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NN_in_Trading/Experts/RL/QRDQN.mqh

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Split NeuroNet.mqh
2026-06-05 22:00:59 +03:00
//+------------------------------------------------------------------+
//| QR-DQN.mqh |
//| Copyright DNG® |
//| https://www.mql5.com/ru/users/dng |
//+------------------------------------------------------------------+
#property copyright "Copyright DNG®"
#property link "https://www.mql5.com/ru/users/dng"
#property version "1.00"
//+------------------------------------------------------------------+
//| Includes |
//+------------------------------------------------------------------+
#include "..\NeuroNet_DNG\NeuroNet.mqh"
//---
#define defQRDQN 0x7791 ///<Neuron Net \details Identified class #CQRDQN
//+------------------------------------------------------------------+
//| |
//+------------------------------------------------------------------+
class CQRDQN : protected CNet
{
private:
uint iCountBackProp;
protected:
uint iNumbers;
uint iActions;
uint iUpdateTarget;
matrix<float> mTaus;
//---
CNet cTargetNet;
public:
/** Constructor */
CQRDQN(void);
CQRDQN(CArrayObj *Description) { Create(Description, iActions); }
bool Create(CArrayObj *Description, uint actions);
/** Destructor */~CQRDQN(void);
bool feedForward(CArrayFloat *inputVals, int window = 1, bool tem = true) ///< Feed Forward method.@param[in] prevLayer Pointer to previos layer. @param[in] window Window of input data. @param[in] tem Use Time Embedding.
{ return CNet::feedForward(inputVals, window, tem); }
bool backProp(CBufferFloat *targetVals, float discount, CArrayFloat *nextState=NULL, int window = 1, bool tem = true); ///< Back propagation method. @param[in] targetVals Target values
void getResults(CBufferFloat *&resultVals); ///< Method to get results of feed forward process.@param[out] resultVals Array of result values
int getAction(void); ///< Method to get results of feed forward process.@param[out] resultVals Array of result values
int getSample(void);
float getRecentAverageError() { return recentAverageError; } ///< Method to check quality of study. @return Average error
bool Save(string file_name, datetime time, bool common = true)
{ return CNet::Save(file_name, getRecentAverageError(), (float)iActions, 0, time, common); }
///< Save method. @param[in] file_name File name to save @param[in] error Average error @param[in] undefine Undefined percent @param[in] Foecast percent @param[in] time Last study time @param[in] common Common flag
virtual bool Save(const int file_handle);
virtual bool Load(string file_name, datetime &time, bool common = true);
///< Load method. @param[in] file_name File name to save @param[out] error Average error @param[out] undefine Undefined percent @param[out] Foecast percent @param[out] time Last study time @param[in] common Common flag
virtual bool Load(const int file_handle);
//---
virtual int Type(void) const { return defQRDQN; }///< Identificator of class.@return Type of class
virtual bool TrainMode(bool flag) { return CNet::TrainMode(flag); } ///< Set Training Mode Flag
virtual bool GetLayerOutput(uint layer, CBufferFloat *&result) ///< Retutn Output data of layer. @param[in] layer Number of layer @param[out] return Buffer with data
{ return CNet::GetLayerOutput(layer, result); }
//---
virtual void SetUpdateTarget(uint batch) { iUpdateTarget = batch; }
virtual bool UpdateTarget(string file_name);
//---
virtual bool SetActions(uint actions);
};
//+------------------------------------------------------------------+
//| |
//+------------------------------------------------------------------+
CQRDQN::CQRDQN() : iNumbers(31),
iActions(2),
iUpdateTarget(1000)
{
mTaus = matrix<float>::Ones(1, iNumbers) / iNumbers;
mTaus[0, 0] /= 2;
mTaus = mTaus.CumSum(0);
cTargetNet.Create(NULL);
Create(NULL, iActions);
}
//+------------------------------------------------------------------+
//| |
//+------------------------------------------------------------------+
CQRDQN::~CQRDQN()
{
}
//+------------------------------------------------------------------+
//| |
//+------------------------------------------------------------------+
bool CQRDQN::Create(CArrayObj *Description, uint actions)
{
if(actions <= 0 || !CNet::Create(Description))
return false;
int last_layer = Description.Total() - 1;
CLayer *layer = layers.At(last_layer);
if(!layer)
return false;
CNeuronBaseOCL *neuron = layer.At(0);
if(!neuron)
return false;
iActions = actions;
iNumbers = neuron.Neurons() / actions;
mTaus = matrix<float>::Ones(1, iNumbers) / iNumbers;
mTaus[0, 0] /= 2;
mTaus = mTaus.CumSum(0);
cTargetNet.Create(NULL);
//---
return true;
}
//+------------------------------------------------------------------+
//| |
//+------------------------------------------------------------------+
bool CQRDQN::backProp(CBufferFloat *targetVals, float discount, CArrayFloat *nextState=NULL, int window = 1, bool tem = true)
{
//---
if(!targetVals)
return false;
vectorf target;
if(!targetVals.GetData(target) || target.Size() != iActions)
return false;
//---
if(!!nextState)
