Differential Dynamic Programming (DDP) solver. More...

#include <crocoddyl/core/solvers/ddp.hpp>

Inheritance diagram for SolverDDP:

[legend]

Collaboration diagram for SolverDDP:

Public Member Functions
	SolverDDP (boost::shared_ptr< ShootingProblem > problem)
	Initialize the DDP solver. More...

virtual void	allocateData ()
	Allocate all the internal data needed for the solver.

virtual void	backwardPass ()
	Run the backward pass (Riccati sweep) More...

virtual double	calcDiff ()
	Update the Jacobian, Hessian and feasibility of the optimal control problem. More...

virtual void	computeDirection (const bool recalc=true)
	Compute the search direction \((\delta\mathbf{x}^k,\delta\mathbf{u}^k)\) for the current guess \((\mathbf{x}^k_s,\mathbf{u}^k_s)\). More...

virtual void	computeGains (const std::size_t t)
	Compute the feedforward and feedback terms using a Cholesky decomposition. More...

void	decreaseRegularization ()
	Decrease the state and control regularization values by a `regfactor_` factor.

	DEPRECATED ("Use get_reg_incfactor() or get_reg_decfactor()", double get_regfactor() const ;) double get_reg_min() const
	Return the regularization factor used to decrease / increase it. More...

	DEPRECATED ("Use get_reg_max()", double get_regmax() const)

	DEPRECATED ("Use get_reg_min()", double get_regmin() const)

	DEPRECATED ("Use set_reg_incfactor() or set_reg_decfactor()", void set_regfactor(const double reg_factor);) void set_reg_min(const double regmin)
	Modify the regularization factor used to decrease / increase it. More...

	DEPRECATED ("Use set_reg_max()", void set_regmax(const double regmax))

	DEPRECATED ("Use set_reg_min()", void set_regmin(const double regmin))

virtual const Eigen::Vector2d &	expectedImprovement ()
	Return the expected improvement \(dV_{exp}\) from a given current search direction \((\delta\mathbf{x}^k,\delta\mathbf{u}^k)\). More...

virtual void	forwardPass (const double stepLength)
	Run the forward pass or rollout. More...

const std::vector< double > &	get_alphas () const
	Return the set of step lengths using by the line-search procedure.

const std::vector< MatrixXdRowMajor > &	get_K () const
	Return the feedback gains \(\mathbf{K}_{s}\).

const std::vector< Eigen::VectorXd > &	get_k () const
	Return the feedforward gains \(\mathbf{k}_{s}\).

const std::vector< Eigen::VectorXd > &	get_Qu () const
	Return the Jacobian of the Hamiltonian function \(\mathbf{Q}_{\mathbf{u}_s}\).

const std::vector< Eigen::MatrixXd > &	get_Quu () const
	Return the Hessian of the Hamiltonian function \(\mathbf{Q}_{\mathbf{uu}_s}\).

const std::vector< Eigen::VectorXd > &	get_Qx () const
	Return the Jacobian of the Hamiltonian function \(\mathbf{Q}_{\mathbf{x}_s}\).

const std::vector< Eigen::MatrixXd > &	get_Qxu () const
	Return the Hessian of the Hamiltonian function \(\mathbf{Q}_{\mathbf{xu}_s}\).

const std::vector< Eigen::MatrixXd > &	get_Qxx () const
	Return the Hessian of the Hamiltonian function \(\mathbf{Q}_{\mathbf{xx}_s}\).

double	get_reg_decfactor () const
	Return the regularization factor used to decrease the damping value.

double	get_reg_incfactor () const
	Return the regularization factor used to increase the damping value.

double	get_reg_max () const
	Return the maximum regularization value.

double	get_th_grad () const
	Return the tolerance of the expected gradient used for testing the step.

double	get_th_stepdec () const
	Return the step-length threshold used to decrease regularization.

double	get_th_stepinc () const
	Return the step-length threshold used to increase regularization.

