public FitnessInfo Evaluate(IBlackBox phenome)
{
var fitness = 0.0;
foreach (var world in worlds)
{
var lidar = createLidarFor(world.Track);
var agent = new NeuralNetworkAgent(lidar, phenome);
var simulation = new Simulation.Simulation(agent, world);
var simulationResult = simulation.Simulate(simulationStep, perceptionStep, maximumSimulationTime);
fitness += fitnessOf(simulationResult);
}
lock (evaluationLock)
{
EvaluationCount++;
}
return(new FitnessInfo(
fitness: fitness / worlds.Length,
new AuxFitnessInfo[0]));
}
public static void Main(string[] args)
{
var random = new Random();
var circuits = circuitsFilePaths().ToArray();
var perceptionStep = TimeSpan.FromSeconds(0.1);
var simulationStep = TimeSpan.FromSeconds(0.05); // 20Hz
var maximumSimulationTime = TimeSpan.FromSeconds(60);
var tracks = circuits.Select(circuitPath => Track.Load($"{circuitPath}/circuit_definition.json"));
var worlds = tracks.Select(track => new StandardWorld(track, simulationStep)).ToArray();
var inputSamplesCount = 3;
var maximumScanningDistance = 200;
ILidar createLidarFor(ITrack track)
=> new Lidar(track, inputSamplesCount, Angle.FromDegrees(135), maximumScanningDistance);
var settings = new EvolutionSettings
{
PopulationSize = 1000,
SpeciesCount = 30,
ElitismProportion = 0,
ComplexityThreshold = 50
};
// prepare simulation
var parameters = new NeatEvolutionAlgorithmParameters
{
ElitismProportion = settings.ElitismProportion,
SpecieCount = settings.SpeciesCount
};
var distanceMetric = new ManhattanDistanceMetric(1.0, 0.0, 10.0);
var parallelOptions = new ParallelOptions {
MaxDegreeOfParallelism = 4
};
var speciationStrategy = new ParallelKMeansClusteringStrategy <NeatGenome>(distanceMetric, parallelOptions);
var complexityRegulationStrategy = new DefaultComplexityRegulationStrategy(ComplexityCeilingType.Absolute, settings.ComplexityThreshold);
var evolutionaryAlgorithm = new NeatEvolutionAlgorithm <NeatGenome>(
parameters,
speciationStrategy,
complexityRegulationStrategy);
var phenomeEvaluator = new RaceSimulationEvaluator(
random,
simulationStep,
perceptionStep,
maximumSimulationTime,
worlds,
createLidarFor);
var genomeDecoder = new NeatGenomeDecoder(NetworkActivationScheme.CreateAcyclicScheme());
var genomeListEvaluator = new ParallelGenomeListEvaluator <NeatGenome, IBlackBox>(
genomeDecoder,
phenomeEvaluator);
evolutionaryAlgorithm.Initialize(
genomeListEvaluator,
genomeFactory: new NeatGenomeFactory(
inputNeuronCount: inputSamplesCount,
outputNeuronCount: 2,
DefaultActivationFunctionLibrary.CreateLibraryNeat(new BipolarSigmoid()),
new NeatGenomeParameters
{
FeedforwardOnly = true,
AddNodeMutationProbability = 0.03,
DeleteConnectionMutationProbability = 0.05,
ConnectionWeightMutationProbability = 0.08,
FitnessHistoryLength = 10,
}),
settings.PopulationSize);
var lastVisualization = DateTimeOffset.Now;
evolutionaryAlgorithm.UpdateEvent += onUpdate;
evolutionaryAlgorithm.StartContinue();
Console.WriteLine("Press enter to stop the evolution.");
Console.ReadLine();
Console.WriteLine("Finishing the evolution.");
evolutionaryAlgorithm.Stop();
Console.WriteLine("Evolution is stopped.");
// simulate best individual
Console.WriteLine("Simulating best individual...");
evaluate(evolutionaryAlgorithm.CurrentChampGenome);
Console.WriteLine("Done.");
void onUpdate(object sender, EventArgs e)
{
Console.WriteLine($"Generation #{evolutionaryAlgorithm.CurrentGeneration}");
Console.WriteLine($"- max fitness: {evolutionaryAlgorithm.Statistics._maxFitness}");
Console.WriteLine($"- mean fitness: {evolutionaryAlgorithm.Statistics._meanFitness}");
Console.WriteLine();
if (DateTimeOffset.Now - lastVisualization > TimeSpan.FromSeconds(35))
{
lastVisualization = DateTimeOffset.Now;
Console.WriteLine("Simulating currently best individual...");
evaluate(evolutionaryAlgorithm.CurrentChampGenome);
}
}
void evaluate(NeatGenome genome)
{
var worldId = random.Next(0, worlds.Length - 1);
var world = worlds[worldId];
var bestIndividual = genomeDecoder.Decode(genome);
var agent = new NeuralNetworkAgent(createLidarFor(world.Track), bestIndividual);
var simulation = new Simulation.Simulation(agent, world);
var summary = simulation.Simulate(simulationStep, perceptionStep, maximumSimulationTime);
File.Copy($"{circuits[worldId]}/visualization.svg", "C:/Users/simon/Projects/racer-experiment/simulator/src/visualization.svg", overwrite: true);
IO.Simulation.StoreResult(world.Track, world.VehicleModel, summary, "", "C:/Users/simon/Projects/racer-experiment/simulator/src/report.json");
}
}