burlap.behavior.singleagent.pomdp.qmdp

Class QMDP

java.lang.Object

burlap.behavior.singleagent.MDPSolver

burlap.behavior.singleagent.pomdp.qmdp.QMDP

All Implemented Interfaces:

MDPSolverInterface, Planner, QFunction, ValueFunction

public class QMDP
extends MDPSolver
implements Planner, QFunction

An implementation of QMDP for POMDP domains. This is a fast approximation method that has the agent acting as though it would obtain perfect knowledge of the state in the next time step. It works by solving the underling fully observable MDP, and then setting the Q-value for belief states to be the expected fully observable Q-value. Therefore, planning is only as hard as MDP planning. This implementation can take different sources for the MDP QFunction.

Nested Class Summary

Nested classes/interfaces inherited from interface burlap.behavior.valuefunction.QFunction

QFunction.QFunctionHelper

Field Summary

Fields

Modifier and Type	Field and Description
protected QFunction	mdpQSource The fully observable MDP QFunction source.

Fields inherited from class burlap.behavior.singleagent.MDPSolver

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

Constructor Summary

Constructors

Constructor and Description
QMDP(PODomain domain, QFunction mdpQSource) Initializes.
QMDP(PODomain domain, RewardFunction rf, TerminalFunction tf, double discount, HashableStateFactory hashingFactory, double maxDelta, int maxIterations) Initializes and creates a ValueIteration planner to solve the underling MDP.

Method Summary

Methods

Modifier and Type	Method and Description
void	forceMDPPlanningFromAllStates() Calls the Planner.planFromState(burlap.oomdp.core.states.State) method on all states defined in the POMDP.
QValue	getQ(State s, AbstractGroundedAction a) Returns the QValue for the given state-action pair.
java.util.List<QValue>	getQs(State s) Returns a List of QValue objects for ever permissible action for the given input state.
Policy	planFromState(State initialState) This method will cause the Planner to begin planning from the specified initial State.
double	qForBelief(EnumerableBeliefState bs, GroundedAction ga) Computes the expected Q-value of the underlying hidden MDP by marginalizing over of the states in the belief state.
protected double	qForBeliefList(java.util.List<EnumerableBeliefState.StateBelief> beliefs, GroundedAction ga) Computes the expected Q-value of the underlying hidden MDP by marginalizing over of the states in the belief state.
void	resetSolver() This method resets all solver results so that a solver can be restarted fresh as if had never solved the MDP.
double	value(State s) Returns the value function evaluation of the given state.

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

mdpQSource

protected QFunction mdpQSource

The fully observable MDP QFunction source.

Constructor Detail

QMDP

public QMDP(PODomain domain,
    QFunction mdpQSource)

Initializes.

Parameters:

domain - the POMDP domain

mdpQSource - the underlying fully observable MDP QFunction source.

QMDP

public QMDP(PODomain domain,
    RewardFunction rf,
    TerminalFunction tf,
    double discount,
    HashableStateFactory hashingFactory,
    double maxDelta,
    int maxIterations)

Initializes and creates a ValueIteration planner to solve the underling MDP. You should call the forceMDPPlanningFromAllStates() method after construction to have the constructed ValueIteration instance perform planning.

Parameters:

domain - the POMDP domain

rf - the POMDP hidden state reward function

tf - the POMDP hidden state terminal function

discount - the discount factor

hashingFactory - the HashableStateFactory to use for the ValueIteration instance to use.

maxDelta - the maximum value function change threshold that will cause planning to terminiate

maxIterations - the maximum number of value iteration iterations.

Method Detail

forceMDPPlanningFromAllStates

public void forceMDPPlanningFromAllStates()

Calls the Planner.planFromState(burlap.oomdp.core.states.State) method on all states defined in the POMDP. Calling this method requires that the PODomain provides a StateEnumerator, otherwise an exception will be thrown.

getQs

public java.util.List<QValue> getQs(State s)

Description copied from interface: QFunction

Returns a List of QValue objects for ever permissible action for the given input state.

Specified by:

getQs in interface QFunction

Parameters:

s - the state for which Q-values are to be returned.

Returns:

a List of QValue objects for ever permissible action for the given input state.

getQ

public QValue getQ(State s,
          AbstractGroundedAction a)

Description copied from interface: QFunction

Returns the QValue for the given state-action pair.

Specified by:

getQ in interface QFunction

Parameters:

s - the input state

a - the input action

Returns:

the QValue for the given state-action pair.

value

public double value(State s)

Description copied from interface: ValueFunction

Returns the value function evaluation of the given state. If the value is not stored, then the default value specified by the ValueFunctionInitialization object of this class is returned.

Specified by:

value in interface ValueFunction

Parameters:

s - the state to evaluate.

Returns:

the value function evaluation of the given state.

qForBelief

public double qForBelief(EnumerableBeliefState bs,
                GroundedAction ga)

Computes the expected Q-value of the underlying hidden MDP by marginalizing over of the states in the belief state.

Parameters:

bs - the belief state

ga - the action whose Q-value is to be computed

Returns:

the expected Q-value of the underlying hidden MDP by marginalizing over of the states in the belief state.

qForBeliefList

protected double qForBeliefList(java.util.List<EnumerableBeliefState.StateBelief> beliefs,
                    GroundedAction ga)

Computes the expected Q-value of the underlying hidden MDP by marginalizing over of the states in the belief state.

Parameters:

beliefs - belief state distribution

ga - the action whose Q-value is to be computed

Returns:

the expected Q-value of the underlying hidden MDP by marginalizing over of the states in the belief state.

planFromState

public Policy planFromState(State initialState)

Description copied from interface: Planner

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.

Specified by:

planFromState in interface Planner

Parameters:

initialState - the initial state of the planning problem

Returns:

a Policy that captures the planning results from input State.

resetSolver

public void resetSolver()

Description copied from interface: MDPSolverInterface

This method resets all solver results so that a solver can be restarted fresh as if had never solved the MDP.

Specified by:

resetSolver in interface MDPSolverInterface

Specified by:

resetSolver in class MDPSolver