const std::vector< Eigen::VectorXd > &	get_Vx () const
	Return the Hessian of the Value function \(V_{\mathbf{x}_s}\).

const std::vector< Eigen::MatrixXd > &	get_Vxx () const
	Return the Hessian of the Value function \(V_{\mathbf{xx}_s}\).

void	increaseRegularization ()
	Increase the state and control regularization values by a `regfactor_` factor.

virtual void	resizeData ()
	Resizing the solver data. More...

void	set_alphas (const std::vector< double > &alphas)
	Modify the set of step lengths using by the line-search procedure.

void	set_reg_decfactor (const double reg_factor)
	Modify the regularization factor used to decrease the damping value.

void	set_reg_incfactor (const double reg_factor)
	Modify the regularization factor used to increase the damping value.

void	set_reg_max (const double regmax)
	Modify the maximum regularization value.

void	set_th_grad (const double th_grad)
	Modify the tolerance of the expected gradient used for testing the step.

void	set_th_stepdec (const double th_step)
	Modify the step-length threshold used to decrease regularization.

void	set_th_stepinc (const double th_step)
	Modify the step-length threshold used to increase regularization.

virtual bool	solve (const std::vector< Eigen::VectorXd > &init_xs=DEFAULT_VECTOR, const std::vector< Eigen::VectorXd > &init_us=DEFAULT_VECTOR, const std::size_t maxiter=100, const bool is_feasible=false, const double init_reg=1e-9)
	Compute the optimal trajectory \(\mathbf{x}^_s,\mathbf{u}^_s\) as lists of \(T+1\) and \(T\) terms. More...

virtual double	stoppingCriteria ()
	Return a positive value that quantifies the algorithm termination. More...

virtual double	tryStep (const double steplength=1)
	Try a predefined step length \(\alpha\) and compute its cost improvement \(dV\). More...

Public Member Functions inherited from SolverAbstract
EIGEN_MAKE_ALIGNED_OPERATOR_NEW	SolverAbstract (boost::shared_ptr< ShootingProblem > problem)
	Initialize the solver. More...

double	computeDynamicFeasibility ()
	Compute the dynamic feasibility \(\\|\mathbf{f}_{\mathbf{s}}\\|_{\infty,1}\) for the current guess \((\mathbf{x}^k,\mathbf{u}^k)\). More...

double	get_cost () const
	Return the total cost.

const Eigen::Vector2d &	get_d () const
	Return the LQ approximation of the expected improvement.

double	get_dV () const
	Return the cost reduction \(dV\).

double	get_dVexp () const
	Return the expected cost reduction \(dV_{exp}\).

double	get_ffeas () const
	Return the feasibility of the dynamic constraints \(\\|\mathbf{f}_{\mathbf{s}}\\|_{\infty,1}\) of the current guess.

const std::vector< Eigen::VectorXd > &	get_fs () const
	Return the gaps \(\mathbf{f}_{s}\).

bool	get_inffeas () const
	Return the norm used for the computing the feasibility (true for \(\ell_\infty\), false for \(\ell_1\))

bool	get_is_feasible () const
	Return the feasibility status of the \((\mathbf{x}_s,\mathbf{u}_s)\) trajectory.

std::size_t	get_iter () const
	Return the number of iterations performed by the solver.

const boost::shared_ptr< ShootingProblem > &	get_problem () const
	Return the shooting problem.

double	get_steplength () const
	Return the step length \(\alpha\).

double	get_stop () const
	Return the value computed by `stoppingCriteria()`

double	get_th_acceptstep () const
	Return the threshold used for accepting a step.

double	get_th_gaptol () const
	Return the threshold for accepting a gap as non-zero.

double	get_th_stop () const
	Return the tolerance for stopping the algorithm.

double	get_ureg () const
	Return the control regularization value.

