Evolutionary Neuro-Controller for the MIT Spheres (perception-action defined in the body frame) More...

#include <spheres_q.h>

Inheritance diagram for pagmo::problem::spheres_q:

Public Member Functions
	spheres_q (int n_evaluations=10, int n_hidden=10, double ode_prec=1E-3, unsigned int seed=0)
	Constructor. More...

	spheres_q (const spheres_q &)
	Copy Constructor. More...

	~spheres_q ()
	Destructor. More...

std::vector< std::vector< double > >	post_evaluate (const decision_vector &x, int N=25000, unsigned int seed=0) const
	Post evaluation of the neural controller. More...

std::vector< std::vector< double > >	simulate (const decision_vector &x, const std::vector< double > &ic, int N) const
	Performs one "detailed" simulation. More...

base_ptr	clone () const
	Clone method.

Public Member Functions inherited from pagmo::problem::base_stochastic
	base_stochastic (int, unsigned int=0u)
	Constructor from global dimension and random seed. More...

	base_stochastic (int, int, int, int, int, const double &, unsigned int)
	Constructor from global dimension, integer dimension, fitness dimension, global constraints dimension, inequality constraints dimension and constraints tolerance. More...

	base_stochastic (int, int, int, int, int, const std::vector< double > &, unsigned int)
	Constructor from global dimension, integer dimension, fitness dimension, global constraints dimension, inequality constraints dimension and constraints tolerance vector. More...

unsigned int	get_seed () const
	Gets the pseudo random generator seed. More...

void	set_seed (unsigned int) const
	Sets the pseudo random generator seed. More...

Public Member Functions inherited from pagmo::problem::base
	base (int, int=0, int=1, int=0, int=0, const double &=0)
	Constructor from global dimension, integer dimension, fitness dimension, global constraints dimension, inequality constraints dimension and constraints tolerance. More...

	base (int, int, int, int, int, const std::vector< double > &)
	Constructor from global dimension, integer dimension, fitness dimension, global constraints dimension, inequality constraints dimension and constraints tolerance. More...

	base (const double &, const double &, int, int=0, int=1, int=0, int=0, const double &=0)
	Constructor from values for lower and upper bounds, global dimension, integer dimension, fitness dimension, global constraints dimension, inequality constraints dimension and constraints tolerance. More...

	base (const decision_vector &, const decision_vector &, int=0, int=1, int=0, int=0, const double &=0)
	Constructor from upper/lower bounds, integer dimension, fitness dimension, global constraints dimension, inequality constraints dimension and constraints tolerance. More...

template<std::size_t N>
	base (const double(&v1)[N], const double(&v2)[N], int ni=0, int nf=1, int nc=0, int nic=0, const double &c_tol=0)
	Constructor from raw arrays, integer dimension, fitness dimension, global constraints dimension, inequality constraints dimension and constraints tolerance. More...

template<class Iterator1 , class Iterator2 >
	base (Iterator1 start1, Iterator1 end1, Iterator2 start2, Iterator2 end2, int ni=0, int nf=1, int nc=0, int nic=0, const double &c_tol=0)
	Constructor from iterators, integer dimension, fitness dimension, global constraints dimension, inequality constraints dimension and constraints tolerance. More...

virtual	~base ()
	Trivial destructor. More...

std::string	human_readable () const
	Return human readable representation of the problem. More...

virtual std::string	human_readable_extra () const
	Extra information in human readable format. More...

bool	operator== (const base &) const
	Equality operator. More...

bool	operator!= (const base &) const
	Inequality operator. More...

bool	is_compatible (const base &) const
	Compatibility operator. More...

bool	compare_x (const decision_vector &, const decision_vector &) const
	Compare decision vectors. More...

bool	verify_x (const decision_vector &) const
	Verify compatibility of decision vector x with problem. More...

bool	compare_fc (const fitness_vector &, const constraint_vector &, const fitness_vector &, const constraint_vector &) const
	Simultaneous fitness-constraint comparison. More...

virtual void	pre_evolution (population &) const
	Pre-evolution hook. More...

virtual void	post_evolution (population &) const
	Post-evolution hook. More...

virtual void	set_sparsity (int &lenG, std::vector< int > &iGfun, std::vector< int > &jGvar) const
	Sets the sparsity pattern of the gradient. More...

const decision_vector &	get_lb () const
	Lower bounds getter. More...

const decision_vector &	get_ub () const
	Upper bounds getter. More...

void	set_bounds (const decision_vector &, const decision_vector &)
	Bounds setter from pagmo::decision_vector. More...

template<class Iterator1 , class Iterator2 >
void	set_bounds (Iterator1 start1, Iterator1 end1, Iterator2 start2, Iterator2 end2)
	Bounds setter from iterators. More...

