public override Dictionary <QFeature, decimal> GetFeatures(QAction action)
{
Point self = ((Maze_With_No_Walls)GetNewState(action)).self;
QSearch qsearch = new QSearch(this);
Maze_With_No_Walls simpleMaze = new Maze_With_No_Walls()
{
maze = maze, self = self, goal = goal, width = width, height = height
};
QSearchResult bestPath = qsearch.AStar(simpleMaze);
return(QFeature_String.FromStringDecimalDictionary(new Dictionary <string, decimal>()
{
//{this.GetHashCode().ToString()+"_"+action, 1}, // Identity to convert this back to QLearning
{ "Goal", goal == self? 1:0 },
{ "Distance_To_Goal", bestPath == null? 1:(decimal)bestPath.Count / (decimal)(width * height) },
}));
}
public override Dictionary <QFeature, decimal> GetFeatures(QAction action)
{
int newVal = value;
if (action == UP)
{
newVal++;
}
else
{
newVal--;
}
return(QFeature_String.FromStringDecimalDictionary(new Dictionary <string, decimal>()
{
//{value.ToString()+"_"+action, 1}, // Identity for sanity check
//{"Distance", (decimal)Math.Abs(end-newVal)}
{ "Distance_Change", Math.Abs(end - newVal) - Math.Abs(end - value) }
}));
}
public override Dictionary <QFeature, decimal> GetFeatures(QAction action)
{
Point self = ((Maze)GetNewState(action)).self;
QSearch qsearch = new QSearch(this);
Maze simpleMaze = new Maze()
{
maze = maze, self = self, goal = goal, width = width, height = height, walls = walls.ToArray()
};
QSearchResult bestPath = qsearch.AStar(simpleMaze);
List <Point> bestOppMoves = new List <Point>();
foreach (Point o in opponent)
{
bestOppMoves.Add(o);
bestOppMoves.Add(new Point(o.X + 1, o.Y));
bestOppMoves.Add(new Point(o.X - 1, o.Y));
bestOppMoves.Add(new Point(o.X, o.Y + 1));
bestOppMoves.Add(new Point(o.X, o.Y - 1));
}
foreach (Point o in bestOppMoves.ToArray())
{
bestOppMoves.Add(new Point(o.X + 1, o.Y));
bestOppMoves.Add(new Point(o.X - 1, o.Y));
bestOppMoves.Add(new Point(o.X, o.Y + 1));
bestOppMoves.Add(new Point(o.X, o.Y - 1));
}
Maze safeMaze = new Maze()
{
maze = maze, self = self, goal = goal, width = width, height = height, walls = walls.Concat(bestOppMoves).ToArray()
};
QSearchResult safePath = qsearch.AStar(safeMaze);
Dictionary <string, decimal> features = new Dictionary <string, decimal>()
{
//{this.GetHashCode().ToString()+"_"+action, 1}, // Identity to convert this back to QLearning
{ "Goal", goal == self? 1:0 },
{ "Direct_Distance_To_Goal", bestPath == null? 1:(decimal)bestPath.Count / (decimal)(width * height) },
{ "Safe_Distance_To_Goal", (safePath == null? 1: (decimal)safePath.Count / (decimal)(width * height)) }
};
decimal distanceToOpponent = decimal.MaxValue;
if (opponent.Any())
{
features["Opponent"] = goal != self && opponent.Where(p => (Math.Abs(p.X - self.X) <= 1 && p.Y == self.Y) || (Math.Abs(p.Y - self.Y) <= 1 && p.X == self.X)).Any() ? 1 : 0;
distanceToOpponent = opponent.Select(o => qsearch.AStar(new Maze()
{
maze = maze, self = self, goal = o, width = width, height = height, walls = walls
})).Select(x => x == null? width * height:x.Count).Min();
features["Distance_To_Opponent"] = distanceToOpponent >= 5? 1:distanceToOpponent / (decimal)(width * height);
if (goal != self)
{
Maze deadEnd = new Maze()
{
maze = maze, self = self, goal = goal, width = width, height = height, walls = walls.Concat(new Point[] { new Point(this.self.X - 1, this.self.Y), new Point(this.self.X + 1, this.self.Y), new Point(this.self.X, this.self.Y - 1), new Point(this.self.X, this.self.Y + 1) }).ToArray()
};
QSearchResult deadPath = qsearch.Depth_First(deadEnd);
if (deadPath == null)
{
features["Dead_End"] = 1;
}
}
}
return(QFeature_String.FromStringDecimalDictionary(features));
}
