public void SelectMove(Board board, out int column, out double score, int depth = 1)
{
// Lambda percent of the time, select a random move.
if (LambdaType == LambdaType.Threshold && RANDOM.NextDouble() <= Lambda)
{
int[] cols = Enumerable.Range(0, board.Columns).Where(x => !board.IsColumnFull(x)).ToArray();
column = cols[RANDOM.Next(cols.Count())];
board.AddChecker(MyColor, column);
score = EvaluateBoard(board);
board.RemoveChecker(column);
return;
}
List <double> columnEvaluations;
recSelectMove(board, depth, true, out column, out score, out columnEvaluations);
//we pick a move randomly, using a probability distribution such that the moves with the "best" board positions have a
// higher probability of being selected higher values of Lambda mean choices will have more equal probability, even if they had different
// low values of Lambda will have the opposite effect Lambda should be positive number. Otherwise, no exploration will take place.
// If non-positive, just return the "best" move now, to avoid divide-by-zero type issues.
if (LambdaType == LambdaType.ProbabilityDistribution && Lambda > 0)
{
double sum = 0.0;
double[] weights = new double[columnEvaluations.Count];
for (int i = 0; i < columnEvaluations.Count; i++)
{
// the closer this column's evaluation to the "best", the
// greater weight it will have
double w = 1 / (Lambda + (score - columnEvaluations[i]));
weights[i] = w;
sum += w;
}
double r = RANDOM.NextDouble() * sum;
int c;
for (c = 0; c + 1 < weights.Length; c++)
{
r -= weights[c];
if (r <= 0)
{
break;
}
}
column = c;
score = columnEvaluations[c];
}
}
public void recSelectMove(Board board, int depth, bool max, out int column, out double score, out List <double> columnEvaluations)
{
int bestX = 0;
columnEvaluations = Enumerable.Repeat(double.NegativeInfinity, board.Columns).ToList();
double bestV = max ? Double.NegativeInfinity : Double.PositiveInfinity;;
for (int x = 0; x < board.Columns; x++)
{
if (!board.IsColumnFull(x))
{
board.AddChecker(MyColor, x);
int col;
double v;
if (depth <= 1 || board.IsGameOver)
{
col = x;
v = EvaluateBoard(board);
}
else
{
List <double> ignore = (new double[board.Columns]).ToList();
recSelectMove(board, depth - 1, !max, out col, out v, out ignore);
}
board.RemoveChecker(x);
columnEvaluations[x] = v;
if (v > bestV && max || v < bestV && !max)
{
bestV = v;
bestX = col;
}
}
}
column = bestX;
score = bestV;
}
public void recSelectMove(Board board, int depth, bool max, out int column, out double score, out List<double> columnEvaluations)
{
int bestX = 0;
columnEvaluations = Enumerable.Repeat(double.NegativeInfinity, board.Columns).ToList();
double bestV = max ? Double.NegativeInfinity : Double.PositiveInfinity; ;
for (int x = 0; x < board.Columns; x++)
{
if (!board.IsColumnFull(x))
{
board.AddChecker(MyColor, x);
int col;
double v;
if (depth <= 1 || board.IsGameOver)
{
col = x;
v = EvaluateBoard(board);
}
else
{
List<double> ignore = (new double[board.Columns]).ToList();
recSelectMove(board, depth - 1, !max, out col, out v, out ignore);
}
board.RemoveChecker(x);
columnEvaluations[x] = v;
if (v > bestV && max || v < bestV && !max)
{
bestV = v;
bestX = col;
}
}
}
column = bestX;
score = bestV;
}
public void SelectMove(Board board, out int column, out double score, int depth = 1)
{
// Lambda percent of the time, select a random move.
if (LambdaType == LambdaType.Threshold && RANDOM.NextDouble() <= Lambda)
{
int[] cols = Enumerable.Range(0, board.Columns).Where(x => !board.IsColumnFull(x)).ToArray();
column = cols[RANDOM.Next(cols.Count())];
board.AddChecker(MyColor, column);
score = EvaluateBoard(board);
board.RemoveChecker(column);
return;
}
List<double> columnEvaluations;
recSelectMove(board, depth, true, out column, out score, out columnEvaluations);
//we pick a move randomly, using a probability distribution such that the moves with the "best" board positions have a
// higher probability of being selected higher values of Lambda mean choices will have more equal probability, even if they had different
// low values of Lambda will have the opposite effect Lambda should be positive number. Otherwise, no exploration will take place.
// If non-positive, just return the "best" move now, to avoid divide-by-zero type issues.
if (LambdaType == LambdaType.ProbabilityDistribution && Lambda > 0)
{
double sum = 0.0;
double[] weights = new double[columnEvaluations.Count];
for (int i = 0; i < columnEvaluations.Count; i++)
{
// the closer this column's evaluation to the "best", the
// greater weight it will have
double w = 1 / (Lambda + (score - columnEvaluations[i]));
weights[i] = w;
sum += w;
}
double r = RANDOM.NextDouble() * sum;
int c;
for (c = 0; c + 1 < weights.Length; c++)
{
r -= weights[c];
if (r <= 0)
break;
}
column = c;
score = columnEvaluations[c];
}
}
