darkpowerxo/NN_in_Trading
1
0
Fork 0
forked from dng/NN_in_Trading
Code Pull requests Activity
NN_in_Trading/Experts/SAC-D_DICE/Net_SAC_D_DICE.mqh

587 lines
45 KiB
MQL5
Raw Permalink Normal View History

new files added
2026-03-12 15:02:23 +02:00
<EFBFBD><EFBFBD>//+------------------------------------------------------------------+
//| Net_SAC_DICE.mqh |
//| Copyright DNG<EFBFBD> |
//| https://www.mql5.com/ru/users/dng |
//+------------------------------------------------------------------+
#property copyright "Copyright DNG<00>"
#property link "https://www.mql5.com/ru/users/dng"
#property version "1.00"
//+------------------------------------------------------------------+
//| Includes |
//+------------------------------------------------------------------+
#include "..\RL\FQF.mqh"
//---
#define defSACDICE 0x7795 ///<Neuron Net
#define LogProbMultiplier 1.0e-5f
//+------------------------------------------------------------------+
//| |
//+------------------------------------------------------------------+
class CNet_SAC_D_DICE : protected CNet
{
protected:
CNet cActorExploer;
CNet cCritic1;
CNet cCritic2;
CNet cTargetCritic1;
CNet cTargetCritic2;
CNet cZeta;
CNet cNu;
CNet cTargetNu;
vector<float> fLambda;
vector<float> fLambda_m;
vector<float> fLambda_v;
int iLatentLayer;
float fCAGrad_C;
int iCAGrad_Iters;
int iUpdateDelay;
int iUpdateDelayCount;
//---
float fLoss1;
float fLoss2;
vector<float> fZeta;
vector<float> fQWeights;
//---
vector<float> GetLogProbability(CBufferFloat *Actions);
vector<float> CAGrad(vector<float> &grad);
public:
//---
CNet_SAC_D_DICE(void);
~CNet_SAC_D_DICE(void) {}
//---
bool Create(CArrayObj *actor, CArrayObj *critic, CArrayObj *zeta, CArrayObj *nu, int latent_layer = -1);
//---
virtual bool Study(CArrayFloat *State, CArrayFloat *SecondInput, CBufferFloat *Actions, vector<float> &Rewards, CBufferFloat *NextState, CBufferFloat *NextSecondInput, float discount, float tau);
virtual void GetLoss(float &loss1, float &loss2) { loss1 = fLoss1; loss2 = fLoss2; }
virtual bool TargetsUpdate(float tau);
//---
virtual void SetQWeights(vector<float> &weights) { fQWeights=weights; }
virtual void SetCAGradC(float c) { fCAGrad_C=c; }
virtual void SetLambda(vector<float> &lambda) { fLambda=lambda;
fLambda_m=vector<float>::Zeros(lambda.Size());
fLambda_v=fLambda_m; }
virtual void TargetsUpdateDelay(int delay) { iUpdateDelay=delay; iUpdateDelayCount=delay; }
//---
virtual bool Save(string file_name, bool common = true);
bool Load(string file_name, bool common = true);
};
//+------------------------------------------------------------------+
//| |
//+------------------------------------------------------------------+
CNet_SAC_D_DICE::CNet_SAC_D_DICE(void) : fLoss1(0),
fLoss2(0),
fCAGrad_C(0.5f),
iCAGrad_Iters(15),
iUpdateDelay(100),
iUpdateDelayCount(100)
{
fLambda = vector<float>::Full(1, 1.0e-5f);
fLambda_m = vector<float>::Zeros(1);
fLambda_v = vector<float>::Zeros(1);
fZeta = vector<float>::Zeros(1);
fQWeights = vector<float>::Ones(1);
}
//+------------------------------------------------------------------+
//| |
//+------------------------------------------------------------------+
