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Warrior_EA/references/MQL5/Experts/ExORL/Trajectory.mqh

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fix(cl): stabilize network training with tighter limits, weight decay, and sign-agreement gate - Tighten MAX_WEIGHT from 1.0e6 to 100.0 to prevent unbounded weight growth. - Add MIN_ACTIVATION_DERIVATIVE (1e-4) to avoid zero gradients for saturated units. - Introduce WEIGHT_DECAY (0.01) to decouple decay from Adam updates. - Add MAX_WEIGHT_DELTA (0.1) to clamp per-step Adam updates and prevent overshoot. - Apply sign-agreement gate on weight updates in Adam kernel to only apply steps aligned with current gradient direction. - Fix tanh/sigmoid derivative calculations to use the new floor instead of hard-coded edge-case values.
2026-07-15 21:47:37 -04:00
<EFBFBD><EFBFBD>//+------------------------------------------------------------------+
//| Trajectory.mqh |
//| Copyright DNG<EFBFBD> |
//| https://www.mql5.com/en/users/dng |
//+------------------------------------------------------------------+
#property copyright "Copyright DNG<00>"
#property link "https://www.mql5.com/en/users/dng"
#property version "1.00"
//+------------------------------------------------------------------+
//| Rewards structure |
//| 0 - Delta Balance |
//| 1 - Delta Equity ( "-" Drawdown / "+" Profit) |
//| 2 - Penalty for no open positions |
//| 3 - NNM |
//| 4 - Latent NNM |
//+------------------------------------------------------------------+
#include "..\NeuroNet_DNG\NeuroNet.mqh"
//---
#define HistoryBars 20 //Depth of history
#define BarDescr 9 //Elements for 1 bar description
#define AccountDescr 12 //Account description
#define NActions 6 //Number of possible Actions
#define NRewards 5 //Number of rewards
#define EmbeddingSize 16
#define Buffer_Size 6500
#define DiscFactor 0.99f
#define FileName "ExORL"
#define LatentLayer 9
#define LatentCount 1024
#define SamplLatentStates 32
#define MaxSL 1000
#define MaxTP 1000
#define MaxReplayBuffer 500
#define StartTargetIteration 50000
#define fCAGrad_C 0.5f
#define iCAGrad_Iters 15
#define Quant 0.001
//+------------------------------------------------------------------+
//| |
//+------------------------------------------------------------------+
struct STarget
{
vector<float> rewards;
vector<float> actions;
//---
STarget(void)
{
rewards=vector<float>::Zeros(NRewards);
actions=vector<float>::Zeros(NActions);
}
};
//+------------------------------------------------------------------+
//| |
//+------------------------------------------------------------------+
struct SState
{
float state[HistoryBars * BarDescr];
float account[AccountDescr - 4];
float action[NActions];
float rewards[NRewards];
//---
SState(void);
//---
bool Save(int file_handle);
bool Load(int file_handle);
//--- overloading
void operator=(const SState &obj)
{
ArrayCopy(state, obj.state);
ArrayCopy(account, obj.account);
ArrayCopy(action, obj.action);
ArrayCopy(rewards, obj.rewards);
}
};
//+------------------------------------------------------------------+
//| |
//+------------------------------------------------------------------+
SState::SState(void)
{
ArrayInitialize(state, 0);
ArrayInitialize(account, 0);
ArrayInitialize(action, 0);
ArrayInitialize(rewards, 0);
}
//+------------------------------------------------------------------+
//| |
//+------------------------------------------------------------------+
bool SState::Save(int file_handle)
{
if(file_handle == INVALID_HANDLE)
return false;
//---
int total = ArraySize(state);
if(FileWriteInteger(file_handle, total) < sizeof(int))
return false;
for(int i = 0; i < total; i++)