const std::vector< Eigen::VectorXd > &	get_us () const
	Return the control trajectory \(\mathbf{u}_s\).

double	get_xreg () const
	Return the state regularization value.

const std::vector< Eigen::VectorXd > &	get_xs () const
	Return the state trajectory \(\mathbf{x}_s\).

const std::vector< boost::shared_ptr< CallbackAbstract > > &	getCallbacks () const
	Return the list of callback functions using for diagnostic.

void	set_inffeas (const bool inffeas)
	Modify the current norm used for computed the feasibility.

void	set_th_acceptstep (const double th_acceptstep)
	Modify the threshold used for accepting step.

void	set_th_gaptol (const double th_gaptol)
	Modify the threshold for accepting a gap as non-zero.

void	set_th_stop (const double th_stop)
	Modify the tolerance for stopping the algorithm.

void	set_ureg (const double ureg)
	Modify the control regularization value.

void	set_us (const std::vector< Eigen::VectorXd > &us)
	Modify the control trajectory \(\mathbf{u}_s\).

void	set_xreg (const double xreg)
	Modify the state regularization value.

void	set_xs (const std::vector< Eigen::VectorXd > &xs)
	Modify the state trajectory \(\mathbf{x}_s\).

void	setCallbacks (const std::vector< boost::shared_ptr< CallbackAbstract > > &callbacks)
	Set a list of callback functions using for the solver diagnostic. More...

void	setCandidate (const std::vector< Eigen::VectorXd > &xs_warm=DEFAULT_VECTOR, const std::vector< Eigen::VectorXd > &us_warm=DEFAULT_VECTOR, const bool is_feasible=false)
	Set the solver candidate trajectories \((\mathbf{x}_s,\mathbf{u}_s)\). More...

Public Attributes
EIGEN_MAKE_ALIGNED_OPERATOR_NEW typedef MathBaseTpl< double >::MatrixXsRowMajor	MatrixXdRowMajor

Protected Attributes
std::vector< double >	alphas_
	Set of step lengths using by the line-search procedure.

double	cost_try_
	Total cost computed by line-search procedure.

std::vector< Eigen::VectorXd >	dx_
	State error during the roll-out/forward-pass (size T)

Eigen::VectorXd	fTVxx_p_
	Store the value of \(\mathbf{\bar{f}}^T\mathbf{V_{xx}}^{'}\).

std::vector< MatrixXdRowMajor >	FuTVxx_p_
	Store the values of \(\mathbf{f_u}^T\mathbf{V_{xx}}^{'}\) per each running node.

MatrixXdRowMajor	FxTVxx_p_
	Store the value of \(\mathbf{f_x}^T\mathbf{V_{xx}}^{'}\).

std::vector< MatrixXdRowMajor >	K_
	Feedback gains \(\mathbf{K}\).

std::vector< Eigen::VectorXd >	k_
	Feed-forward terms \(\mathbf{l}\).

std::vector< Eigen::VectorXd >	Qu_
	Gradient of the Hamiltonian \(\mathbf{Q_u}\).

std::vector< Eigen::MatrixXd >	Quu_
	Hessian of the Hamiltonian \(\mathbf{Q_{uu}}\).

std::vector< Eigen::LLT< Eigen::MatrixXd > >	Quu_llt_
	Cholesky LLT solver.

std::vector< Eigen::VectorXd >	Quuk_
	Store the values of.

std::vector< Eigen::VectorXd >	Qx_
	Gradient of the Hamiltonian \(\mathbf{Q_x}\).

std::vector< Eigen::MatrixXd >	Qxu_
	Hessian of the Hamiltonian \(\mathbf{Q_{xu}}\).

std::vector< Eigen::MatrixXd >	Qxx_
	Hessian of the Hamiltonian \(\mathbf{Q_{xx}}\).

double	reg_decfactor_
	Regularization factor used to decrease the damping value.

double	reg_incfactor_
	Regularization factor used to increase the damping value.

