d\t%d\t%1.2f\t", x, valCnt[x], valSum[x]);
//ai_debug(msg);
printf("%d\t%d\t%1.3f\t%1.3f\t%1.3f\t%1.3f\t%1.3f\t%1.4f", x, valCnt[x], valSum[x], valMin[x],
valSum[x]/valCnt[x], mean[x]-sqrt(S[x]/(valCnt[x]-1)), sqrt(S[x]/(valCnt[x]-1)), valWeighted[x]);
val[x]->Print(0);//variance = S/(n - 1)
if (x == maxIndex)
printf("*");
printf("\n");
}
}
#endif
for (int x = 0; x < MAXMOVES; x++)
{
if (val[x] && (x != maxIndex))
delete val[x];
}
//printf("Using %d for answer!\n", maxIndex);
return val[maxIndex];
}
void iiMonteCarlo::GetGameStates(GameState *g, Player *p,
std::vector<GameState *> &states,
std::vector<double> &probs)
{
probs.resize(0);
states.resize(0);
std::vector<GameState *> candidates;
std::vector<double> pr;
iiGameState *iiState;
iiState = g->getiiGameState(true, g->getPlayerNum(p), player);
iiState->getGameStates(numChoices, candidates, pr);
NormalizeProbs(pr);
while ((int)states.size() < numModels)
{
double val = rand.rand_double();
for (unsigned int x = 0; x < pr.size(); x++)
{
if (fless(val, pr[x]))
{
if (candidates[x] == 0)
continue;
states.push_back(candidates[x]);
probs.push_back(1.0/(double)numModels);
candidates[x] = 0;
break;
}
}
}
while (candidates.size() > 0)
{
GameState *gg = candidates.back();
if (gg)
{
//gg->deletePlayers();
delete gg;
}
candidates.pop_back();
}
delete iiState;
}
void iiMonteCarlo::NormalizeProbs(std::vector<double> &pr)
{
double sum = 0, minval = 1, maxval = 0;
for (unsigned int x = 0; x < pr.size(); x++)
{
if (fless(pr[x], minval))
minval = pr[x];
if (fgreater(pr[x], maxval))
maxval = pr[x];
}
maxval -= minval;
// // scale down to 0
for (unsigned int x = 0; x < pr.size(); x++)
{
if (!fequal(maxval, 0))
{
pr[x] -= minval;
pr[x] = 7.0*pr[x]/maxval+1.0;
}
sum += pr[x];
}
for (unsigned int x = 0; x < pr.size(); x++)
{
pr[x]/=sum;
if (x != 0)
pr[x] += pr[x-1];
}
}
} // namespace hearts