Here are the classes, structs, unions and interfaces with brief descriptions:

[detail level 12]

▼NDmpBbo
CCostFunction	Interface for cost functions, which define a cost_function
CDistributionGaussian	A class for representing a Gaussian distribution
CDmp	Implementation of Dynamical Movement Primitives
CDmpContextual	Implementation of Contextual Dynamical Movement Primitives
CDmpContextualOneStep	Implementation of Contextual Dynamical Movement Primitives
CDmpContextualTwoStep	Implementation of Contextual Dynamical Movement Primitives
CDmpWithGainSchedules	Implementation of DMPs which contain extra dimensions to represent variable gain schedules, as described in [1]
CDynamicalSystem	Interface for implementing dynamical systems
CExperimentBBO	POD class to store all objects relevant to running and evolutionary optimization
CExponentialSystem	Dynamical System modelling the evolution of an exponential system: $\dot{x} = -\alpha (x-x^g)$
CFunctionApproximator	Base class for all function approximators
CFunctionApproximatorGMR	GMR (Gaussian Mixture Regression) function approximator
CFunctionApproximatorGPR	GPR (Gaussian Process Regression) function approximator
CFunctionApproximatorLWPR	LWPR (Locally Weighted Projection Regression) function approximator
CFunctionApproximatorLWR	LWR (Locally Weighted Regression) function approximator
CFunctionApproximatorRBFN	RBFN (Radial Basis Function Network) function approximator
CFunctionApproximatorRRRFF	RRRFF (Ridge Regression with Random Fourier Features) function approximatorhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC3935121/
CMetaParameters	Base class for all meta-parameters of function approximators
CMetaParametersGMR	Meta-parameters for the GMR function approximator
CMetaParametersGPR	Meta-parameters for the Gaussian Process Regression (GPR) function approximator
CMetaParametersLWPR	Meta-parameters for the Locally Weighted Projection Regression (LWPR) function approximator
CMetaParametersLWR	Meta-parameters for the Locally Weighted Regression (LWR) function approximator
CMetaParametersRBFN	Meta-parameters for the Radial Basis Function Network (RBFN) function approximator
CMetaParametersRRRFF	Meta-parameters for the RRRFF function approximator
CModelParameters	Base class for all model parameters of function approximators
CModelParametersGMR	Model parameters for the GMR function approximator
CModelParametersGPR	Model parameters for the Gaussian Process Regression (GPR) function approximator
CModelParametersLWPR	Model parameters for the Locally Weighted Projection Regression (LWPR) function approximator
CModelParametersLWR	Model parameters for the Locally Weighted Regression (LWR) function approximator
CModelParametersRBFN	Model parameters for the Radial Basis Function Network (RBFN) function approximator
CModelParametersRRRFF	Model parameters for the RRRFF function approximator
CParameterizable	Class for providing access to a model's parameters as a vector
CRollout	Class for storing the information in a rollout, the result of executing a policy once
CSigmoidSystem	Dynamical System modelling the evolution of a sigmoidal system $\dot{x} = -\alpha x(1-x/K)$
CSpringDamperSystem	Dynamical System modelling the evolution of a spring-damper system: $m\ddot{x} = -k(x-x^g) -c\dot{x}$
CTask	Interface for cost functions, which define a task
CTaskSolver	Interface for classes that can perform rollouts
CTaskSolverDmp	TaskSolver for the viapoint task, that generates trajectories with a DMP
CTaskSolverDmpArm2D	TaskSolver for the viapoint task, that generates trajectories with a DMP
CTaskViapoint	Task for passing through a viapoint with minimal acceleration
CTaskViapointArm2D	Task where a articulated arm should pass through a viapoint
CTaskWithTrajectoryDemonstrator	Interface for tasks that are able to provide demonstrations that solve the task (optimally)
CTimeSystem	Dynamical System modelling the evolution of a time: $\dot{x} = 1/\tau$
CTrajectory	A class for storing trajectories: positions, velocities and accelerations of variables over time
CUnifiedModel	The unified model, which can be used to represent the model of all other function approximators
CUpdater	Interface for the distribution update step in evolution strategies
CUpdaterCovarAdaptation	Updater that updates the mean and also implements Covariance Matrix Adaptation
CUpdaterCovarDecay	Updater that updates the mean and decreases the size of the covariance matrix over time
CUpdaterMean	Updater that updates the mean (but not the covariance matrix) of the parameter distribution
CDemoCostFunctionDistanceToPoint	CostFunction in which the distance to a pre-defined point must be minimized
CDemoTaskApproximateQuadraticFunction	The task is to choose the parameters a and c such that the function best matches a set of target values y_target for a set of input values x
CDemoTaskSolverApproximateQuadraticFunction	The task solver tunes the parameters a and c such that the function best matches a set of target values y_target for a set of input values x