public BotFactory(Maze maze, EngineSettings settings, WallGrid walls, BrainFactory brainFactory, EyeFactory eyeFactory)
{
_maze = maze;
_settings = settings;
_walls = walls;
_brainFactory = brainFactory;
_eyeFactory = eyeFactory;
}
public static void Run()
{
CerebroML.Cerebro net = BrainFactory.Create()
.WithInput(2)
.WithLayer(3, LayerType.Tanh)
.WithLayer(1, LayerType.Sigmoid)
.Build();
float[] values = net.Run(new float[] { 1, 0 });
System.Console.WriteLine(values[0]);
}
void Start()
{
ball_rigidBody = Ball.GetComponent <Rigidbody2D>();
var input = new BrainFactoryInput()
{
ActivationFunctionInputOutput = new TanH(),
ActivationFunctionHiddenLayers = new TanH(), //do not use TanH for the xorbrain it is counter productive. The xor needs 0 or 1, TanH brings in negative values as well
Inputs = 6,
Outputs = 1,
HiddenLayers = 1,
NeuronsPerHiddenLayer = 4,
Alpha = 0.11 //how much impact the training has, sometimes you'll see NaN come back and this dials back the calculations a bit
};
pongBrain = BrainFactory.CreateBrain <PongBrain>(input);
}
public XORPopulation()
{
int popMax = 250;
List <CerebroML.Cerebro> population = new List <CerebroML.Cerebro>();
BrainFactory factory = BrainFactory.Create()
.WithWeightBiasAmplitude(10f)
.WithInput(2)
.WithLayer(2, LayerType.Tanh)
.WithLayer(1, LayerType.Sigmoid);
for (int i = 0; i < popMax; i++)
{
population.Add(factory.Build());
}
this.SetUp(population.ToArray(), 0.05f, 10f);
}
// ==============================================
// Static runner
// ==============================================
public static void Run()
{
// Generate first population
XORPopulation pop = new XORPopulation();
// The record
float kingFitness = 0;
CerebroML.Cerebro king = BrainFactory.Create()
.WithWeightBiasAmplitude(10f)
.WithInput(2)
.WithLayer(2, LayerType.Tanh)
.WithLayer(1, LayerType.Sigmoid)
.Build();
// Iterate until the king has a good fitness
while (kingFitness < 0.999) // && generation < GENERATION_LIMT)
{
pop.Next();
kingFitness = pop.bestFitness;
king.SetGenome(pop.best);
// Write to the console the current king and the average fitness
if (pop.generationNumber % 250 == 0)
{
Console.WriteLine($"Gen {pop.generationNumber}");
Console.WriteLine($"Fitness: King- {kingFitness:0.00} AVG- {pop.GetAVGFitness():0.00}");
}
}
// Show the final results
if (king != null)
{
Console.WriteLine($"FINAL - Gen: {pop.generationNumber}");
Console.WriteLine($"Fitness: King- {kingFitness:0.00}");
Console.WriteLine($" - (0, 0) = {king.Run(new float[] { 0.0f, 0.0f })[0]:0.00}");
Console.WriteLine($" - (1, 0) = {king.Run(new float[] { 1.0f, 0.0f })[0]:0.00}");
Console.WriteLine($" - (0, 1) = {king.Run(new float[] { 0.0f, 1.0f })[0]:0.00}");
Console.WriteLine($" - (1, 1) = {king.Run(new float[] { 1.0f, 1.0f })[0]:0.00}");
}
}
//-------------------------------------------------------------------------
// 生成・準備に関するモノ
/// <summary>
/// 対戦に要するオブジェクトの準備、ロケーション情報を持ったオブジェクトを受け取る。
/// </summary>
public void Setup(GameObject locations)
{
// ロケーション生成
this.locations.Add(App.Player.P1, new Location("P1", "P2", locations));
this.locations.Add(App.Player.P2, new Location("P2", "P1", locations));
// プレイヤーを生成し、脳を設定
this.p1 = CreatePlayer(App.Player.P1);
this.p2 = CreatePlayer(App.Player.P2);
this.p1.SetBrain(BrainFactory.Create(App.Brain.Player, this.p1, this.p2));
this.p2.SetBrain(BrainFactory.Create(App.Brain.Player, this.p2, this.p1));
// ガイド生成
this.guide = new GameObject("Guide").AddComponent <Guide>();
this.guide.SetParent(CacheTransform);
this.guide.Init(
this.locations[App.Player.P1].Center,
this.locations[App.Player.P2].Center
);
this.guide.Setup();
this.state.SetState(State.Ready);
}
public static void Run()
{
// Generate first population
int popMax = 250;
List <CerebroML.Cerebro> population = new List <CerebroML.Cerebro>();
