601 lines
30 KiB
MQL5
601 lines
30 KiB
MQL5
//+------------------------------------------------------------------+
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//| Study.mq5 |
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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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#property strict
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#include "Trajectory.mqh"
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//---
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input group "---- D2Skill training ----"
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input ENUM_D2SKILL_STAGE InpD2SkillStage = D2Skill_D2_STAGE_FORMATION; //Lifecycle stage
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input ENUM_D2SKILL_MODE InpD2SkillExecutionMode = D2_COLLECT; //Bank execution mode
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input ED2SkillRepresentation InpD2SkillRepresentation = D2SkillDirectionMagnitude; //Bank representation
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input bool InpD2SkillResetBanksOnRepresentationMismatch = false; //Reset incompatible banks
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input bool InpD2SkillRecreateIncompatibleCheckpoint = false; //Recreate incompatible policy checkpoint
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//---
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input group "---- Actor-Critic training ----"
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input datetime Start = D'2024.01.01'; //Training period start
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input datetime End = D'2026.01.01'; //Training period end
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input int Iterations = 1000000; //Training iterations
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input int EpisodeBars = 2 * StackSize; //Bars per episode
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input double MinBalance = 50.0; //Minimum account balance
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input int UpdatePolicy = 5; //Actor update interval
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//+------------------------------------------------------------------+
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//| |
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//+------------------------------------------------------------------+
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int Epochs = 0;
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CNet Actor;
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CNet Q1;
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CNet Q2;
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CBufferFloat State;
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CBufferFloat TimeState;
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CBufferFloat Account;
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CBufferFloat NextAccount;
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CBufferFloat CurrentAction;
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CBufferFloat BaselineAction;
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CBufferFloat BaselineAccount;
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CBufferFloat BaselineNextAccount;
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CBufferFloat TeacherAction;
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CBufferFloat RandomAction;
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CBufferFloat CriticInput;
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CBufferFloat ScalarTarget;
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CBufferFloat ActorObjective;
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ulong TeacherTransitions = 0;
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ulong TeacherCriticTransitions = 0;
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ulong RandomCriticTransitions = 0;
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double TeacherRewardSum = 0;
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ulong PolicyTransitions = 0;
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ulong PolicySkippedInvalid = 0;
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ulong PairedTransitions = 0;
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double PairedDeltaSum = 0;
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SD2SkillActorForwardState ActorForwardState;
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SD2SkillActorForwardState BaselineActorForwardState;
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SD2SkillEpisodeOutcome BaselineEpisodeOutcome;
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SD2SkillEpisodeOutcome SkillEpisodeOutcome;
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bool PairedEpisodeActive = false;
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//+------------------------------------------------------------------+
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//| Resets only D2Skill episode influence, preserving bank params. |
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//+------------------------------------------------------------------+
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bool ResetD2SkillEpisodeInfluence(CNet &actor)
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{
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CNeuronBaseOCL *layer = actor.Layer(1);
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if(!layer || layer.Type() != defNeuronD2Skill)
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ReturnFalse;
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CD2Skill *skill = (CD2Skill*)layer;
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if(!skill.Ready() || !skill.ResetEpisodeInfluence())
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ReturnFalse;
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return(true);
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}
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//+------------------------------------------------------------------+
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//| Adds one simulated account transition to its own episode result.|
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//+------------------------------------------------------------------+
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bool AccumulateEpisodeOutcome(SD2SkillEpisodeOutcome &outcome,
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CBufferFloat *account,
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const double reward)
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{
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if(!account || account.Total() != AccountDescr || !MathIsValidNumber(reward) ||
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(account.GetIndex() >= 0 && !account.BufferRead()))
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ReturnFalse;
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const double balance = MathMax(0.0, double(account[0]) * EtalonBalance);
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const double equity = MathMax(0.0, double(account[2]) * MathMax(balance, 1.0));
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const double drawdown = MathMin(0.0, double(account[1]));
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const double cost = MathMax(0.0, -reward);
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const double risk = MathMax(0.0, double(account[4]) + double(account[5]));
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return(outcome.Accumulate(reward, balance, equity, drawdown, cost, 1, risk));
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}
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//+------------------------------------------------------------------+
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//| Loads OrCreateD2SkillPolicies. |
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//+------------------------------------------------------------------+
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bool LoadOrCreateD2SkillPolicies(void)
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{
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return D2SkillLoadOrCreatePolicySet(Actor, Q1, Q2, true);
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}
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//+------------------------------------------------------------------+
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//| Saves D2SkillPolicies. |
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//+------------------------------------------------------------------+
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bool SaveD2SkillPolicies(void)
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{
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//--- Forecast artifacts are immutable throughout Actor-Critic study. This
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//--- bounded lifecycle check avoids hashing three large files every batch.