bool CNet_SAC_D_DICE::Create(CArrayObj *actor, CArrayObj *critic, CArrayObj *zeta, CArrayObj *nu, int latent_layer = -1)
{
ResetLastError();
//---
if(!cActorExploer.Create(actor) || !CNet::Create(actor))
{
PrintFormat("Error of create Actor: %d", GetLastError());
return false;
}
//---
if(!opencl)
{
Print("Don't opened OpenCL context");
return false;
}
//---
if(!cCritic1.Create(critic) || !cCritic2.Create(critic))
{
PrintFormat("Error of create Critic: %d", GetLastError());
return false;
}
//---
if(!cZeta.Create(zeta) || !cNu.Create(nu))
{
PrintFormat("Error of create function nets: %d", GetLastError());
return false;
}
//---
if(!cTargetCritic1.Create(critic) || !cTargetCritic2.Create(critic) ||
!cTargetNu.Create(nu))
{
PrintFormat("Error of create target models: %d", GetLastError());
return false;
}
//---
cActorExploer.SetOpenCL(opencl);
cCritic1.SetOpenCL(opencl);
cCritic2.SetOpenCL(opencl);
cZeta.SetOpenCL(opencl);
cNu.SetOpenCL(opencl);
cTargetCritic1.SetOpenCL(opencl);
cTargetCritic2.SetOpenCL(opencl);
cTargetNu.SetOpenCL(opencl);
//---
if(!cTargetCritic1.WeightsUpdate(GetPointer(cCritic1), 1.0) ||
!cTargetCritic2.WeightsUpdate(GetPointer(cCritic2), 1.0) ||
!cTargetNu.WeightsUpdate(GetPointer(cNu), 1.0))
{
PrintFormat("Error of update target models: %d", GetLastError());
return false;
}
//---
cZeta.getResults(fZeta);
ulong size = fZeta.Size();
fLambda = vector<float>::Full(size,1.0e-5f);
fLambda_m = vector<float>::Zeros(size);
fLambda_v = vector<float>::Zeros(size);
fQWeights = vector<float>::Ones(size);
iLatentLayer = latent_layer;
//---
return true;
}
//+------------------------------------------------------------------+
//| |
//+------------------------------------------------------------------+
bool CNet_SAC_D_DICE::Study(CArrayFloat *State,
CArrayFloat *SecondInput,
CBufferFloat *Actions,
vector<float> &Rewards,
CBufferFloat *NextState,
CBufferFloat *NextSecondInput,
float discount,
float tau)
{
//---
if(!Actions)
return false;
//---
if(!!NextState)
if(!CNet::feedForward(NextState, 1, false, NextSecondInput))
return false;
if(!cTargetCritic1.feedForward(GetPointer(this), iLatentLayer, GetPointer(this), layers.Total() - 1) ||
!cTargetCritic2.feedForward(GetPointer(this), iLatentLayer, GetPointer(this), layers.Total() - 1))
return false;
//---
if(!cTargetNu.feedForward(GetPointer(this), iLatentLayer, GetPointer(this), layers.Total() - 1))
return false;
//---
if(!CNet::feedForward(State, 1, false, SecondInput))
return false;
CBufferFloat *output = ((CNeuronBaseOCL*)((CLayer*)layers.At(layers.Total() - 1)).At(0)).getOutput();
output.AssignArray(Actions);
output.BufferWrite();
if(!cNu.feedForward(GetPointer(this), iLatentLayer, GetPointer(this)))
return false;
if(!cZeta.feedForward(GetPointer(this), iLatentLayer, GetPointer(this)))
return false;
//---
vector<float> nu, next_nu, zeta, ones;
cNu.getResults(nu);
cZeta.getResults(zeta);
if(!!NextState)
cTargetNu.getResults(next_nu);
else
next_nu = vector<float>::Zeros(nu.Size());
ones = vector<float>::Ones(zeta.Size());
vector<float> log_prob = GetLogProbability(output);
int shift = (int)(Rewards.Size() - log_prob.Size());
