public double[] CreateInput(NeuralAgent agent, CardinalMovement action)
{
var input = InputCoder.Encode(agent);
InputCoder.EncodeAction(input, action);
return(input);
}
public AgentNetworkPrediction Predict(double[] input)
{
double bestValue = double.MinValue;
CardinalMovement bestAction = null;
foreach (CardinalMovement action in CardinalMovement.All)
{
InputCoder.EncodeAction(input, action);
double predictedValue = Network.Evaluate(input)[0];
if (predictedValue > bestValue)
{
bestValue = predictedValue;
bestAction = action;
}
}
InputCoder.EncodeAction(input, bestAction);
return(new AgentNetworkPrediction(bestAction, bestValue, input));
}
private void CalculatePathToGoal()
{
var space = Anchor.Field.Space;
int width = space.Width;
int height = space.Height;
Dictionary <UShortPoint, int> distances = new Dictionary <UShortPoint, int>();
Dictionary <UShortPoint, CardinalMovement> predecessors = new Dictionary <UShortPoint, CardinalMovement>();
List <UShortPoint> unvisited = new List <UShortPoint>();
List <UShortPoint> visited = new List <UShortPoint>();
UShortPoint goalPosition = new UShortPoint((ushort)Goal.Position.X, (ushort)Goal.Position.Y);
for (ushort y = 0; y < height; y++)
{
for (ushort x = 0; x < width; x++)
{
var point = new UShortPoint(x, y);
distances[point] = int.MaxValue;
predecessors[point] = default;
if (space[x, y].IsEmpty || space[x, y].HasAgent)
{
unvisited.Add(point);
}
}
}
void removeUnvisitedMin()
{
int minDistance = int.MaxValue;
UShortPoint minVisited = default;
UShortPoint minUnvisited = default;
CardinalMovement minMove = default;
foreach (UShortPoint visitedPoint in visited)
{
foreach (CardinalMovement move in CardinalMovement.AllExceptNothing)
{
UShortPoint neighbor = visitedPoint.Add(move);
if (unvisited.Contains(neighbor))
{
var neighborField = space[neighbor.X, neighbor.Y];
var distanceOverNeighbor = distances[visitedPoint] + 1 + neighbor.Distance(goalPosition);
if (distanceOverNeighbor < minDistance)
{
minDistance = distanceOverNeighbor;
minVisited = visitedPoint;
minUnvisited = neighbor;
minMove = move;
}
}
}
}
distances[minUnvisited] = distances[minVisited] + 1;
predecessors[minUnvisited] = minMove;
unvisited.Remove(minUnvisited);
visited.Add(minUnvisited);
}
UShortPoint startPosition = new UShortPoint((ushort)Anchor.Field.X, (ushort)Anchor.Field.Y);
distances[startPosition] = 0;
unvisited.Remove(startPosition);
visited.Add(startPosition);
while (visited.Contains(goalPosition) == false)
{
removeUnvisitedMin();
}
while (goalPosition.Equals(startPosition) == false)
{
var action = predecessors[goalPosition];
UShortPoint predecessor = goalPosition.Add(action.Opposite);
PathToGoal.AddFirst(action);
goalPosition = predecessor;
}
}
public AgentNetworkPrediction(CardinalMovement action, double value, double[] input)
{
Action = action;
Value = value;
Input = input;
}
public void EncodeAction(double[] input, CardinalMovement action)
{
Array.Clear(input, ActionsOffset, CardinalMovement.All.Length);
input[ActionsOffset + action.Index] = 1;
}
