44 namespace stomp_motion_planner
88 virtual bool getBasisFunctions(std::vector<Eigen::MatrixXd>& basis_functions) = 0;
97 virtual bool getControlCosts(std::vector<Eigen::MatrixXd>& control_costs) = 0;
104 virtual bool updateParameters(
const std::vector<Eigen::MatrixXd>& updates) = 0;
111 virtual bool getParameters(std::vector<Eigen::VectorXd>& parameters) = 0;
118 virtual bool setParameters(
const std::vector<Eigen::VectorXd>& parameters) = 0;
128 virtual bool computeControlCosts(
const std::vector<Eigen::MatrixXd>& control_cost_matrices,
const std::vector<std::vector<Eigen::VectorXd> >& parameters,
129 const double weight, std::vector<Eigen::VectorXd>& control_costs) = 0;
131 virtual bool computeControlCosts(
const std::vector<Eigen::MatrixXd>& control_cost_matrices,
const std::vector<Eigen::VectorXd>& parameters,
132 const std::vector<Eigen::VectorXd>& noise,
const double weight, std::vector<Eigen::VectorXd>& control_costs) = 0;
Definition: policy.hpp:47
virtual bool getBasisFunctions(std::vector< Eigen::MatrixXd > &basis_functions)=0
Gets the basis functions that multiply the policy parameters in the dynamical system.
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 ...
virtual bool setParameters(const std::vector< Eigen::VectorXd > ¶meters)=0
Set the policy parameters per dimension.
virtual bool getParameters(std::vector< Eigen::VectorXd > ¶meters)=0
Get the policy parameters per dimension.
virtual bool computeControlCosts(const std::vector< Eigen::MatrixXd > &control_cost_matrices, const std::vector< std::vector< Eigen::VectorXd > > ¶meters, 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 ...
virtual bool getNumTimeSteps(int &num_time_steps)=0
Gets the number of time steps used in reinforcement learning.
virtual bool setNumTimeSteps(const int num_time_steps)=0
Sets the number of time steps used in reinforcement learning.
virtual bool getNumParameters(std::vector< int > &num_params)=0
Gets the number of policy parameters per dimension.
virtual bool getNumDimensions(int &num_dimensions)=0
Gets the number of dimensions.
virtual bool updateParameters(const std::vector< Eigen::MatrixXd > &updates)=0
Update the policy parameters based on the updates per timestep.