/***** * * * * * CEM Helpers * * * * * *****/
private Layers TrainRearAxelBicycleModel()
{
Int32 numElite = (Int32)(cemBatchSize * cemEliteFrac);
// Initialize our base set of layers.
// This value will be iterated on each simulation iteration and
// is the basis for the current iteration's batches
Layers bestLayers = new Layers();
bestLayers.Init(inputSize, hiddenSize, outputSize);
bestLayers.SetStdDev(cemInitStddev);
bestLayers.SetMean(0);
// Run through our CEM
for (Int32 i = 0; i < cemIterations; i++)
{
// Run batches based off of our current 'bestLayers' and store them
// on order to find the elite results.
List <Layers> batchLayers = new List <Layers>(cemBatchSize);
List <double> batchRewards = new List <double>(cemBatchSize);
for (Int32 j = 0; j < cemBatchSize; j++)
{
// Init a set of layers off of our current best set.
Layers batchLayer = new Layers();
batchLayer.Init(bestLayers, (float)cemInitStddev);
// Calculate the reward for this batch and store it.
batchRewards.Add(Reward(batchLayer));
batchLayers.Add(batchLayer);
}
double meanReward = batchRewards.Average();
// We now have our run-throughs, keep the best elite and set our layers back up.
Int32[] indexArray = batchRewards
.Select((value, index) => new { value, index })
.OrderByDescending(item => item.value)
.Take(numElite)
.Select(item => item.index)
.ToArray();
// Store the mean and stdDev of our elite layers into our best layer.
BatchMeanAndStdDev(in batchLayers, in indexArray, ref bestLayers);
bestLayers.AddNoise(cemNoiseFactor / (i + 1));
Debug.Log("Iteration: " + i + "\tmeanReward: " + meanReward + "\t Reward(CurrMatrix): " + Reward(bestLayers));
}
UnityEngine.Debug.Log("done");
return(bestLayers);
}
