449 lines
38 KiB
MQL5
449 lines
38 KiB
MQL5
//+------------------------------------------------------------------+
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//| Study.mq5 |
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//| Copyright DNG® |
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//| https://www.mql5.com/ru/users/dng |
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//+------------------------------------------------------------------+
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#property copyright "Copyright DNG®"
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#property link "https://www.mql5.com/ru/users/dng"
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#property version "1.00"
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//+------------------------------------------------------------------+
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//| |
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//+------------------------------------------------------------------+
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#define Study
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#include "Trajectory.mqh"
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//+------------------------------------------------------------------+
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//| Input parameters |
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//+------------------------------------------------------------------+
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input int Iterations = 100000;
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input float Tau = 0.001f;
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//+------------------------------------------------------------------+
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//| |
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//+------------------------------------------------------------------+
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STrajectory Buffer[];
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CNet Encoder;
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CNet TargetEncoder;
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CNet Actor;
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CNet TargetActor;
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CNet Critic1;
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CNet Critic2;
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CNet TargetCritic1;
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CNet TargetCritic2;
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CNet Convolution;
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CNet Scheduler;
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//---
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float dError;
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datetime dtStudied;
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//---
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CBufferFloat State;
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CBufferFloat Account;
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CBufferFloat TargetState;
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CBufferFloat TargetAccount;
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CBufferFloat Actions;
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CBufferFloat Gradient;
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CBufferFloat Skills;
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CBufferFloat *Result;
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vector<float> check;
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//---
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COpenCLMy *OpenCL;
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//+------------------------------------------------------------------+
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//| Expert initialization function |
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//+------------------------------------------------------------------+
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int OnInit()
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{
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//---
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ResetLastError();
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if(!LoadTotalBase())
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{
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PrintFormat("Error of load study data: %d", GetLastError());
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return INIT_FAILED;
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}
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//--- load models
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float temp;
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if(!Encoder.Load(FileName + "Enc.nnw", temp, temp, temp, dtStudied, true) ||
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!Actor.Load(FileName + "Act.nnw", temp, temp, temp, dtStudied, true) ||
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!Critic1.Load(FileName + "Crt1.nnw", temp, temp, temp, dtStudied, true) ||
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!Critic2.Load(FileName + "Crt2.nnw", temp, temp, temp, dtStudied, true) ||
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!Convolution.Load(FileName + "CNN.nnw", temp, temp, temp, dtStudied, true) ||
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!TargetEncoder.Load(FileName + "Enc.nnw", temp, temp, temp, dtStudied, true) ||
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!TargetActor.Load(FileName + "Act.nnw", temp, temp, temp, dtStudied, true) ||
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!TargetCritic1.Load(FileName + "Crt1.nnw", temp, temp, temp, dtStudied, true) ||
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!TargetCritic2.Load(FileName + "Crt2.nnw", temp, temp, temp, dtStudied, true))
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{
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Print("No pretrained models found");
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return INIT_FAILED;
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}
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if(!Scheduler.Load(FileName + "Sch.nnw", temp, temp, temp, dtStudied, true))
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{
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CArrayObj *descr = new CArrayObj();
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if(!SchedulerDescriptions(descr) || !Scheduler.Create(descr))
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{
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delete descr;
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return INIT_FAILED;
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}
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delete descr;
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}
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//---
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OpenCL = Actor.GetOpenCL();
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Encoder.SetOpenCL(OpenCL);
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Critic1.SetOpenCL(OpenCL);
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Critic2.SetOpenCL(OpenCL);
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TargetEncoder.SetOpenCL(OpenCL);
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TargetActor.SetOpenCL(OpenCL);