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return D2SkillSavePolicySet(Actor, Q1, Q2);
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}
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//+------------------------------------------------------------------+
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//| Implements PrepareHistory. |
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//+------------------------------------------------------------------+
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bool PrepareHistory(int &first_position, int &last_position)
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{
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int start = iBarShift(Symb.Name(), TimeFrame, Start);
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int end = iBarShift(Symb.Name(), TimeFrame, End);
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int bars = CopyRates(Symb.Name(), TimeFrame, 0, start, Rates);
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if(bars <= 0 || !RSI.BufferResize(bars) || !CCI.BufferResize(bars) ||
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!ATR.BufferResize(bars) || !MACD.BufferResize(bars))
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ReturnFalse;
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int wait = -1;
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bool calculated = false;
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do
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{
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calculated = (RSI.BarsCalculated() >= bars && CCI.BarsCalculated() >= bars &&
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ATR.BarsCalculated() >= bars && MACD.BarsCalculated() >= bars);
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Sleep(100);
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wait++;
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}
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while(!calculated && wait < 100);
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if(!calculated)
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ReturnFalse;
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RSI.Refresh();
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CCI.Refresh();
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ATR.Refresh();
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MACD.Refresh();
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if(!ArraySetAsSeries(Rates, true))
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ReturnFalse;
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first_position = end + 1;
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//--- CreateBuffers(position,...,forecast) forms its state from
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//--- position+NForecast and needs HistoryBars bars behind it. position is
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//--- therefore the end of the realized future window, not its first bar.
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last_position = start - HistoryBars - NForecast;
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return (last_position > first_position);
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}
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//+------------------------------------------------------------------+
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//| Implements PolicyBackward. |
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//+------------------------------------------------------------------+
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bool PolicyBackward(void)
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{
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//--- PolicyBackward builds Q+1 in this buffer. ScalarTarget must remain the
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//--- immutable TD target consumed by both Critic backward passes.
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return PolicyBackward(Actor, Q1, GetPointer(ActorObjective), GetPointer(D2SkillMarket), -1);
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}
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//+------------------------------------------------------------------+
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//| Builds one causal baseline/skill pair on the same state. |
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//+------------------------------------------------------------------+
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bool BuildPairedTransition(const int position, const bool trace_td, bool &terminal,
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double &reward, double &delta_j, bool &paired,
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bool &baseline_terminal)
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{
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terminal = false;
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reward = 0;
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delta_j = 0;
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paired = false;
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baseline_terminal = false;
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CNeuronBaseOCL *layer = Actor.Layer(1);
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CD2Skill *skill = (layer && layer.Type() == defNeuronD2Skill ? (CD2Skill*)layer : NULL);
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paired = D2SkillUsePairedHindsight(skill, ActorForwardState);
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bool task_enabled = false;
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bool step_enabled = false;
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D2SkillD2BankFlags(task_enabled, step_enabled);
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static bool pair_setup_logged = false;
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if(!pair_setup_logged)
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{
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PrintFormat("D2Skill paired setup stage=%d layer_type=%d skill_ready=%s paired=%s",
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D2SkillD2Stage, (layer ? layer.Type() : -1),
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(skill != NULL && skill.Ready() ? "true" : "false"),
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(paired ? "true" : "false"));
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pair_setup_logged = true;
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}
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double baseline_reward = 0;
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if(paired)
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{
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if(!D2SkillActorForwardStateReady(BaselineActorForwardState) ||
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!D2SkillRestorePairedActorState(BaselineActorForwardState, skill,
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false, false, "baseline_start"))
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{
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Print("TrainTransition stage=baseline_restore");
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ReturnFalse;
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}
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if(!Actor.feedForward(GetPointer(BaselineAccount), 1, false, GetPointer(D2SkillMarket), -1) ||
