public double EvaluateNetwork(INetwork cppn)
{
lock (locker)
{
m_evalCount++;
SharpNeatLib.NeatGenome.NeatGenome subsGenome = m_substrate.GenerateGenome(cppn);
INetwork subsNet = subsGenome.Decode(HyperNEATParameters.substrateActivationFunction);
m_neatQTable.NetworkToEvaluateBallOwner = subsNet;
m_neatQTable.NetworkToEvaluateNotBallOwner = subsNet;
m_neatQTable.m_eventNewNetReady.Set();
m_neatQTable.m_eventNetFitnessReady.WaitOne();
double reward = m_neatQTable.PerformanceNetworkToEvaluate.Reward;
double maxTime = NeatExpParams.EpisodesPerGenome * 3 * (m_neatQTable.Rows + m_neatQTable.Cols);
double globalFitness = (m_neatQTable.PerformanceNetworkToEvaluate.GoalsScoredByTeam * NeatExpParams.GoalScoreEffect);
double localFitness = (m_neatQTable.PerformanceNetworkToEvaluate.GoalsScoredByMe * NeatExpParams.GoalScoreEffect);
double fitness = globalFitness;
switch (NeatExpParams.CreditAssignment)
{
case CreditAssignments.Global:
fitness = globalFitness;
break;
case CreditAssignments.Local:
fitness = localFitness;
break;
case CreditAssignments.Mid:
fitness = (localFitness + globalFitness) * 0.5;
break;
default:
break;
}
if (m_neatQTable.PerformanceNetworkToEvaluate.GoalsScoredByTeam > 0)
{
fitness += (maxTime - m_neatQTable.PerformanceNetworkToEvaluate.PerformanceTime);
}
//- m_neatQTable.PerformanceNetworkToEvaluate.GoalsReceived * NeatExpParams.GoalRecvEffect
if (fitness < 0)
{
fitness = 0;
}
Console.WriteLine("G: {0,-3} - Rew: {1,-10:F3} - Fitness: {2,-15:F3}",
m_neatQTable.GenerationNo,
reward, fitness);
return(fitness);
}
}
//private Position GetHomePos(int unum, int r)
//{
// if (unum == 1)
// return new Position(2, 2);
// else if (unum == 2)
// return new Position(r - 1, 2);
// else if (unum == 3)
// return new Position(r / 2 + 1, 4);
// else
// return new Position(0, 0);
//}
public double EvaluateNetwork(INetwork cppn)
{
// lazy loading
//if (m_substratesBallOwner == null)
//{
// m_substratesBallOwner = new ISubstrate[m_neatQTables.Length];
// m_substratesNotBallOwner = new ISubstrate[m_neatQTables.Length];
// //Debug.Assert(m_neatQTable != null);
// // we put (cols + 2) columns in substrate to allaw players to go beyond the columns and score a goal
// for (int i = 0; i < m_neatQTables.Length; i++)
// {
// //Position homPos = GetHomePos(i + 1, m_neatQTables[i].Rows);
// float homex = ((float) m_neatQTables[i].NeatClient.MyHomePosCol / ((float)m_neatQTables[i].Cols * 0.5f));
// float homey = ((float)m_neatQTables[i].NeatClient.MyHomePosRow / (float)m_neatQTables[i].Rows);
// m_substratesBallOwner[i] = new FourDFieldSubstrate(homex, homey, m_neatQTables[i].Rows, m_neatQTables[i].Cols + 2,
// NeatExpParams.AddBiasToSubstrate, HyperNEATParameters.substrateActivationFunction, 0);
// m_substratesNotBallOwner[i] = new FourDFieldSubstrate(homex, homey, m_neatQTables[i].Rows, m_neatQTables[i].Cols + 2,
// NeatExpParams.AddBiasToSubstrate, HyperNEATParameters.substrateActivationFunction,
// NeatExpParams.AddBiasToSubstrate ? 2 : 1);
// }
//} // end of lazy loading
lock (locker)
{
m_evalCount++;
for (int i = 0; i < m_neatQTables.Length; i++)
{
SharpNeatLib.NeatGenome.NeatGenome tempGenomeBallOwner = m_substratesBallOwner[i].GenerateGenome(cppn);
INetwork tempNetBallOwner = tempGenomeBallOwner.Decode(HyperNEATParameters.substrateActivationFunction);
SharpNeatLib.NeatGenome.NeatGenome tempGenomeNotBallOwner = m_substratesNotBallOwner[i].GenerateGenome(cppn);
INetwork tempNetNotBallOwner = tempGenomeNotBallOwner.Decode(HyperNEATParameters.substrateActivationFunction);
m_neatQTables[i].NetworkToEvaluateBallOwner = tempNetBallOwner;
m_neatQTables[i].NetworkToEvaluateNotBallOwner = tempNetNotBallOwner;
m_neatQTables[i].m_eventNewNetReady.Set();
}
double[] fitnesses = new double[m_neatQTables.Length];
for (int i = 0; i < m_neatQTables.Length; i++)
{
m_neatQTables[i].m_eventNetFitnessReady.WaitOne();
double reward = m_neatQTables[i].PerformanceNetworkToEvaluate.Reward;