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TargetCritic1.SetOpenCL(OpenCL);
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TargetCritic2.SetOpenCL(OpenCL);
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Scheduler.SetOpenCL(OpenCL);
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Convolution.SetOpenCL(OpenCL);
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//---
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Actor.TrainMode(false);
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Encoder.TrainMode(false);
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//---
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vector<float> ActorResult;
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Actor.getResults(ActorResult);
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if(ActorResult.Size() != NActions)
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{
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PrintFormat("The scope of the actor does not match the actions count (%d <> %d)", NActions, Result.Total());
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return INIT_FAILED;
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}
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//---
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Encoder.GetLayerOutput(0, Result);
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if(Result.Total() != (HistoryBars * BarDescr))
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{
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PrintFormat("Input size of State Encoder doesn't match state description (%d <> %d)", Result.Total(), (HistoryBars * BarDescr));
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return INIT_FAILED;
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}
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//---
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vector<float> EncoderResults;
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Actor.GetLayerOutput(0,Result);
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Encoder.getResults(EncoderResults);
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if(Result.Total() != int(EncoderResults.Size()))
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{
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PrintFormat("Input size of Actor doesn't match Encoder outputs (%d <> %d)", Result.Total(), EncoderResults.Size());
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return INIT_FAILED;
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}
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//---
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Actor.GetLayerOutput(LatentLayer, Result);
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int latent_state = Result.Total();
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Critic1.GetLayerOutput(0, Result);
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if(Result.Total() != latent_state)
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{
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PrintFormat("Input size of Critic doesn't match latent state Actor (%d <> %d)", Result.Total(), latent_state);
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return INIT_FAILED;
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}
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//---
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Gradient.BufferInit(AccountDescr, 0);
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//---
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if(!EventChartCustom(ChartID(), 1, 0, 0, "Init"))
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{
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PrintFormat("Error of create study event: %d", GetLastError());
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return INIT_FAILED;
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}
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//---
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return(INIT_SUCCEEDED);
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}
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//+------------------------------------------------------------------+
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//| Expert deinitialization function |
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//+------------------------------------------------------------------+
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void OnDeinit(const int reason)
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{
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//---
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TargetCritic1.WeightsUpdate(GetPointer(Critic1), Tau);
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TargetCritic2.WeightsUpdate(GetPointer(Critic2), Tau);
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TargetCritic1.Save(FileName + "Crt1.nnw", Critic1.getRecentAverageError(), 0, 0, TimeCurrent(), true);
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TargetCritic2.Save(FileName + "Crt2.nnw", Critic2.getRecentAverageError(), 0, 0, TimeCurrent(), true);
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Scheduler.Save(FileName + "Sch.nnw", 0, 0, 0, TimeCurrent(), true);
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delete Result;
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}
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//+------------------------------------------------------------------+
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//| ChartEvent function |
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//+------------------------------------------------------------------+
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void OnChartEvent(const int id,
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const long &lparam,
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const double &dparam,
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const string &sparam)
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{
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//---
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if(id == 1001)
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Train();
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}
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//+------------------------------------------------------------------+
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//| Train function |
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//+------------------------------------------------------------------+
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void Train(void)
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{
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int total_tr = ArraySize(Buffer);
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uint ticks = GetTickCount();
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float loss = 0;
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//---
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int total_states = Buffer[0].Total - 1;
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for(int i = 1; i < total_tr; i++)
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total_states += Buffer[i].Total - 1;
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vector<float> temp;
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Convolution.getResults(temp);
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matrix<float> state_embedding = matrix<float>::Zeros(total_states,temp.Size());
