private void learnNN(NeuronNet pNN, List <double> inputs, List <double> expected)
{
pNN.Update(inputs);
var outputs = pNN.Outputs;
Debug.Assert(outputs.Count == expected.Count);
double tempFitness = pNN.Fitness;
for (int ipOutput = 0; ipOutput < outputs.Count; ipOutput++)
{
tempFitness += calculateFitness(outputs[ipOutput], expected[ipOutput]);
}
pNN.Fitness = tempFitness;
}
//同一时间内屏幕中只有3个子弹
Action GetActionFromNetWork(List <Bullet> bullets, Vector3 gunPos)
{
List <float> netInputs = new List <float>();
List <float> outputs;
Action action = Action.Drift;
float toTurretX = gunPos.x - pos.x;
float toTurretY = gunPos.y - pos.y;
netInputs.Add(toTurretX);
netInputs.Add(toTurretY);
for (int i = 0; i < bullets.Count; i++)
{
if (bullets[i].Active)
{
float xComp = bullets[i].Pos.x - pos.x;
float yComp = bullets[i].Pos.y - pos.y;
netInputs.Add(xComp);
netInputs.Add(yComp);
}
else
{
//子弹未则输入一个指向Turret的向量
netInputs.Add(toTurretX);
netInputs.Add(toTurretY);
}
}
outputs = brain.Update(netInputs);
float bestSoFar = 0;
for (int i = 0; i < outputs.Count; i++)
{
if (outputs[i] > bestSoFar && outputs[i] > 0.9f)
{
bestSoFar = outputs[i];
action = (Action)i;
}
}
return(action);
}