if(FileWriteFloat(file_handle, state[i]) < sizeof(float))
return false;
//---
total = ArraySize(account);
if(FileWriteInteger(file_handle, total) < sizeof(int))
return false;
for(int i = 0; i < total; i++)
if(FileWriteFloat(file_handle, account[i]) < sizeof(float))
return false;
//---
total = ArraySize(action);
if(FileWriteInteger(file_handle, total) < sizeof(int))
return false;
for(int i = 0; i < total; i++)
if(FileWriteFloat(file_handle, action[i]) < sizeof(float))
return false;
total = ArraySize(rewards);
if(FileWriteInteger(file_handle, total) < sizeof(int))
return false;
for(int i = 0; i < total; i++)
if(FileWriteFloat(file_handle, rewards[i]) < sizeof(float))
return false;
//---
return true;
}
//+------------------------------------------------------------------+
//| |
//+------------------------------------------------------------------+
bool SState::Load(int file_handle)
{
if(file_handle == INVALID_HANDLE)
return false;
if(FileIsEnding(file_handle))
return false;
//---
int total = FileReadInteger(file_handle);
if(total != ArraySize(state))
return false;
//---
for(int i = 0; i < total; i++)
{
if(FileIsEnding(file_handle))
return false;
state[i] = FileReadFloat(file_handle);
}
//---
total = FileReadInteger(file_handle);
if(total != ArraySize(account))
return false;
//---
for(int i = 0; i < total; i++)
{
if(FileIsEnding(file_handle))
return false;
account[i] = FileReadFloat(file_handle);
}
//---
total = FileReadInteger(file_handle);
if(total != ArraySize(action))
return false;
//---
for(int i = 0; i < total; i++)
{
if(FileIsEnding(file_handle))
return false;
action[i] = FileReadFloat(file_handle);
}
//---
total = FileReadInteger(file_handle);
if(total != ArraySize(rewards))
return false;
//---
for(int i = 0; i < total; i++)
{
if(FileIsEnding(file_handle))
return false;
rewards[i] = FileReadFloat(file_handle);
}
//---
return true;
}
//+------------------------------------------------------------------+
//| |
//+------------------------------------------------------------------+
struct STrajectory
{
SState States[Buffer_Size];
int Total;
float DiscountFactor;
bool CumCounted;
//---
STrajectory(void);
//---
bool Add(SState &state);
void CumRevards(void);
//---
bool Save(int file_handle);
bool Load(int file_handle);
};
//+------------------------------------------------------------------+
//| |
//+------------------------------------------------------------------+
STrajectory::STrajectory(void) : Total(0),
DiscountFactor(DiscFactor),
CumCounted(false)
{
}
//+------------------------------------------------------------------+
//| |
//+------------------------------------------------------------------+
bool STrajectory::Save(int file_handle)
{
if(file_handle == INVALID_HANDLE)
return false;
//---
if(!CumCounted)
CumRevards();
if(FileWriteInteger(file_handle, Total) < sizeof(int))
return false;
if(FileWriteFloat(file_handle, DiscountFactor) < sizeof(float))
return false;
for(int i = 0; i < Total; i++)
if(!States[i].Save(file_handle))
return false;
//---
return true;
}
//+------------------------------------------------------------------+
//| |
//+------------------------------------------------------------------+
bool STrajectory::Load(int file_handle)
{
if(file_handle == INVALID_HANDLE)
return false;
//---
Total = FileReadInteger(file_handle);
if(FileIsEnding(file_handle) || Total >= ArraySize(States))
return false;
DiscountFactor = FileReadFloat(file_handle);
CumCounted = true;
//---
for(int i = 0; i < Total; i++)
if(!States[i].Load(file_handle))
return false;
//---
return true;
}
//+------------------------------------------------------------------+
//| |