BrainFactory factory = BrainFactory.Create()
.WithWeightBiasAmplitude(10f)
.WithInput(2)
.WithLayer(2, LayerType.Tanh)
.WithLayer(1, LayerType.Sigmoid);
for (int i = 0; i < popMax; i++)
{
population.Add(factory.Build());
}
// The record
float kingFitness = 0;
CerebroML.Cerebro king = null;
int generation = 0;
// Iterate until the king has a good fitness
while (kingFitness < 0.9) // && generation < GENERATION_LIMT)
{
generation++;
float total = 0;
// Search for the best one
for (int e = 0; e < popMax; e++)
{
float f = CalcFitness(population[e]);
if (f > kingFitness)
{
kingFitness = f;
king = population[e];
}
total += f;
}
// Write to the console the current king and the average fitness
if (generation % 250 == 0)
{
Console.WriteLine($"Gen {generation}");
Console.WriteLine($"Fitness: King- {kingFitness:0.00} AVG- {total / popMax:0.00}");
Console.WriteLine($" - (0, 0) = {king.Run(new float[] { 0.0f, 0.0f })[0]:0.00}");
Console.WriteLine($" - (1, 0) = {king.Run(new float[] { 1.0f, 0.0f })[0]:0.00}");
Console.WriteLine($" - (0, 1) = {king.Run(new float[] { 0.0f, 1.0f })[0]:0.00}");
Console.WriteLine($" - (1, 1) = {king.Run(new float[] { 1.0f, 1.0f })[0]:0.00}");
}
// Make new population
List <CerebroML.Cerebro> newPop = new List <CerebroML.Cerebro>();
for (int e = 0; e < popMax; e++)
{
// Find Two parents to make cross over.
List <CerebroML.Cerebro> parents = new List <CerebroML.Cerebro>();
int control = 0;
Random rnd = new Random();
while (parents.Count < 2 && control < 10000)
{
control++;
int index = rnd.Next(popMax);
float dice = (float)rnd.NextDouble();
if (CalcFitness(population[index]) > dice)
{
parents.Add(population[index]);
}
}
CerebroML.Cerebro offspring = factory.Build();
// Perform cross over and 'inject' the new Genome into the new network
if (parents.Count == 2)
{
Genome e1 = parents[0].GetGenome();
Genome e2 = parents[1].GetGenome();
Genome child = Genome.Crossover(e1, e2);
child.Mutate(0.01f, 10f);
offspring.SetGenome(child);
}
newPop.Add(offspring);
}
population = newPop;
}
// Show the final results
if (king != null)
{
Console.WriteLine("FINAL");
Console.WriteLine($"Fitness: King- {kingFitness:0.00}");
Console.WriteLine($" - (0, 0) = {king.Run(new float[] { 0.0f, 0.0f })[0]:0.00}");
Console.WriteLine($" - (1, 0) = {king.Run(new float[] { 1.0f, 0.0f })[0]:0.00}");
Console.WriteLine($" - (0, 1) = {king.Run(new float[] { 0.0f, 1.0f })[0]:0.00}");
Console.WriteLine($" - (1, 1) = {king.Run(new float[] { 1.0f, 1.0f })[0]:0.00}");
}
}
public override void Initialize(Unit owner)
{
base.Initialize(owner); this.Brain = BrainFactory.CreateBrainFromUnit(owner);
}
// =======================================
void Awake()
{
// Sensor data
/**
* x
* y
* health_percent
* Can shoot
* forward_x
* forward_y
*
* eye_active
* eye_x
* eye_y
* .
* . drive by eyeCount
* .
* hit_from_active
* hit_from_x
* hit_from_y
*/
this.eyes = new Vector3[this.eyeCount];
this.sensorBuffer = new NameIndexedBuffer();
this.sensorBuffer.AddIndex("time", 1);
this.sensorBuffer.AddIndex("position", 2);
this.sensorBuffer.AddIndex("health", 1);
this.sensorBuffer.AddIndex("shoot_time", 1);
this.sensorBuffer.AddIndex("forward", 2);
for (int i = 0; i < this.eyeCount; i++)
{
this.sensorBuffer.AddIndex($"eye_{i}", 3);
}
this.sensorBuffer.AddIndex("hit_from", 3);
// Reaction data
/**
* forward
* backwards
* turn left
* turn right
* shoot
*/
this.reaction = new float[5];
this.brain = BrainFactory.Create()
.WithInput(this.sensorBuffer.size)
.WithLayer(16, LayerType.Tanh)
.WithLayer(8, LayerType.Tanh)
.WithLayer(this.reaction.Length, LayerType.Sigmoid)
.WithWeightBiasAmplitude(10f)
.Build();
this.physics = GetComponent <Rigidbody>();
this.positionRecorder = GetComponent <PositionRecorder>();
this.lookToFriend = new FloatRecorder();
this.lookToFoe = new FloatRecorder();
}
