/// <summary>
/// Mutates the weights of a neural net. For each connection in the genotype there is a random chance of different mutations occuring according to the properties defined on this class.
/// </summary>
/// <param name="genotype">Genotype to mutate.</param>
/// <param name="random">Random for number generation.</param>
/// <param name="innovationIdManager"></param>
public override void Mutate(NeatBrainGenotype genotype, Random random, InnovationIdManager innovationIdManager)
{
foreach (ConnectionGene gene in genotype.ConnectionGenes)
{
if (random.NextDouble() < this.ProbPerturbWeight)
{
// Perturb the weights
gene.Weight += (float)random.Normal(0.0d, NewWeightStd) * WeightPerturbScale;
}
if (random.NextDouble() < this.ProbResetWeight)
{
// Reset the weight to a new random value
gene.Weight = (float)random.Normal(0.0d, NewWeightStd) * NewWeightPower;
}
if (random.NextDouble() < this.ProbEnableConn)
{
if (!genotype.CreatesCycle(gene))
{
gene.Enabled = true;
}
}
if (random.NextDouble() < this.ProbDisableConn)
{
gene.Enabled = false;
}
}
}
public void TestAdder()
{
NeatBrainGenotype test = new NeatBrainGenotype();
test.AddNamedNode("input1", NodeType.INPUT, ActivationFunctionType.RELU);
test.AddNamedNode("hidden1", NodeType.HIDDEN, ActivationFunctionType.RELU);
test.AddNamedNode("output1", NodeType.OUTPUT, ActivationFunctionType.RELU);
test.ConnectionGenes.Add(new ConnectionGene(1, 0, 1, 1)); // Input to hidden
test.ConnectionGenes.Add(new ConnectionGene(2, 1, 1, 1, true, true)); // Hidden to hidden (recurrent)
test.ConnectionGenes.Add(new ConnectionGene(3, 1, 2, 1)); //# Hidden to output
var network = new NeatBrainPhenotype(test);
network.SetInput("input1", 1.0f);
Assert.AreEqual(1.0f, network.Get("input1"));
network.Calculate();
Assert.AreEqual(0, network.Get("output1"));
network.SetInput("input1", 2.0f);
network.Calculate();
Assert.AreEqual(1, network.Get("output1"));
network.SetInput("input1", 10.0f);
network.Calculate();
Assert.AreEqual(3, network.Get("output1"));
Assert.AreEqual(13, network.Get("hidden1"));
Assert.AreEqual(10, network.Get("input1"));
}
public void TestCreatesCycleIgnoresRecurrentConnections()
{
// Test that a gene already in the genome can be tested fro recurrence
NeatBrainGenotype test = new NeatBrainGenotype();
test.AddNamedNode("input1", NodeType.INPUT, ActivationFunctionType.RELU);
test.AddNamedNode("input2", NodeType.INPUT, ActivationFunctionType.RELU);
test.AddNamedNode("hidden1", NodeType.HIDDEN, ActivationFunctionType.RELU);
test.AddNamedNode("hidden2", NodeType.HIDDEN, ActivationFunctionType.RELU);
test.AddNamedNode("output1", NodeType.OUTPUT, ActivationFunctionType.RELU);
test.ConnectionGenes.Add(new ConnectionGene(1, 0, 2, 1));
test.ConnectionGenes.Add(new ConnectionGene(3, 1, 3, 1));
test.ConnectionGenes.Add(new ConnectionGene(4, 2, 4, 1));
test.ConnectionGenes.Add(new ConnectionGene(5, 3, 4, 1));
var nonRecurrentGene = new ConnectionGene(2, 2, 3, 1, true, true);
Assert.IsFalse(test.CreatesCycle(nonRecurrentGene));
test.ConnectionGenes.Add(nonRecurrentGene);
var recurrentGene = new ConnectionGene(2, 3, 2, 1);
Assert.IsFalse(test.CreatesCycle(recurrentGene));
}
/// <summary>
/// Mutates the given genotype with the assigned mutators.
