A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

U

u(String) - Static method in class burlap.debugtools.DPrint

A universal print whose behavior is determined by the universalPrint field

UCT - Class in burlap.behavior.singleagent.planning.stochastic.montecarlo.uct

An implementation of UCT [1].

UCT(SADomain, double, HashableStateFactory, int, int, int) - Constructor for class burlap.behavior.singleagent.planning.stochastic.montecarlo.uct.UCT

Initializes UCT

UCTActionConstructor() - Constructor for class burlap.behavior.singleagent.planning.stochastic.montecarlo.uct.UCTActionNode.UCTActionConstructor

UCTActionNode - Class in burlap.behavior.singleagent.planning.stochastic.montecarlo.uct

UCT Action node that stores relevant action statics necessary for UCT.

UCTActionNode(Action) - Constructor for class burlap.behavior.singleagent.planning.stochastic.montecarlo.uct.UCTActionNode

Generates a new action node for a given action.

UCTActionNode.UCTActionConstructor - Class in burlap.behavior.singleagent.planning.stochastic.montecarlo.uct

A factory for generating UCTActionNode objects.

UCTInit(SADomain, double, HashableStateFactory, int, int, int) - Method in class burlap.behavior.singleagent.planning.stochastic.montecarlo.uct.UCT

UCTStateConstructor() - Constructor for class burlap.behavior.singleagent.planning.stochastic.montecarlo.uct.UCTStateNode.UCTStateConstructor

UCTStateNode - Class in burlap.behavior.singleagent.planning.stochastic.montecarlo.uct

UCT State Node that wraps a hashed state object and provided additional state statistics necessary for UCT.

UCTStateNode(HashableState, int, List<ActionType>, UCTActionNode.UCTActionConstructor) - Constructor for class burlap.behavior.singleagent.planning.stochastic.montecarlo.uct.UCTStateNode

Initializes the UCT state node.

UCTStateNode.UCTStateConstructor - Class in burlap.behavior.singleagent.planning.stochastic.montecarlo.uct

A factory for generating UCTStateNode objects

UCTTreeWalkPolicy - Class in burlap.behavior.singleagent.planning.stochastic.montecarlo.uct

This policy is for use with UCT.

UCTTreeWalkPolicy(UCT) - Constructor for class burlap.behavior.singleagent.planning.stochastic.montecarlo.uct.UCTTreeWalkPolicy

Initializes the policy with the UCT valueFunction

uf(String, Object...) - Static method in class burlap.debugtools.DPrint

A universal printf whose behavior is determined by the universalPrint field

ul(String) - Static method in class burlap.debugtools.DPrint

A universal print line whose behavior is determined by the universalPrint field

UniformCostRF - Class in burlap.mdp.singleagent.common

Defines a reward function that always returns -1.

UniformCostRF() - Constructor for class burlap.mdp.singleagent.common.UniformCostRF

UniformRandomSARSCollector(SADomain) - Constructor for class burlap.behavior.singleagent.learning.lspi.SARSCollector.UniformRandomSARSCollector

Initializes the collector's action set using the actions that are part of the domain.

UniformRandomSARSCollector(List<ActionType>) - Constructor for class burlap.behavior.singleagent.learning.lspi.SARSCollector.UniformRandomSARSCollector

Initializes this collector's action set to use for collecting data.

uniqueActionNames - Variable in class burlap.domain.stochasticgames.normalform.SingleStageNormalFormGame

The unique action names for the domain to be generated.

uniqueStatesInTree - Variable in class burlap.behavior.singleagent.planning.stochastic.montecarlo.uct.UCT

UniversalActionType - Class in burlap.mdp.core.action

An ActionType implementation for unparameterized actions (or at least a single action whose parameters are full specified at construction time of this ActionType) that have no preconditions (can be executed anywhere).

UniversalActionType(String) - Constructor for class burlap.mdp.core.action.UniversalActionType

Initializes with the type name and sets to return a SimpleAction whose action name is the same as the type name.

UniversalActionType(Action) - Constructor for class burlap.mdp.core.action.UniversalActionType

Initializes to return the given action.

UniversalActionType(String, Action) - Constructor for class burlap.mdp.core.action.UniversalActionType

Initializes.

universalLR - Variable in class burlap.behavior.learningrate.ExponentialDecayLR

The state independent learning rate

universalTime - Variable in class burlap.behavior.learningrate.SoftTimeInverseDecayLR

The universal number of learning rate polls

UnknownClassException - Exception in burlap.mdp.core.oo.state.exceptions

UnknownClassException(String) - Constructor for exception burlap.mdp.core.oo.state.exceptions.UnknownClassException

UnknownKeyException - Exception in burlap.mdp.core.state

A runtime exception for when a State variable key is unknown.