double	reg_max_
	Maximum allowed regularization value.

double	reg_min_
	Minimum allowed regularization value.

double	th_grad_
	Tolerance of the expected gradient used for testing the step.

double	th_stepdec_
	Step-length threshold used to decrease regularization.

double	th_stepinc_
	Step-length threshold used to increase regularization.

std::vector< Eigen::VectorXd >	us_try_
	Control trajectory computed by line-search procedure.

std::vector< Eigen::VectorXd >	Vx_
	Gradient of the Value function \(\mathbf{V_x}\).

std::vector< Eigen::MatrixXd >	Vxx_
	Hessian of the Value function \(\mathbf{V_{xx}}\).

Eigen::MatrixXd	Vxx_tmp_
	Temporary variable for ensuring symmetry of Vxx.

Eigen::VectorXd	xnext_
	Next state \(\mathbf{x}^{'}\).

std::vector< Eigen::VectorXd >	xs_try_
	State trajectory computed by line-search procedure.

Protected Attributes inherited from SolverAbstract
std::vector< boost::shared_ptr< CallbackAbstract > >	callbacks_
	Callback functions.

double	cost_
	Total cost.

Eigen::Vector2d	d_
	LQ approximation of the expected improvement.

double	dV_
	Cost reduction obtained by `tryStep()`

double	dVexp_
	Expected cost reduction.

double	ffeas_
	Feasibility of the dynamic constraints.

std::vector< Eigen::VectorXd >	fs_
	Gaps/defects between shooting nodes.

bool	inffeas_

bool	is_feasible_
	Label that indicates is the iteration is feasible.

std::size_t	iter_
	Number of iteration performed by the solver.

boost::shared_ptr< ShootingProblem >	problem_
	optimal control problem

double	steplength_
	Current applied step-length.

double	stop_
	Value computed by `stoppingCriteria()`

double	th_acceptstep_
	Threshold used for accepting step.

double	th_gaptol_
	Threshold limit to check non-zero gaps.

double	th_stop_
	Tolerance for stopping the algorithm.

double	tmp_feas_
	Temporal variables used for computed the feasibility.

double	ureg_
	Current control regularization values.

std::vector< Eigen::VectorXd >	us_
	Control trajectory.

bool	was_feasible_
	Label that indicates in the previous iterate was feasible.

double	xreg_
	Current state regularization value.

std::vector< Eigen::VectorXd >	xs_
	State trajectory.

Detailed Description

Differential Dynamic Programming (DDP) solver.

The DDP solver computes an optimal trajectory and control commands by iterates running backwardPass() and forwardPass(). The backward-pass updates locally the quadratic approximation of the problem and computes descent direction. If the warm-start is feasible, then it computes the gaps \(\mathbf{f}_s\) and run a modified Riccati sweep:

\begin{eqnarray*} \mathbf{Q}_{\mathbf{x}_k} &=& \mathbf{l}_{\mathbf{x}_k} + \mathbf{f}^\top_{\mathbf{x}_k} (V_{\mathbf{x}_{k+1}} + V_{\mathbf{xx}_{k+1}}\mathbf{\bar{f}}_{k+1}),\\ \mathbf{Q}_{\mathbf{u}_k} &=& \mathbf{l}_{\mathbf{u}_k} + \mathbf{f}^\top_{\mathbf{u}_k} (V_{\mathbf{x}_{k+1}} + V_{\mathbf{xx}_{k+1}}\mathbf{\bar{f}}_{k+1}),\\ \mathbf{Q}_{\mathbf{xx}_k} &=& \mathbf{l}_{\mathbf{xx}_k} + \mathbf{f}^\top_{\mathbf{x}_k} V_{\mathbf{xx}_{k+1}} \mathbf{f}_{\mathbf{x}_k},\\ \mathbf{Q}_{\mathbf{xu}_k} &=& \mathbf{l}_{\mathbf{xu}_k} + \mathbf{f}^\top_{\mathbf{x}_k} V_{\mathbf{xx}_{k+1}} \mathbf{f}_{\mathbf{u}_k},\\ \mathbf{Q}_{\mathbf{uu}_k} &=& \mathbf{l}_{\mathbf{uu}_k} + \mathbf{f}^\top_{\mathbf{u}_k} V_{\mathbf{xx}_{k+1}} \mathbf{f}_{\mathbf{u}_k}. \end{eqnarray*}