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!ReadAction(Actor, GetPointer(BaselineAction)) ||
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!AdvanceAccount(GetPointer(BaselineAccount), GetPointer(BaselineAction), position, MinBalance,
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GetPointer(BaselineNextAccount), baseline_reward, baseline_terminal) ||
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!D2SkillCaptureActorForwardState(BaselineActorForwardState) ||
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!AccumulateEpisodeOutcome(BaselineEpisodeOutcome, GetPointer(BaselineNextAccount),
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baseline_reward))
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{
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D2SkillRestorePairedActorState(ActorForwardState, skill,
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task_enabled, step_enabled,
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"baseline_failure");
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Print("TrainTransition stage=baseline_transition");
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ReturnFalse;
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}
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if(!D2SkillRestorePairedActorState(ActorForwardState, skill,
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task_enabled, step_enabled,
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"baseline_to_skill"))
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{ Print("TrainTransition stage=restore_skill_state"); ReturnFalse; }
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}
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if(!Actor.feedForward(GetPointer(Account), 1, false, GetPointer(D2SkillMarket), -1) ||
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!ReadAction(Actor, GetPointer(CurrentAction)) ||
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!AdvanceAccount(GetPointer(Account), GetPointer(CurrentAction), position, MinBalance,
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GetPointer(NextAccount), reward, terminal) ||
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(paired && (!D2SkillCaptureActorForwardState(ActorForwardState) ||
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!AccumulateEpisodeOutcome(SkillEpisodeOutcome, GetPointer(NextAccount), reward))))
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{
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if(paired)
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D2SkillRestorePairedActorState(ActorForwardState, skill,
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task_enabled, step_enabled,
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"skill_failure");
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Print("TrainTransition stage=skill_transition");
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ReturnFalse;
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}
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if(paired)
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{
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delta_j = reward - baseline_reward;
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if(!MathIsValidNumber(delta_j))
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{
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D2SkillRestorePairedActorState(ActorForwardState, skill,
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task_enabled, step_enabled,
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"nonfinite_delta");
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Print("TrainTransition stage=paired_delta_nonfinite");
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ReturnFalse;
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}
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if(trace_td)
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PrintFormat("D2Skill paired iteration=%d JBase=%.8f JSkill=%.8f DeltaJ=%.8f baseline_terminal=%s skill_terminal=%s",
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position, baseline_reward, reward, delta_j,
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(baseline_terminal ? "true" : "false"), (terminal ? "true" : "false"));
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}
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return(true);
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}
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//+------------------------------------------------------------------+
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//| Implements TrainTransition. |
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//+------------------------------------------------------------------+
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bool TrainTransition(const int position, const int iteration, bool &skill_terminal,
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bool &pair_terminal)
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{
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skill_terminal = false;
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pair_terminal = false;
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const bool trace_td = (iteration < 8 || iteration % 10000 == 0);
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if(!D2SkillForwardForecast(position, GetPointer(State), GetPointer(TimeState)))
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{ Print("TrainTransition stage=current_forecast"); ReturnFalse; }
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double reward = 0;
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double delta_j = 0;
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bool paired = false;
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bool baseline_terminal = false;
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if(!BuildPairedTransition(position, trace_td, skill_terminal, reward, delta_j, paired,
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baseline_terminal))
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ReturnFalse;
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pair_terminal = (paired && D2SkillPairReachedTerminal(baseline_terminal,
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skill_terminal));
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const double balance = MathMax(0.0, double(Account[0]) * EtalonBalance);
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const double buy_lot = MathMax(0.0, double(CurrentAction[0] - CurrentAction[3]));
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const double sell_lot = MathMax(0.0, double(CurrentAction[3] - CurrentAction[0]));
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if(trace_td)
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PrintFormat("D2Skill action iteration=%d balance=%.2f buy_lot=%.8f buy_tp=%.8f buy_sl=%.8f sell_lot=%.8f sell_tp=%.8f sell_sl=%.8f",
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iteration, balance, buy_lot, double(CurrentAction[1]), double(CurrentAction[2]),
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sell_lot, double(CurrentAction[4]), double(CurrentAction[5]));
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//--- Utility is emitted only at the common episode boundary. The two branch
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//--- outcomes are accumulated independently, never inferred from this reward.
