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Warrior_EA/references/MQL5/Experts/DWSL/Study.mq5

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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>//+------------------------------------------------------------------+
//| Study.mq5 |
//| 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"
//+------------------------------------------------------------------+
//| |
//+------------------------------------------------------------------+
#define Study
#include "Trajectory.mqh"
//+------------------------------------------------------------------+
//| Input parameters |
//+------------------------------------------------------------------+
input int Iterations = 10000;
input float Tau = 0.01f;
input float MaxErrorActorStudy = 0.15f;
//+------------------------------------------------------------------+
//| |
//+------------------------------------------------------------------+
STrajectory Buffer[];
CNet Actor;
CNet Critic1;
CNet Critic2;
CNet TargetCritic1;
CNet TargetCritic2;
CNet Convolution;
//---
float dError;
datetime dtStudied;
//---
CBufferFloat State;
CBufferFloat Account;
CBufferFloat Actions;
CBufferFloat Gradient;
CBufferFloat *Result;
vector<float> check;
int StartTargetIter;
//---
COpenCLMy *OpenCL;
//+------------------------------------------------------------------+
//| Expert initialization function |
//+------------------------------------------------------------------+
int OnInit()
{
//---
ResetLastError();
if(!LoadTotalBase())
{
PrintFormat("Error of load study data: %d", GetLastError());
return INIT_FAILED;
}
//--- load models
float temp;
if(!Actor.Load(FileName + "Act.nnw", temp, temp, temp, dtStudied, true) ||
!Critic1.Load(FileName + "Crt1.nnw", temp, temp, temp, dtStudied, true) ||
!Critic2.Load(FileName + "Crt2.nnw", temp, temp, temp, dtStudied, true) ||
!Convolution.Load(FileName + "CNN.nnw", temp, temp, temp, dtStudied, true) ||
!TargetCritic1.Load(FileName + "Crt1.nnw", temp, temp, temp, dtStudied, true) ||
!TargetCritic2.Load(FileName + "Crt2.nnw", temp, temp, temp, dtStudied, true))
{
Print("Init new models");
CArrayObj *actor = new CArrayObj();
CArrayObj *critic = new CArrayObj();
CArrayObj *convolution = new CArrayObj();
if(!CreateDescriptions(actor, critic, convolution))
{
delete actor;
delete critic;
delete convolution;
return INIT_FAILED;
}
if(!Actor.Create(actor) || !Critic1.Create(critic) || !Critic2.Create(critic) ||
!Convolution.Create(convolution))
{
delete actor;
delete critic;
delete convolution;
return INIT_FAILED;
}
if(!TargetCritic1.Create(critic) || !TargetCritic2.Create(critic))
{
delete actor;
delete critic;
delete convolution;
return INIT_FAILED;
}
delete actor;
delete critic;
delete convolution;
//---
TargetCritic1.WeightsUpdate(GetPointer(Critic1), 1.0f);
TargetCritic2.WeightsUpdate(GetPointer(Critic2), 1.0f);
StartTargetIter = StartTargetIteration;
}
else
StartTargetIter = 0;
//---
OpenCL = Actor.GetOpenCL();
Critic1.SetOpenCL(OpenCL);
Critic2.SetOpenCL(OpenCL);
TargetCritic1.SetOpenCL(OpenCL);
TargetCritic2.SetOpenCL(OpenCL);
Convolution.SetOpenCL(OpenCL);
//---
Actor.getResults(Result);
if(Result.Total() != NActions)
{
PrintFormat("The scope of the actor does not match the actions count (%d <> %d)", NActions, Result.Total());
return INIT_FAILED;
}
//---
Actor.GetLayerOutput(0, Result);
if(Result.Total() != (HistoryBars * BarDescr))
{
PrintFormat("Input size of Actor doesn't match state description (%d <> %d)", Result.Total(), (HistoryBars * BarDescr));
