burlap.behavior.singleagent.planning.deterministic.informed.astar

Class WeightedGreedy

java.lang.Object

burlap.behavior.singleagent.MDPSolver

burlap.behavior.singleagent.planning.deterministic.DeterministicPlanner

burlap.behavior.singleagent.planning.deterministic.informed.BestFirst

burlap.behavior.singleagent.planning.deterministic.informed.astar.AStar

burlap.behavior.singleagent.planning.deterministic.informed.astar.WeightedGreedy

All Implemented Interfaces:

MDPSolverInterface, Planner

public class WeightedGreedy
extends AStar

A weighted greedy search in which the g(n) function (the cost to the current node) is weighted by a fraction 0 <= w <= 1. When w = 0, the search is fully greedy. When w = 1, the search is optimal and equivalent to A*.

If a terminal function is provided via the setter method defined for OO-MDPs, then the search algorithm will not expand any nodes that are terminal states, as if there were no actions that could be executed from that state. Note that terminal states are not necessarily the same as goal states, since there could be a fail condition from which the agent cannot act, but that is not explicitly represented in the transition dynamics.

Author:

James MacGlashan

Nested Class Summary

Nested classes/interfaces inherited from class burlap.behavior.singleagent.planning.deterministic.DeterministicPlanner

DeterministicPlanner.PlanningFailedException

Field Summary

Fields

Modifier and Type	Field and Description
protected double	costWeight The cost function weight.

Fields inherited from class burlap.behavior.singleagent.planning.deterministic.informed.astar.AStar

cumulatedRewardMap, heuristic, lastComputedCumR

Fields inherited from class burlap.behavior.singleagent.planning.deterministic.DeterministicPlanner

gc, internalPolicy

Fields inherited from class burlap.behavior.singleagent.MDPSolver

actions, debugCode, domain, gamma, hashingFactory, mapToStateIndex, rf, tf

Constructor Summary

Constructors

Constructor and Description
WeightedGreedy(Domain domain, RewardFunction rf, StateConditionTest gc, HashableStateFactory hashingFactory, Heuristic heuristic, double costWeight) Initializes the valueFunction.

Method Summary

Methods

Modifier and Type	Method and Description
double	computeF(PrioritizedSearchNode parentNode, GroundedAction generatingAction, HashableState successorState) This method returns the f-score for a state given the parent search node, the generating action, the state that was produced.

Methods inherited from class burlap.behavior.singleagent.planning.deterministic.informed.astar.AStar

insertIntoOpen, postPlanPrep, prePlanPrep, updateOpen

Methods inherited from class burlap.behavior.singleagent.planning.deterministic.informed.BestFirst

planFromState

Methods inherited from class burlap.behavior.singleagent.planning.deterministic.DeterministicPlanner

deterministicPlannerInit, encodePlanIntoPolicy, hasCachedPlanForState, planContainsOption, planHasDupilicateStates, querySelectedActionForState, resetSolver

Methods inherited from class burlap.behavior.singleagent.MDPSolver

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

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface burlap.behavior.singleagent.MDPSolverInterface

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

Field Detail

costWeight

protected double costWeight

The cost function weight.

Constructor Detail

WeightedGreedy

public WeightedGreedy(Domain domain,
              RewardFunction rf,
              StateConditionTest gc,
              HashableStateFactory hashingFactory,
              Heuristic heuristic,
              double costWeight)

Initializes the valueFunction.

Parameters:

domain - the domain in which to plan

rf - the reward function that represents costs as negative reward

gc - should evaluate to true for goal states; false otherwise

hashingFactory - the state hashing factory to use

heuristic - the planning heuristic. Should return non-positive values.

costWeight - a fraction 0 <= w <= 1. When w = 0, the search is fully greedy. When w = 1, the search is optimal and equivalent to A*.

Method Detail

computeF

public double computeF(PrioritizedSearchNode parentNode,
              GroundedAction generatingAction,
              HashableState successorState)

Description copied from class: BestFirst

This method returns the f-score for a state given the parent search node, the generating action, the state that was produced.

Overrides:

computeF in class AStar

Parameters:

parentNode - the parent search node (and its priority) that from which the next state was generated.

generatingAction - the action that was used to generate the next state.

successorState - the next state that was generated

Returns:

the f-score for the next state.