if(shift < 0)
return false;
float policy_ratio = 0;
for(ulong i = 0; i < log_prob.Size(); i++)
policy_ratio += log_prob[i] - Rewards[shift + i] / LogProbMultiplier;
policy_ratio = MathExp(policy_ratio / log_prob.Size());
vector<float> bellman_residuals = (next_nu * discount + Rewards) * policy_ratio - nu;
vector<float> zeta_loss = MathPow(zeta, 2.0f) / 2.0f - zeta * (MathAbs(bellman_residuals) - fLambda) ;
vector<float> nu_loss = zeta * MathAbs(bellman_residuals) + MathPow(nu, 2.0f) / 2.0f;
vector<float> lambda_los = fLambda * (ones - zeta);
//--- update lambda
vector<float> grad_lambda = CAGrad((ones - zeta) * (lambda_los * (-1.0f)));
fLambda_m = fLambda_m * b1 + grad_lambda * (1 - b1);
fLambda_v = fLambda_v * b2 + MathPow(grad_lambda, 2) * (1.0f - b2);
fLambda += fLambda_m * lr / MathSqrt(fLambda_v + lr / 100.0f);
//---
CBufferFloat temp;
temp.BufferInit(MathMax(Actions.Total(), SecondInput.Total()), 0);
temp.BufferCreate(opencl);
//--- update nu
int last_layer = cNu.layers.Total() - 1;
CLayer *layer = cNu.layers.At(last_layer);
if(!layer)
return false;
CNeuronBaseOCL *neuron = layer.At(0);
if(!neuron)
return false;
CBufferFloat *buffer = neuron.getGradient();
if(!buffer)
return false;
vector<float> nu_grad = CAGrad(nu_loss * (zeta * bellman_residuals / MathAbs(bellman_residuals) - nu));
if(!buffer.AssignArray(nu_grad) || !buffer.BufferWrite())
return false;
if(!cNu.backPropGradient(output, GetPointer(temp)))
return false;
//--- update zeta
last_layer = cZeta.layers.Total() - 1;
layer = cZeta.layers.At(last_layer);
if(!layer)
return false;
neuron = layer.At(0);
if(!neuron)
return false;
buffer = neuron.getGradient();
if(!buffer)
return false;
vector<float> zeta_grad = CAGrad(zeta_loss * (zeta - MathAbs(bellman_residuals) + fLambda) * (-1.0f));
if(!buffer.AssignArray(zeta_grad) || !buffer.BufferWrite())
return false;
if(!cZeta.backPropGradient(output, GetPointer(temp)))
return false;
//--- feed forward critics
if(!cCritic1.feedForward(GetPointer(this), iLatentLayer, output) ||
!cCritic2.feedForward(GetPointer(this), iLatentLayer, output))
return false;
//--- target
vector<float> result;
if(fZeta.CompareByDigits(vector<float>::Zeros(fZeta.Size()),8) == 0)
fZeta = MathAbs(zeta);
else
fZeta = fZeta * 0.9f + MathAbs(zeta) * 0.1f;
zeta = MathPow(MathAbs(zeta), 1.0f / 3.0f) / (MathPow(fZeta, 1.0f / 3.0f) * 10.0f);
vector<float> target = vector<float>::Zeros(Rewards.Size());
if(!!NextState)
{
cTargetCritic1.getResults(target);
cTargetCritic2.getResults(result);
if(fQWeights.Dot(result) < fQWeights.Dot(target))
target = result;
}
target = (target * discount + Rewards);
ulong total = log_prob.Size();
for(ulong i = 0; i < total; i++)
target[shift + i] = log_prob[i] * LogProbMultiplier;
//--- update critic1
cCritic1.getResults(result);
vector<float> loss = zeta * MathPow(result - target, 2.0f);
if(fLoss1 == 0)
fLoss1 = MathSqrt(fQWeights.Dot(loss) / fQWeights.Sum());
else
fLoss1 = MathSqrt(0.999f * MathPow(fLoss1, 2.0f) + 0.001f * fQWeights.Dot(loss) / fQWeights.Sum());
vector<float> grad = CAGrad(loss * zeta * (target - result) * 2.0f);
last_layer = cCritic1.layers.Total() - 1;