return INIT_FAILED;
}
//---
Actor.GetLayerOutput(LatentLayer, Result);
int latent_state = Result.Total();
Critic1.GetLayerOutput(0, Result);
if(Result.Total() != latent_state)
{
PrintFormat("Input size of Critic doesn't match latent state Actor (%d <> %d)", Result.Total(), latent_state);
return INIT_FAILED;
}
//---
Gradient.BufferInit(AccountDescr, 0);
//---
if(!EventChartCustom(ChartID(), 1, 0, 0, "Init"))
{
PrintFormat("Error of create study event: %d", GetLastError());
return INIT_FAILED;
}
//---
return(INIT_SUCCEEDED);
}
//+------------------------------------------------------------------+
//| Expert deinitialization function |
//+------------------------------------------------------------------+
void OnDeinit(const int reason)
{
//---
TargetCritic1.WeightsUpdate(GetPointer(Critic1), Tau);
TargetCritic2.WeightsUpdate(GetPointer(Critic2), Tau);
Actor.Save(FileName + "Act.nnw", 0, 0, 0, TimeCurrent(), true);
TargetCritic1.Save(FileName + "Crt1.nnw", Critic1.getRecentAverageError(), 0, 0, TimeCurrent(), true);
TargetCritic2.Save(FileName + "Crt2.nnw", Critic2.getRecentAverageError(), 0, 0, TimeCurrent(), true);
Convolution.Save(FileName + "CNN.nnw", 0, 0, 0, TimeCurrent(), true);
delete Result;
}
//+------------------------------------------------------------------+
//| ChartEvent function |
//+------------------------------------------------------------------+
void OnChartEvent(const int id,
const long &lparam,
const double &dparam,
const string &sparam)
{
//---
if(id == 1001)
Train();
}
//+------------------------------------------------------------------+
//| Train function |
//+------------------------------------------------------------------+
void Train(void)
{
int total_tr = ArraySize(Buffer);
uint ticks = GetTickCount();
//---
int total_states = Buffer[0].Total;
for(int i = 1; i < total_tr; i++)
total_states += Buffer[i].Total;
vector<float> temp, next;
Convolution.getResults(temp);
matrix<float> state_embedding = matrix<float>::Zeros(total_states, temp.Size());
matrix<float> rewards = matrix<float>::Zeros(total_states, NRewards);
matrix<float> actions = matrix<float>::Zeros(total_states, NActions);
int state = 0;
for(int tr = 0; tr < total_tr; tr++)
{
for(int st = 0; st < Buffer[tr].Total; st++)
{
State.AssignArray(Buffer[tr].States[st].state);
float PrevBalance = Buffer[tr].States[MathMax(st - 1, 0)].account[0];
float PrevEquity = Buffer[tr].States[MathMax(st - 1, 0)].account[1];
State.Add((Buffer[tr].States[st].account[0] - PrevBalance) / PrevBalance);
State.Add(Buffer[tr].States[st].account[1] / PrevBalance);
State.Add((Buffer[tr].States[st].account[1] - PrevEquity) / PrevEquity);
State.Add(Buffer[tr].States[st].account[2]);
State.Add(Buffer[tr].States[st].account[3]);
State.Add(Buffer[tr].States[st].account[4] / PrevBalance);
State.Add(Buffer[tr].States[st].account[5] / PrevBalance);
State.Add(Buffer[tr].States[st].account[6] / PrevBalance);
double x = (double)Buffer[tr].States[st].account[7] / (double)(D'2024.01.01' - D'2023.01.01');
State.Add((float)MathSin(x != 0 ? 2.0 * M_PI * x : 0));
x = (double)Buffer[tr].States[st].account[7] / (double)PeriodSeconds(PERIOD_MN1);
State.Add((float)MathCos(x != 0 ? 2.0 * M_PI * x : 0));
x = (double)Buffer[tr].States[st].account[7] / (double)PeriodSeconds(PERIOD_W1);
State.Add((float)MathSin(x != 0 ? 2.0 * M_PI * x : 0));
x = (double)Buffer[tr].States[st].account[7] / (double)PeriodSeconds(PERIOD_D1);
State.Add((float)MathSin(x != 0 ? 2.0 * M_PI * x : 0));