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if(paired && trace_td)
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PrintFormat("D2Skill paired transition base_reward=%.8f skill_reward=%.8f",
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reward - delta_j, reward);
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const double target_value = reward;
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if(trace_td)
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PrintFormat("D2Skill return iteration=%d reward=%.8f target=%.8f balance=%.2f equity=%.2f "
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"floating_buy=%.8f floating_sell=%.8f next_buy=%.8f next_sell=%.8f terminal=%s",
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iteration, reward, target_value, double(NextAccount[0])*EtalonBalance,
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double(NextAccount[2])*balance, double(NextAccount[6])*MathMax(double(NextAccount[0])*EtalonBalance, 1.0),
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double(NextAccount[7])*MathMax(double(NextAccount[0])*EtalonBalance, 1.0),
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double(NextAccount[4]), double(NextAccount[5]),
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(skill_terminal ? "true" : "false"));
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if(!MathIsValidNumber(target_value) || !ScalarTarget.BufferInit(1, 0) ||
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!ScalarTarget.Update(0, float(target_value)))
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{ Print("TrainTransition stage=scalar_target"); ReturnFalse; }
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CNeuronBaseOCL *context_layer = Actor.Layer(0);
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CNeuronBaseOCL *actor_layer = Actor.Layer(3);
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if(!context_layer || !actor_layer)
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{ Print("TrainTransition stage=actor_layer"); ReturnFalse; }
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//--- Use the live device activation, not the CPU account object.
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if(!BuildCriticInput(context_layer.getOutput(), actor_layer.getOutput(), GetPointer(CriticInput)))
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{ Print("TrainTransition stage=critic_input"); ReturnFalse; }
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if(!Q1.feedForward(GetPointer(CriticInput), 1, false, GetPointer(D2SkillMarket), -1))
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{ Print("TrainTransition stage=q1_forward"); ReturnFalse; }
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if(!Q2.feedForward(GetPointer(CriticInput), 1, false, GetPointer(D2SkillMarket), -1))
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{ Print("TrainTransition stage=q2_forward"); ReturnFalse; }
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//+------------------------------------------------------------------+
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//| Function if. |
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//+------------------------------------------------------------------+
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if(trace_td)
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{
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CNeuronBaseOCL *q1_layer = Q1.Layer(3);
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CNeuronBaseOCL *q2_layer = Q2.Layer(3);
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CBufferFloat *q1_output = (q1_layer ? q1_layer.getOutput() : NULL);
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CBufferFloat *q2_output = (q2_layer ? q2_layer.getOutput() : NULL);
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if(!q1_output || !q2_output || q1_output.GetIndex() < 0 || q2_output.GetIndex() < 0 ||
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q1_output.Total() != 1 || q2_output.Total() != 1 ||
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!q1_output.BufferRead() || !q2_output.BufferRead())
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{ Print("TrainTransition stage=current_q_read"); ReturnFalse; }
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PrintFormat("D2Skill Q iteration=%d q1=%.8f q2=%.8f td_q1=%.8f td_q2=%.8f",
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iteration, double(q1_output[0]), double(q2_output[0]),
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target_value - double(q1_output[0]), target_value - double(q2_output[0]));
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}
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//--- A critic gradient has no valid direction on the discontinuous no-trade
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//--- plateau. Keep learning Q there, but let only the realized-future teacher
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//--- move Actor back into the executable action manifold.
