burlap.behavior.singleagent.planning

Interface Planner

All Superinterfaces:

MDPSolverInterface

All Known Subinterfaces:

ModelLearningPlanner

All Known Implementing Classes:

AStar, BeliefSparseSampling, BestFirst, BFS, BFSRTDP, BoundedRTDP, DeterministicPlanner, DFS, DifferentiableSparseSampling, DifferentiableVI, DynamicWeightedAStar, FittedVI, GradientDescentSarsaLam, IDAStar, LimitedMemoryDFS, LSPI, PolicyIteration, PrioritizedSweeping, QLearning, QMDP, RTDP, SarsaLam, SparseSampling, StaticWeightedAStar, UCT, ValueIteration, VIModelLearningPlanner, VITutorial, WeightedGreedy

public interface Planner
extends MDPSolverInterface

Author:

James MacGlashan.

Method Summary

Methods

Modifier and Type	Method and Description
Policy	planFromState(State initialState) This method will cause the Planner to begin planning from the specified initial State.

Methods inherited from interface burlap.behavior.singleagent.MDPSolverInterface

addNonDomainReferencedAction, getActions, getDebugCode, getDomain, getGamma, getHashingFactory, getRf, getRF, getTf, getTF, resetSolver, setActions, setDebugCode, setDomain, setGamma, setHashingFactory, setRf, setTf, solverInit, toggleDebugPrinting

Method Detail

planFromState

Policy planFromState(State initialState)

This method will cause the Planner to begin planning from the specified initial State. It will then return an appropriate Policy object that captured the planning results. Note that typically you can use a variety of different Policy objects in conjunction with this Planner to get varying behavior and the returned Policy is not required to be used.

Parameters:

initialState - the initial state of the planning problem

Returns:

a Policy that captures the planning results from input State.