//+------------------------------------------------------------------+
void STrajectory::CumRevards(void)
{
if(CumCounted)
return;
//---
for(int i = Total - 2; i >= 0; i--)
for(int r = 0; r < NRewards; r++)
States[i].rewards[r] += States[i + 1].rewards[r] * DiscountFactor;
CumCounted = true;
}
//+------------------------------------------------------------------+
//| |
//+------------------------------------------------------------------+
bool STrajectory::Add(SState &state)
{
if(Total + 1 >= ArraySize(States))
return false;
States[Total] = state;
Total++;
//---
return true;
}
//+------------------------------------------------------------------+
//| |
//+------------------------------------------------------------------+
bool SaveTotalBase(void)
{
int total = ArraySize(Buffer);
if(total < 0)
return true;
int handle = FileOpen(FileName + ".bd", FILE_WRITE | FILE_BIN | FILE_COMMON);
if(handle < 0)
return false;
int start = MathMax(total - MaxReplayBuffer, 0);
if(FileWriteInteger(handle, total - start) < INT_VALUE)
{
FileClose(handle);
return false;
}
for(int i = start; i < total; i++)
if(!Buffer[i].Save(handle))
{
FileClose(handle);
return false;
}
FileFlush(handle);
FileClose(handle);
//---
return true;
}
//+------------------------------------------------------------------+
//| |
//+------------------------------------------------------------------+
bool LoadTotalBase(void)
{
int handle = FileOpen(FileName + ".bd", FILE_READ | FILE_BIN | FILE_COMMON | FILE_SHARE_READ);
if(handle < 0)
return false;
int total = FileReadInteger(handle);
if(total <= 0)
{
FileClose(handle);
return false;
}
if(ArrayResize(Buffer, total) < total)
{
FileClose(handle);
return false;
}
for(int i = 0; i < total; i++)
if(!Buffer[i].Load(handle))
{
FileClose(handle);
return false;
}
FileClose(handle);
//---
return true;
}
//+------------------------------------------------------------------+
//| |
//+------------------------------------------------------------------+
bool CreateDescriptions(CArrayObj *actor, CArrayObj *critic, CArrayObj *convolution)
{
//---
CLayerDescription *descr;
//---
if(!actor)
{
actor = new CArrayObj();
if(!actor)
return false;
}
if(!critic)
{
critic = new CArrayObj();
if(!critic)
return false;
}
if(!convolution)
{
convolution = new CArrayObj();
if(!convolution)
return false;
}
//--- Actor
actor.Clear();
//--- Input layer
if(!(descr = new CLayerDescription()))
return false;
descr.type = defNeuronBaseOCL;
int prev_count = descr.count = (HistoryBars * BarDescr);
descr.activation = None;
descr.optimization = ADAM;
if(!actor.Add(descr))
{
delete descr;
return false;
}
//--- layer 1
if(!(descr = new CLayerDescription()))
return false;
descr.type = defNeuronBatchNormOCL;
descr.count = prev_count;
descr.batch = 1000;
descr.activation = None;
descr.optimization = ADAM;
if(!actor.Add(descr))
{
delete descr;
return false;
}
//--- layer 2
if(!(descr = new CLayerDescription()))
return false;
descr.type = defNeuronConvOCL;
prev_count = descr.count = HistoryBars;
descr.window = BarDescr;
descr.step = BarDescr;
int prev_wout = descr.window_out = BarDescr / 2;
descr.activation = LReLU;
descr.optimization = ADAM;
if(!actor.Add(descr))
{
delete descr;
return false;
}
//--- layer 3
if(!(descr = new CLayerDescription()))
return false;
descr.type = defNeuronSoftMaxOCL;
descr.count = prev_count;
descr.step = prev_wout;
descr.optimization = ADAM;
descr.activation = None;
if(!convolution.Add(descr))
{
delete descr;
return false;
}
//--- layer 4
if(!(descr = new CLayerDescription()))
return false;
descr.type = defNeuronConvOCL;
prev_count = descr.count = prev_count;
descr.window = prev_wout;
descr.step = prev_wout;