Then, the forward-pass rollouts this new policy by integrating the system dynamics along a tuple of optimized control commands \(\mathbf{u}^*_s\), i.e.

\begin{eqnarray} \mathbf{\hat{x}}_0 &=& \mathbf{\tilde{x}}_0,\\ \mathbf{\hat{u}}_k &=& \mathbf{u}_k + \alpha\mathbf{k}_k + \mathbf{K}_k(\mathbf{\hat{x}}_k-\mathbf{x}_k),\\ \mathbf{\hat{x}}_{k+1} &=& \mathbf{f}_k(\mathbf{\hat{x}}_k,\mathbf{\hat{u}}_k). \end{eqnarray}

See also: SolverAbstract(), backwardPass() and forwardPass()

Definition at line 50 of file ddp.hpp.

Constructor & Destructor Documentation

◆ SolverDDP()

SolverDDP ( boost::shared_ptr< ShootingProblem > problem )

explicit

Initialize the DDP solver.

Parameters

[in] problem shooting problem

Definition at line 17 of file ddp.cpp.

Member Function Documentation

◆ solve()

bool solve	(	const std::vector< Eigen::VectorXd > &	init_xs = `DEFAULT_VECTOR`,
		const std::vector< Eigen::VectorXd > &	init_us = `DEFAULT_VECTOR`,
		const std::size_t	maxiter = `100`,
		const bool	is_feasible = `false`,
		const double	reg_init = `1e-9`
	)

virtual

Compute the optimal trajectory \(\mathbf{x}^*_s,\mathbf{u}^*_s\) as lists of \(T+1\) and \(T\) terms.

From an initial guess init_xs, init_us (feasible or not), iterate over computeDirection() and tryStep() until stoppingCriteria() is below threshold. It also describes the globalization strategy used during the numerical optimization.

Parameters

[in]	init_xs	initial guess for state trajectory with \(T+1\) elements (default [])
[in]	init_us	initial guess for control trajectory with \(T\) elements (default [])
[in]	maxiter	maximum allowed number of iterations (default 100)
[in]	is_feasible	true if the `init_xs` are obtained from integrating the `init_us` (rollout) (default false)
[in]	init_reg	initial guess for the regularization value. Very low values are typical used with very good guess points (default 1e-9).

Returns: A boolean that describes if convergence was reached.

Implements SolverAbstract.

Reimplemented in SolverFDDP.

Definition at line 43 of file ddp.cpp.

◆ computeDirection()

void computeDirection ( const bool recalc = true )

virtual

Compute the search direction \((\delta\mathbf{x}^k,\delta\mathbf{u}^k)\) for the current guess \((\mathbf{x}^k_s,\mathbf{u}^k_s)\).

You must call setCandidate() first in order to define the current guess. A current guess defines a state and control trajectory \((\mathbf{x}^k_s,\mathbf{u}^k_s)\) of \(T+1\) and \(T\) elements, respectively.

Parameters

[in] recalc true for recalculating the derivatives at current state and control

Returns: The search direction \((\delta\mathbf{x},\delta\mathbf{u})\) and the dual lambdas as lists of \(T+1\), \(T\) and \(T+1\) lengths, respectively

Implements SolverAbstract.

Definition at line 129 of file ddp.cpp.

◆ tryStep()

double tryStep ( const double steplength = 1 )

virtual

Try a predefined step length \(\alpha\) and compute its cost improvement \(dV\).

