shark::NonMarkovPole Class Reference

Objective function for single and double non-Markov poles. More...

#include <shark/ObjectiveFunctions/Benchmarks/NonMarkovPole.h>

+ Inheritance diagram for shark::NonMarkovPole:

Public Member Functions

 NonMarkovPole (bool single, std::size_t hidden, bool bias, RecurrentStructure::SigmoidType sigmoidType=RecurrentStructure::FastSigmoid, bool normalize=true, std::size_t max_pole_evaluations=100000)
 
 ~NonMarkovPole ()
 
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< PointType, ResultT >
const Featuresfeatures () 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< PointType, ResultT >
enum  Feature {
  HAS_VALUE = 1, HAS_FIRST_DERIVATIVE = 2, HAS_SECOND_DERIVATIVE = 4, CAN_PROPOSE_STARTING_POINT = 8,
  IS_CONSTRAINED_FEATURE = 16, HAS_CONSTRAINT_HANDLER = 32, CAN_PROVIDE_CLOSEST_FEASIBLE = 64, IS_THREAD_SAFE = 128,
  IS_NOISY = 256
}
 List of features that are supported by an implementation. More...
 
typedef PointType SearchPointType
 
typedef ResultT ResultType
 
typedef boost::mpl::if_< std::is_arithmetic< ResultT >, SearchPointType, RealMatrix >::type FirstOrderDerivative
 
typedef TypedFlags< FeatureFeatures
 This statement declares the member m_features. See Core/Flags.h for details. More...
 
typedef TypedFeatureNotAvailableException< FeatureFeatureNotAvailableException
 
- Protected Member Functions inherited from shark::AbstractObjectiveFunction< PointType, ResultT >
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< PointType, ResultT >
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

Objective function for single and double non-Markov poles.

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 59 of file NonMarkovPole.h.

Constructor & Destructor Documentation

◆ NonMarkovPole()

shark::NonMarkovPole::NonMarkovPole ( bool  single,
std::size_t  hidden,
bool  bias,
RecurrentStructure::SigmoidType  sigmoidType = RecurrentStructure::FastSigmoid,
bool  normalize = true,
std::size_t  max_pole_evaluations = 100000 
)
inline
Parameters
singleIs this an instance of the single pole problem?
hiddenNumber of hidden neurons in underlying neural network
biasWhether to use bias in neural network
sigmoidTypeActivation sigmoid function for neural network
normalizeWhether to normalize input before use in neural network
max_pole_evaluationsBalance goal of the function, i.e. number of steps that pole should be able to balance without failure

Definition at line 68 of file NonMarkovPole.h.

References shark::AbstractObjectiveFunction< PointType, ResultT >::CAN_PROPOSE_STARTING_POINT, shark::AbstractObjectiveFunction< PointType, ResultT >::m_evaluationCounter, and shark::AbstractObjectiveFunction< PointType, ResultT >::m_features.

◆ ~NonMarkovPole()

shark::NonMarkovPole::~NonMarkovPole ( )
inline

Definition at line 120 of file NonMarkovPole.h.

Member Function Documentation

◆ eval()

ResultType shark::NonMarkovPole::eval ( const SearchPointType input) const
inlinevirtual

Evaluates weight vector on fitness function.

Parameters
inputVector to be evaluated.
Returns
Fitness of vector

Reimplemented from shark::AbstractObjectiveFunction< PointType, ResultT >.

Definition at line 146 of file NonMarkovPole.h.

References shark::SinglePole::getState(), shark::SinglePole::init(), shark::AbstractObjectiveFunction< PointType, ResultT >::m_evaluationCounter, and SIZE_CHECK.

◆ name()

std::string shark::NonMarkovPole::name ( )
inline

Definition at line 125 of file NonMarkovPole.h.

◆ numberOfVariables()

std::size_t shark::NonMarkovPole::numberOfVariables ( ) const
inlinevirtual

Returns degrees of freedom.

Implements shark::AbstractObjectiveFunction< PointType, ResultT >.

Definition at line 130 of file NonMarkovPole.h.

◆ proposeStartingPoint()

SearchPointType shark::NonMarkovPole::proposeStartingPoint ( ) const
inlinevirtual

Always proposes to start in a zero vector with appropriate degrees of freedom.

Reimplemented from shark::AbstractObjectiveFunction< PointType, ResultT >.

Definition at line 135 of file NonMarkovPole.h.


The documentation for this class was generated from the following file: