public QNetworkSimple(int stateSize, int actionSize, int numLayers, int hiddenSize, DeviceDescriptor device, float initialWeightScale = 0.01f)
{
Device = device;
StateSize = stateSize;
ActionSize = actionSize;
//create actor network part
var inputA = new InputLayerDense(stateSize);
var outputA = new OutputLayerDense(hiddenSize, null, OutputLayerDense.LossFunction.None);
outputA.HasBias = false;
outputA.InitialWeightScale = initialWeightScale;
SequentialNetworkDense qNetwork = new SequentialNetworkDense(inputA, LayerDefineHelper.DenseLayers(numLayers, hiddenSize, false, NormalizationMethod.None, 0, initialWeightScale, new ReluDef()), outputA, device);
//seperate the advantage and value part. It is said to be better
var midStream = outputA.GetOutputVariable();
var advantageStream = CNTKLib.Slice(midStream, AxisVector.Repeat(new Axis(0), 1), IntVector.Repeat(0, 1), IntVector.Repeat(hiddenSize / 2, 1));
var valueStream = CNTKLib.Slice(midStream, AxisVector.Repeat(new Axis(0), 1), IntVector.Repeat(hiddenSize / 2, 1), IntVector.Repeat(hiddenSize, 1));
var adv = Layers.Dense(advantageStream, actionSize, device, false, "QNetworkAdvantage", initialWeightScale);
var value = Layers.Dense(valueStream, 1, device, false, "QNetworkValue", initialWeightScale);
InputState = inputA.InputVariable;
//OutputQs = outputA.GetOutputVariable();
OutputQs = value.Output + CNTKLib.Minus(adv, CNTKLib.ReduceMean(adv, Axis.AllStaticAxes())).Output;
}
public PPONetworkContinuousSimple(int stateSize, int actionSize, int numLayers, int hiddenSize, DeviceDescriptor device, float initialWeightScale = 0.01f)
{
Device = device;
StateSize = stateSize;
ActionSize = actionSize;
//create actor network part
var inputA = new InputLayerDense(stateSize);
var outputA = new OutputLayerDense(actionSize, null, OutputLayerDense.LossFunction.None);
outputA.InitialWeightScale = initialWeightScale;
valueNetwork = new SequentialNetworkDense(inputA, LayerDefineHelper.DenseLayers(numLayers, hiddenSize, true, NormalizationMethod.None, 0, initialWeightScale, new TanhDef()), outputA, device);
InputState = inputA.InputVariable;
OutputMean = outputA.GetOutputVariable();
OutputProbabilities = null; //this is for discrete action only.
//the variance output will use a seperate parameter as in Unity's implementation
var log_sigma_sq = new Parameter(new int[] { actionSize }, DataType.Float, CNTKLib.ConstantInitializer(0), device, "PPO.log_sigma_square");
//test
OutputVariance = CNTKLib.Exp(log_sigma_sq);
PolicyFunction = Function.Combine(new Variable[] { OutputMean, OutputVariance });
//create value network
var inputC = new InputLayerCNTKVar(InputState);
var outputC = new OutputLayerDense(1, null, OutputLayerDense.LossFunction.None);
outputC.InitialWeightScale = initialWeightScale;
policyNetwork = new SequentialNetworkDense(inputC, LayerDefineHelper.DenseLayers(numLayers, hiddenSize, true, NormalizationMethod.None, 0, initialWeightScale, new TanhDef()), outputC, device);
OutputValue = outputC.GetOutputVariable();
ValueFunction = OutputValue.ToFunction();
//PolicyParameters.Add(log_sigma_sq);
}
public PPONetworkDiscreteSimple(int stateSize, int actionSize, int numLayers, int hiddenSize, DeviceDescriptor device, float initialWeightScale = 0.01f)
{
Device = device;
StateSize = stateSize;
ActionSize = actionSize;
//create actor network part
var inputA = new InputLayerDense(stateSize);
var outputA = new OutputLayerDense(actionSize, new SoftmaxDef(), OutputLayerDense.LossFunction.None);
outputA.InitialWeightScale = initialWeightScale;
valueNetwork = new SequentialNetworkDense(inputA, LayerDefineHelper.DenseLayers(numLayers, hiddenSize, true, NormalizationMethod.None, 0, initialWeightScale, new TanhDef()), outputA, device);
InputState = inputA.InputVariable;
OutputMean = null;
OutputVariance = null;
OutputProbabilities = outputA.GetOutputVariable(); //this is for discrete action only.
