#include <shark/ObjectiveFunctions/Benchmarks/MarkovPole.h>
Inheritance diagram for shark::MarkovPole< HiddenNeuron, OutputNeuron >:
Public Member Functions | |
| MarkovPole (bool single_pole, std::size_t hidden, bool shortcuts, bool bias, bool normalize=true, std::size_t max_pole_evaluations=100000) | |
| ~MarkovPole () | |
| std::string | name () |
| std::size_t | numberOfVariables () const |
| Returns degrees of freedom. More... | |
| SearchPointType | proposeStartingPoint () const |
| Always proposes to start in a zero vector with appropriate degrees of freedom. More... | |
| ResultType | eval (const SearchPointType &input) const |
| Evaluates weight vector on fitness function. More... | |
| Public Member Functions inherited from shark::AbstractObjectiveFunction< RealVector, double > | |
| const Features & | features () const |
| virtual void | updateFeatures () |
| bool | hasValue () const |
| returns whether this function can calculate it's function value More... | |
| bool | hasFirstDerivative () const |
| returns whether this function can calculate the first derivative More... | |
| bool | hasSecondDerivative () const |
| returns whether this function can calculate the second derivative More... | |
| bool | canProposeStartingPoint () const |
| returns whether this function can propose a starting point. More... | |
| bool | isConstrained () const |
| returns whether this function can return More... | |
| bool | hasConstraintHandler () const |
| returns whether this function can return More... | |
| bool | canProvideClosestFeasible () const |
| Returns whether this function can calculate thee closest feasible to an infeasible point. More... | |
| bool | isThreadSafe () const |
| Returns true, when the function can be usd in parallel threads. More... | |
| bool | isNoisy () const |
| Returns true, when the function can be usd in parallel threads. More... | |
| AbstractObjectiveFunction () | |
| Default ctor. More... | |
| virtual | ~AbstractObjectiveFunction () |
| Virtual destructor. More... | |
| virtual void | init () |
| void | setRng (random::rng_type *rng) |
| Sets the Rng used by the objective function. More... | |
| virtual bool | hasScalableDimensionality () const |
| virtual void | setNumberOfVariables (std::size_t numberOfVariables) |
| Adjusts the number of variables if the function is scalable. More... | |
| virtual std::size_t | numberOfObjectives () const |
| virtual bool | hasScalableObjectives () const |
| virtual void | setNumberOfObjectives (std::size_t numberOfObjectives) |
| Adjusts the number of objectives if the function is scalable. More... | |
| std::size_t | evaluationCounter () const |
| Accesses the evaluation counter of the function. More... | |
| AbstractConstraintHandler< SearchPointType > const & | getConstraintHandler () const |
| Returns the constraint handler of the function if it has one. More... | |
| virtual bool | isFeasible (const SearchPointType &input) const |
| Tests whether a point in SearchSpace is feasible, e.g., whether the constraints are fulfilled. More... | |
| virtual void | closestFeasible (SearchPointType &input) const |
| If supported, the supplied point is repaired such that it satisfies all of the function's constraints. More... | |
| ResultType | operator() (SearchPointType const &input) const |
| Evaluates the function. Useful together with STL-Algorithms like std::transform. More... | |
| virtual ResultType | evalDerivative (SearchPointType const &input, FirstOrderDerivative &derivative) const |
| Evaluates the objective function and calculates its gradient. More... | |
| virtual ResultType | evalDerivative (SearchPointType const &input, SecondOrderDerivative &derivative) const |
| Evaluates the objective function and calculates its gradient. More... | |
| Public Member Functions inherited from shark::INameable | |
| virtual | ~INameable () |
| virtual std::string | name () const |
| returns the name of the object More... | |
Additional Inherited Members | |
| Public Types inherited from shark::AbstractObjectiveFunction< RealVector, double > | |
| enum | Feature |