template<std::size_t N>
void	set_bounds (const double(&v1)[N], const double(&v2)[N])
	Bounds setter from raw arrays. More...

void	set_bounds (const double &, const double &)
	Set bounds to specified values. More...

void	set_bounds (int, const double &, const double &)
	Set bounds to specified values. More...

void	set_lb (const decision_vector &)
	Set lower bounds from pagmo::decision_vector. More...

void	set_lb (int, const double &)
	Set specific lower bound to value. More...

void	set_lb (const double &)
	Set all lower bounds to value. More...

template<class Iterator >
void	set_lb (Iterator start, Iterator end)
	Lower bounds setter from iterators. More...

template<std::size_t N>
void	set_lb (const double(&v)[N])
	Lower bounds setter from raw array. More...

void	set_ub (const decision_vector &)
	Set upper bounds from pagmo::decision_vector. More...

void	set_ub (int, const double &)
	Set specific upper bound to value. More...

void	set_ub (const double &)
	Set all upper bounds to value. More...

template<class Iterator >
void	set_ub (Iterator start, Iterator end)
	Upper bounds setter from iterators. More...

template<std::size_t N>
void	set_ub (const double(&v)[N])
	Upper bounds setter from raw array. More...

unsigned int	get_fevals () const
	Return number of function evaluations. More...

unsigned int	get_cevals () const
	Return number of constraints function evaluations. More...

size_type	get_dimension () const
	Return global dimension. More...

size_type	get_i_dimension () const
	Return integer dimension. More...

f_size_type	get_f_dimension () const
	Return fitness dimension. More...

c_size_type	get_c_dimension () const
	Return global constraints dimension. More...

c_size_type	get_ic_dimension () const
	Return inequality constraints dimension. More...

const std::vector< double > &	get_c_tol () const
	Return constraints tolerance. More...

double	get_diameter () const
	Get the diameter of the problem. More...

virtual std::string	get_name () const
	Get problem's name. More...

constraint_vector	compute_constraints (const decision_vector &) const
	Compute constraints and return constraint vector. More...

void	compute_constraints (constraint_vector &, const decision_vector &) const
	Compute constraints and write them into contraint vector. More...

bool	compare_constraints (const constraint_vector &, const constraint_vector &) const
	Compare constraint vectors. More...

bool	test_constraint (const constraint_vector &, const c_size_type &) const
	Test i-th constraint of c (using tolerance information). More...

bool	feasibility_x (const decision_vector &) const
	Test feasibility of decision vector. More...

bool	feasibility_c (const constraint_vector &) const
	Test feasibility of constraint vector. More...

fitness_vector	objfun (const decision_vector &) const
	Return fitness of pagmo::decision_vector. More...

void	objfun (fitness_vector &, const decision_vector &) const
	Write fitness of pagmo::decision_vector into pagmo::fitness_vector. More...

bool	compare_fitness (const fitness_vector &, const fitness_vector &) const
	Compare fitness vectors. More...

void	reset_caches () const
	Reset internal caches. More...

const std::vector< constraint_vector > &	get_best_c (void) const
	Get the best known constraint vector. More...

const std::vector< decision_vector > &	get_best_x (void) const
	Get the best known decision vector. More...

const std::vector< fitness_vector > &	get_best_f (void) const
	Get the best known fitness vector. More...

void	set_best_x (const std::vector< decision_vector > &)
	Sets the best known decision vectors. More...

Protected Member Functions
void	objfun_impl (fitness_vector &, const decision_vector &) const
	Objective function implementation. More...

Protected Member Functions inherited from pagmo::problem::base
virtual bool	equality_operator_extra (const base &) const
	Extra requirements for equality. More...

virtual bool	compare_fc_impl (const fitness_vector &, const constraint_vector &, const fitness_vector &, const constraint_vector &) const
	Implementation of simultaneous fitness-constraint comparison. More...

void	estimate_sparsity (const decision_vector &, int &lenG, std::vector< int > &iGfun, std::vector< int > &jGvar) const
	Heuristics to estimate the sparsity pattern of the problem. More...

void	estimate_sparsity (int &lenG, std::vector< int > &iGfun, std::vector< int > &jGvar) const
	Heuristics to estimate the sparsity pattern of the problem. More...

virtual void	compute_constraints_impl (constraint_vector &, const decision_vector &) const
	Implementation of constraint computation. More...

virtual bool	compare_constraints_impl (const constraint_vector &, const constraint_vector &) const
	Implementation of constraint vector comparison. More...

virtual bool	compare_fitness_impl (const fitness_vector &, const fitness_vector &) const
	Implementation of fitness vectors comparison. More...