layer = cCritic1.layers.At(last_layer);
if(!layer)
return false;
neuron = layer.At(0);
if(!neuron)
return false;
buffer = neuron.getGradient();
if(!buffer)
return false;
if(!buffer.AssignArray(grad) || !buffer.BufferWrite())
return false;
if(!cCritic1.backPropGradient(output, GetPointer(temp)) || !backPropGradient(SecondInput, GetPointer(temp), iLatentLayer))
return false;
//--- update critic2
cCritic2.getResults(result);
loss = zeta * MathPow(result - target, 2.0f);
if(fLoss2 == 0)
fLoss2 = MathSqrt(fQWeights.Dot(loss) / fQWeights.Sum());
else
fLoss2 = MathSqrt(0.999f * MathPow(fLoss2, 2.0f) + 0.001f * fQWeights.Dot(loss) / fQWeights.Sum());
grad = CAGrad(loss * zeta * (target - result) * 2.0f);
last_layer = cCritic2.layers.Total() - 1;
layer = cCritic2.layers.At(last_layer);
if(!layer)
return false;
neuron = layer.At(0);
if(!neuron)
return false;
buffer = neuron.getGradient();
if(!buffer)
return false;
if(!buffer.AssignArray(grad) || !buffer.BufferWrite())
return false;
if(!cCritic2.backPropGradient(output, GetPointer(temp)) || !backPropGradient(SecondInput, GetPointer(temp), iLatentLayer))
return false;
//--- update policy
vector<float> mean;
CNet *critic = NULL;
if(fLoss1 <= fLoss2)
{
cCritic1.getResults(result);
cCritic2.getResults(mean);
critic = GetPointer(cCritic1);
}
else
{
cCritic1.getResults(mean);
cCritic2.getResults(result);
critic = GetPointer(cCritic2);
}
vector<float> var = MathAbs(mean - result) / 2.0f;
mean += result;
mean /= 2.0f;
target = mean;
for(ulong i = 0; i < log_prob.Size(); i++)
target[shift + i] = discount * log_prob[i] * LogProbMultiplier;
target = CAGrad(zeta * (target - var * 2.5f) - result) + result;
CBufferFloat bTarget;
bTarget.AssignArray(target);
critic.TrainMode(false);
if(!critic.backProp(GetPointer(bTarget), GetPointer(this)) ||
!backPropGradient(SecondInput, GetPointer(temp)))
{
critic.TrainMode(true);
return false;
}
//--- update exploration policy
if(!cActorExploer.feedForward(State, 1, false, SecondInput))
{
critic.TrainMode(true);
return false;
}
output = ((CNeuronBaseOCL*)((CLayer*)cActorExploer.layers.At(layers.Total() - 1)).At(0)).getOutput();
output.AssignArray(Actions);
output.BufferWrite();
cActorExploer.GetLogProbs(log_prob);
target = mean;
for(ulong i = 0; i < log_prob.Size(); i++)
target[shift + i] = discount * log_prob[i] * LogProbMultiplier;
target = CAGrad(zeta * (target + var * 2.0f) - result) + result;
bTarget.AssignArray(target);
if(!critic.backProp(GetPointer(bTarget), GetPointer(cActorExploer)) ||
!cActorExploer.backPropGradient(SecondInput, GetPointer(temp)))
{
critic.TrainMode(true);
return false;
}
critic.TrainMode(true);
//---
if(!!NextState)
{
if(iUpdateDelayCount > 0)
{
iUpdateDelayCount--;
return true;
}
iUpdateDelayCount = iUpdateDelay;
}
if(!cTargetCritic1.WeightsUpdate(GetPointer(cCritic1), tau) ||
!cTargetCritic2.WeightsUpdate(GetPointer(cCritic2), tau) ||
!cTargetNu.WeightsUpdate(GetPointer(cNu), tau))
{
PrintFormat("Error of update target models: %d", GetLastError());
return false;
}
//---
return true;
}
//+------------------------------------------------------------------+
//| |