double maxTime = NeatExpParams.EpisodesPerGenome * 3 * (m_neatQTables[i].Rows + m_neatQTables[i].Cols);
double globalFitness = (m_neatQTables[i].PerformanceNetworkToEvaluate.GoalsScoredByTeam * NeatExpParams.GoalScoreEffect);
double localFitness = (m_neatQTables[i].PerformanceNetworkToEvaluate.GoalsScoredByMe * NeatExpParams.GoalScoreEffect);
double fitness = globalFitness;
switch (NeatExpParams.CreditAssignment)
{
case CreditAssignments.Global:
fitness = globalFitness;
break;
case CreditAssignments.Local:
fitness = localFitness;
break;
case CreditAssignments.Mid:
fitness = (localFitness + globalFitness) * 0.5;
break;
default:
break;
}
if (m_neatQTables[i].PerformanceNetworkToEvaluate.GoalsScoredByTeam > 0)
{
fitness += (maxTime - m_neatQTables[i].PerformanceNetworkToEvaluate.PerformanceTime);
}
//- m_neatQTable.PerformanceNetworkToEvaluate.GoalsReceived * NeatExpParams.GoalRecvEffect
if (fitness < 0)
{
fitness = 0;
}
fitnesses[i] = fitness;
Console.WriteLine("[{0}] G: {1,-3} - Rew: {2,-10:F3} - Fitness: {3,-15:F3}",
m_neatQTables[i].NeatClient.MyUnum,
m_neatQTables[i].GenerationNo,
reward, fitness);
}
Console.WriteLine("These are all fitnesses:");
foreach (var f in fitnesses)
{
Console.Write("{0} ", f);
}
Console.WriteLine("::::::::::::::::::::::::::::::::");
Console.WriteLine();
return(fitnesses[0]);
}
}
//saves a CPPN in dot file format.
//Assumes that inputs are X1, Y1, X2, Y2, Z
public static void saveCPPNasDOT(SharpNeatLib.NeatGenome.NeatGenome genome, string filename)
{
StreamWriter SW = File.CreateText(filename);
SW.WriteLine("digraph g { ");
String activationType = "";
foreach (NeuronGene neuron in genome.NeuronGeneList)
{
switch (neuron.NeuronType)
{
case NeuronType.Bias: SW.WriteLine("N0 [shape=box, label=Bias]"); break;
case NeuronType.Input:
string str = "?";
switch (neuron.InnovationId)
{
case 1: str = "X1"; break;
case 2: str = "Y1"; break;
case 3: str = "X2"; break;
case 4: str = "Y2"; break;
case 5: str = "Z"; break;
}
SW.WriteLine("N" + neuron.InnovationId + "[shape=box label=" + str + "]");
break;
case NeuronType.Output: SW.WriteLine("N" + neuron.InnovationId + "[shape=triangle]"); break;
case NeuronType.Hidden:
if (neuron.ActivationFunction.FunctionDescription.Equals("bipolar steepend sigmoid"))
{
activationType = "S";
}
if (neuron.ActivationFunction.FunctionDescription.Equals("bimodal gaussian"))
{
activationType = "G";
}
if (neuron.ActivationFunction.FunctionDescription.Equals("Linear"))
{
activationType = "L";
}
if (neuron.ActivationFunction.FunctionDescription.Equals("Sin function with doubled period"))
{
activationType = "Si";
}
if (neuron.ActivationFunction.FunctionDescription.Equals("Returns the sign of the input"))
{
activationType = "Sign";
}
SW.WriteLine("N" + neuron.InnovationId + "[shape=circle, label=N" + neuron.InnovationId + "_" + activationType + ", fillcolor=gray]");
break;
}
}
foreach (ConnectionGene gene in genome.ConnectionGeneList)
{
SW.Write("N" + gene.SourceNeuronId + " -> N" + gene.TargetNeuronId + " ");
if (gene.Weight > 0)
{
SW.WriteLine("[color=black] ");
}
else if (gene.Weight < -0)
{
SW.WriteLine("[color=red] [arrowType=inv]");
}
}
foreach (ModuleGene mg in genome.ModuleGeneList)
{
foreach (uint sourceID in mg.InputIds)
{
foreach (uint targetID in mg.OutputIds)
{
SW.Write("N" + sourceID + " -> N" + targetID + " ");
SW.WriteLine("[color=gray]");
}
}
}
SW.WriteLine(" { rank=same; ");
foreach (NeuronGene neuron in genome.NeuronGeneList)
{
if (neuron.NeuronType == NeuronType.Output)
{
SW.WriteLine("N" + neuron.InnovationId);
}
}
SW.WriteLine(" } ");
SW.WriteLine(" { rank=same; ");
foreach (NeuronGene neuron in genome.NeuronGeneList)
{
if (neuron.NeuronType == NeuronType.Input)
{
SW.Write("N" + neuron.InnovationId + " ->");
}
}
//Also the bias neuron on the same level
SW.WriteLine("N0 [style=invis]");
SW.WriteLine(" } ");
SW.WriteLine("}");
SW.Close();
}