public virtual ISignalArray activateNetwork(double[] sensors)
{
Brain.ResetState();
// Convert the sensors into an input array for the network
for (int i = 0; i < sensors.Length; i++)
{
Brain.InputSignalArray[i] = sensors[i];
}
// Activate the network
Brain.Activate();
return(Brain.OutputSignalArray);
}
/// <summary>
/// Gets the next move as dictated by the neural network.
/// </summary>
public Move GetMove(SquareTypes[,] board)
{
// Clear the network
Brain.ResetState();
// Convert the game board into an input array for the network
setInputSignalArray(Brain.InputSignalArray, board);
// Activate the network
Brain.Activate();
// Find the highest-scoring available move
Move move = null;
double max = double.MinValue;
for (int i = 0; i < 3; i++)
{
for (int j = 0; j < 3; j++)
{
// If the square is taken, skip it.
if (board[i, j] != SquareTypes.N)
{
continue;
}
// Set the score for this square.
double score = Brain.OutputSignalArray[i * 3 + j];
// If this is the first available move we've found,
// set it to the current best.
if (move == null)
{
move = new Move(i, j);
max = score;
}
// If this square has a higher score than any we've
// found, set it to the current best.
else if (max < score)
{
move.X = i;
move.Y = j;
max = score;
}
}
}
return(move);
}
public void Move(bool canMove)
{
var foodNear = FoodNear ? 0.7 : 0.3;
var agentsNear = AgentsNear ? 0.7 : 0.3;
var nearWalls = (DistanceToNearestVertical < 15 || DistanceToNearestHorizontal < 15) ? 0.7 : 0.3;
var result = Brain.Activate(new List <double> {
nearWalls, Angle, DistanceToNearestFood, foodNear, AngleToFood, DistanceToNearestAgent, AngleToAgent, agentsNear
});
Angle += result[0];
Angle = Angle % 360;
if (canMove)
{
base.Move();
}
}
public bool Accept(LinkedList <StateActionReward> memory)
{
var last = memory.Last.Value;
Debug.Assert(last.State.Length + last.Action.Length + 1 == Brain.InputCount);
for (int i = 0; i < last.State.Length; i++)
{
Brain.InputSignalArray[i] = last.State[i];
}
for (int i = 0; i < last.Action.Length; i++)
{
Brain.InputSignalArray[i + last.State.Length] = last.Action[i];
}
Brain.InputSignalArray[last.State.Length + last.Action.Length] =
Math.Min(1, Math.Max(0, (RewardNormalizer + last.Reward) / (2 * RewardNormalizer)));
// Activate the network
Brain.Activate();
return(Brain.OutputSignalArray[0] >= AcceptThreshold);
}