prev_wout = descr.window_out = 8;
descr.activation = LReLU;
descr.optimization = ADAM;
if(!actor.Add(descr))
{
delete descr;
return false;
}
//--- layer 5
if(!(descr = new CLayerDescription()))
return false;
descr.type = defNeuronSoftMaxOCL;
descr.count = prev_count;
descr.step = prev_wout;
descr.optimization = ADAM;
descr.activation = None;
if(!convolution.Add(descr))
{
delete descr;
return false;
}
//--- layer 6
if(!(descr = new CLayerDescription()))
return false;
descr.type = defNeuronBaseOCL;
descr.count = LatentCount;
descr.optimization = ADAM;
descr.activation = LReLU;
if(!actor.Add(descr))
{
delete descr;
return false;
}
//--- layer 7
if(!(descr = new CLayerDescription()))
return false;
descr.type = defNeuronBaseOCL;
prev_count = descr.count = LatentCount;
descr.activation = LReLU;
descr.optimization = ADAM;
if(!actor.Add(descr))
{
delete descr;
return false;
}
//--- layer 8
if(!(descr = new CLayerDescription()))
return false;
descr.type = defNeuronConcatenate;
descr.count = 2 * LatentCount;
descr.window = prev_count;
descr.step = AccountDescr;
descr.optimization = ADAM;
descr.activation = SIGMOID;
if(!actor.Add(descr))
{
delete descr;
return false;
}
//--- layer 9
if(!(descr = new CLayerDescription()))
return false;
descr.type = defNeuronVAEOCL;
descr.count = LatentCount;
descr.optimization = ADAM;
if(!actor.Add(descr))
{
delete descr;
return false;
}
//--- layer 10
if(!(descr = new CLayerDescription()))
return false;
descr.type = defNeuronBaseOCL;
descr.count = LatentCount;
descr.activation = LReLU;
descr.optimization = ADAM;
if(!actor.Add(descr))
{
delete descr;
return false;
}
//--- layer 11
if(!(descr = new CLayerDescription()))
return false;
descr.type = defNeuronBaseOCL;
descr.count = LatentCount;
descr.activation = LReLU;
descr.optimization = ADAM;
if(!actor.Add(descr))
{
delete descr;
return false;
}
//--- layer 12
if(!(descr = new CLayerDescription()))
return false;
descr.type = defNeuronBaseOCL;
descr.count = 2 * NActions;
descr.activation = SIGMOID;
descr.optimization = ADAM;
if(!actor.Add(descr))
{
delete descr;
return false;
}
//--- layer 13
if(!(descr = new CLayerDescription()))
return false;
descr.type = defNeuronVAEOCL;
descr.count = NActions;
descr.optimization = ADAM;
if(!actor.Add(descr))
{
delete descr;
return false;
}
//--- Critic
critic.Clear();
//--- Input layer
if(!(descr = new CLayerDescription()))
return false;
descr.type = defNeuronBaseOCL;
prev_count = descr.count = LatentCount;
descr.activation = None;
descr.optimization = ADAM;
if(!critic.Add(descr))
{
delete descr;
return false;
}
//--- layer 1
if(!(descr = new CLayerDescription()))
return false;
descr.type = defNeuronConcatenate;
descr.count = LatentCount;
descr.window = prev_count;
descr.step = NActions;
descr.optimization = ADAM;
descr.activation = LReLU;
if(!critic.Add(descr))
{
delete descr;
return false;
}
//--- layer 2
if(!(descr = new CLayerDescription()))
return false;
descr.type = defNeuronBaseOCL;
descr.count = LatentCount;
descr.activation = LReLU;
descr.optimization = ADAM;
if(!critic.Add(descr))
{
delete descr;
return false;
}
//--- layer 3
if(!(descr = new CLayerDescription()))
return false;
descr.type = defNeuronBaseOCL;
descr.count = LatentCount;
descr.activation = LReLU;
descr.optimization = ADAM;
if(!critic.Add(descr))
{
delete descr;
return false;
}
//--- layer 4
if(!(descr = new CLayerDescription()))
return false;
descr.type = defNeuronBaseOCL;
descr.count = NRewards;
descr.optimization = ADAM;
descr.activation = None;
if(!critic.Add(descr))
{
delete descr;
return false;
}
//--- Convolution
convolution.Clear();
//--- Input layer
if(!(descr = new CLayerDescription()))