PolicyFunction = OutputProbabilities.ToFunction();
//create value network
var inputC = new InputLayerCNTKVar(InputState);
var outputC = new OutputLayerDense(1, null, OutputLayerDense.LossFunction.None);
outputC.InitialWeightScale = initialWeightScale;
policyNetwork = new SequentialNetworkDense(inputC, LayerDefineHelper.DenseLayers(numLayers, hiddenSize, true, NormalizationMethod.None, 0, initialWeightScale, new TanhDef()), outputC, device);
OutputValue = outputC.GetOutputVariable();
ValueFunction = OutputValue.ToFunction();
}
public void CreateResnode()
{
var input = new InputLayerDense(2);
//outputLayer = new OutputLayerDenseBayesian(1);
outputLayer = new OutputLayerDense(1, null, OutputLayerDense.LossFunction.Square);
network = new SequentialNetworkDense(input, LayerDefineHelper.DenseLayers(10, 5, true, NormalizationMethod.None), outputLayer, DeviceDescriptor.CPUDevice);
//network = new SequentialNetworkDense(input, LayerDefineHelper.ResNodeLayers(10, 5), outputLayer, DeviceDescriptor.CPUDevice);
trainer = new TrainerSimpleNN(network, LearnerDefs.AdamLearner(lr), DeviceDescriptor.CPUDevice);
dataPlane.network = this;
}
public QNetworkConvSimple(int inputWidth, int inputHeight, int inputDepth, int actionSize,
int[] filterSizes, int[] filterDepths, int[] strides, bool[] pooling,
int densehiddenLayers, int densehiddenSize, bool denseUseBias, DeviceDescriptor device, float denseInitialWeightScale = 0.01f)
{
Device = device;
StateSize = inputWidth * inputHeight * inputDepth;
ActionSize = actionSize;
InputDimension = new int[3] {
inputWidth, inputHeight, inputDepth
};
//create actor network part
InputState = CNTKLib.InputVariable(InputDimension, DataType.Float);
Debug.Assert(filterSizes.Length == strides.Length && filterDepths.Length == filterSizes.Length, "Length of filterSizes,strides and filterDepth are not the same");
var lastLayer = InputState;
for (int i = 0; i < filterSizes.Length; ++i)
{
//conv layers. Use selu activaion and selu initlaization
lastLayer = Layers.Convolution2D(lastLayer, filterDepths[i],
filterSizes[i], filterSizes[i], device, strides[i], true, true, "QConv_" + i, Mathf.Sqrt((1.0f / (filterSizes[i] * filterSizes[i]))));
lastLayer = new SELUDef().BuildNew(lastLayer, device, "");
//pooling
if (pooling[i])
{
lastLayer = CNTKLib.Pooling(lastLayer, PoolingType.Max, new int[] { 2, 2 }, new int[] { 2, 2 }, BoolVector.Repeat(true, 2), false, true, "pool2");
}
}
lastLayer = CNTKLib.Flatten(lastLayer, new Axis(3), "Flatten");
//dense layers
var inputA = new InputLayerCNTKVar(lastLayer);
var outputA = new OutputLayerDense(actionSize, null, OutputLayerDense.LossFunction.None);
outputA.HasBias = false;
outputA.InitialWeightScale = denseInitialWeightScale;
SequentialNetworkDense qNetwork = new SequentialNetworkDense(inputA, LayerDefineHelper.DenseLayers(densehiddenLayers, densehiddenSize, denseUseBias, NormalizationMethod.None, 0, denseInitialWeightScale, new ReluDef()), outputA, device);
//OutputQs = outputA.GetOutputVariable();
OutputQs = outputA.GetOutputVariable();
}