/// Mutators each have an assigned probability of activating (ProbabiltiyOfMutation). The list of mutators is read from the lowest index up until the highest.
/// If a mutator activates, then no more mutators can be activated and the mutattion finishes.
/// A mutator with a ProbabiltiyOfMutation will always activate if we reach it in the list.
/// </summary>
/// <param name="genotype">Genotype to mutate</param>
/// <param name="innovationIdManager">Innovation id manager. New innovation ids will be taken from here.</param>
public void Mutate(NeatBrainGenotype genotype, InnovationIdManager innovationIdManager)
{
foreach (AbstractMutator mutator in Mutators)
{
if (Random.NextDouble() < mutator.ProbabiltiyOfMutation)
{
mutator.Mutate(genotype, Random, innovationIdManager);
}
}
}
public void TestRecurrentFromOutput()
{
NeatBrainGenotype test = new NeatBrainGenotype();
test.AddNamedNode("input1", NodeType.INPUT, ActivationFunctionType.RELU);
test.AddNamedNode("input2", NodeType.INPUT, ActivationFunctionType.RELU);
test.AddNamedNode("input3", NodeType.INPUT, ActivationFunctionType.RELU);
// Hidden genes
test.NodeGenes.Add(new NodeGene(3, NodeType.HIDDEN, ActivationFunctionType.RELU));
test.NodeGenes.Add(new NodeGene(4, NodeType.HIDDEN, ActivationFunctionType.RELU));
// Output genes
test.AddNamedNode("output", NodeType.OUTPUT, ActivationFunctionType.RELU);
test.NodeGenes.Add(new NodeGene(5, NodeType.OUTPUT, ActivationFunctionType.RELU));
// Input connections
test.ConnectionGenes.Add(new ConnectionGene(1, 0, 3, 1));
test.ConnectionGenes.Add(new ConnectionGene(2, 1, 3, 1));
test.ConnectionGenes.Add(new ConnectionGene(3, 1, 4, 1));
test.ConnectionGenes.Add(new ConnectionGene(4, 2, 4, 1));
// Hidden connection
test.ConnectionGenes.Add(new ConnectionGene(5, 3, 4, 1));
// The recurrent connection from 4 to 3
test.ConnectionGenes.Add(new ConnectionGene(6, 4, 3, 1, true, true));
// Hidden to output
test.ConnectionGenes.Add(new ConnectionGene(7, 3, 5, 1));
test.ConnectionGenes.Add(new ConnectionGene(8, 4, 5, 1));
// The recurrent connection from 5 to 3
test.ConnectionGenes.Add(new ConnectionGene(9, 5, 3, -1));
var network = new NeatBrainPhenotype(test);
network.SetInput("input1", 1.0f);
network.SetInput("input2", 2.0f);
network.SetInput("input3", 3.0f);
network.Calculate();
Assert.AreEqual(0.0d, network.Get(5));
network.SetInput("input1", 1.0f);
network.SetInput("input2", 2.0f);
network.SetInput("input3", 3.0f);
network.Calculate();
Assert.AreEqual(8.0d, network.Get(5));
network.SetInput("input1", 1.0f);
network.SetInput("input2", 2.0f);
network.SetInput("input3", 3.0f);
network.Calculate();
Assert.AreEqual(16.0d, network.Get(5));
}
public void TestNamedNodes()
{
NeatBrainGenotype test = new NeatBrainGenotype();
test.AddNamedNode("bias", NodeType.BIAS, ActivationFunctionType.RELU);
test.AddNamedNode("input1", NodeType.INPUT, ActivationFunctionType.RELU);
test.AddNamedNode("input2", NodeType.INPUT, ActivationFunctionType.RELU);
Assert.IsTrue(test.NodeGenes.Count == 3);
Assert.IsTrue(test.NodeNameMap.ContainsKey("bias"));
Assert.IsTrue(test.NodeNameMap.ContainsKey("input1"));
Assert.IsTrue(test.NodeNameMap.ContainsKey("input2"));
}
public void TestReEnableCreatesCycle()
{
// Test that a gene already in the genome can be tested fro recurrence
NeatBrainGenotype test = new NeatBrainGenotype();
test.AddNamedNode("input1", NodeType.INPUT, ActivationFunctionType.RELU);
test.AddNamedNode("hidden1", NodeType.HIDDEN, ActivationFunctionType.RELU);
test.AddNamedNode("output1", NodeType.OUTPUT, ActivationFunctionType.RELU);
test.ConnectionGenes.Add(new ConnectionGene(1, 0, 1, 1));
var recurrentGene = new ConnectionGene(2, 1, 1, 1, false, true);
test.ConnectionGenes.Add(recurrentGene);
test.ConnectionGenes.Add(new ConnectionGene(3, 1, 2, 1));
// This new gene should not create a cycle
Assert.IsTrue(test.CreatesCycle(recurrentGene));
}
public void Update(uint tick, float _gameSpeed)
{
Random r = new Random();
for (int i = 0; i < Compatible.Count; i++)
{
var parentEntity = Compatible[i];
var reproduction = parentEntity.GetComponent <ReproductionComponent>();
if (reproduction.Reproduce > reproduction.ReproductionThreshold)
{
var energy = parentEntity.GetComponent <EnergyComponent>();
// check if we have the energy
if (!energy.CanAfford(reproduction.ReproductionEnergyCost))
{
continue;
}
if (energy.Energy - reproduction.ReproductionEnergyCost < reproduction.RequiredRemainingEnergy)
{
continue;
}
// charge teh parent energy. Each critter has an efficency rating, so some energy gets wasted an the rest gets sent to the child.
float charged = energy.ChargeEnergy(reproduction.ReproductionEnergyCost);
float toChild = reproduction.Efficency * charged;
float wasted = charged - toChild;
// Let the energy manager know about the wasted energy.
_energyManager.DepositEnergy(wasted);
// Some set up for the new child, setting positions/giving energy.
var babyEntity = Pool.AddEntityFromDefinition(reproduction.ChildDefinitionId, _simulation.JsonSettings, parentEntity.Tag);
babyEntity.GetComponent <EnergyComponent>().Energy = toChild;
var parentPosition = parentEntity.GetComponent <TransformComponent>().LocalPosition;
babyEntity.GetComponent <TransformComponent>().LocalPosition = parentPosition;
// The new child will need a mutated brain
var originalBrainComponent = parentEntity.GetComponent <BrainComponent>();
var originalBrain = originalBrainComponent.Brain;
AbstractBrainGenotype parentGenotype = ((NeatBrainPhenotype)originalBrain).Genotype;
NeatBrainGenotype childGenotype = (NeatBrainGenotype)parentGenotype.Clone();
_muationManager.Mutate(childGenotype, _innovationIdManager);
var newBrain = babyEntity.GetComponent <BrainComponent>();
newBrain.SetGenotype(childGenotype);
// Do not need to pass it type as we have already set the brain, therefore it does not need initializing.
newBrain.SetUpLinks(null, _simulation.Settings);
newBrain.ParentSpecies = originalBrainComponent.Species;
BirthEventInfo e = new BirthEventInfo(parentPosition, tick * _gameSpeed, parentEntity.Id, parentEntity.Tag, babyEntity.Id, babyEntity.Tag, childGenotype, originalBrainComponent.Species.Id);
BirthEvent?.Invoke(e);
}
}
}
/// <summary> /// Mutates a given genotype /// </summary> /// <param name="genotype">Genotype to mutate</param> public abstract void Mutate(NeatBrainGenotype genotype, Random random, InnovationIdManager innovationIdManager);
