private void instantiateAgents()
{
for (int i = 0; i < numClones; i++)
{
agents[i] = Instantiate(agentPrefab);
MeshRenderer pendulumMesh = agents[i].GetComponent <MeshRenderer>();
if (pendulumMesh != null)
{
pendulumMesh.material.color = Random.ColorHSV(0f, 1f, 1f, 1f, 0.5f, 1f);//colours[i];//Random.Range(0, 1) * 255, Random.Range(0, 1) * 255, Random.Range(0, 1) * 255, 1);
}
AgentTrainer trainer = agents[i].GetComponent <AgentTrainer>();
trainer.Initialize(newTrainers[i].getNeuralNetwork(), Random.Range(-15, 15));
//Debug.Log("Fitness: " + trainer.fitness);
}
}
void Start()
{
//Note: Different instances are set to not collide given Edit>ProjectSettings>Physics>LayerCollisionMatrix settings
Debug.Log("Starting! Godspeed");
Time.timeScale = timeScale;
//TODO: Make random neural networks or read in NNs from file
NeuralNetwork[] initNetworks = new NeuralNetwork[numClones];
for (int i = 0; i < initNetworks.Length; i++)
{
initNetworks[i] = new NeuralNetwork(topology);
initNetworks[i].SetRandomWeights(-10.0, 10.0);
}
for (int i = 0; i < numClones; i++)
{
newTrainers[i] = new AgentTrainer(initNetworks[i]);
}
instantiateAgents();
}
public AgentTrainer[] evolve(AgentTrainer[] trainers)
{
if (trainers.Length < 2)
{
throw new System.Exception("Not enough trainers passed to Evolver.evolve(). Cannot breed.");
}
trainers = trainers.OrderByDescending(t => t.fitness).ToArray <AgentTrainer>();
NeuralNetwork mother = trainers[0].getNeuralNetwork();
NeuralNetwork father = trainers[1].getNeuralNetwork();
NeuralNetwork[] childrenNetworks = new NeuralNetwork[trainers.Length];
if (!validateNetworks(mother, father))
{
throw new System.Exception("Invalid parents");
}
//////////////////////////////////////////////////////
for (int i = 0; i < childrenNetworks.Length; i++)
{
childrenNetworks[i] = new NeuralNetwork(mother.Topology);
}
foreach (NeuralNetwork childNetwork in childrenNetworks)
{
for (int layer = 0; layer < childNetwork.Layers.Length; layer++)
{
NeuralLayer Layer = childNetwork.Layers[layer];
for (int weightRow = 0; weightRow < Layer.Weights.GetLength(0); weightRow++)
{
for (int weightCol = 0; weightCol < Layer.Weights.GetLength(1); weightCol++)
{
if (!mutate())
{
double parentWeight = Mathf.RoundToInt(Random.Range(0, 1)) >= biasTowardsMother ? mother.Layers[layer].Weights[weightRow, weightCol] : father.Layers[layer].Weights[weightRow, weightCol];
childNetwork.Layers[layer].Weights[weightRow, weightCol] = parentWeight;
}
else
{
childNetwork.Layers[layer].Weights[weightRow, weightCol] = Random.Range(-mutationRange, mutationRange);
Debug.Log("MUTATED!");
}
}
}
}
}
NeuralNetwork[] randomChildren = childrenNetworks.Take((int)(percentRandomChildren * childrenNetworks.Length)).ToArray();
foreach (NeuralNetwork randomChild in randomChildren)
{
randomChild.SetRandomWeights(-mutationRange, mutationRange);
}
//////////////////////////////////////////////////////
AgentTrainer[] newTrainers = new AgentTrainer[trainers.Length];
for (int trainer = 0; trainer < newTrainers.Length; trainer++)
{
newTrainers[trainer] = new AgentTrainer(childrenNetworks[trainer]);
}
return(newTrainers);
}