UnknownKeyException(Object) - Constructor for exception burlap.mdp.core.state.UnknownKeyException

UnknownObjectException - Exception in burlap.mdp.core.oo.state.exceptions

An exception for when an OOState is queried for an unknown object.

UnknownObjectException(String) - Constructor for exception burlap.mdp.core.oo.state.exceptions.UnknownObjectException

unmarkAllTerminalPositions() - Method in class burlap.domain.singleagent.gridworld.GridWorldTerminalFunction

Unmarks all agent positions as terminal positions.

unmarkTerminalPosition(int, int) - Method in class burlap.domain.singleagent.gridworld.GridWorldTerminalFunction

Unmarks an agent position as a terminal position.

unmodeledActions(KWIKModel, List<ActionType>, State) - Static method in class burlap.behavior.singleagent.learning.modellearning.KWIKModel.Helper

UnmodeledFavoredPolicy - Class in burlap.behavior.singleagent.learning.modellearning.rmax

UnmodeledFavoredPolicy(Policy, KWIKModel, List<ActionType>) - Constructor for class burlap.behavior.singleagent.learning.modellearning.rmax.UnmodeledFavoredPolicy

unstack(BlocksWorldState, ObjectParameterizedAction) - Method in class burlap.domain.singleagent.blocksworld.BWModel

UnstackActionType(String) - Constructor for class burlap.domain.singleagent.blocksworld.BlocksWorld.UnstackActionType

update(double) - Method in class burlap.behavior.singleagent.planning.stochastic.montecarlo.uct.UCTActionNode

Updates the node statistics with a sample return

update(BeliefState, State, Action) - Method in interface burlap.mdp.singleagent.pomdp.beliefstate.BeliefUpdate

Computes a new belief distribution from a previous belief and given a new observation received after taking a specific action.

update(BeliefState, State, Action) - Method in class burlap.mdp.singleagent.pomdp.beliefstate.TabularBeliefUpdate

updateAERSeris() - Method in class burlap.behavior.singleagent.auxiliary.performance.PerformancePlotter

Updates the average reward by episode series.

updateAERSeris(MultiAgentPerformancePlotter.DatasetsAndTrials) - Method in class burlap.behavior.stochasticgames.auxiliary.performance.MultiAgentPerformancePlotter

Updates the average reward by episode series.

updateAndWait(State) - Method in class burlap.mdp.stochasticgames.common.VisualWorldObserver

updateCERSeries() - Method in class burlap.behavior.singleagent.auxiliary.performance.PerformancePlotter

Updates the cumulative reward by episode series.

updateCERSeries(MultiAgentPerformancePlotter.DatasetsAndTrials) - Method in class burlap.behavior.stochasticgames.auxiliary.performance.MultiAgentPerformancePlotter

Updates the cumulative reward by episode series.

updateCSESeries() - Method in class burlap.behavior.singleagent.auxiliary.performance.PerformancePlotter

Updates the cumulative steps by episode series.

updateCSESeries(MultiAgentPerformancePlotter.DatasetsAndTrials) - Method in class burlap.behavior.stochasticgames.auxiliary.performance.MultiAgentPerformancePlotter

Updates the cumulative steps by episode series.

updateCSRSeries() - Method in class burlap.behavior.singleagent.auxiliary.performance.PerformancePlotter

Updates the cumulative reward by step series.

updateCSRSeries(MultiAgentPerformancePlotter.DatasetsAndTrials) - Method in class burlap.behavior.stochasticgames.auxiliary.performance.MultiAgentPerformancePlotter

Updates the cumulative reward by step series.

updateDatasetWithLearningEpisode(Episode) - Method in class burlap.behavior.singleagent.learning.lspi.LSPI

Updates this object's SARSData to include the results of a learning episode.

updateFromCritique(CritiqueResult) - Method in class burlap.behavior.singleagent.learning.actorcritic.actor.BoltzmannActor

updateFromCritique(CritiqueResult) - Method in class burlap.behavior.singleagent.learning.actorcritic.Actor

Causes this object to update its behavior is response to a critique of its behavior.

updateGBConstraint(GridBagConstraints, int) - Method in class burlap.behavior.singleagent.auxiliary.performance.PerformancePlotter

Increments the x-y position of a constraint to the next position.