It uses the search direction found by computeDirection() to try a determined step length \(\alpha\). Therefore, it assumes that we have run computeDirection() first. Additionally, it returns the cost improvement \(dV\) along the predefined step length \(\alpha\).

Parameters

[in] steplength applied step length ( \(0\leq\alpha\leq1\))

Returns: the cost improvement

Implements SolverAbstract.

Definition at line 138 of file ddp.cpp.

◆ stoppingCriteria()

double stoppingCriteria ( )

virtual

Return a positive value that quantifies the algorithm termination.

These values typically represents the gradient norm which tell us that it's been reached the local minima. The stopping criteria strictly speaking depends on the search direction (calculated by computeDirection()) but it could also depend on the chosen step length, tested by tryStep().

Implements SolverAbstract.

Definition at line 145 of file ddp.cpp.

◆ expectedImprovement()

const Eigen::Vector2d & expectedImprovement ( )

virtual

Return the expected improvement \(dV_{exp}\) from a given current search direction \((\delta\mathbf{x}^k,\delta\mathbf{u}^k)\).

For computing the expected improvement, you need to compute the search direction first via computeDirection().

Implements SolverAbstract.

Reimplemented in SolverFDDP.

Definition at line 159 of file ddp.cpp.

◆ resizeData()

void resizeData ( )

virtual

Resizing the solver data.

If the shooting problem has changed after construction, then this function resizes all the data before starting resolve the problem.

Reimplemented from SolverAbstract.

Reimplemented in SolverBoxDDP, and SolverBoxFDDP.

Definition at line 173 of file ddp.cpp.

◆ calcDiff()

double calcDiff ( )

virtual

Update the Jacobian, Hessian and feasibility of the optimal control problem.

These derivatives are computed around the guess state and control trajectory. These trajectory can be set by using setCandidate().

Returns: the total cost around the guess trajectory

Definition at line 195 of file ddp.cpp.

◆ backwardPass()

void backwardPass ( )

virtual

Run the backward pass (Riccati sweep)

It assumes that the Jacobian and Hessians of the optimal control problem have been compute (i.e., calcDiff()). The backward pass handles infeasible guess through a modified Riccati sweep:

\begin{eqnarray*} \mathbf{Q}_{\mathbf{x}_k} &=& \mathbf{l}_{\mathbf{x}_k} + \mathbf{f}^\top_{\mathbf{x}_k} (V_{\mathbf{x}_{k+1}} + V_{\mathbf{xx}_{k+1}}\mathbf{\bar{f}}_{k+1}),\\ \mathbf{Q}_{\mathbf{u}_k} &=& \mathbf{l}_{\mathbf{u}_k} + \mathbf{f}^\top_{\mathbf{u}_k} (V_{\mathbf{x}_{k+1}} + V_{\mathbf{xx}_{k+1}}\mathbf{\bar{f}}_{k+1}),\\ \mathbf{Q}_{\mathbf{xx}_k} &=& \mathbf{l}_{\mathbf{xx}_k} + \mathbf{f}^\top_{\mathbf{x}_k} V_{\mathbf{xx}_{k+1}} \mathbf{f}_{\mathbf{x}_k},\\ \mathbf{Q}_{\mathbf{xu}_k} &=& \mathbf{l}_{\mathbf{xu}_k} + \mathbf{f}^\top_{\mathbf{x}_k} V_{\mathbf{xx}_{k+1}} \mathbf{f}_{\mathbf{u}_k},\\ \mathbf{Q}_{\mathbf{uu}_k} &=& \mathbf{l}_{\mathbf{uu}_k} + \mathbf{f}^\top_{\mathbf{u}_k} V_{\mathbf{xx}_{k+1}} \mathbf{f}_{\mathbf{u}_k}, \end{eqnarray*}

