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aGrUM 3.2.0
a C++ library for (probabilistic) graphical models
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aGrUM 3.2.0
a C++ library for (probabilistic) graphical models
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This is the complete list of members for gum::SDYNA, including all inherited members.
| _actionReward_ | gum::SDYNA | private |
| _bin_ | gum::SDYNA | private |
| _decider_ | gum::SDYNA | private |
| _lastAction_ | gum::SDYNA | private |
| _learner_ | gum::SDYNA | private |
| _nbObservation_ | gum::SDYNA | private |
| _nbValueIterationStep_ | gum::SDYNA | private |
| _observationPhaseLenght_ | gum::SDYNA | private |
| _planer_ | gum::SDYNA | private |
| addAction(const Idx actionId, std::string_view actionName) | gum::SDYNA | |
| addVariable(const DiscreteVariable *var) | gum::SDYNA | |
| feedback(const Instantiation &originalState, const Instantiation &reachedState, Idx performedAction, double obtainedReward) | gum::SDYNA | |
| feedback(const Instantiation &reachedState, double obtainedReward) | gum::SDYNA | |
| fmdp_ | gum::SDYNA | protected |
| initialize() | gum::SDYNA | |
| initialize(const Instantiation &initialState) | gum::SDYNA | |
| lastState_ | gum::SDYNA | protected |
| learnerSize() | gum::SDYNA | |
| makePlanning(Idx nbStep) | gum::SDYNA | |
| modelSize() | gum::SDYNA | |
| optimalPolicy2String() | gum::SDYNA | |
| optimalPolicySize() | gum::SDYNA | |
| RandomMDDInstance(double attributeSelectionThreshold=0.99, double similarityThreshold=0.3, double discountFactor=0.9, double epsilon=1, Idx observationPhaseLenght=100, Idx nbValueIterationStep=10) | gum::SDYNA | static |
| RandomTreeInstance(double attributeSelectionThreshold=0.99, double discountFactor=0.9, double epsilon=1, Idx observationPhaseLenght=100, Idx nbValueIterationStep=10) | gum::SDYNA | static |
| RMaxMDDInstance(double attributeSelectionThreshold=0.99, double similarityThreshold=0.3, double discountFactor=0.9, double epsilon=1, Idx observationPhaseLenght=100, Idx nbValueIterationStep=10) | gum::SDYNA | static |
| RMaxTreeInstance(double attributeSelectionThreshold=0.99, double discountFactor=0.9, double epsilon=1, Idx observationPhaseLenght=100, Idx nbValueIterationStep=10) | gum::SDYNA | static |
| SDYNA(ILearningStrategy *learner, IPlanningStrategy< double > *planer, IDecisionStrategy *decider, Idx observationPhaseLenght, Idx nbValueIterationStep, bool actionReward, bool verbose=true) | gum::SDYNA | private |
| setCurrentState(const Instantiation ¤tState) | gum::SDYNA | |
| spimddiInstance(double attributeSelectionThreshold=0.99, double similarityThreshold=0.3, double discountFactor=0.9, double epsilon=1, Idx observationPhaseLenght=100, Idx nbValueIterationStep=10) | gum::SDYNA | static |
| spitiInstance(double attributeSelectionThreshold=0.99, double discountFactor=0.9, double epsilon=1, Idx observationPhaseLenght=100, Idx nbValueIterationStep=10) | gum::SDYNA | static |
| takeAction(const Instantiation &curState) | gum::SDYNA | |
| takeAction() | gum::SDYNA | |
| toString() | gum::SDYNA | |
| valueFunctionSize() | gum::SDYNA | |
| verbose_ | gum::SDYNA | private |
| ~SDYNA() | gum::SDYNA |