return false;
descr.type = defNeuronBaseOCL;
prev_count = descr.count = (HistoryBars * BarDescr) + AccountDescr + NActions;
descr.activation = None;
descr.optimization = ADAM;
if(!convolution.Add(descr))
{
delete descr;
return false;
}
//--- layer 1
if(!(descr = new CLayerDescription()))
return false;
descr.type = defNeuronBaseOCL;
descr.count = HistoryBars * BarDescr;
descr.optimization = ADAM;
descr.activation = None;
if(!convolution.Add(descr))
{
delete descr;
return false;
}
//--- layer 2
if(!(descr = new CLayerDescription()))
return false;
descr.type = defNeuronSoftMaxOCL;
descr.count = HistoryBars;
descr.step = BarDescr;
descr.optimization = ADAM;
descr.activation = None;
if(!convolution.Add(descr))
{
delete descr;
return false;
}
//--- layer 3
if(!(descr = new CLayerDescription()))
return false;
descr.type = defNeuronConvOCL;
prev_count = descr.count = HistoryBars;
descr.window = BarDescr;
descr.step = BarDescr;
prev_wout = descr.window_out = BarDescr / 2;
descr.activation = LReLU;
descr.optimization = ADAM;
if(!convolution.Add(descr))
{
delete descr;
return false;
}
//--- layer 4
if(!(descr = new CLayerDescription()))
return false;
descr.type = defNeuronConvOCL;
prev_count = descr.count = prev_count;
descr.window = prev_wout;
descr.step = prev_wout;
prev_wout = descr.window_out = prev_wout / 2;
descr.activation = LReLU;
descr.optimization = ADAM;
if(!convolution.Add(descr))
{
delete descr;
return false;
}
//--- layer 5
if(!(descr = new CLayerDescription()))
return false;
descr.type = defNeuronConvOCL;
prev_count = descr.count = prev_count;
descr.window = prev_wout;
descr.step = prev_wout;
prev_wout = descr.window_out = 2;
descr.activation = LReLU;
descr.optimization = ADAM;
if(!convolution.Add(descr))
{
delete descr;
return false;
}
//--- layer 6
if(!(descr = new CLayerDescription()))
return false;
descr.type = defNeuronSoftMaxOCL;
descr.count = prev_count * prev_wout;
descr.optimization = ADAM;
descr.activation = None;
if(!convolution.Add(descr))
{
delete descr;
return false;
}
//--- layer 7
if(!(descr = new CLayerDescription()))
return false;
descr.type = defNeuronBaseOCL;
descr.count = EmbeddingSize;
descr.activation = LReLU;
descr.optimization = ADAM;
if(!convolution.Add(descr))
{
delete descr;
return false;
}
//---
return true;
}
#ifndef Study
//+------------------------------------------------------------------+
//| |
//+------------------------------------------------------------------+
bool IsNewBar(void)
{
static datetime last_bar = 0;
if(last_bar >= iTime(Symb.Name(), TimeFrame, 0))
return false;
//---
last_bar = iTime(Symb.Name(), TimeFrame, 0);
return true;
}
//+------------------------------------------------------------------+
//| |
//+------------------------------------------------------------------+
bool CloseByDirection(ENUM_POSITION_TYPE type)
{
int total = PositionsTotal();
bool result = true;
for(int i = total - 1; i >= 0; i--)
{
if(PositionGetSymbol(i) != Symb.Name())
continue;
if(PositionGetInteger(POSITION_TYPE) != type)
continue;
result = (Trade.PositionClose(PositionGetInteger(POSITION_TICKET)) && result);
}
//---
return result;
}
//+------------------------------------------------------------------+
//| |
//+------------------------------------------------------------------+
bool TrailPosition(ENUM_POSITION_TYPE type, double sl, double tp)
{
int total = PositionsTotal();
bool result = true;
//---
for(int i = 0; i < total; i++)
{
if(PositionGetSymbol(i) != Symb.Name())
continue;
if(PositionGetInteger(POSITION_TYPE) != type)
continue;
bool modify = false;
double psl = PositionGetDouble(POSITION_SL);
double ptp = PositionGetDouble(POSITION_TP);
