/// <summary>
/// Default constructor which gets the simulation object reference from VISSIM
/// </summary>
/// <param name="vissimConnection">Vissim connection from <see cref="VissimConnection"/></param>
public Simulation(VissimConnection vissimConnection)
{
currentSimulation = vissimConnection.GetVissimInstance().Simulation;
this.vissimConnection = vissimConnection;
vissimConnection.GetVissimInstance().SuspendUpdateGUI();
int numOfCounters = vissimConnection.GetVissimInstance().Net.QueueCounters.Count;
queueCounterResultsMax = new double[numOfCounters];
queueCounterResultsAvg = new double[numOfCounters];
}
private void button1_Click(object sender, EventArgs e)
{
vissim = new VissimConnection();
}
private void button13_Click(object sender, EventArgs e)
{
vissim = new VissimConnection();
OpenFileDialog openFileDialog1 = new OpenFileDialog();
if (openFileDialog1.ShowDialog() == System.Windows.Forms.DialogResult.OK)
{
COM.LoadVissimNetwork(vissim.GetVissimInstance(), openFileDialog1.FileName);
}
sim = new Simulation(vissim);
sim.SetSimulationResolution(1);
BLL.Neural.SOM som = new BLL.Neural.SOM(25, 5, 5, 0.5, 1, 18, 100);
OpenFileDialog openFileDialog2 = new OpenFileDialog();
if (openFileDialog2.ShowDialog() == System.Windows.Forms.DialogResult.OK)
{
double[][] importedData = BLL.HelpMethods.LoadTrainingSet(openFileDialog2.FileName, ',');
if (importedData != null)
{
som.TrainSOM(importedData, 1000);
}
}
EpsilonGreedyExploration greedyExploration = new EpsilonGreedyExploration(1);
double beta = 0;
BLL.Neural.Q q = new BLL.Neural.Q(25, 7, greedyExploration);
q.qLearning.DiscountFactor = 0.1;
BLL.Thesis.Actions actions = new BLL.Thesis.Actions(25, 22);
double simulationPeriod = COM.getSimulationPeriod(sim.currentSimulation);
ISignalController SignalController = vissim.GetVissimInstance().Net.SignalControllers.get_ItemByKey(3);
StreamWriter writer = new StreamWriter("Results1000.csv");
for (int j = 0; j < 1000; j++)
{
sim.RunContinuos(299);
for (int i = 599; i <= simulationPeriod - 1; i = i + 300)
{
double[] qLenMax = sim.queueCounterResultsMax;
double[] qLenAvg = sim.queueCounterResultsAvg;
double[] qLenAll = new double[18];
Array.Copy(qLenAvg, qLenAll, qLenAvg.Length);
Array.Copy(qLenMax, 0, qLenAll, qLenAvg.Length, qLenMax.Length);
double delayAvgBefore = vissim.GetVissimInstance().Net.VehicleNetworkPerformanceMeasurement.get_AttValue("DelayAvg(Current, Current, All)");
int state1 = som.GetWinningNeuronNumber(qLenAll);
int action = q.GetAction(state1);
actions.PerformAction(state1, action, SignalController);
sim.RunContinuos(i);
qLenMax = sim.queueCounterResultsMax;
qLenAvg = sim.queueCounterResultsAvg;
Array.Copy(qLenAvg, qLenAll, qLenAvg.Length);
Array.Copy(qLenMax, 0, qLenAll, qLenAvg.Length, qLenMax.Length);
int state2 = som.GetWinningNeuronNumber(qLenAll);
double delayAvgAfter = vissim.GetVissimInstance().Net.VehicleNetworkPerformanceMeasurement.get_AttValue("DelayAvg(Current, Current, All)");
double reward = BLL.Neural.Q.CalculateReward(qLenAvg, delayAvgBefore, delayAvgAfter, beta);
q.UpdateQTable(state1, action, reward, state2);
}
double resultTTS = vissim.GetVissimInstance().Net.VehicleNetworkPerformanceMeasurement.get_AttValue("TravTmTot(Current, Total, All)");
// int seed = sim.currentSimulation.get_AttValue("RandSeed");
writer.WriteLine(j + "," + resultTTS);
q.qLearning.ExplorationPolicy = new EpsilonGreedyExploration((Math.Pow(0.995, (j + 1)) * 0.99) + 0.01);
// beta = (j + 1) * 0.001;
writer.Flush();
sim.currentSimulation.RunSingleStep();
}
writer.Close();
}