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const bool executable_action = (IsExecutableOrder(buy_lot, CurrentAction[1], CurrentAction[2]) ||
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IsExecutableOrder(sell_lot, CurrentAction[4], CurrentAction[5]));
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//---
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if(iteration > 0 && UpdatePolicy > 0 && iteration % UpdatePolicy == 0)
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{
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if(executable_action)
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{
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if(!PolicyBackward())
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{ Print("TrainTransition stage=policy_backward"); ReturnFalse; }
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PolicyTransitions++;
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}
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else
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PolicySkippedInvalid++;
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}
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if(!Q1.backProp(GetPointer(ScalarTarget), GetPointer(D2SkillMarket), -1) ||
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!Q2.backProp(GetPointer(ScalarTarget), GetPointer(D2SkillMarket), -1))
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{ Print("TrainTransition stage=critic_backward"); ReturnFalse; }
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//--- The teacher uses realized future bars, not one selected Forecast scenario.
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//--- It is deliberately a second Critic-only sample: Q1/Q2 are stateless,
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//--- whereas Actor has already performed its only forward for this state.
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double teacher_reward = 0;
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if(!BuildTeacherAction(position, GetPointer(Account), GetPointer(TeacherAction), teacher_reward))
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{ Print("TrainTransition stage=teacher_action"); ReturnFalse; }
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//---
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if(teacher_reward > 0)
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{
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if(trace_td)
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PrintFormat("D2Skill teacher iteration=%d reward=%.8f buy_lot=%.8f sell_lot=%.8f",
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iteration, teacher_reward,
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MathMax(0.0, double(TeacherAction[0] - TeacherAction[3])),
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MathMax(0.0, double(TeacherAction[3] - TeacherAction[0])));
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//--- Supervised Actor update reuses the current activation; it never advances
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//--- the history stack or invokes a second Actor forward.
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if(!Actor.backProp(GetPointer(TeacherAction), GetPointer(D2SkillMarket), -1))
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{ Print("TrainTransition stage=teacher_actor_backward"); ReturnFalse; }
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TeacherTransitions++;
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TeacherRewardSum += teacher_reward;
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}
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//--- Critic receives every oracle action, including a losing one. Only Actor
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//--- is restricted to profitable oracle supervision above.
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if(!ScalarTarget.BufferInit(1, 0) || !ScalarTarget.Update(0, float(teacher_reward)) ||
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!BuildCriticInput(context_layer.getOutput(), GetPointer(TeacherAction), GetPointer(CriticInput)))
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{ Print("TrainTransition stage=teacher_critic_input"); ReturnFalse; }
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if(!Q1.feedForward(GetPointer(CriticInput), 1, false, GetPointer(D2SkillMarket), -1) ||
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!Q2.feedForward(GetPointer(CriticInput), 1, false, GetPointer(D2SkillMarket), -1))
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{ Print("TrainTransition stage=teacher_critic_forward"); ReturnFalse; }
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if(!Q1.backProp(GetPointer(ScalarTarget), GetPointer(D2SkillMarket), -1) ||
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!Q2.backProp(GetPointer(ScalarTarget), GetPointer(D2SkillMarket), -1))
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{ Print("TrainTransition stage=teacher_critic_backward"); ReturnFalse; }
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TeacherCriticTransitions++;
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//--- A second critic-only sample is uniformly random over executable lots and
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//--- stop distances. It never runs Actor backward or advances policy history.
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double random_reward = 0;
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if(!BuildRandomAction(position, GetPointer(Account), GetPointer(RandomAction), random_reward))
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{ Print("TrainTransition stage=random_action"); ReturnFalse; }
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//+------------------------------------------------------------------+
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//| Function if. |
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//+------------------------------------------------------------------+
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if(random_reward > 0)
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{
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if(!Actor.backProp(GetPointer(RandomAction), GetPointer(D2SkillMarket), -1))
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{ Print("TrainTransition stage=random_actor_backward"); ReturnFalse; }
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}
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if(!ScalarTarget.BufferInit(1, 0) || !ScalarTarget.Update(0, float(random_reward)) ||
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!BuildCriticInput(context_layer.getOutput(), GetPointer(RandomAction), GetPointer(CriticInput)))
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{ Print("TrainTransition stage=random_critic_input"); ReturnFalse; }
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if(!Q1.feedForward(GetPointer(CriticInput), 1, false, GetPointer(D2SkillMarket), -1) ||
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!Q2.feedForward(GetPointer(CriticInput), 1, false, GetPointer(D2SkillMarket), -1))
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{ Print("TrainTransition stage=random_critic_forward"); ReturnFalse; }
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if(!Q1.backProp(GetPointer(ScalarTarget), GetPointer(D2SkillMarket), -1) ||
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!Q2.backProp(GetPointer(ScalarTarget), GetPointer(D2SkillMarket), -1))
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{ Print("TrainTransition stage=random_critic_backward"); ReturnFalse; }
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RandomCriticTransitions++;
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//--- Only the three declared Forecast trainable buffers are read here. The
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//--- complete persistent Codebook is checked at checkpoint/lifecycle bounds.
