public void Initialize(Track track, MLPNeuralNetwork controller)
{
var carAi = new CarAI(random, controller);
// Load the simulation
simulation = appService.Kernel.Get <RacingSimulationLogic>();
simulation.SetTrack(track);
// Add the 1:1 trained AI to the list of cars
var cars = new List <ICarController>()
{
carAi
};
// Spawn 100 mutated versions of our AI
for (int i = 0; i < 100; i++)
{
var car = carAi.Clone();
car.Mutate();
cars.Add((CarAI)car);
}
// Initialize the simulation
simulation.SetCars(cars);
simulation.ResetCars();
// If the visualization is turned on, create it, set the track and add the cars.
simulationVisualization = appService.Kernel.Get <RacingSimulationVisualization>();
simulationVisualization.SetTrack(track);
simulationVisualization.InitializeCars(simulation.GetCars());
}
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());
}
}
