/*
* private bool ChestReallyDead(FutureStateWorldModel state, GOB.Action action, string enemyName, string chestName)
* {
* bool cond1 = !(bool)state.GetProperty(enemyName);
* bool cond1b = GameObject.Find(enemyName)
* bool cond2 = (bool)state.GetProperty(chestName);
* bool cond3 = action is PickUpChest;
* bool cond4 = action is PickUpChest && ((PickUpChest)action).Target.tag.Equals(chestName);
* /*
* if (cond3)
* cond4 = action is PickUpChest && ((PickUpChest)action).Target.tag.Equals(chestName);
* else
* cond4 = false;
*/
//return cond1 && cond2 && cond3 && cond4;
//return !(bool)state.GetProperty(enemyName) && (bool)state.GetProperty(chestName) && action is PickUpChest && ((PickUpChest)action).Target.tag.Equals(chestName);
//}
private GOB.Action ChooseBias(FutureStateWorldModel state)
{
GOB.Action[] actions = state.GetExecutableActions();
int[] features = new int[2];
int size = features.Length;
double H = 0;
double[] exp = new double[actions.Length]; //array com as exponenciais ja calculadas
double[] P = new double[actions.Length]; //array com as probabilidades ja calculadas para escolher a melhor
for (int j = 0; j < actions.Length; j++)
{
float h = 0;
if (actions[j] is SwordAttack && (int)state.GetProperty(Properties.HP) + ((SwordAttack)actions[j]).hpChange <= 0)
{
//actions = actions.Where(val => val != action).ToArray(); //para a nao optimizacao
exp[j] = 0;
continue; //do for, para passa a proxima accao
}
if (ChestDead(state, actions[j], "Skeleton1", "Chest1") || ChestDead(state, actions[j], "Skeleton2", "Chest4") ||
ChestDead(state, actions[j], "Orc1", "Chest3") || ChestDead(state, actions[j], "Orc2", "Chest2") || ChestDead(state, actions[j], "Dragon", "Chest5"))
{
h = 91;
exp[j] = Mathf.Exp(h);
H += Mathf.Exp(h);
continue; //do for, para passa a proxima accao
}
else
{
FutureStateWorldModel possibleState = (FutureStateWorldModel)state.GenerateChildWorldModel();
actions[j].ApplyActionEffects(possibleState);
possibleState.CalculateNextPlayer();
features[WMoney] = (int)possibleState.GetProperty(Properties.MONEY);
//features[WTime] = (int) (float) possibleState.GetProperty(Properties.TIME);
features[WXP] = (int)possibleState.GetProperty(Properties.XP);
//features[WLevel] = (int)possibleState.GetProperty(Properties.LEVEL);
for (int i = 0; i < size; i++)
{
h += features[i] * weights[i]; //cada peso para uma accao
}
exp[j] = Mathf.Exp(h); //queremos guardar logo a exponencial para nao ter de calcular outra vez
H += Mathf.Exp(h);
}
}
if (H == 0)
{
return(actions[0]);
}
else
{
P[0] = exp[0] / H; //o primeiro nao acumula
for (int j = 1; j < actions.Length; j++)
{
P[j] = P[j - 1] + exp[j] / H; //para ser cumulativo
}
double rand = RandomGenerator.NextDouble();
//prob maior mais pequena que o random
return(actions[Array.FindIndex(P, val => val >= rand)]);
}
}