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

Class UCT

java.lang.Object

burlap.behavior.singleagent.MDPSolver

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

All Implemented Interfaces:

MDPSolverInterface, Planner, QFunction, QProvider, ValueFunction

public class UCT
extends MDPSolver
implements Planner, QProvider

An implementation of UCT [1]. This class can be augmented with a goal state specification (using a StateConditionTest) that will cause the planning algorithm to terminate early once it has found a path to the goal. This may be useful if randomly finding the goal state is rare.

The class also implements the QProvider interface. However, it will only return the Q-value for a state if that state is the root node of the tree. If it is not the root node of the tree, then it will automatically reset the planning results and replan from that state as the root node and then return the result. This allows the client to use a GreedyQPolicy with this valueFunction in which it replans with each step in the world, thereby forcing the Q-values for every state to be for the same horizon. Replanning fresh after each step in the world is the standard UCT approach. If you instead want a policy that walks through the tree it generated from some source state, (so that each step computes a Q-value for a shorter horizon than the step before), you can use the UCTTreeWalkPolicy. The TreeWalkPolicy will be more computationally efficient than replanning at each step, but may have degrading performance after each step since each step has a shorter horizon from which to plan and may not have as many samples from which it estimated its Q-value.

1. Kocsis, Levente, and Csaba Szepesvari. "Bandit based monte-carlo planning." ECML (2006). 282-293.

Author:

James MacGlashan

Nested Class Summary

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

QProvider.Helper

Field Summary

Fields

Modifier and Type	Field and Description
protected UCTActionNode.UCTActionConstructor	actionNodeConstructor
protected double	explorationBias
protected boolean	foundGoal
protected boolean	foundGoalOnRollout
protected StateConditionTest	goalCondition
protected int	maxHorizon
protected int	maxRollOutsFromRoot
protected int	numRollOutsFromRoot
protected int	numVisits
protected java.util.Random	rand
protected UCTStateNode	root
protected java.util.List<java.util.Map<HashableState,UCTStateNode>>	stateDepthIndex
protected UCTStateNode.UCTStateConstructor	stateNodeConstructor
protected java.util.Map<HashableState,java.util.List<UCTStateNode>>	statesToStateNodes
protected int	treeSize
protected java.util.Set<HashableState>	uniqueStatesInTree

Fields inherited from class burlap.behavior.singleagent.MDPSolver

actionTypes, debugCode, domain, gamma, hashingFactory, model, usingOptionModel

Constructor Summary

Constructors

Constructor and Description
UCT(SADomain domain, double gamma, HashableStateFactory hashingFactory, int horizon, int nRollouts, int explorationBias) Initializes UCT

Method Summary

Modifier and Type	Method and Description
protected void	addNodeToIndexTree(UCTStateNode snode) Adds a UCTStateNode to the UCT tree
protected UCTActionNode	bestReturnAction(UCTStateNode snode) Returns the UCTActionNode with the highest average sample Q-value.
protected double	computeUCTQ(UCTStateNode snode, UCTActionNode anode) Returns the upper confidence Q-value for a given state node and action node.
protected boolean	containsActionPreference(UCTStateNode snode) Returns true if the sample returns for any actions are different
protected double	explorationQBoost(int ns, int na) Returns the extra value added to the average sample Q-value that is sued to produce the upper confidence Q-value.
UCTStateNode	getRoot() Returns the root node of the UCT tree.
protected void	initializeRollOut()
GreedyQPolicy	planFromState(State initialState) Plans from the input state and then returns a GreedyQPolicy that greedily selects the action with the highest Q-value and breaks ties uniformly randomly.
protected UCTStateNode	queryTreeIndex(HashableState sh, int d) Returns the UCTStateNode for the given (hashed) state at the given depth.
double	qValue(State s, Action a) Returns the QValue for the given state-action pair.
java.util.List<QValue>	qValues(State s) Returns a List of QValue objects for ever permissible action for the given input state.
void	resetSolver() This method resets all solver results so that a solver can be restarted fresh as if had never solved the MDP.
protected UCTActionNode	selectActionNode(UCTStateNode snode) Selections which action to take.
boolean	stopPlanning() Returns true if rollouts and planning should cease.
double	treeRollOut(UCTStateNode node, int depth, int childrenLeftToAdd) Performs a rollout in the UCT tree from the given node, keeping track of how many new nodes can be added to the tree.
protected void	UCTInit(SADomain domain, double gamma, HashableStateFactory hashingFactory, int horizon, int nRollouts, int explorationBias)
void	useGoalConditionStopCriteria(StateConditionTest gc) Tells the valueFunction to stop planning if a goal state is ever found.
double	value(State s) Returns the value function evaluation of the given state.

