burlap.behavior.singleagent.learning.modellearning

Interface ModelLearningPlanner

All Superinterfaces:

MDPSolverInterface, Planner

All Known Implementing Classes:

VIModelLearningPlanner

public interface ModelLearningPlanner
extends Planner, MDPSolverInterface

Interface for defining planning algorithms that operate on iteratively learned models. Planning algorithms that operate on iteratively learned models must support features for replanning when the model changes and returning the policy of the plan under the current model.

Author:

James MacGlashan

Method Summary

Modifier and Type	Method and Description
void	initializePlannerIn(State s) This is method is expected to be called at the beginning of any new learning episode.
void	modelChanged(State changedState) Tells the valueFunction that the model has changed and that it will need to replan accordingly
Policy	modelPlannedPolicy() Returns a policy encoding the planner's results.

Methods inherited from interface burlap.behavior.singleagent.planning.Planner

planFromState

Methods inherited from interface burlap.behavior.singleagent.MDPSolverInterface

addActionType, getActionTypes, getDebugCode, getDomain, getGamma, getHashingFactory, getModel, resetSolver, setActionTypes, setDebugCode, setDomain, setGamma, setHashingFactory, setModel, solverInit, toggleDebugPrinting

Method Detail

initializePlannerIn

void initializePlannerIn(State s)

This is method is expected to be called at the beginning of any new learning episode. This may be useful for planning algorithms that do not solve the policy for every state since new episodes may starts in states the planning algorithm had not previously considered. before a learning episode begins.

Parameters:

s - the input state

modelChanged

void modelChanged(State changedState)

Tells the valueFunction that the model has changed and that it will need to replan accordingly

Parameters:

changedState - the source state that caused a change in the model.

modelPlannedPolicy

Policy modelPlannedPolicy()

Returns a policy encoding the planner's results.

Returns:

a policy object