switch(type)
{
case POSITION_TYPE_BUY:
if((sl - psl) >= Symb.Point())
{
psl = sl;
modify = true;
}
if(MathAbs(tp - ptp) >= Symb.Point())
{
ptp = tp;
modify = true;
}
break;
case POSITION_TYPE_SELL:
if((psl - sl) >= Symb.Point())
{
psl = sl;
modify = true;
}
if(MathAbs(tp - ptp) >= Symb.Point())
{
ptp = tp;
modify = true;
}
break;
}
if(modify)
result = (Trade.PositionModify(PositionGetInteger(POSITION_TICKET), psl, ptp) && result);
}
//---
return result;
}
//+------------------------------------------------------------------+
//| |
//+------------------------------------------------------------------+
bool ClosePartial(ENUM_POSITION_TYPE type, double value)
{
if(value <= 0)
return true;
//---
for(int i = 0; (i < PositionsTotal() && value > 0); i++)
{
if(PositionGetSymbol(i) != Symb.Name())
continue;
if(PositionGetInteger(POSITION_TYPE) != type)
continue;
double pvalue = PositionGetDouble(POSITION_VOLUME);
if(pvalue <= value)
{
if(Trade.PositionClose(PositionGetInteger(POSITION_TICKET)))
{
value -= pvalue;
i--;
}
}
else
{
if(Trade.PositionClosePartial(PositionGetInteger(POSITION_TICKET), value))
value = 0;
}
}
//---
return (value <= 0);
}
#endif
//+------------------------------------------------------------------+
//| |
//+------------------------------------------------------------------+
vector<float> ForecastAccount(float &prev_account[], vector<float> &actions, double prof_1l, float time_label)
{
vector<float> account;
double min_lot = SymbolInfoDouble(_Symbol, SYMBOL_VOLUME_MIN);
double step_lot = SymbolInfoDouble(_Symbol, SYMBOL_VOLUME_STEP);
double stops = MathMax(SymbolInfoInteger(_Symbol, SYMBOL_TRADE_STOPS_LEVEL), 1) * Point();
double margin_buy, margin_sell;
if(!OrderCalcMargin(ORDER_TYPE_BUY, _Symbol, 1.0, SymbolInfoDouble(_Symbol, SYMBOL_ASK), margin_buy) ||
!OrderCalcMargin(ORDER_TYPE_SELL, _Symbol, 1.0, SymbolInfoDouble(_Symbol, SYMBOL_BID), margin_sell))
return vector<float>::Zeros(prev_account.Size());
//---
account.Assign(prev_account);
//---
if(actions[0] >= actions[3])
{
actions[0] -= actions[3];
actions[3] = 0;
if(actions[0]*margin_buy >= MathMin(account[0], account[1]))
actions[0] = 0;
}
else
{
actions[3] -= actions[0];
actions[0] = 0;
if(actions[3]*margin_sell >= MathMin(account[0], account[1]))
actions[3] = 0;
}
//--- buy control
if(actions[0] < min_lot || (actions[1] * MaxTP * Point()) <= stops || (actions[2] * MaxSL * Point()) <= stops)
{
account[0] += account[4];
account[2] = 0;
account[4] = 0;
}
else
{
double buy_lot = min_lot + MathRound((double)(actions[0] - min_lot) / step_lot) * step_lot;
if(account[2] > buy_lot)
{
float koef = (float)buy_lot / account[2];
account[0] += account[4] * (1 - koef);
account[4] *= koef;
}
account[2] = (float)buy_lot;
account[4] += float(buy_lot * prof_1l);
}
//--- sell control
if(actions[3] < min_lot || (actions[4] * MaxTP * Point()) <= stops || (actions[5] * MaxSL * Point()) <= stops)
{
account[0] += account[5];
account[3] = 0;
account[5] = 0;
}
else
{
double sell_lot = min_lot + MathRound((double)(actions[3] - min_lot) / step_lot) * step_lot;
if(account[3] > sell_lot)
{
float koef = float(sell_lot / account[3]);
account[0] += account[5] * (1 - koef);
account[5] *= koef;
}
account[3] = float(sell_lot);
account[5] -= float(sell_lot * prof_1l);
}
account[6] = account[4] + account[5];
account[1] = account[0] + account[6];
//---
vector<float> result = vector<float>::Zeros(AccountDescr);
result[0] = (account[0] - prev_account[0]) / prev_account[0];
result[1] = account[1] / prev_account[0];
result[2] = (account[1] - prev_account[1]) / prev_account[1];
result[3] = account[2];
result[4] = account[3];
result[5] = account[4] / prev_account[0];
result[6] = account[5] / prev_account[0];
result[7] = account[6] / prev_account[0];
double x = (double)time_label / (double)(D'2024.01.01' - D'2023.01.01');
result[8] = (float)MathSin(2.0 * M_PI * x);
x = (double)time_label / (double)PeriodSeconds(PERIOD_MN1);
result[9] = (float)MathCos(2.0 * M_PI * x);
x = (double)time_label / (double)PeriodSeconds(PERIOD_W1);
result[10] = (float)MathSin(2.0 * M_PI * x);
x = (double)time_label / (double)PeriodSeconds(PERIOD_D1);
result[11] = (float)MathSin(2.0 * M_PI * x);
//--- return result
return result;
}
//+------------------------------------------------------------------+
//| |
//+------------------------------------------------------------------+
float EntropyLatentState(CNet &net)
{
//--- random values
double random[];
Math::MathRandomNormal(0, 1, LatentCount * SamplLatentStates, random);
matrix<float> states;
states.Assign(random);
states.Reshape(SamplLatentStates, LatentCount);
//--- get means and std
vector<float> temp;
matrix<float> stats = matrix<float>::Zeros(SamplLatentStates, 2 * LatentCount);
net.GetLayerOutput(LatentLayer - 1, temp);
stats.Row(temp, 0);
stats = stats.CumSum(0);
matrix<float> split[];
stats.Vsplit(2, split);
//--- calculate latent values
states = states * split[1] + split[0];
//--- add current latent value
net.GetLayerOutput(LatentLayer, temp);
states.Resize(SamplLatentStates + 1, LatentCount);
states.Row(temp, SamplLatentStates);
//--- calculate entropy
states.SVD(split[0], split[1], temp);
float result = temp.Sum() / (MathSqrt(MathPow(states, 2.0f).Sum() * MathMax(SamplLatentStates + 1, LatentCount)));
//---
return result;
}
//+------------------------------------------------------------------+
//| |
//+------------------------------------------------------------------+
vector<float> GetProbTrajectories(STrajectory &buffer[], double lambda)
{
ulong total = buffer.Size();
vector<float> result = vector<float>::Zeros(total);
vector<float> temp;
for(ulong i = 0; i < total; i++)
{
temp.Assign(buffer[i].States[0].rewards);
result[i] = temp.Sum();
if(!MathIsValidNumber(result[i]))
result[i] = -FLT_MAX;
}
float max_reward = result.Max();
//---
vector<float> sorted = result;
bool sort = true;
int iter = 0;
while(sort)
{
sort = false;
for(ulong i = 0; i < sorted.Size() - 1; i++)
if(sorted[i] > sorted[i + 1])
{
float temp = sorted[i];
sorted[i] = sorted[i + 1];
sorted[i + 1] = temp;
sort = true;
}
iter++;
}
//---
float min = result.Min() - 0.1f * MathAbs(max_reward);
if(max_reward > min)
{
float k = sorted.Percentile(80) - max_reward;
vector<float> multipl = MathExp(MathAbs(result - max_reward) / (k == 0 ? -1 : k));
result = (result - min) / (max_reward - min);
result = result / (result + lambda) * multipl;
result.ReplaceNan(0);
}
else
result.Fill(1);
result = result / result.Sum();
result = result.CumSum();
//---
return result;
}
//+------------------------------------------------------------------+
//| |
//+------------------------------------------------------------------+
int SampleTrajectory(vector<float> &probability)
{
//--- check
ulong total = probability.Size();
if(total <= 0)
return -1;
//--- randomize
float rnd = float(MathRand() / 32767.0);
//--- search
if(rnd <= probability[0] || total == 1)
return 0;
if(rnd > probability[total - 2])
return int(total - 1);
int result = int(rnd * total);
if(probability[result] < rnd)
while(probability[result] < rnd)
result++;
else
while(probability[result - 1] >= rnd)
result--;
//--- return result
return result;
}
//+------------------------------------------------------------------+
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