Inheritance diagram for stomp_motion_planner::Policy:

Public Member Functions
virtual bool	setNumTimeSteps (const int num_time_steps)=0
	Sets the number of time steps used in reinforcement learning. More...

virtual bool	getNumTimeSteps (int &num_time_steps)=0
	Gets the number of time steps used in reinforcement learning. More...

virtual bool	getNumDimensions (int &num_dimensions)=0
	Gets the number of dimensions. More...

virtual bool	getNumParameters (std::vector< int > &num_params)=0
	Gets the number of policy parameters per dimension. More...

virtual bool	getBasisFunctions (std::vector< Eigen::MatrixXd > &basis_functions)=0
	Gets the basis functions that multiply the policy parameters in the dynamical system. More...

virtual bool	getControlCosts (std::vector< Eigen::MatrixXd > &control_costs)=0
	Gets the positive semi-definite matrix of the quadratic control cost The weight of this control cost is provided by the task. More...

virtual bool	updateParameters (const std::vector< Eigen::MatrixXd > &updates)=0
	Update the policy parameters based on the updates per timestep. More...

virtual bool	getParameters (std::vector< Eigen::VectorXd > &parameters)=0
	Get the policy parameters per dimension. More...

virtual bool	setParameters (const std::vector< Eigen::VectorXd > &parameters)=0
	Set the policy parameters per dimension. More...

virtual bool	computeControlCosts (const std::vector< Eigen::MatrixXd > &control_cost_matrices, const std::vector< std::vector< Eigen::VectorXd > > &parameters, const double weight, std::vector< Eigen::VectorXd > &control_costs)=0
	Compute the control costs over time, given the control cost matrix per dimension and parameters over time. More...

virtual bool	computeControlCosts (const std::vector< Eigen::MatrixXd > &control_cost_matrices, const std::vector< Eigen::VectorXd > &parameters, const std::vector< Eigen::VectorXd > &noise, const double weight, std::vector< Eigen::VectorXd > &control_costs)=0

Member Function Documentation

virtual bool stomp_motion_planner::Policy::computeControlCosts	(	const std::vector< Eigen::MatrixXd > &	control_cost_matrices,
		const std::vector< std::vector< Eigen::VectorXd > > &	parameters,
		const double	weight,
		std::vector< Eigen::VectorXd > &	control_costs
	)

pure virtual

Compute the control costs over time, given the control cost matrix per dimension and parameters over time.

Parameters

control_cost_matrices	(input) [num_dimensions] num_parameters x num_parameters: Quadratic control cost matrix (R)
parameters	(input) [num_dimensions][num_time_steps] num_parameters: Parameters over time (can also be theta + projected noise)
weight	(input) constant multiplier for the control costs
control_costs	(output) [num_dimensions] num_time_steps: Control costs over time

Returns

Implemented in stomp_motion_planner::CovariantTrajectoryPolicy.

virtual bool stomp_motion_planner::Policy::getBasisFunctions ( std::vector< Eigen::MatrixXd > & basis_functions )

pure virtual

Gets the basis functions that multiply the policy parameters in the dynamical system.

Parameters

basis_function_matrix_array (output) Array of "num_time_steps x num_parameters" matrices, per dimension

Returns: true on success, false on failure

Implemented in stomp_motion_planner::CovariantTrajectoryPolicy.

virtual bool stomp_motion_planner::Policy::getControlCosts ( std::vector< Eigen::MatrixXd > & control_costs )

pure virtual

Gets the positive semi-definite matrix of the quadratic control cost The weight of this control cost is provided by the task.

Parameters

control_cost_matrix (output) Array of square, positive semi-definite matrix: num_params x num_params

Returns: true on success, false on failure

Implemented in stomp_motion_planner::CovariantTrajectoryPolicy.

virtual bool stomp_motion_planner::Policy::getNumDimensions ( int & num_dimensions )

pure virtual

Gets the number of dimensions.

Parameters

num_dimensions (output) number of dimensions

Returns: true on success, false on failure

Implemented in stomp_motion_planner::CovariantTrajectoryPolicy.

virtual bool stomp_motion_planner::Policy::getNumParameters ( std::vector< int > & num_params )

pure virtual

Gets the number of policy parameters per dimension.

Parameters

num_params (output) vector of number of parameters per dimension

Returns: true on success, false on failure

Implemented in stomp_motion_planner::CovariantTrajectoryPolicy.

virtual bool stomp_motion_planner::Policy::getNumTimeSteps ( int & num_time_steps )

pure virtual

Gets the number of time steps used in reinforcement learning.

Parameters

num_time_steps

Returns: true on success, fase on failure

Implemented in stomp_motion_planner::CovariantTrajectoryPolicy.

virtual bool stomp_motion_planner::Policy::getParameters ( std::vector< Eigen::VectorXd > & parameters )

pure virtual

Get the policy parameters per dimension.

Parameters

parameters (output) array of parameter vectors

Returns: true on success, false on failure

Implemented in stomp_motion_planner::CovariantTrajectoryPolicy.

virtual bool stomp_motion_planner::Policy::setNumTimeSteps ( const int num_time_steps )

pure virtual

Sets the number of time steps used in reinforcement learning.

Parameters

num_time_steps

Returns: true on success, false on failure

Implemented in stomp_motion_planner::CovariantTrajectoryPolicy.

virtual bool stomp_motion_planner::Policy::setParameters ( const std::vector< Eigen::VectorXd > & parameters )

pure virtual

Set the policy parameters per dimension.

Parameters

parameters (input) array of parameter vectors

Returns: true on success, false on failure

Implemented in stomp_motion_planner::CovariantTrajectoryPolicy.

virtual bool stomp_motion_planner::Policy::updateParameters ( const std::vector< Eigen::MatrixXd > & updates )

pure virtual

Update the policy parameters based on the updates per timestep.

Parameters

updates (input) parameter updates per time-step, num_time_steps x num_parameters

Returns: true on success, false on failure

Implemented in stomp_motion_planner::CovariantTrajectoryPolicy.

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

src/planner/TrajectoryOptim/Stomp/policy.hpp

Public Member Functions

Member Function Documentation