fmdpLearner_tpl.h

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/****************************************************************************
 *   This file is part of the aGrUM/pyAgrum library.                        *
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 *   Copyright (c) 2005-2026 by                                             *
 *       - Pierre-Henri WUILLEMIN(_at_LIP6)                                 *
 *       - Christophe GONZALES(_at_AMU)                                     *
 *                                                                          *
 *   The aGrUM/pyAgrum library is free software; you can redistribute it    *
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 *   SPDX-FileCopyrightText: Copyright 2005-2026                            *
 *       - Pierre-Henri WUILLEMIN(_at_LIP6)                                 *
 *       - Christophe GONZALES(_at_AMU)                                     *
 *   SPDX-License-Identifier: LGPL-3.0-or-later OR MIT                      *
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#pragma once
 

 
// =========================================================================
#include <agrum/FMDP/learning/fmdpLearner.h>
 
// =========================================================================
 
namespace gum {
 
  // ==========================================================================
  // Constructor & destructor.
  // ==========================================================================
 
  // ###################################################################
  // Default constructor
  // ###################################################################
  template < TESTNAME    VariableAttributeSelection,
             TESTNAME    RewardAttributeSelection,
             LEARNERNAME LearnerSelection >
  FMDPLearner< VariableAttributeSelection, RewardAttributeSelection, LearnerSelection >::
      FMDPLearner(double lT, bool actionReward, double sT) :
      _actionReward_(actionReward), _learningThreshold_(lT), _similarityThreshold_(sT) {
    GUM_CONSTRUCTOR(FMDPLearner);
    _rewardLearner_ = nullptr;
  }
 
  // ###################################################################
  // Default destructor
  // ###################################################################
  template < TESTNAME    VariableAttributeSelection,
             TESTNAME    RewardAttributeSelection,
             LEARNERNAME LearnerSelection >
  FMDPLearner< VariableAttributeSelection, RewardAttributeSelection, LearnerSelection >::
      ~FMDPLearner() {
    for (auto actionIter = _actionLearners_.beginSafe(); actionIter != _actionLearners_.endSafe();
         ++actionIter) {
      for (auto learnerIter = actionIter.val()->beginSafe();
           learnerIter != actionIter.val()->endSafe();
           ++learnerIter)
        delete learnerIter.val();
      delete actionIter.val();
      if (_actionRewardLearners_.exists(actionIter.key()))
        delete _actionRewardLearners_[actionIter.key()];
    }
 
    if (_rewardLearner_) delete _rewardLearner_;
 
    GUM_DESTRUCTOR(FMDPLearner);
  }
 
  // ==========================================================================
  //
  // ==========================================================================
 
  // ###################################################################
  //
  // ###################################################################
  template < TESTNAME    VariableAttributeSelection,
             TESTNAME    RewardAttributeSelection,
             LEARNERNAME LearnerSelection >
  void FMDPLearner< VariableAttributeSelection, RewardAttributeSelection, LearnerSelection >::
      initialize(FMDP< double >* fmdp) {
    _fmdp_ = fmdp;
 
    _modaMax_ = 0;
    _rmax_    = 0.0;
 
    gum::VariableSet mainVariables;
    for (auto varIter = _fmdp_->beginVariables(); varIter != _fmdp_->endVariables(); ++varIter) {
      mainVariables.insert(*varIter);
      _modaMax_ = _modaMax_ < (*varIter)->domainSize() ? (*varIter)->domainSize() : _modaMax_;
    }
 
    for (auto actionIter = _fmdp_->beginActions(); actionIter != _fmdp_->endActions();
         ++actionIter) {
      // Adding a Hashtable for the action
      _actionLearners_.insert(*actionIter, new VarLearnerTable());
 
      // Adding a learner for each variable
      for (auto varIter = _fmdp_->beginVariables(); varIter != _fmdp_->endVariables(); ++varIter) {
        MultiDimFunctionGraph< double >* varTrans = _instantiateFunctionGraph_();
        varTrans->setTableName("ACTION : " + _fmdp_->actionName(*actionIter)
                               + " - VARIABLE : " + (*varIter)->name());
        _fmdp_->addTransitionForAction(*actionIter, *varIter, varTrans);
        _actionLearners_[*actionIter]->insert(
            (*varIter),
            _instantiateVarLearner_(varTrans, mainVariables, _fmdp_->main2prime(*varIter)));
      }
 
      if (_actionReward_) {
        MultiDimFunctionGraph< double >* reward = _instantiateFunctionGraph_();
        reward->setTableName("REWARD - ACTION : " + _fmdp_->actionName(*actionIter));
        _fmdp_->addRewardForAction(*actionIter, reward);
        _actionRewardLearners_.insert(*actionIter,
                                      _instantiateRewardLearner_(reward, mainVariables));
      }
    }
 
    if (!_actionReward_) {
      MultiDimFunctionGraph< double >* reward = _instantiateFunctionGraph_();
      reward->setTableName("REWARD");
      _fmdp_->addReward(reward);
      _rewardLearner_ = _instantiateRewardLearner_(reward, mainVariables);
    }
  }
 