where \(\mathbf{l}_{\mathbf{x}_k}\), \(\mathbf{l}_{\mathbf{u}_k}\), \(\mathbf{f}_{\mathbf{x}_k}\) and \(\mathbf{f}_{\mathbf{u}_k}\) are the Jacobians of the cost function and dynamics, \(\mathbf{l}_{\mathbf{xx}_k}\), \(\mathbf{l}_{\mathbf{xu}_k}\) and \(\mathbf{l}_{\mathbf{uu}_k}\) are the Hessians of the cost function, \(V_{\mathbf{x}_{k+1}}\) and \(V_{\mathbf{xx}_{k+1}}\) defines the linear-quadratic approximation of the Value function, and \(\mathbf{\bar{f}}_{k+1}\) describes the gaps of the dynamics.

Definition at line 207 of file ddp.cpp.

◆ forwardPass()

void forwardPass ( const double stepLength )

virtual

Run the forward pass or rollout.

It rollouts the action model given the computed policy (feedforward terns and feedback gains) by the backwardPass():

\begin{eqnarray} \mathbf{\hat{x}}_0 &=& \mathbf{\tilde{x}}_0,\\ \mathbf{\hat{u}}_k &=& \mathbf{u}_k + \alpha\mathbf{k}_k + \mathbf{K}_k(\mathbf{\hat{x}}_k-\mathbf{x}_k),\\ \mathbf{\hat{x}}_{k+1} &=& \mathbf{f}_k(\mathbf{\hat{x}}_k,\mathbf{\hat{u}}_k). \end{eqnarray}

We can define different step lengths \(\alpha\).

Parameters

stepLength applied step length ( \(0\leq\alpha\leq1\))

Reimplemented in SolverFDDP, SolverBoxDDP, and SolverBoxFDDP.

Definition at line 290 of file ddp.cpp.

◆ computeGains()

void computeGains ( const std::size_t t )

virtual

Compute the feedforward and feedback terms using a Cholesky decomposition.

To compute the feedforward \(\mathbf{k}_k\) and feedback \(\mathbf{K}_k\) terms, we use a Cholesky decomposition to solve \(\mathbf{Q}_{\mathbf{uu}_k}^{-1}\) term:

\begin{eqnarray} \mathbf{k}_k &=& \mathbf{Q}_{\mathbf{uu}_k}^{-1}\mathbf{Q}_{\mathbf{u}},\\ \mathbf{K}_k &=& \mathbf{Q}_{\mathbf{uu}_k}^{-1}\mathbf{Q}_{\mathbf{ux}}. \end{eqnarray}

Note that if the Cholesky decomposition fails, then we re-start the backward pass and increase the state and control regularization values.

Reimplemented in SolverBoxDDP, and SolverBoxFDDP.

Definition at line 338 of file ddp.cpp.

◆ DEPRECATED() [1/2]

DEPRECATED	(	"Use get_reg_incfactor() or get_reg_decfactor()"	,
		double get_regfactor() const ;
	)		const

Return the regularization factor used to decrease / increase it.

Return the minimum regularization value

◆ DEPRECATED() [2/2]

DEPRECATED	(	"Use set_reg_incfactor() or set_reg_decfactor()"	,
		void set_regfactor(const double reg_factor);
	)		const

Modify the regularization factor used to decrease / increase it.

Modify the minimum regularization value

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

include/crocoddyl/core/solvers/ddp.hpp
src/core/solvers/ddp.cpp

Public Member Functions

Public Attributes

Protected Attributes

Detailed Description

Constructor & Destructor Documentation

◆ SolverDDP()

Member Function Documentation

◆ solve()

◆ computeDirection()

◆ tryStep()

◆ stoppingCriteria()

◆ expectedImprovement()

◆ resizeData()

◆ calcDiff()

◆ backwardPass()

◆ forwardPass()

◆ computeGains()

◆ DEPRECATED() [1/2]

◆ DEPRECATED() [2/2]