private void Awake()
{
SP = transform.position;
SR = transform.eulerAngles;
EnvironmentControler = GetComponent <NNetwork>();
EnvironmentControler.Initialise(LAYER, NEURON);
}
public void ResetCar()
{
EnvironmentControler.Initialise(LAYER, NEURON);
transform.position = SP;
transform.eulerAngles = SR;
time = 0f;
totalDistance = 0f;
avgSpeed = 0f;
lastPosition = SP;
overallPerformance = 0f;
}
private void Crossover(NNetwork[] newPopulation)
{
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;
}
}
}
NNetwork Child1 = new NNetwork();
NNetwork Child2 = new NNetwork();
Child1.Initialise(controller.LAYER, controller.NEURON);
Child2.Initialise(controller.LAYER, controller.NEURON);
Child1.performance = 0;
Child2.performance = 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];
}
else
{
Child2.weights[w] = population[AIndex].weights[w];
Child1.weights[w] = population[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];
}
else
{
Child2.biases[w] = population[AIndex].biases[w];
Child1.biases[w] = population[BIndex].biases[w];
}
}
newPopulation[naturallySelected] = Child1;
naturallySelected++;
newPopulation[naturallySelected] = Child2;
naturallySelected++;
}
}