Methods inherited from class burlap.behavior.singleagent.MDPSolver

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

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

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

Field Detail

stateDepthIndex

protected java.util.List<java.util.Map<HashableState,UCTStateNode>> stateDepthIndex

statesToStateNodes

protected java.util.Map<HashableState,java.util.List<UCTStateNode>> statesToStateNodes

root

protected UCTStateNode root

maxHorizon

protected int maxHorizon

maxRollOutsFromRoot

protected int maxRollOutsFromRoot

numRollOutsFromRoot

protected int numRollOutsFromRoot

explorationBias

protected double explorationBias

stateNodeConstructor

protected UCTStateNode.UCTStateConstructor stateNodeConstructor

actionNodeConstructor

protected UCTActionNode.UCTActionConstructor actionNodeConstructor

goalCondition

protected StateConditionTest goalCondition

foundGoal

protected boolean foundGoal

foundGoalOnRollout

protected boolean foundGoalOnRollout

uniqueStatesInTree

protected java.util.Set<HashableState> uniqueStatesInTree

treeSize

protected int treeSize

numVisits

protected int numVisits

rand

protected java.util.Random rand

Constructor Detail

UCT

public UCT(SADomain domain,
           double gamma,
           HashableStateFactory hashingFactory,
           int horizon,
           int nRollouts,
           int explorationBias)

Initializes UCT

Parameters:

domain - the domain in which to plan

gamma - the discount factor

hashingFactory - the state hashing factory

horizon - the planning horizon

nRollouts - the number of rollouts to perform

explorationBias - the exploration bias constant (suggested >2)

Method Detail

UCTInit

protected void UCTInit(SADomain domain,
                       double gamma,
                       HashableStateFactory hashingFactory,
                       int horizon,
                       int nRollouts,
                       int explorationBias)

getRoot

public UCTStateNode getRoot()

Returns the root node of the UCT tree.

Returns:

the root node of the UCT tree.

useGoalConditionStopCriteria

public void useGoalConditionStopCriteria(StateConditionTest gc)

Tells the valueFunction to stop planning if a goal state is ever found.

Parameters:

gc - a StateConditionTest object used to specify goal states (whereever it evaluates as true).

planFromState

public GreedyQPolicy planFromState(State initialState)

Plans from the input state and then returns a GreedyQPolicy that greedily selects the action with the highest Q-value and breaks ties uniformly randomly.

Specified by:

planFromState in interface Planner

Parameters:

initialState - the initial state of the planning problem

Returns:

a GreedyQPolicy.

qValues

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

Description copied from interface: QProvider

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

Specified by:

qValues in interface QProvider

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.

qValue

public double qValue(State s,
                     Action a)

Description copied from interface: QFunction

Returns the QValue for the given state-action pair.

Specified by:

qValue 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.

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

initializeRollOut

protected void initializeRollOut()

treeRollOut

public double treeRollOut(UCTStateNode node,
                          int depth,
                          int childrenLeftToAdd)

Performs a rollout in the UCT tree from the given node, keeping track of how many new nodes can be added to the tree.

Parameters:

node - the node from which to rollout

depth - the depth of the node

childrenLeftToAdd - the number of new subsequent nodes that can be connected to the tree

Returns:

the sample return from rolling out from this node

stopPlanning

public boolean stopPlanning()

Returns true if rollouts and planning should cease. Planning will stop if the valueFunction is told to terminate upon finding a goal and one was found, or if the maximum number of rollouts have already been performed.

Returns:

true if rollouts and planning should cease; false otherwise.

selectActionNode

protected UCTActionNode selectActionNode(UCTStateNode snode)

Selections which action to take. Unexplored actions from the node are selected first. If all actions have been explored, then the action with the highest upper confidence Q-value is selected, ties are broken randomly.

Parameters:

snode - the UCT node from which to select an action.

Returns:

the UCTActionNode to be taken.

computeUCTQ

protected double computeUCTQ(UCTStateNode snode,
                             UCTActionNode anode)

Returns the upper confidence Q-value for a given state node and action node.

Parameters:

snode - the state node

anode - the action node

Returns:

the upper confidence Q-value

explorationQBoost

protected double explorationQBoost(int ns,
                                   int na)

Returns the extra value added to the average sample Q-value that is sued to produce the upper confidence Q-value.

Parameters:

ns - the number of times the state node has been visited

na - the number of times the action node has been visited

Returns:

the extra value added to the average sample Q-value that is sued to produce the upper confidence Q-value.

queryTreeIndex

protected UCTStateNode queryTreeIndex(HashableState sh,
                                      int d)

Returns the UCTStateNode for the given (hashed) state at the given depth.

Parameters:

sh - the state whose node should be returned

d - the depth of the state

Returns:

the corresponding UCTStateNode

addNodeToIndexTree

protected void addNodeToIndexTree(UCTStateNode snode)

Adds a UCTStateNode to the UCT tree

Parameters:

snode - the UCTStateNode to add

bestReturnAction

protected UCTActionNode bestReturnAction(UCTStateNode snode)

Returns the UCTActionNode with the highest average sample Q-value. Ties are broken by returning the first UCTActionNode with the highest value.

Parameters:

snode - the UCTStateNode to query

Returns:

the UCTActionNode with the highest average sample Q-value

containsActionPreference

protected boolean containsActionPreference(UCTStateNode snode)

Returns true if the sample returns for any actions are different

Parameters:

snode - the node to check for an action preference

Returns:

true if the sample returns for any actions are different; false otherwise or if there is only one action to take.