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if(!D2SkillVerifyFrozenWeightsExact())
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{ Print("TrainTransition stage=frozen_forecast_check"); ReturnFalse; }
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return(true);
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}
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//+------------------------------------------------------------------+
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//| Implements TrainD2SkillActorCritic. |
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//+------------------------------------------------------------------+
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void TrainD2SkillActorCritic(void)
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{
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int first = 0, last = 0;
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if(!PrepareHistory(first, last))
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{ PrintFormat("%s -> %d history unavailable", __FUNCTION__, __LINE__); return; }
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uint shown = GetTickCount();
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int completed_iterations = 0;
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int failed_iteration = -1;
|
|
int failed_position = -1;
|
|
int position = last;
|
|
int episode = 0;
|
|
bool episode_reset_failed = false;
|
|
TeacherTransitions = 0;
|
|
TeacherCriticTransitions = 0;
|
|
RandomCriticTransitions = 0;
|
|
TeacherRewardSum = 0;
|
|
PolicyTransitions = 0;
|
|
PolicySkippedInvalid = 0;
|
|
PairedTransitions = 0;
|
|
PairedDeltaSum = 0;
|
|
for(int iteration = 0; iteration < Iterations && !IsStopped(); iteration++)
|
|
{
|
|
if(episode == 0)
|
|
{
|
|
if(!ResetD2SkillEpisodeInfluence(Actor) ||
|
|
!D2SkillForwardForecast(position, GetPointer(State), GetPointer(TimeState)))
|
|
break;
|
|
const vector<float> sampled = SampleAccount(GetPointer(State), Rates[position].time, EtalonBalance, MinBalance);
|
|
if(sampled.Size() != AccountDescr || !Account.AssignArray(sampled) ||
|
|
(Account.GetIndex() >= 0 && !Account.BufferWrite()) ||
|
|
!BaselineAccount.AssignArray(GetPointer(Account)) ||
|
|
(BaselineAccount.GetIndex() >= 0 && !BaselineAccount.BufferWrite()) ||
|
|
!BaselineEpisodeOutcome.Reset() || !SkillEpisodeOutcome.Reset() || !Actor.Clear())
|
|
break;
|
|
CNeuronBaseOCL *layer = Actor.Layer(1);
|
|
CD2Skill *skill = (layer && layer.Type() == defNeuronD2Skill ? (CD2Skill*)layer : NULL);
|
|
PairedEpisodeActive = (D2SkillUsePairedHindsight(skill, ActorForwardState) &&
|
|
D2SkillActorForwardStateReady(BaselineActorForwardState));
|
|
if(PairedEpisodeActive && (!D2SkillCaptureActorForwardState(ActorForwardState) ||
|
|
!D2SkillCaptureActorForwardState(BaselineActorForwardState)))
|
|
break;
|
|
}
|
|
bool skill_terminal = false;
|
|
bool pair_terminal = false;
|
|
//---
|
|
if(!TrainTransition(position, iteration, skill_terminal, pair_terminal))
|
|
{
|
|
failed_iteration = iteration;
|
|
failed_position = position;
|
|
if(!ResetD2SkillEpisodeInfluence(Actor))
|
|
episode_reset_failed = true;
|
|
break;
|
|
}
|
|
completed_iterations++;
|
|
//---
|
|
if((NextAccount.GetIndex() >= 0 && !NextAccount.BufferRead()) || !Account.AssignArray(GetPointer(NextAccount)) ||
|
|
(Account.GetIndex() >= 0 && !Account.BufferWrite()) ||
|
|
(PairedEpisodeActive &&
|
|
((BaselineNextAccount.GetIndex() >= 0 && !BaselineNextAccount.BufferRead()) ||
|
|
!BaselineAccount.AssignArray(GetPointer(BaselineNextAccount)) ||
|
|
(BaselineAccount.GetIndex() >= 0 && !BaselineAccount.BufferWrite()))))
|
|
{
|
|
failed_iteration = iteration;
|
|
failed_position = position;
|
|
if(!ResetD2SkillEpisodeInfluence(Actor))
|
|
episode_reset_failed = true;
|
|
break;
|
|
}
|
|
position--;
|
|
episode++;
|
|
//---
|
|
const bool episode_limit = (position < first ||
|
|
episode >= MathMax(1, EpisodeBars));
|
|
double terminal_delta = 0.0;
|
|
bool utility_applied = false;
|
|
bool influence_reset = false;
|
|
bool episode_closed = false;
|
|
if(!D2SkillCloseOfflineEpisode(Actor, PairedEpisodeActive, pair_terminal,
|
|
episode_limit, BaselineEpisodeOutcome,
|
|
SkillEpisodeOutcome, terminal_delta,
|
|
utility_applied, influence_reset,
|
|
episode_closed))
|
|
{
|
|
failed_iteration = iteration;
|
|
failed_position = position;
|
|
episode_reset_failed = true;
|
|
break;
|
|
}
|
|
if(episode_closed && !influence_reset)
|
|
{
|
|
failed_iteration = iteration;
|
|
failed_position = position;
|
|
episode_reset_failed = true;
|
|
break;
|
|
}
|
|
if(episode_closed)
|
|
{
|
|
if(PairedEpisodeActive)
|
|
{
|
|
PairedTransitions++;
|
|
PairedDeltaSum += terminal_delta;
|
|
PrintFormat("D2Skill paired terminal JBase=%.8f JSkill=%.8f DeltaJ=%.8f utility_mutated=%s base_balance=%.2f skill_balance=%.2f base_duration=%u skill_duration=%u",
|
|
BaselineEpisodeOutcome.Outcome(), SkillEpisodeOutcome.Outcome(), terminal_delta,
|
|
(utility_applied ? "true" : "false"),
|
|
BaselineEpisodeOutcome.Balance(), SkillEpisodeOutcome.Balance(),
|
|
BaselineEpisodeOutcome.Duration(), SkillEpisodeOutcome.Duration());
|
|
}
|
|
if(position < first)
|
|
position = last;
|
|
episode = 0;
|
|
}
|
|
//---
|
|
if(GetTickCount() - shown > 500)
|
|
{
|
|
const double q1_rmse = MathSqrt(MathMax(0.0, double(Q1.getRecentAverageError()))) / MathMax(EtalonBalance, 1.0);
|
|
const double q2_rmse = MathSqrt(MathMax(0.0, double(Q2.getRecentAverageError()))) / MathMax(EtalonBalance, 1.0);
|
|
const double teacher_mean = (TeacherTransitions > 0 ? TeacherRewardSum / double(TeacherTransitions) : 0.0);
|
|
Comment(StringFormat("D2Skill AC %6.2f%% Q1 rRMSE %.8f Q2 rRMSE %.8f teacherA %I64u teacherQ %I64u randomQ %I64u R %.2f policy %I64u skip %I64u",
|
|
100.0 * iteration / MathMax(Iterations, 1), q1_rmse, q2_rmse,
|
|
TeacherTransitions, TeacherCriticTransitions, RandomCriticTransitions,
|
|
teacher_mean, PolicyTransitions, PolicySkippedInvalid));
|
|
shown = GetTickCount();
|
|
}
|
|
}
|
|
if(!ResetD2SkillEpisodeInfluence(Actor))
|
|
episode_reset_failed = true;
|
|
Comment("");
|
|
//---
|
|
if(completed_iterations != Iterations || episode_reset_failed)
|
|
{
|
|
if(episode_reset_failed)
|
|
PrintFormat("%s -> %d publication aborted: episode influence reset failed completed=%d/%d",
|
|
__FUNCTION__, __LINE__, completed_iterations, Iterations);
|
|
else
|
|
if(failed_iteration >= 0)
|
|