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matrix<float> rewards = matrix<float>::Zeros(total_states,NRewards);
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int state = 0;
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for(int tr = 0; tr < total_tr; tr++)
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{
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for(int st = 0; st < Buffer[tr].Total - 1; st++)
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{
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State.AssignArray(Buffer[tr].States[st].state);
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float PrevBalance = Buffer[tr].States[MathMax(st,0)].account[0];
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float PrevEquity = Buffer[tr].States[MathMax(st,0)].account[1];
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State.Add((Buffer[tr].States[st].account[0] - PrevBalance) / PrevBalance);
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State.Add(Buffer[tr].States[st].account[1] / PrevBalance);
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State.Add((Buffer[tr].States[st].account[1] - PrevEquity) / PrevEquity);
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State.Add(Buffer[tr].States[st].account[2]);
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State.Add(Buffer[tr].States[st].account[3]);
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State.Add(Buffer[tr].States[st].account[4] / PrevBalance);
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State.Add(Buffer[tr].States[st].account[5] / PrevBalance);
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State.Add(Buffer[tr].States[st].account[6] / PrevBalance);
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double x = (double)Buffer[tr].States[st].account[7] / (double)(D'2024.01.01' - D'2023.01.01');
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State.Add((float)MathSin(x != 0 ? 2.0 * M_PI * x : 0));
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x = (double)Buffer[tr].States[st].account[7] / (double)PeriodSeconds(PERIOD_MN1);
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State.Add((float)MathCos(x != 0 ? 2.0 * M_PI * x : 0));
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x = (double)Buffer[tr].States[st].account[7] / (double)PeriodSeconds(PERIOD_W1);
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State.Add((float)MathSin(x != 0 ? 2.0 * M_PI * x : 0));
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x = (double)Buffer[tr].States[st].account[7] / (double)PeriodSeconds(PERIOD_D1);
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State.Add((float)MathSin(x != 0 ? 2.0 * M_PI * x : 0));
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//---
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State.AddArray(Buffer[tr].States[st + 1].state);
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State.Add((Buffer[tr].States[st + 1].account[0] - PrevBalance) / PrevBalance);
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State.Add(Buffer[tr].States[st + 1].account[1] / PrevBalance);
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State.Add((Buffer[tr].States[st + 1].account[1] - PrevEquity) / PrevEquity);
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State.Add(Buffer[tr].States[st + 1].account[2]);
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State.Add(Buffer[tr].States[st + 1].account[3]);
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State.Add(Buffer[tr].States[st + 1].account[4] / PrevBalance);
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State.Add(Buffer[tr].States[st + 1].account[5] / PrevBalance);
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State.Add(Buffer[tr].States[st + 1].account[6] / PrevBalance);
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x = (double)Buffer[tr].States[st + 1].account[7] / (double)(D'2024.01.01' - D'2023.01.01');
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State.Add((float)MathSin(x != 0 ? 2.0 * M_PI * x : 0));
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x = (double)Buffer[tr].States[st + 1].account[7] / (double)PeriodSeconds(PERIOD_MN1);
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State.Add((float)MathCos(x != 0 ? 2.0 * M_PI * x : 0));
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x = (double)Buffer[tr].States[st + 1].account[7] / (double)PeriodSeconds(PERIOD_W1);
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State.Add((float)MathSin(x != 0 ? 2.0 * M_PI * x : 0));
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x = (double)Buffer[tr].States[st + 1].account[7] / (double)PeriodSeconds(PERIOD_D1);
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State.Add((float)MathSin(x != 0 ? 2.0 * M_PI * x : 0));
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if(!Convolution.feedForward(GetPointer(State),1,false,(CBufferFloat*)NULL))
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{
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PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
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ExpertRemove();
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return;
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}
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Convolution.getResults(temp);
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state_embedding.Row(temp,state);
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temp.Assign(Buffer[tr].States[st].rewards);
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for(ulong r = 0; r < temp.Size(); r++)
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temp[r] -= Buffer[tr].States[st + 1].rewards[r] * DiscFactor;
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rewards.Row(temp,state);
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state++;
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if(GetTickCount() - ticks > 500)
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{
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string str = StringFormat("%-15s %6.2f%%", "Embedding ", state * 100.0 / (double)(total_states));
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Comment(str);
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ticks = GetTickCount();
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}
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}
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}
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if(state != total_states)
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{
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state_embedding.Reshape(state,state_embedding.Cols());
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rewards.Reshape(state,NRewards);
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total_states = state;
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}
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//---
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vector<float> reward, rewards1, rewards2, target_reward;
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int bar = (HistoryBars - 1) * BarDescr;
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for(int iter = 0; (iter < Iterations && !IsStopped()); iter ++)
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{
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int tr = (int)((MathRand() / 32767.0) * (total_tr - 1));
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int i = (int)((MathRand() * MathRand() / MathPow(32767, 2)) * (Buffer[tr].Total - 2));
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if(i < 0)
