Objective function for single and double non-Markov poles. More...
#include <shark/ObjectiveFunctions/Benchmarks/NonMarkovPole.h>
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 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... | |
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.
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inline |
single | Is this an instance of the single pole problem? |
hidden | Number of hidden neurons in underlying neural network |
bias | Whether to use bias in neural network |
sigmoidType | Activation sigmoid function for 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 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.
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inline |
Definition at line 120 of file NonMarkovPole.h.
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inlinevirtual |
Evaluates weight vector on fitness function.
input | Vector to be evaluated. |
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.
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inline |
Definition at line 125 of file NonMarkovPole.h.
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inlinevirtual |
Returns degrees of freedom.
Implements shark::AbstractObjectiveFunction< PointType, ResultT >.
Definition at line 130 of file NonMarkovPole.h.
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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.