PrintFormat("%s -> %d publication aborted: TrainTransition failed iteration=%d position=%d completed=%d/%d",
|
|
__FUNCTION__, __LINE__, failed_iteration, failed_position, completed_iterations, Iterations);
|
|
else
|
|
PrintFormat("%s -> %d publication aborted: stop requested completed=%d/%d",
|
|
__FUNCTION__, __LINE__, completed_iterations, Iterations);
|
|
return;
|
|
}
|
|
if(!SaveD2SkillPolicies())
|
|
PrintFormat("%s -> %d save failed", __FUNCTION__, __LINE__);
|
|
if(PairedTransitions > 0)
|
|
PrintFormat("D2Skill paired summary transitions=%I64u mean_delta_j=%.8f",
|
|
PairedTransitions, PairedDeltaSum / double(PairedTransitions));
|
|
}
|
|
//+------------------------------------------------------------------+
|
|
//| Function OnInit. |
|
|
//+------------------------------------------------------------------+
|
|
int OnInit()
|
|
{
|
|
ResetLastError();
|
|
if(!D2SkillConfigureRuntime(InpD2SkillStage, InpD2SkillMode,
|
|
InpD2SkillExecutionMode, InpD2SkillUtilityAware,
|
|
InpD2SkillMinUtility, InpD2SkillUtilityScale,
|
|
false, false, InpD2SkillRepresentation,
|
|
InpD2SkillResetBanksOnRepresentationMismatch,
|
|
InpD2SkillRecreateIncompatibleCheckpoint) ||
|
|
!D2SkillInitIndicators() || !D2SkillLoadForecastInference() || !LoadOrCreateD2SkillPolicies() ||
|
|
!D2SkillConfigureActorCriticUpdates(Actor, Q1, Q2) ||
|
|
!D2SkillVerifyFrozenForecastExact())
|
|
{
|
|
PrintFormat("D2Skill Actor-Critic initialization failed at line %d error=%d", __LINE__, GetLastError());
|
|
return INIT_FAILED;
|
|
}
|
|
if(!D2SkillConfigureD2UtilityMode(D2Skill_D2_UTILITY_PAIRED_HINDSIGHT) ||
|
|
((D2SkillD2ExecutionMode == D2_EVALUATE ||
|
|
D2SkillD2ExecutionMode == D2_ONLINE_CALIBRATION) &&
|
|
D2SkillD2Mode != D2Skill_D2_MODE_BASE &&
|
|
(!D2SkillInitActorForwardState(ActorForwardState, Actor) ||
|
|
!D2SkillInitActorForwardState(BaselineActorForwardState, Actor))))
|
|
{
|
|
PrintFormat("D2Skill paired snapshot initialization failed at line %d error=%d",
|
|
__LINE__, GetLastError());
|
|
return INIT_FAILED;
|
|
}
|
|
if(!EventSetMillisecondTimer(1))
|
|
{
|
|
PrintFormat("D2Skill Actor-Critic timer initialization failed at line %d error=%d",
|
|
__LINE__, GetLastError());
|
|
return(INIT_FAILED);
|
|
}
|
|
return(INIT_SUCCEEDED);
|
|
}
|
|
//+------------------------------------------------------------------+
|
|
//| Function OnDeinit. |
|
|
//+------------------------------------------------------------------+
|
|
void OnDeinit(const int reason)
|
|
{
|
|
EventKillTimer();
|
|
//--- Only a completed explicit training loop persists all six artifacts and
|
|
//--- writes the hash manifest last. Deinit must not create a mixed generation.
|
|
if(D2SkillFrozenBaselineReady && D2SkillForecast != NULL && !D2SkillVerifyFrozenForecastExact())
|
|
PrintFormat("%s -> %d forecast mutation", __FUNCTION__, __LINE__);
|
|
D2SkillForecast = NULL;
|
|
}
|
|
//+------------------------------------------------------------------+
|
|
//| Runs the one-shot training lifecycle. |
|
|
//+------------------------------------------------------------------+
|
|
void OnTimer(void)
|
|
{
|
|
TrainD2SkillActorCritic();
|
|
ExpertRemove();
|
|
}
|
|
//+------------------------------------------------------------------+
|