if(!Convolution.feedForward(GetPointer(State), 1, false, NULL))
{
PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
ExpertRemove();
return;
}
Convolution.getResults(temp);
if(!state_embedding.Row(temp, state))
continue;
if(!temp.Assign(Buffer[tr].States[st].rewards) ||
!next.Assign(Buffer[tr].States[st + 1].rewards) ||
!rewards.Row(temp - next * DiscFactor, state))
continue;
if(!temp.Assign(Buffer[tr].States[st].action) ||
!actions.Row(temp, state))
continue;
state++;
if(GetTickCount() - ticks > 500)
{
string str = StringFormat("%-15s %6.2f%%", "Embedding ", state * 100.0 / (double)(total_states));
Comment(str);
ticks = GetTickCount();
}
}
}
if(state != total_states)
{
rewards.Resize(state, NRewards);
actions.Resize(state, NActions);
state_embedding.Reshape(state, state_embedding.Cols());
total_states = state;
}
//---
vector<float> rewards1, rewards2, target_reward;
STarget target;
//---
vector<float> probability = GetProbTrajectories(Buffer, 0.9);
int bar = (HistoryBars - 1) * BarDescr;
for(int iter = 0; (iter < Iterations && !IsStopped()); iter ++)
{
int tr = SampleTrajectory(probability);
int i = (int)((MathRand() * MathRand() / MathPow(32767, 2)) * (Buffer[tr].Total - 2));
if(i < 0)
{
iter--;
continue;
}
target_reward = vector<float>::Zeros(NRewards);
//--- Target
if(iter >= StartTargetIter)
{
State.AssignArray(Buffer[tr].States[i + 1].state);
float PrevBalance = Buffer[tr].States[i].account[0];
float PrevEquity = Buffer[tr].States[i].account[1];
Account.Clear();
Account.Add((Buffer[tr].States[i + 1].account[0] - PrevBalance) / PrevBalance);
Account.Add(Buffer[tr].States[i + 1].account[1] / PrevBalance);
Account.Add((Buffer[tr].States[i + 1].account[1] - PrevEquity) / PrevEquity);
Account.Add(Buffer[tr].States[i + 1].account[2]);
Account.Add(Buffer[tr].States[i + 1].account[3]);
Account.Add(Buffer[tr].States[i + 1].account[4] / PrevBalance);
Account.Add(Buffer[tr].States[i + 1].account[5] / PrevBalance);
Account.Add(Buffer[tr].States[i + 1].account[6] / PrevBalance);
double x = (double)Buffer[tr].States[i + 1].account[7] / (double)(D'2024.01.01' - D'2023.01.01');
Account.Add((float)MathSin(x != 0 ? 2.0 * M_PI * x : 0));
x = (double)Buffer[tr].States[i + 1].account[7] / (double)PeriodSeconds(PERIOD_MN1);
Account.Add((float)MathCos(x != 0 ? 2.0 * M_PI * x : 0));
x = (double)Buffer[tr].States[i + 1].account[7] / (double)PeriodSeconds(PERIOD_W1);
Account.Add((float)MathSin(x != 0 ? 2.0 * M_PI * x : 0));
x = (double)Buffer[tr].States[i + 1].account[7] / (double)PeriodSeconds(PERIOD_D1);
Account.Add((float)MathSin(x != 0 ? 2.0 * M_PI * x : 0));
//---
if(Account.GetIndex() >= 0)
Account.BufferWrite();
if(!Actor.feedForward(GetPointer(State), 1, false, GetPointer(Account)))
{
PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
break;
}
//---
if(!TargetCritic1.feedForward(GetPointer(Actor), LatentLayer, GetPointer(Actor)) ||
!TargetCritic2.feedForward(GetPointer(Actor), LatentLayer, GetPointer(Actor)))
{
PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
break;
}
TargetCritic1.getResults(rewards1);
TargetCritic2.getResults(rewards2);
target_reward.Assign(Buffer[tr].States[i + 1].rewards);
if(rewards1.Sum() <= rewards2.Sum())
target_reward = rewards1 - target_reward;
else
target_reward = rewards2 - target_reward;
target_reward *= DiscFactor;
target_reward[NRewards - 1] = EntropyLatentState(Actor);
}
//--- Q-function study
State.AssignArray(Buffer[tr].States[i].state);
float PrevBalance = Buffer[tr].States[MathMax(i - 1, 0)].account[0];
