burlap.behavior.singleagent.planning.stochastic.montecarlo.uct

Class UCTTreeWalkPolicy

java.lang.Object

burlap.behavior.policy.Policy

burlap.behavior.singleagent.planning.stochastic.montecarlo.uct.UCTTreeWalkPolicy

All Implemented Interfaces:

SolverDerivedPolicy

public class UCTTreeWalkPolicy
extends Policy
implements SolverDerivedPolicy

This policy is for use with UCT. Note that UCT can only guarantee the policy for the initial state of planning. However, the policy from states that lie on the greedy path from the initial state are likely "okay" as well since they were used for the determining which action to take in the initial state. This class defines the policy for states that lie along the the greedy path of the UCT tree. Any state not visited by the greedy path in the UCT tree is excluded from the policy and will result in an error if this policy is queried for such a state. A more robust policy would cause the valueFunction to be called at each state to build a new tree.

Author:

James MacGlashan

Nested Class Summary

Nested classes/interfaces inherited from class burlap.behavior.policy.Policy

Policy.ActionProb, Policy.GroundedAnnotatedAction, Policy.PolicyUndefinedException

Field Summary

Fields inherited from class burlap.behavior.policy.Policy

annotateOptionDecomposition, evaluateDecomposesOptions

Constructor Summary

Constructors

Constructor and Description
UCTTreeWalkPolicy(UCT planner) Initializes the policy with the UCT valueFunction

Method Summary

Methods

Modifier and Type	Method and Description
void	computePolicyFromTree() computes a hash-backed policy for every state visited along the greedy path of the UCT tree.
AbstractGroundedAction	getAction(State s) This method will return an action sampled by the policy for the given state.
java.util.List<Policy.ActionProb>	getActionDistributionForState(State s) This method will return action probability distribution defined by the policy.
protected UCTActionNode	getQGreedyNode(UCTStateNode snode) Returns the UCTActionNode with the highest average sample return.
boolean	isDefinedFor(State s) Specifies whether this policy is defined for the input state.
boolean	isStochastic() Indicates whether the policy is stochastic or deterministic.
void	setSolver(MDPSolverInterface solver) Sets the valueFunction whose results affect this policy.

Methods inherited from class burlap.behavior.policy.Policy

evaluateBehavior, evaluateBehavior, evaluateBehavior, evaluateBehavior, evaluateBehavior, evaluateMethodsShouldAnnotateOptionDecomposition, evaluateMethodsShouldDecomposeOption, followAndRecordPolicy, followAndRecordPolicy, getDeterministicPolicy, getProbOfAction, getProbOfActionGivenDistribution, getProbOfActionGivenDistribution, sampleFromActionDistribution

Methods inherited from class java.lang.Object

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

Constructor Detail

UCTTreeWalkPolicy

public UCTTreeWalkPolicy(UCT planner)

Initializes the policy with the UCT valueFunction

Parameters:

planner - the UCT valueFunction whose tree should be walked.

Method Detail

setSolver

public void setSolver(MDPSolverInterface solver)

Description copied from interface: SolverDerivedPolicy

Sets the valueFunction whose results affect this policy.

Specified by:

setSolver in interface SolverDerivedPolicy

Parameters:

solver - the solver from which this policy is derived

computePolicyFromTree

public void computePolicyFromTree()

computes a hash-backed policy for every state visited along the greedy path of the UCT tree.

getQGreedyNode

protected UCTActionNode getQGreedyNode(UCTStateNode snode)

Returns the UCTActionNode with the highest average sample return. Note that this does not use the upper confidence since planning is completed.

Parameters:

snode - the UCTStateNode for which to get the best UCTActionNode.

Returns:

the UCTActionNode with the highest average sample return.

getAction

public AbstractGroundedAction getAction(State s)

Description copied from class: Policy

This method will return an action sampled by the policy for the given state. If the defined policy is stochastic, then multiple calls to this method for the same state may return different actions. The sampling should be with respect to defined action distribution that is returned by getActionDistributionForState

Specified by:

getAction in class Policy

Parameters:

s - the state for which an action should be returned

Returns:

a sample action from the action distribution; null if the policy is undefined for s

getActionDistributionForState

public java.util.List<Policy.ActionProb> getActionDistributionForState(State s)

Description copied from class: Policy

This method will return action probability distribution defined by the policy. The action distribution is represented by a list of ActionProb objects, each which specifies a grounded action and a probability of that grounded action being taken. The returned list does not have to include actions with probability 0.

Specified by:

getActionDistributionForState in class Policy

Parameters:

s - the state for which an action distribution should be returned

Returns:

a list of possible actions taken by the policy and their probability.

isStochastic

public boolean isStochastic()

Description copied from class: Policy

Indicates whether the policy is stochastic or deterministic.

Specified by:

isStochastic in class Policy

Returns:

true when the policy is stochastic; false when it is deterministic.

isDefinedFor

public boolean isDefinedFor(State s)

Description copied from class: Policy

Specifies whether this policy is defined for the input state.

Specified by:

isDefinedFor in class Policy

Parameters:

s - the input state to test for whether this policy is defined

Returns:

true if this policy is defined for State s, false otherwise.