//+------------------------------------------------------------------+
vector<float> CNet_SAC_D_DICE::GetLogProbability(CBufferFloat *Actions)
{
CBufferFloat temp;
vector<float> result = vector<float>::Zeros(0);
temp.AssignArray(Actions);
if(!CalcLogProbs(GetPointer(temp)))
return result;
temp.GetData(result);
//---
return result;
}
//+------------------------------------------------------------------+
//| |
//+------------------------------------------------------------------+
bool CNet_SAC_D_DICE::Save(string file_name, bool common = true)
{
if(file_name == NULL)
return false;
//---
int handle = FileOpen(file_name + ".set", (common ? FILE_COMMON : 0) | FILE_BIN | FILE_WRITE);
if(handle == INVALID_HANDLE)
return false;
ulong size = fLambda.Size();
if(FileWriteInteger(handle, iLatentLayer) < sizeof(iLatentLayer) ||
FileWriteLong(handle, (long)size) < sizeof(long))
return false;
for(ulong i = 0; i < size; i++)
if(FileWriteFloat(handle, fLambda[i]) < sizeof(fLambda[i]) ||
FileWriteFloat(handle, fLambda_m[i]) < sizeof(fLambda_m[i]) ||
FileWriteFloat(handle, fLambda_v[i]) < sizeof(fLambda_v[i]) ||
FileWriteFloat(handle, fQWeights[i]) < sizeof(fQWeights[i]))
return false;
FileFlush(handle);
FileClose(handle);
//---
if(!CNet::Save(file_name + "Act.nnw", 0, 0, 0, TimeCurrent(), common))
return false;
//---
if(!cActorExploer.Save(file_name + "ActExp.nnw", 0, 0, 0, TimeCurrent(), common))
return false;
//---
if(!cTargetCritic1.Save(file_name + "Crt1.nnw", fLoss1, 0, 0, TimeCurrent(), common))
return false;
//---
if(!cTargetCritic2.Save(file_name + "Crt2.nnw", fLoss2, 0, 0, TimeCurrent(), common))
return false;
//---
if(!cZeta.Save(file_name + "Zeta.nnw", 0, 0, 0, TimeCurrent(), common))
return false;
//---
if(!cTargetNu.Save(file_name + "Nu.nnw", 0, 0, 0, TimeCurrent(), common))
return false;
//---
return true;
}
//+------------------------------------------------------------------+
//| |
//+------------------------------------------------------------------+
bool CNet_SAC_D_DICE::Load(string file_name, bool common = true)
{
if(file_name == NULL)
return false;
//---
int handle = FileOpen(file_name + ".set", (common ? FILE_COMMON : 0) | FILE_BIN | FILE_READ);
if(handle == INVALID_HANDLE)
return false;
if(FileIsEnding(handle))
return false;
iLatentLayer = FileReadInteger(handle);
if(FileIsEnding(handle))
return false;
ulong size = (ulong)FileReadLong(handle);
fLambda = vector<float>::Zeros(size);
fLambda_m = fLambda;
fLambda_v = fLambda;
fQWeights = vector<float>::Ones(size);
for(ulong i = 0; i < size; i++)
{
if(FileIsEnding(handle))
return false;
fLambda[i] = FileReadFloat(handle);
if(FileIsEnding(handle))
return false;
fLambda_m[i] = FileReadFloat(handle);
if(FileIsEnding(handle))
return false;
fLambda_v[i] = FileReadFloat(handle);
if(FileIsEnding(handle))
return false;
fQWeights[i] = FileReadFloat(handle);
}
FileClose(handle);
//---
float temp;
datetime dt;
if(!CNet::Load(file_name + "Act.nnw", temp, temp, temp, dt, common))
return false;
//---
if(!cActorExploer.Load(file_name + "ActExp.nnw", temp, temp, temp, dt, common))
return false;
//---
if(!cCritic1.Load(file_name + "Crt1.nnw", fLoss1, temp, temp, dt, common) ||