float PrevEquity = Buffer[tr].States[MathMax(i - 1, 0)].account[1];
Account.Clear();
Account.Add((Buffer[tr].States[i].account[0] - PrevBalance) / PrevBalance);
Account.Add(Buffer[tr].States[i].account[1] / PrevBalance);
Account.Add((Buffer[tr].States[i].account[1] - PrevEquity) / PrevEquity);
Account.Add(Buffer[tr].States[i].account[2]);
Account.Add(Buffer[tr].States[i].account[3]);
Account.Add(Buffer[tr].States[i].account[4] / PrevBalance);
Account.Add(Buffer[tr].States[i].account[5] / PrevBalance);
Account.Add(Buffer[tr].States[i].account[6] / PrevBalance);
double x = (double)Buffer[tr].States[i].account[7] / (double)(D'2024.01.01' - D'2023.01.01');
Account.Add((float)MathSin(x != 0 ? 2.0 * M_PI * x : 0));
x = (double)Buffer[tr].States[i].account[7] / (double)PeriodSeconds(PERIOD_MN1);
Account.Add((float)MathCos(x != 0 ? 2.0 * M_PI * x : 0));
x = (double)Buffer[tr].States[i].account[7] / (double)PeriodSeconds(PERIOD_W1);
Account.Add((float)MathSin(x != 0 ? 2.0 * M_PI * x : 0));
x = (double)Buffer[tr].States[i].account[7] / (double)PeriodSeconds(PERIOD_D1);
Account.Add((float)MathSin(x != 0 ? 2.0 * M_PI * x : 0));
if(Account.GetIndex() >= 0)
Account.BufferWrite();
//---
if(!Actor.feedForward(GetPointer(State), 1, false, GetPointer(Account)))
{
PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
break;
}
//---
Actions.AssignArray(Buffer[tr].States[i].action);
if(Actions.GetIndex() >= 0)
Actions.BufferWrite();
//---
if(!Critic1.feedForward(GetPointer(Actor), LatentLayer, GetPointer(Actions)) ||
!Critic2.feedForward(GetPointer(Actor), LatentLayer, GetPointer(Actions)))
{
PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
break;
}
//---
if(!State.AddArray(GetPointer(Account)) || !Convolution.feedForward(GetPointer(State), 1, false, NULL))
{
PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
break;
}
Convolution.getResults(temp);
target = GetTargets(Quant, temp, state_embedding, rewards, actions);
//---
Critic1.getResults(rewards1);
Result.AssignArray(CAGrad(target.rewards + target_reward - rewards1) + rewards1);
if(!Critic1.backProp(Result, GetPointer(Actions), GetPointer(Gradient)) ||
!Actor.backPropGradient(GetPointer(Account), GetPointer(Gradient), LatentLayer))
{
PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
break;
}
Critic2.getResults(rewards2);
Result.AssignArray(CAGrad(target.rewards + target_reward - rewards2) + rewards2);
if(!Critic2.backProp(Result, GetPointer(Actions), GetPointer(Gradient)) ||
!Actor.backPropGradient(GetPointer(Account), GetPointer(Gradient), LatentLayer))
{
PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
break;
}
//--- Policy study
Actor.getResults(rewards1);
Result.AssignArray(CAGrad(target.actions - rewards1) + rewards1);
if(!Actor.backProp(Result, GetPointer(Account), GetPointer(Gradient)))
{
PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
break;
}
//---
CNet *critic = NULL;
if(Critic1.getRecentAverageError() <= Critic2.getRecentAverageError())
critic = GetPointer(Critic1);
else
critic = GetPointer(Critic2);
if(MathAbs(critic.getRecentAverageError()) <= MaxErrorActorStudy)
{
if(!critic.feedForward(GetPointer(Actor), LatentLayer, GetPointer(Actor)))
{
PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
break;
}
critic.getResults(rewards1);
Result.AssignArray(CAGrad(target.rewards + target_reward - rewards1) + rewards1);
critic.TrainMode(false);
if(!critic.backProp(Result, GetPointer(Actor)) ||
!Actor.backPropGradient(GetPointer(Account), GetPointer(Gradient)))
{
PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
