| //+------------------------------------------------------------------+
|
| //| Study.mq5 |
|
| //| 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"
|
| //+------------------------------------------------------------------+
|
| //| |
|
| //+------------------------------------------------------------------+
|
| #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,(CBufferFloat*)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,(CBufferFloat*)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();
|
| //---
|
| }
|
| //+------------------------------------------------------------------+
|
| //| |
|
| //+------------------------------------------------------------------+
|
| //| | Creates and manages object lifecycle for CAGrad. |
|
| //+------------------------------------------------------------------+
|
| 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;
|
| }
|
| //+------------------------------------------------------------------+
|
| //| |
|
| //+------------------------------------------------------------------+
|
| //| | Gets Targets. |
|
| //+------------------------------------------------------------------+
|
| 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;
|
| }
|
| //+------------------------------------------------------------------+
|
|
|