Friends
class	boost::serialization::access

Additional Inherited Members
Public Types inherited from pagmo::problem::base
typedef decision_vector::size_type	size_type
	Problem's size type: the same as pagmo::decision_vector's size type.

typedef fitness_vector::size_type	f_size_type
	Fitness' size type: the same as pagmo::fitness_vector's size type.

typedef constraint_vector::size_type	c_size_type
	Constraints' size type: the same as pagmo::constraint_vector's size type.

Static Public Attributes inherited from pagmo::problem::base
static const std::size_t	cache_capacity = 5
	Capacity of the internal caches.

Protected Attributes inherited from pagmo::problem::base_stochastic
rng_double	m_drng
	Random number generator for double-precision floating point values.

rng_uint32	m_urng
	Random number generator for unsigned integer values.

unsigned int	m_seed
	Seed of the random number generator.

Detailed Description

Evolutionary Neuro-Controller for the MIT Spheres (perception-action defined in the body frame)

The MIT spheres test-bed on board of the ISS.

This problem (a stochastic optimization problem) aims at 'evolving' an artificial neural network able to steer the positions of three satellites representing the SPHERES MIT test-bed on-board of the ISS. It requies GSL libraries and thus it is compiled only if that option is activated when compiling pagmo.

The objective function is the average over multiple runs of the following fitness

$F = |L^2 - r_{12}^2| + |L^2 - r_{13}^2| + |L^2 - r_{23}^2| + (|v_1^2| + |v_2^2| + |v_3^2|) / 2$

rewarding neurocontrollers that drive the spheres towards a triangular configuration in space with zero absolute velocity.

NOTE: the dynamical model of the spheres is here that of six-degrees of freedom bodies. With respect to pagmo::problem::spheres, this requires the intorduction of quaternion dynamics, hence the name spheres_q (q is the classical letter to indicate a quaternion)

Author: Dario Izzo (dario.nosp@m..izz.nosp@m.o@esa.nosp@m..int)

Definition at line 66 of file spheres_q.h.

Constructor & Destructor Documentation

pagmo::problem::spheres_q::spheres_q	(	int	n_evaluations = `10`,
		int	n_hidden = `10`,
		double	ode_prec = `1E-3`,
		unsigned int	seed = `0`
	)

Constructor.

Initializes the Sphere simulator, the neural net and everything else!!!

Parameters

[in]	n_evaluations	Number of initial conditions each neural network fitness is evaluated upon
[in]	n_hidden	number of hidden neurons in the neural net
[in]	ode_prec	precision requested to adapt the ode-solver step size
[in]	seed	seed used to produce all random initial conditions

Definition at line 126 of file spheres_q.cpp.

pagmo::problem::spheres_q::spheres_q ( const spheres_q & other )

Copy Constructor.

Necessary to properly handle the gsl variables (pointers!!!)

Definition at line 141 of file spheres_q.cpp.

pagmo::problem::spheres_q::~spheres_q ( )

Destructor.

Necessary to properly clear memory allocated by gsl routines

Definition at line 153 of file spheres_q.cpp.

Member Function Documentation

void pagmo::problem::spheres_q::objfun_impl	(	fitness_vector &	f,
		const decision_vector &	x
	)		const

protectedvirtual

Objective function implementation.

Takes a pagmo::decision_vector x as input and writes its pagmo::fitness_vector to f. This function is not to be called directly, it is invoked by objfun() after a series of safety checks is performed on x and f.

Parameters

[out]	f	fitness vector into which x's fitness will be written.
[in]	x	decision vector whose fitness will be calculated.

Implements pagmo::problem::base.

Definition at line 312 of file spheres_q.cpp.

std::vector< std::vector< double > > pagmo::problem::spheres_q::post_evaluate	(	const decision_vector &	x,
		int	N = `25000`,
		unsigned int	seed = `0`
	)		const

Post evaluation of the neural controller.

It tests a given neural controller over a large set of initial conditions (different from those the controller's fitness was actually evaluated upon during evolution)

Parameters

[in]	x	chromosome encoding the neural network
[in]	N	number of initial conditions we want it to be tested against
[in]	seed	seed used to produce all random initial conditions

Returns: A vector containing, for each simulation, a vector of [ic, fitness] sorted by fitness

Definition at line 363 of file spheres_q.cpp.

std::vector< std::vector< double > > pagmo::problem::spheres_q::simulate	(	const decision_vector &	x,
		const std::vector< double > &	ic,
		int	N
	)		const

Performs one "detailed" simulation.

One single run of the ode solver is made, maintaining the states along the simulation ....)

Parameters

[in]	x	chromosome encoding the neural network
[in]	ic	initial condition for the simulation
[in]	N	number of points to retain along the simulation (plus the ic, of course)

Returns: A vector containing, for each time, a vector of [t, states]

Definition at line 417 of file spheres_q.cpp.

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

Public Member Functions

Protected Member Functions

Friends

Additional Inherited Members

Detailed Description

Constructor & Destructor Documentation

Member Function Documentation