  // ###################################################################
  //
  // ###################################################################
  template < TESTNAME    VariableAttributeSelection,
             TESTNAME    RewardAttributeSelection,
             LEARNERNAME LearnerSelection >
  bool FMDPLearner< VariableAttributeSelection, RewardAttributeSelection, LearnerSelection >::
      addObservation(Idx actionId, const Observation* newObs) {
    for (SequenceIteratorSafe< const DiscreteVariable* > varIter = _fmdp_->beginVariables();
         varIter != _fmdp_->endVariables();
         ++varIter) {
      _actionLearners_[actionId]->getWithDefault(*varIter, nullptr)->addObservation(newObs);
      _actionLearners_[actionId]->getWithDefault(*varIter, nullptr)->updateGraph();
    }
 
    if (_actionReward_) {
      _actionRewardLearners_[actionId]->addObservation(newObs);
      _actionRewardLearners_[actionId]->updateGraph();
    } else {
      _rewardLearner_->addObservation(newObs);
      _rewardLearner_->updateGraph();
    }
 
    _rmax_ = _rmax_ < std::abs(newObs->reward()) ? std::abs(newObs->reward()) : _rmax_;
 
    return false;
  }
 
  // ###################################################################
  //
  // ###################################################################
  template < TESTNAME    VariableAttributeSelection,
             TESTNAME    RewardAttributeSelection,
             LEARNERNAME LearnerSelection >
  Size FMDPLearner< VariableAttributeSelection, RewardAttributeSelection, LearnerSelection >::
      size() {
    Size s = 0;
    for (SequenceIteratorSafe< Idx > actionIter = _fmdp_->beginActions();
         actionIter != _fmdp_->endActions();
         ++actionIter) {
      for (SequenceIteratorSafe< const DiscreteVariable* > varIter = _fmdp_->beginVariables();
           varIter != _fmdp_->endVariables();
           ++varIter)
        s += _actionLearners_[*actionIter]->getWithDefault(*varIter, nullptr)->size();
      if (_actionReward_) s += _actionRewardLearners_[*actionIter]->size();
    }
 
    if (!_actionReward_) s += _rewardLearner_->size();
 
    return s;
  }
 
  // ###################################################################
  //
  // ###################################################################
  template < TESTNAME    VariableAttributeSelection,
             TESTNAME    RewardAttributeSelection,
             LEARNERNAME LearnerSelection >
  void FMDPLearner< VariableAttributeSelection, RewardAttributeSelection, LearnerSelection >::
      updateFMDP() {
    for (SequenceIteratorSafe< Idx > actionIter = _fmdp_->beginActions();
         actionIter != _fmdp_->endActions();
         ++actionIter) {
      for (SequenceIteratorSafe< const DiscreteVariable* > varIter = _fmdp_->beginVariables();
           varIter != _fmdp_->endVariables();
           ++varIter)
        _actionLearners_[*actionIter]->getWithDefault(*varIter, nullptr)->updateFunctionGraph();
      if (_actionReward_) _actionRewardLearners_[*actionIter]->updateFunctionGraph();
    }
 
    if (!_actionReward_) _rewardLearner_->updateFunctionGraph();
  }
 
  // ==========================================================================
  // Instantiation methods
  // ==========================================================================
 
  template < TESTNAME    VariableAttributeSelection,
             TESTNAME    RewardAttributeSelection,
             LEARNERNAME LearnerSelection >
  MultiDimFunctionGraph< double >* FMDPLearner< VariableAttributeSelection,
                                                RewardAttributeSelection,
                                                LearnerSelection >::_instantiateFunctionGraph_() {
    return _instantiateFunctionGraph_(Int2Type< LearnerSelection >());
  }
 
  template < TESTNAME    VariableAttributeSelection,
             TESTNAME    RewardAttributeSelection,
             LEARNERNAME LearnerSelection >
  MultiDimFunctionGraph< double >*
      FMDPLearner< VariableAttributeSelection, RewardAttributeSelection, LearnerSelection >::
          _instantiateFunctionGraph_(Int2Type< IMDDILEARNER >) {
    return MultiDimFunctionGraph< double, ExactTerminalNodePolicy >::getReducedAndOrderedInstance();
  }
 
  template < TESTNAME    VariableAttributeSelection,
             TESTNAME    RewardAttributeSelection,
             LEARNERNAME LearnerSelection >
  MultiDimFunctionGraph< double >*
      FMDPLearner< VariableAttributeSelection, RewardAttributeSelection, LearnerSelection >::
          _instantiateFunctionGraph_(Int2Type< ITILEARNER >) {
    return MultiDimFunctionGraph< double, ExactTerminalNodePolicy >::getTreeInstance();
  }
 