critic.TrainMode(true);
break;
}
critic.TrainMode(true);
}
//--- Update Target Nets
if(iter >= StartTargetIter)
{
TargetCritic1.WeightsUpdate(GetPointer(Critic1), Tau);
TargetCritic2.WeightsUpdate(GetPointer(Critic2), Tau);
}
else
{
TargetCritic1.WeightsUpdate(GetPointer(Critic1), 1);
TargetCritic2.WeightsUpdate(GetPointer(Critic2), 1);
}
//---
if(GetTickCount() - ticks > 500)
{
string str = StringFormat("%-15s %5.2f%% -> Error %15.8f\n", "Critic1", iter * 100.0 / (double)(Iterations), Critic1.getRecentAverageError());
str += StringFormat("%-15s %5.2f%% -> Error %15.8f\n", "Critic2", iter * 100.0 / (double)(Iterations), Critic2.getRecentAverageError());
str += StringFormat("%-14s %5.2f%% -> Error %15.8f\n", "Actor", iter * 100.0 / (double)(Iterations), Actor.getRecentAverageError());
Comment(str);
ticks = GetTickCount();
}
}
Comment("");
//---
PrintFormat("%s -> %d -> %-15s %10.7f", __FUNCTION__, __LINE__, "Critic1", Critic1.getRecentAverageError());
PrintFormat("%s -> %d -> %-15s %10.7f", __FUNCTION__, __LINE__, "Critic2", Critic2.getRecentAverageError());
PrintFormat("%s -> %d -> %-15s %10.7f", __FUNCTION__, __LINE__, "Actor", Actor.getRecentAverageError());
ExpertRemove();
//---
}
//+------------------------------------------------------------------+
//| |
//+------------------------------------------------------------------+
vector<float> 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;
}
//+------------------------------------------------------------------+
//| |
//+------------------------------------------------------------------+
STarget GetTargets(double quant, vector<float> &embedding, matrix<float> &state_embedding, matrix<float> &rewards, matrix<float> &actions)
{
STarget result;
if(embedding.Size() != state_embedding.Cols())
{
PrintFormat("%s -> %d Inconsistent embedding size", __FUNCTION__, __LINE__);
return result;
}
//---
ulong size = embedding.Size();
ulong states = state_embedding.Rows();
ulong k = ulong(states * quant);
matrix<float> temp = matrix<float>::Zeros(states, size);
for(ulong i = 0; i < size; i++)
temp.Col(MathAbs(state_embedding.Col(i) - embedding[i]), i);
float alpha = temp.Max();
vector<float> dist = MathLog(MathExp(temp / (-alpha)).Sum(1)) * (-alpha);
vector<float> min_dist = vector<float>::Zeros(k);
matrix<float> k_rewards = matrix<float>::Zeros(k, NRewards);
matrix<float> k_actions = matrix<float>::Zeros(k, NActions);
matrix<float> k_embedding = matrix<float>::Zeros(k + 1, size);
matrix<float> U, V;
vector<float> S;
float max = dist.Quantile(quant);
float min = dist.Min();
for(ulong i = 0, cur = 0; (i < states && cur < k); i++)
{
if(max < dist[i])
continue;
min_dist[cur] = dist[i];
k_rewards.Row(rewards.Row(i), cur);
k_actions.Row(actions.Row(i), cur);
k_embedding.Row(state_embedding.Row(i), cur);
cur++;
}
k_embedding.Row(embedding, k);
//---
vector<float> sf;
(min_dist * (-1)).Activation(sf, AF_SOFTMAX);
result.rewards = sf.MatMul(k_rewards);
//---
k_embedding.SVD(U, V, S);
result.rewards[NRewards - 2] = S.Sum() / (MathSqrt(MathPow(k_embedding, 2.0f).Sum() * MathMax(k + 1, size)));
result.rewards[NRewards - 1] = EntropyLatentState(Actor);
//---
vector<float> act_sf;
alpha = MathAbs(k_rewards).Max();
dist = MathLog(MathExp(k_rewards / (-alpha)).Sum(1)) * (-alpha);
dist.Activation(act_sf, AF_SOFTMAX);
result.actions = act_sf.MatMul(k_actions);
//---
return result;
}
//+------------------------------------------------------------------+
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