{
if(!cTargetNet.feedForward(nextState, window, tem))
return false;
vectorf temp;
cTargetNet.getResults(targetVals);
if(!targetVals.GetData(temp))
return false;
matrixf q = matrixf::Zeros(1, temp.Size());
if(!q.Row(temp, 0) || !q.Reshape(iActions, iNumbers))
return false;
temp = q.Mean(0);
target = target + discount * temp.Max();
}
//---
vectorf quantils;
getResults(targetVals);
if(!targetVals.GetData(quantils))
return false;
matrixf Q = matrixf::Zeros(1, quantils.Size());
if(!Q.Row(quantils, 0) || !Q.Reshape(iActions, iNumbers))
return false;
//---
for(uint a = 0; a < iActions; a++)
{
vectorf q = Q.Row(a);
vectorf dp = q - target[a], dn = dp;
if(!dp.Clip(0, FLT_MAX) || !dn.Clip(-FLT_MAX, 0))
return false;
dp = (mTaus.Row(0) - 1) * dp;
dn = mTaus.Row(0) * dn * (-1);
if(!Q.Row(dp + dn + q, a))
return false;
}
if(!targetVals.AssignArray(Q))
return false;
//---
if(iCountBackProp >= iUpdateTarget)
{
#ifdef FileName
if(UpdateTarget(FileName + ".nnw"))
#else
if(UpdateTarget("QRDQN.upd"))
#endif
iCountBackProp = 0;
}
else
iCountBackProp++;
//---
return CNet::backProp(targetVals);
}
//+------------------------------------------------------------------+
//| |
//+------------------------------------------------------------------+
void CQRDQN::getResults(CBufferFloat *&resultVals)
{
CNet::getResults(resultVals);
if(!resultVals)
return;
vectorf temp;
if(!resultVals.GetData(temp))
{
delete resultVals;
return;
}
matrixf q;
if(!q.Init(1, temp.Size()) || !q.Row(temp, 0) || !q.Reshape(iActions, iNumbers))
{
delete resultVals;
return;
}
//---
if(!resultVals.AssignArray(q.Mean(1)))
{
delete resultVals;
return;
}
//---
}
//+------------------------------------------------------------------+
//| |
//+------------------------------------------------------------------+
int CQRDQN::getAction(void)
{
CBufferFloat *temp;
getResults(temp);
if(!temp)
return -1;
//---
return temp.Maximum(0, temp.Total());
}
//+------------------------------------------------------------------+
//| |
//+------------------------------------------------------------------+
int CQRDQN::getSample(void)
{
CBufferFloat* resultVals;
CNet::getResults(resultVals);
if(!resultVals)
return -1;
vectorf temp;
if(!resultVals.GetData(temp))
{
delete resultVals;
return -1;
}
delete resultVals;
matrixf q;
if(!q.Init(1, temp.Size()) || !q.Row(temp, 0) || !q.Reshape(iActions, iNumbers))
{
delete resultVals;
return -1;
}
//---
if(!q.Mean(1).Activation(temp, AF_SOFTMAX))
return -1;
temp = temp.CumSum();
int err_code;
float random = (float)Math::MathRandomNormal(0.5, 0.5, err_code);
if(random >= 1)
return (int)temp.Size() - 1;
for(int i = 0; i < (int)temp.Size(); i++)
if(random <= temp[i] && temp[i] > 0)
return i;
//---
return -1;
}
//+------------------------------------------------------------------+
//| |
//+------------------------------------------------------------------+
bool CQRDQN::UpdateTarget(string file_name)
{
if(!Save(file_name, 0, false))
return false;
float error, undefine, forecast;
datetime time;
if(!cTargetNet.Load(file_name, error, undefine, forecast, time, false))
return false;
iCountBackProp = 0;
//---
return true;
}
//+------------------------------------------------------------------+
//| |
//+------------------------------------------------------------------+
bool CQRDQN::Save(const int file_handle)
{
if(!CNet::Save(file_handle))
return false;
if(FileWriteInteger(file_handle, (int)iUpdateTarget) < INT_VALUE)
return false;
if(FileWriteInteger(file_handle, (int)iActions) < INT_VALUE)
return false;
//---
return true;
}
//+------------------------------------------------------------------+
//| |
//+------------------------------------------------------------------+
bool CQRDQN::Load(const int file_handle)
{
if(!CNet::Load(file_handle))
return false;
if(FileIsEnding(file_handle))
return true;
iUpdateTarget = (uint)FileReadInteger(file_handle);
if(!SetActions((uint)FileReadInteger(file_handle)))
return false;
if(!UpdateTarget("DQDQN.upd"))
return false;
iCountBackProp = 0;
//---
return true;
}
//+------------------------------------------------------------------+
//| |
//+------------------------------------------------------------------+
bool CQRDQN::SetActions(uint actions)
{
if(actions <= 0)
return false;
iActions = actions;
CLayer *layer = layers.At(layers.Total() - 1);
if(!layer)
return false;
CNeuronBaseOCL *neuron = layer.At(0);
if(!neuron)
return false;
iNumbers = neuron.Neurons() / iActions;
mTaus = matrix<float>::Ones(1, iNumbers) / iNumbers;
mTaus[0, 0] /= 2;
mTaus = mTaus.CumSum(1);
//---
return true;
}
//+------------------------------------------------------------------+
//| |
//+------------------------------------------------------------------+
bool CQRDQN::Load(string file_name, datetime &time, bool common = true)
{
float undefine, forecast;
if(!CNet::Load(file_name, recentAverageError, undefine, forecast, time, common))
return false;
//---
return (undefine>0 ? SetActions((uint)undefine) : true);
}
//+------------------------------------------------------------------+
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