private reinforcement2Child[] PickBestPopulation2()
{
reinforcement2Child[] newPopulation2 = new reinforcement2Child[initialPopulation];
for (int i = 0; i < bestAgentSelection; i++)
{
newPopulation2[naturallySelected] = population2[i].InitialiseCopy2(player2.LAYERS, player2.NEURONS);
newPopulation2[naturallySelected].fitness = 0;
naturallySelected++;
int f = Mathf.RoundToInt(population[i].fitness * 10);
for (int c = 0; c < f; c++)
{
genePool.Add(i);
}
}
for (int i = 0; i < worstAgentSelection; i++)
{
int last = population.Length - 1;
last -= i;
int f = Mathf.RoundToInt(population[last].fitness * 10);
for (int c = 0; c < f; c++)
{
genePool.Add(last);
}
}
return(newPopulation2);
}
public void ResetWithNetwork(reinforcement2Child net)
{
network = net;
Reset();
Debug.Log("resetting");
}
public reinforcement2Child InitialiseCopy2(int hiddenLayerCount, int hiddenNeuronCount)
{
reinforcement2Child n = new reinforcement2Child();
List <Matrix <float> > newWeights = new List <Matrix <float> >();
for (int i = 0; i < this.weights.Count; i++)
{
Matrix <float> currentWeight = Matrix <float> .Build.Dense(weights[i].RowCount, weights[i].ColumnCount);
for (int x = 0; x < currentWeight.RowCount; x++)
{
for (int y = 0; y < currentWeight.ColumnCount; y++)
{
currentWeight[x, y] = weights[i][x, y];
}
}
newWeights.Add(currentWeight);
}
List <float> newBiases = new List <float>();
newBiases.AddRange(biases);
n.weights = newWeights;
n.biases = newBiases;
n.InitialiseHidden2(hiddenLayerCount, hiddenNeuronCount);
return(n);
}
private void FillPopulationWithRandomValues(reinforcementNet[] newPopulation, reinforcement2Child[] newPopulation2, int startingIndex)
{
while (startingIndex < initialPopulation)
{
newPopulation[startingIndex] = new reinforcementNet();
newPopulation[startingIndex].Initialise(controller.LAYERS, controller.NEURONS);
//second person
newPopulation2[startingIndex] = new reinforcement2Child();
newPopulation2[startingIndex].Initialise2(player2.LAYERS, player2.NEURONS);
startingIndex++;
}
}
public void Death2(float fitness2, reinforcement2Child network2)
{
if (currentGenome2 < population2.Length - 1)
{
population2[currentGenome2].fitness = fitness2;
Debug.Log("death first layer");
currentGenome2++;
Debug.Log("death active");
ResetToCurrentGenome();
}
else
{
RePopulate();
}
}
private void SortPopulation()
{
for (int i = 0; i < population.Length; i++)
{
for (int j = i; j < population.Length; j++)
{
if (population[i].fitness < population[j].fitness)
{
reinforcementNet temp = population[i];
population[i] = population[j];
population[j] = temp;
}
if (population2[i].fitness < population2[j].fitness)
{
reinforcement2Child temp2 = population2[i];
population2[i] = population2[j];
population2[j] = temp2;
}
}
}
}
private void Crossover(reinforcementNet[] newPopulation, reinforcement2Child[] newPopulation2)
{
for (int i = 0; i < numberToCrossover; i += 2)
{
int AIndex = i;
int BIndex = i + 1;
if (genePool.Count >= 1)
{
for (int l = 0; l < 100; l++)
{
AIndex = genePool[Random.Range(0, genePool.Count)];
BIndex = genePool[Random.Range(0, genePool.Count)];
if (AIndex != BIndex)
{
break;
}
}
}
reinforcementNet Child1 = new reinforcementNet();
reinforcementNet Child2 = new reinforcementNet();
//
reinforcement2Child Child1_A = new reinforcement2Child();
reinforcement2Child Child2_A = new reinforcement2Child();
Child1.Initialise(controller.LAYERS, controller.NEURONS);
Child2.Initialise(controller.LAYERS, controller.NEURONS);
Child1_A.Initialise2(player2.LAYERS, player2.NEURONS);
Child2_A.Initialise2(player2.LAYERS, player2.NEURONS);
Child1.fitness = 0;
Child2.fitness = 0;
Child1_A.fitness = 0;
Child2_A.fitness = 0;
for (int w = 0; w < Child1.weights.Count; w++)
{
if (Random.Range(0.0f, 1.0f) < 0.5f)
{
Child1.weights[w] = population[AIndex].weights[w];
Child2.weights[w] = population[BIndex].weights[w];
//second person
Child2_A.weights[w] = population2[AIndex].weights[w];
Child1_A.weights[w] = population2[BIndex].weights[w];
}
else
{
Child2.weights[w] = population[AIndex].weights[w];
Child1.weights[w] = population[BIndex].weights[w];
//second person
Child1_A.weights[w] = population2[AIndex].weights[w];
Child2_A.weights[w] = population2[BIndex].weights[w];
}
}
for (int w = 0; w < Child1.biases.Count; w++)
{
if (Random.Range(0.0f, 1.0f) < 0.5f)
{
Child1.biases[w] = population[AIndex].biases[w];
Child2.biases[w] = population[BIndex].biases[w];
//second person
Child2_A.biases[w] = population2[AIndex].biases[w];
Child1_A.biases[w] = population2[BIndex].biases[w];
}
else
{
Child2.biases[w] = population[AIndex].biases[w];
Child1.biases[w] = population[BIndex].biases[w];
//second person
Child1_A.biases[w] = population2[AIndex].biases[w];
Child2_A.biases[w] = population2[BIndex].biases[w];
}
}
newPopulation[naturallySelected] = Child1;
newPopulation2[naturallySelected] = Child1_A;
naturallySelected++;
newPopulation[naturallySelected] = Child2;
newPopulation2[naturallySelected] = Child2_A;
naturallySelected++;
}
}