/// <summary>
/// Start to optimize asynchronized. It is actaually not running in another thread, but running in Update() in each frame of your game.
/// This way the optimization will not block your game.
/// </summary>
/// <param name="optimizeTarget">Target to optimize</param>
/// <param name="onReady">Action to call when optmization is ready. THe input is the best solution found.</param>
/// <param name="initialMean">initial mean guess.</param>
public void StartOptimizingAsync(IESOptimizable optimizeTarget, Action <double[]> onReady = null, double[] initialMean = null)
{
optimizable = optimizeTarget;
optimizer = optimizerType == ESOptimizerType.LMMAES ? (IMAES) new LMMAES() : (IMAES) new MAES();
samples = new OptimizationSample[populationSize];
for (int i = 0; i < populationSize; ++i)
{
samples[i] = new OptimizationSample(optimizable.GetParamDimension());
}
iteration = 0;
//initial mean
double[] actualInitMean = null;
if (initialMean != null && initialMean.Length != optimizeTarget.GetParamDimension())
{
Debug.LogError("Init mean has a wrong dimension " + initialMean.Length + " rather than " + optimizeTarget.GetParamDimension() + ".");
}
if (initialMean == null)
{
actualInitMean = new double[optimizeTarget.GetParamDimension()];
}
else
{
actualInitMean = initialMean;
}
optimizer.init(optimizable.GetParamDimension(), populationSize, actualInitMean, initialStepSize, mode);
IsOptimizing = true;
this.onReady = onReady;
}
protected override void Awake()
{
optimizer = GetComponent <ESOptimizer>();
optimizable = GetComponent <IESOptimizable>();
Debug.Assert(optimizable != null, "DesicionMAES need to attach to a gameobjec with an agent that implements IESOptmizable.");
}
/// <summary>
/// Optimize and return the solution immediately.
/// </summary>
/// <param name="optimizeTarget">Target to optimize</param>
/// <param name="initialMean">initial mean guess.</param>
/// <returns>The best solution found</returns>
public double[] Optimize(IESOptimizable optimizeTarget, double[] initialMean = null)
{
var tempOptimizer = (optimizerType == ESOptimizerType.LMMAES ? (IMAES) new LMMAES() : (IMAES) new MAES());
var tempSamples = new OptimizationSample[populationSize];
for (int i = 0; i < populationSize; ++i)
{
tempSamples[i] = new OptimizationSample(optimizeTarget.GetParamDimension());
}
//initial mean
double[] actualInitMean = null;
if (initialMean != null && initialMean.Length != optimizeTarget.GetParamDimension())
{
Debug.LogError("Init mean has a wrong dimension " + initialMean.Length + " rather than " + optimizeTarget.GetParamDimension() + ".");
}
if (initialMean == null)
{
actualInitMean = new double[optimizeTarget.GetParamDimension()];
}
else
{
actualInitMean = initialMean;
}
//initialize the optimizer
tempOptimizer.init(optimizeTarget.GetParamDimension(), populationSize, actualInitMean, initialStepSize, mode);
//iteration
double[] bestParams = null;
//bool hasInvokeReady = false;
iteration = 0;
for (int it = 0; it < maxIteration; ++it)
{
tempOptimizer.generateSamples(tempSamples);
for (int s = 0; s <= tempSamples.Length / evaluationBatchSize; ++s)
{
List <double[]> paramList = new List <double[]>();
for (int b = 0; b < evaluationBatchSize; ++b)
{
int ind = s * evaluationBatchSize + b;
if (ind < tempSamples.Length)
{
paramList.Add(tempSamples[ind].x);
}
}
var values = optimizeTarget.Evaluate(paramList);
for (int b = 0; b < evaluationBatchSize; ++b)
{
int ind = s * evaluationBatchSize + b;
if (ind < tempSamples.Length)
{
tempSamples[ind].objectiveFuncVal = values[b];
}
}
}
tempOptimizer.update(tempSamples);
BestScore = tempOptimizer.getBestObjectiveFuncValue();
iteration++;
bestParams = tempOptimizer.getBest();
if ((BestScore <= targetValue && mode == OptimizationModes.minimize) ||
(BestScore >= targetValue && mode == OptimizationModes.maximize))
{
//optimizatoin is done
/*if (onReady != null)
* {
* onReady.Invoke(bestParams);
* hasInvokeReady = true;
* }*/
break;
}
}
/*if (onReady != null && !hasInvokeReady)
* {
* onReady.Invoke(bestParams);
* }*/
return(bestParams);
}