| List of features that are supported by an implementation. More... | |
| typedef RealVector | SearchPointType |
| typedef double | ResultType |
| typedef boost::mpl::if_< std::is_arithmetic< double >, SearchPointType, RealMatrix >::type | FirstOrderDerivative |
| typedef TypedFlags< Feature > | Features |
| This statement declares the member m_features. See Core/Flags.h for details. More... | |
| typedef TypedFeatureNotAvailableException< Feature > | FeatureNotAvailableException |
| Protected Member Functions inherited from shark::AbstractObjectiveFunction< RealVector, double > | |
| void | announceConstraintHandler (AbstractConstraintHandler< SearchPointType > const *handler) |
| helper function which is called to announce the presence of an constraint handler. More... | |
| Protected Attributes inherited from shark::AbstractObjectiveFunction< RealVector, double > | |
| Features | m_features |
| std::size_t | m_evaluationCounter |
| Evaluation counter, default value: 0. More... | |
| AbstractConstraintHandler< SearchPointType > const * | m_constraintHandler |
| random::rng_type * | mep_rng |
Detailed Description
template<class HiddenNeuron, class OutputNeuron>
class shark::MarkovPole< HiddenNeuron, OutputNeuron >
Uses templates to allow changing the neural network activation function since FFNet uses templates. The FastSigmoidNeuron is recommended, as it gives better results overall. If errors are encountered using a specific neuron, one can try without normalization, as it fixes it in the single pole LogisticNeuron case at least. Class for balancing one or two poles on a cart using a fitness function that decreases the longer the pole(s) balance(s). Based on code written by Verena Heidrich-Meisner for the paper
V. Heidrich-Meisner and C. Igel. Neuroevolution strategies for episodic reinforcement learn-ing. Journal of Algorithms, 64(4):152–168, 2009.
Definition at line 64 of file MarkovPole.h.
Constructor & Destructor Documentation
◆ MarkovPole()
|
inline |
- Parameters
-
single_pole Indicates whether the cast has a single pole (true) or two poles (false) hidden Number of hidden neurons in underlying neural network shortcuts Whether to use shortcuts in neural network bias Whether to use bias in neural network normalize Whether to normalize input before use in neural network max_pole_evaluations Balance goal of the function, i.e. number of steps that pole should be able to balance without failure
Definition at line 72 of file MarkovPole.h.
References shark::AbstractObjectiveFunction< RealVector, double >::CAN_PROPOSE_STARTING_POINT, shark::AbstractObjectiveFunction< RealVector, double >::m_evaluationCounter, and shark::AbstractObjectiveFunction< RealVector, double >::m_features.
◆ ~MarkovPole()
|
inline |
Definition at line 121 of file MarkovPole.h.
Member Function Documentation
◆ eval()
|
inlinevirtual |
Evaluates weight vector on fitness function.
- Parameters
-
input Vector to be evaluated.
- Returns
- Fitness of vector
Reimplemented from shark::AbstractObjectiveFunction< RealVector, double >.
Definition at line 147 of file MarkovPole.h.
References shark::SinglePole::failure(), shark::DoublePole::failure(), shark::DoublePole::getState(), shark::SinglePole::getState(), shark::SinglePole::init(), shark::DoublePole::init(), shark::AbstractObjectiveFunction< RealVector, double >::m_evaluationCounter, shark::SinglePole::move(), shark::DoublePole::move(), and SIZE_CHECK.
◆ name()
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inline |
Definition at line 126 of file MarkovPole.h.
◆ numberOfVariables()
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inlinevirtual |
Returns degrees of freedom.
Implements shark::AbstractObjectiveFunction< RealVector, double >.
Definition at line 131 of file MarkovPole.h.
◆ proposeStartingPoint()
|
inlinevirtual |
Always proposes to start in a zero vector with appropriate degrees of freedom.
Reimplemented from shark::AbstractObjectiveFunction< RealVector, double >.
Definition at line 136 of file MarkovPole.h.
The documentation for this class was generated from the following file:
- include/shark/ObjectiveFunctions/Benchmarks/MarkovPole.h