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{
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iter--;
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continue;
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}
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reward = vector<float>::Zeros(NRewards);
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rewards1 = reward;
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rewards2 = reward;
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target_reward = reward;
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//--- State
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State.AssignArray(Buffer[tr].States[i].state);
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float PrevBalance = Buffer[tr].States[MathMax(i - 1, 0)].account[0];
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float PrevEquity = Buffer[tr].States[MathMax(i - 1, 0)].account[1];
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if(PrevBalance == 0.0f || PrevEquity == 0.0f)
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continue;
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Account.Clear();
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Account.Add((Buffer[tr].States[i].account[0] - PrevBalance) / PrevBalance);
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Account.Add(Buffer[tr].States[i].account[1] / PrevBalance);
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Account.Add((Buffer[tr].States[i].account[1] - PrevEquity) / PrevEquity);
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Account.Add(Buffer[tr].States[i].account[2]);
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Account.Add(Buffer[tr].States[i].account[3]);
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Account.Add(Buffer[tr].States[i].account[4] / PrevBalance);
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Account.Add(Buffer[tr].States[i].account[5] / PrevBalance);
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Account.Add(Buffer[tr].States[i].account[6] / PrevBalance);
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double x = (double)Buffer[tr].States[i].account[7] / (double)(D'2024.01.01' - D'2023.01.01');
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Account.Add((float)MathSin(x != 0 ? 2.0 * M_PI * x : 0));
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x = (double)Buffer[tr].States[i].account[7] / (double)PeriodSeconds(PERIOD_MN1);
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Account.Add((float)MathCos(x != 0 ? 2.0 * M_PI * x : 0));
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x = (double)Buffer[tr].States[i].account[7] / (double)PeriodSeconds(PERIOD_W1);
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Account.Add((float)MathSin(x != 0 ? 2.0 * M_PI * x : 0));
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x = (double)Buffer[tr].States[i].account[7] / (double)PeriodSeconds(PERIOD_D1);
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Account.Add((float)MathSin(x != 0 ? 2.0 * M_PI * x : 0));
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if(Account.GetIndex() >= 0)
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Account.BufferWrite();
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//--- Encoder State
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if(!Encoder.feedForward(GetPointer(State), 1, false, GetPointer(Account)))
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{
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PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
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break;
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}
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//--- Skills
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if(!Scheduler.feedForward(GetPointer(Encoder), -1, NULL,-1))
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{
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PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
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break;
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}
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//--- Actor
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if(!Actor.feedForward(GetPointer(Encoder), -1, GetPointer(Scheduler),-1))
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{
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PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
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break;
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}
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//--- Next State
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TargetState.AssignArray(Buffer[tr].States[i + 1].state);
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double cl_op = Buffer[tr].States[i + 1].state[bar];
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double prof_1l = SymbolInfoDouble(_Symbol, SYMBOL_TRADE_TICK_VALUE_PROFIT) * cl_op /
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SymbolInfoDouble(_Symbol, SYMBOL_POINT);
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Actor.getResults(Result);
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vector<float> forecast = ForecastAccount(Buffer[tr].States[i].account,Result,prof_1l,Buffer[tr].States[i + 1].account[7]);
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TargetAccount.AssignArray(forecast);
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if(TargetAccount.GetIndex() >= 0 && !TargetAccount.BufferWrite())
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{
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PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
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break;
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}
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if(!TargetEncoder.feedForward(GetPointer(TargetState), 1, false, GetPointer(TargetAccount)))
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{
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PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
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break;
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}
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//--- Target
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if(!TargetActor.feedForward(GetPointer(TargetEncoder), -1, GetPointer(Scheduler),-1))
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{
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PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
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break;
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}
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//---
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if(!TargetCritic1.feedForward(GetPointer(TargetActor), LatentLayer, GetPointer(TargetActor)) ||
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!TargetCritic2.feedForward(GetPointer(TargetActor), LatentLayer, GetPointer(TargetActor)))
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{
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PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
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break;
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}
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TargetCritic1.getResults(rewards1);
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TargetCritic2.getResults(rewards2);