!cTargetCritic1.Load(file_name + "Crt1.nnw", temp, temp, temp, dt, common))
return false;
//---
if(!cCritic2.Load(file_name + "Crt2.nnw", fLoss2, temp, temp, dt, common) ||
!cTargetCritic2.Load(file_name + "Crt2.nnw", temp, temp, temp, dt, common))
return false;
//---
if(!cZeta.Load(file_name + "Zeta.nnw", temp, temp, temp, dt, common))
return false;
//---
if(!cNu.Load(file_name + "Nu.nnw", temp, temp, temp, dt, common) ||
!cTargetNu.Load(file_name + "Nu.nnw", temp, temp, temp, dt, common))
return false;
//---
cActorExploer.SetOpenCL(opencl);
cCritic1.SetOpenCL(opencl);
cCritic2.SetOpenCL(opencl);
cZeta.SetOpenCL(opencl);
cNu.SetOpenCL(opencl);
cTargetCritic1.SetOpenCL(opencl);
cTargetCritic2.SetOpenCL(opencl);
cTargetNu.SetOpenCL(opencl);
//---
return true;
}
//+------------------------------------------------------------------+
//| |
//+------------------------------------------------------------------+
vector<float> CNet_SAC_D_DICE::CAGrad(vector<float> &grad)
{
matrix<float> GG = grad.Outer(grad);
GG.ReplaceNan(0);
if(MathAbs(GG).Sum() == 0)
return grad;
float scale = MathSqrt(GG.Diag() + 1.0e-4f).Mean();
GG = GG / MathPow(scale,2);
vector<float> Gg = GG.Mean(1);
float gg = Gg.Mean();
vector<float> w = vector<float>::Zeros(grad.Size());
float c = MathSqrt(gg + 1.0e-4f) * fCAGrad_C;
vector<float> w_best = w;
float obj_best = FLT_MAX;
vector<float> moment = vector<float>::Zeros(w.Size());
for(int i = 0; i < iCAGrad_Iters; i++)
{
vector<float> ww;
w.Activation(ww,AF_SOFTMAX);
float obj = ww.Dot(Gg) + c * MathSqrt(ww.MatMul(GG).Dot(ww) + 1.0e-4f);
if(MathAbs(obj) < obj_best)
{
obj_best = MathAbs(obj);
w_best = w;
}
if(i < (iCAGrad_Iters - 1))
{
float loss = -obj;
vector<float> derev = Gg + GG.MatMul(ww) * c / (MathSqrt(ww.MatMul(GG).Dot(ww) + 1.0e-4f) * 2) + ww.MatMul(GG) * c / (MathSqrt(ww.MatMul(GG).Dot(ww) + 1.0e-4f) * 2);
vector<float> delta = derev * loss;
ulong size = delta.Size();
matrix<float> ident = matrix<float>::Identity(size, size);
vector<float> ones = vector<float>::Ones(size);
matrix<float> sm_der = ones.Outer(ww);
sm_der = sm_der.Transpose() * (ident - sm_der);
delta = sm_der.MatMul(delta);
if(delta.Ptp() != 0)
delta = delta / delta.Ptp();
moment = delta * 0.8f + moment * 0.5f;
w += moment;
if(w.Ptp() != 0)
w = w / w.Ptp();
}
}
w_best.Activation(w,AF_SOFTMAX);
float gw_norm = MathSqrt(w.MatMul(GG).Dot(w) + 1.0e-4f);
float lmbda = c / (gw_norm + 1.0e-4f);
vector<float> result = ((w * lmbda + 1.0f / (float)grad.Size()) * grad) / (1 + MathPow(fCAGrad_C,2));
//---
return result;
}
//+------------------------------------------------------------------+
//| |
//+------------------------------------------------------------------+
bool CNet_SAC_D_DICE::TargetsUpdate(float tau)
{
if(!cTargetCritic1.WeightsUpdate(GetPointer(cCritic1), tau) ||
!cTargetCritic2.WeightsUpdate(GetPointer(cCritic2), tau) ||
!cTargetNu.WeightsUpdate(GetPointer(cNu), tau))
{
PrintFormat("Error of update target models: %d", GetLastError());
return false;
}
//---
return true;
}
//+------------------------------------------------------------------+
Copy permalink