private void OnBreedingUpdate()
{
// Perform one generation of our GA
genericAlgorithm.DoGeneration();
simulation.SetCars(genericAlgorithm.GetPopulation().Cast <ICarController>());
simulation.ResetCars();
// Add the metrics we will use to judge our cars fitness
simulation.GetCars().ForEach(car => this.AddFitnessMetrics(car));
// Reset our simulation
currentTrackIndex = 0;
simulation.SetTrack(currentTrack);
simulation.Reset();
// Reset the visualisation
if (IsVisualizationEnabled)
{
// Reset all cars
simulationVisualization.InitializeCars(simulation.GetCars());
// Reset all track visuals - not the most efficient but works really clean
simulationVisualization.SetTrack(currentTrack);
simulationVisualization.Reset();
}
trainingState = TrainState.Evaluating;
}
public SelfTrainingScreen(
IApplicationService appService,
bool enableVisualization = true)
{
random = new Random();
List <float> seed = null;
var seedFile = "Resources/CarAi/AI_2.mlpnn";
if (File.Exists(seedFile))
{
var seedText = File.ReadAllText(seedFile);
seed = seedText.Split(',').Select(s => float.Parse(s)).ToList();
}
// Create the GA
genericAlgorithm = this.CreateGeneticAlgorithm(seed);
// Load the tracks used for evaluation
tracks = TrackHelper.LoadTrackFiles("Resources/Tracks");
// Spawn the initial population
genericAlgorithm.SpawnPopulation();
// Load the simulation
simulation = appService.Kernel.Get <RacingSimulationLogic>();
simulation.SetTrack(currentTrack);
// Initialize the simulation
simulation.SetCars(genericAlgorithm.GetPopulation().Cast <ICarController>());
simulation.ResetCars();
// Add the metrics we will use to judge our cars fitness
simulation.GetCars().ForEach(car => this.AddFitnessMetrics(car));
// If the visualization is turned on, create it, set the track and add the cars.
if (enableVisualization)
{
simulationVisualization = appService.Kernel.Get <RacingSimulationVisualization>();
simulationVisualization.SetTrack(currentTrack);
simulationVisualization.InitializeCars(simulation.GetCars());
}
}