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if(rewards1.Sum() <= rewards2.Sum())
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target_reward = rewards1;
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else
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target_reward = rewards2;
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target_reward *= DiscFactor;
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State.AddArray(GetPointer(TargetState));
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State.AddArray(GetPointer(TargetAccount));
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if(!Convolution.feedForward(GetPointer(State),1,false,(CBufferFloat*)NULL))
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{
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PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
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break;
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}
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Convolution.getResults(rewards1);
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reward[0] += KNNReward(7,rewards1,state_embedding,rewards);
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reward += target_reward;
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Result.AssignArray(reward);
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//---
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if(!Critic1.feedForward(GetPointer(Actor), LatentLayer, GetPointer(Actor),-1) ||
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!Critic2.feedForward(GetPointer(Actor), LatentLayer, GetPointer(Actor),-1))
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{
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PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
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break;
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}
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Critic1.getResults(rewards1);
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Critic2.getResults(rewards2);
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if(rewards1.Sum() <= rewards2.Sum())
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{
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loss = (loss * MathMin(iter,999) + (reward - rewards1).Sum()) / MathMin(iter + 1,1000);
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if(!Critic1.backProp(Result, GetPointer(Actor)) ||
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!Actor.backPropGradient(GetPointer(Scheduler),-1,-1) ||
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!Scheduler.backPropGradient() ||
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!Critic2.backProp(Result, GetPointer(Actor)))
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{
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PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
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break;
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}
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}
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else
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{
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loss = (loss * MathMin(iter,999) + (reward - rewards2).Sum()) / MathMin(iter + 1,1000);
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if(!Critic2.backProp(Result, GetPointer(Actor)) ||
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!Actor.backPropGradient(GetPointer(Scheduler),-1,-1) ||
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!Scheduler.backPropGradient() ||
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!Critic1.backProp(Result, GetPointer(Actor)))
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{
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PrintFormat("%s -> %d", __FUNCTION__, __LINE__);
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break;
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}
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}
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//--- Update Target Nets
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TargetCritic1.WeightsUpdate(GetPointer(Critic1), Tau);
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TargetCritic2.WeightsUpdate(GetPointer(Critic2), Tau);
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//---
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if(GetTickCount() - ticks > 500)
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{
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string str = StringFormat("%-20s %5.2f%% -> Error %15.8f\n", "Critic1", iter * 100.0 / (double)(Iterations), Critic1.getRecentAverageError());
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str += StringFormat("%-20s %5.2f%% -> Error %15.8f\n", "Critic2", iter * 100.0 / (double)(Iterations), Critic2.getRecentAverageError());
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str += StringFormat("%-20s %5.2f%% -> Error %15.8f\n", "Scheduler", iter * 100.0 / (double)(Iterations), loss);
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Comment(str);
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ticks = GetTickCount();
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}
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}
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Comment("");
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//---
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PrintFormat("%s -> %d -> %-20s %10.7f", __FUNCTION__, __LINE__, "Critic1", Critic1.getRecentAverageError());
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PrintFormat("%s -> %d -> %-20s %10.7f", __FUNCTION__, __LINE__, "Critic2", Critic2.getRecentAverageError());
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PrintFormat("%s -> %d -> %-15s %10.7f", __FUNCTION__, __LINE__, "Scheduler", loss);
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ExpertRemove();
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//---
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}
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//+------------------------------------------------------------------+
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//| |
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//+------------------------------------------------------------------+
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float KNNReward(ulong k, vector<float> &embedding, matrix<float> &state_embedding, matrix<float> &rewards)
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{
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if(embedding.Size() != state_embedding.Cols())
|
|
{
|
|
PrintFormat("%s -> %d Inconsistent embedding size", __FUNCTION__, __LINE__);
|
|
return (0);
|
|
}
|
|
//---
|
|
ulong size = embedding.Size();
|
|
ulong states = state_embedding.Rows();
|
|
matrix<float> temp = matrix<float>::Zeros(states,size);
|
|
//---
|
|
for(ulong i = 0; i < size; i++)
|
|
temp.Col(MathPow(state_embedding.Col(i) - embedding[i],2.0f),i);
|
|
vector<float> dist = MathSqrt(temp.Sum(1));
|
|
matrix<float> min_dist = matrix<float>::Zeros(k,NRewards + 1);
|
|
for(ulong i = 0; i < k; i++)
|
|
{
|
|
ulong pos = dist.ArgMin();
|
|
min_dist[i,0] = dist[pos];
|
|
dist[pos] = FLT_MAX;
|
|
for(int c = 0; c < NRewards; c++)
|
|
min_dist[i,c + 1] = rewards[pos,c];
|
|
}
|
|
//---
|
|
min_dist.Col(vector<float>::Zeros(k),0);
|
|
//---
|
|
float result = min_dist.Sum() / (k * NRewards);
|
|
//---
|
|
return (result);
|
|
}
|
|
//+------------------------------------------------------------------+
|