  template < TESTNAME    VariableAttributeSelection,
             TESTNAME    RewardAttributeSelection,
             LEARNERNAME LearnerSelection >
  auto FMDPLearner< VariableAttributeSelection, RewardAttributeSelection, LearnerSelection >::
      _instantiateVarLearner_(MultiDimFunctionGraph< double >* target,
                              gum::VariableSet&                mainVariables,
                              const DiscreteVariable*          learnedVar) -> VariableLearnerType* {
    return _instantiateVarLearner_(target,
                                   mainVariables,
                                   learnedVar,
                                   Int2Type< LearnerSelection >());
  }
 
  template < TESTNAME    VariableAttributeSelection,
             TESTNAME    RewardAttributeSelection,
             LEARNERNAME LearnerSelection >
  auto FMDPLearner< VariableAttributeSelection, RewardAttributeSelection, LearnerSelection >::
      _instantiateVarLearner_(MultiDimFunctionGraph< double >* target,
                              gum::VariableSet&                mainVariables,
                              const DiscreteVariable*          learnedVar,
                              Int2Type< IMDDILEARNER >) -> VariableLearnerType* {
    return new VariableLearnerType(target,
                                   _learningThreshold_,
                                   _similarityThreshold_,
                                   mainVariables,
                                   learnedVar);
  }
 
  template < TESTNAME    VariableAttributeSelection,
             TESTNAME    RewardAttributeSelection,
             LEARNERNAME LearnerSelection >
  auto FMDPLearner< VariableAttributeSelection, RewardAttributeSelection, LearnerSelection >::
      _instantiateVarLearner_(MultiDimFunctionGraph< double >* target,
                              gum::VariableSet&                mainVariables,
                              const DiscreteVariable*          learnedVar,
                              Int2Type< ITILEARNER >) -> VariableLearnerType* {
    return new VariableLearnerType(target, _learningThreshold_, mainVariables, learnedVar);
  }
 
  template < TESTNAME    VariableAttributeSelection,
             TESTNAME    RewardAttributeSelection,
             LEARNERNAME LearnerSelection >
  auto FMDPLearner< VariableAttributeSelection, RewardAttributeSelection, LearnerSelection >::
      _instantiateRewardLearner_(MultiDimFunctionGraph< double >* target,
                                 gum::VariableSet& mainVariables) -> RewardLearnerType* {
    return _instantiateRewardLearner_(target, mainVariables, Int2Type< LearnerSelection >());
  }
 
  template < TESTNAME    VariableAttributeSelection,
             TESTNAME    RewardAttributeSelection,
             LEARNERNAME LearnerSelection >
  auto FMDPLearner< VariableAttributeSelection, RewardAttributeSelection, LearnerSelection >::
      _instantiateRewardLearner_(MultiDimFunctionGraph< double >* target,
                                 gum::VariableSet&                mainVariables,
                                 Int2Type< IMDDILEARNER >) -> RewardLearnerType* {
    return new RewardLearnerType(target, _learningThreshold_, _similarityThreshold_, mainVariables);
  }
 
  template < TESTNAME    VariableAttributeSelection,
             TESTNAME    RewardAttributeSelection,
             LEARNERNAME LearnerSelection >
  auto FMDPLearner< VariableAttributeSelection, RewardAttributeSelection, LearnerSelection >::
      _instantiateRewardLearner_(MultiDimFunctionGraph< double >* target,
                                 gum::VariableSet&                mainVariables,
                                 Int2Type< ITILEARNER >) -> RewardLearnerType* {
    return new RewardLearnerType(target, _learningThreshold_, mainVariables);
  }
 
  template < TESTNAME    VariableAttributeSelection,
             TESTNAME    RewardAttributeSelection,
             LEARNERNAME LearnerSelection >
  const IVisitableGraphLearner*
      FMDPLearner< VariableAttributeSelection, RewardAttributeSelection, LearnerSelection >::
          varLearner(Idx actionId, const DiscreteVariable* var) const {
    return _actionLearners_[actionId]->getWithDefault(var, nullptr);
  }
 
  template < TESTNAME    VariableAttributeSelection,
             TESTNAME    RewardAttributeSelection,
             LEARNERNAME LearnerSelection >
  double
      FMDPLearner< VariableAttributeSelection, RewardAttributeSelection, LearnerSelection >::rMax()
          const {
    return _rmax_;
  }
 
  template < TESTNAME    VariableAttributeSelection,
             TESTNAME    RewardAttributeSelection,
             LEARNERNAME LearnerSelection >
  double FMDPLearner< VariableAttributeSelection, RewardAttributeSelection, LearnerSelection >::
      modaMax() const {
    return _modaMax_;
  }
 
}   // End of namespace gum