updateGBConstraint(GridBagConstraints, int) - Method in class burlap.behavior.stochasticgames.auxiliary.performance.MultiAgentPerformancePlotter

Increments the x-y position of a constraint to the next position.

updateLatestQValue() - Method in class burlap.behavior.stochasticgames.agents.maql.MultiAgentQLearning

Updates the Q-value for the most recent observation if it has not already been updated

updateMERSeris() - Method in class burlap.behavior.singleagent.auxiliary.performance.PerformancePlotter

Updates the median reward by episode series.

updateMERSeris(MultiAgentPerformancePlotter.DatasetsAndTrials) - Method in class burlap.behavior.stochasticgames.auxiliary.performance.MultiAgentPerformancePlotter

Updates the median reward by episode series.

updateModel(EnvironmentOutcome) - Method in interface burlap.behavior.singleagent.learning.modellearning.LearnedModel

Updates this model with respect to the observed EnvironmentOutcome.

updateModel(EnvironmentOutcome) - Method in class burlap.behavior.singleagent.learning.modellearning.models.TabularModel

updateModel(EnvironmentOutcome) - Method in class burlap.behavior.singleagent.learning.modellearning.rmax.RMaxModel

updateMostRecentSeriesHelper() - Method in class burlap.behavior.stochasticgames.auxiliary.performance.MultiAgentPerformancePlotter

Updates the series data for the most recent trial plots.

updateMotion(LLState, double) - Method in class burlap.domain.singleagent.lunarlander.LunarLanderModel

Updates the position of the agent/lander given the provided thrust force that has been exerted

updateOpen(HashIndexedHeap<PrioritizedSearchNode>, PrioritizedSearchNode, PrioritizedSearchNode) - Method in class burlap.behavior.singleagent.planning.deterministic.informed.astar.AStar

updateOpen(HashIndexedHeap<PrioritizedSearchNode>, PrioritizedSearchNode, PrioritizedSearchNode) - Method in class burlap.behavior.singleagent.planning.deterministic.informed.astar.DynamicWeightedAStar

updateOpen(HashIndexedHeap<PrioritizedSearchNode>, PrioritizedSearchNode, PrioritizedSearchNode) - Method in class burlap.behavior.singleagent.planning.deterministic.informed.BestFirst

This method is called whenever a search node already in the openQueue needs to have its information or priority updated to reflect a new search node.

updatePropTextArea(State) - Method in class burlap.behavior.singleagent.auxiliary.EpisodeSequenceVisualizer

updatePropTextArea(State) - Method in class burlap.shell.visual.SGVisualExplorer

updatePropTextArea(State) - Method in class burlap.shell.visual.VisualExplorer

Updates the propositional function evaluation text display for the given state.

updater - Variable in class burlap.mdp.singleagent.pomdp.BeliefAgent

The belief update to use

updater - Variable in class burlap.mdp.singleagent.pomdp.BeliefMDPGenerator.BeliefModel

updateRenderedStateAction(State, Action) - Method in class burlap.visualizer.StateActionRenderLayer

Updates the State and Action that will be rendered the next time this class draws

updateSESeries() - Method in class burlap.behavior.singleagent.auxiliary.performance.PerformancePlotter

Updates the steps by episode series.

updateSESeries(MultiAgentPerformancePlotter.DatasetsAndTrials) - Method in class burlap.behavior.stochasticgames.auxiliary.performance.MultiAgentPerformancePlotter

Updates the steps by episode series.

updateState(State, double) - Method in class burlap.domain.singleagent.cartpole.model.IPModel

Updates the given state object given the control force.

updateState(State) - Method in class burlap.shell.visual.SGVisualExplorer

Updates the currently visualized state to the input state.

updateState(State) - Method in class burlap.shell.visual.VisualExplorer

Updates the currently visualized state to the input state.

updateState(State) - Method in class burlap.visualizer.StateRenderLayer

Updates the state that needs to be painted

updateState(State) - Method in class burlap.visualizer.Visualizer

Updates the state that needs to be painted and repaints.

updateStateAction(State, Action) - Method in class burlap.visualizer.Visualizer

Updates the state and action for the StateRenderLayer and StateActionRenderLayer; then repaints.

updateTimeSeries() - Method in class burlap.behavior.singleagent.auxiliary.performance.PerformancePlotter

Updates all the most recent trial time series with the latest data

updateTimeSeries() - Method in class burlap.behavior.stochasticgames.auxiliary.performance.MultiAgentPerformancePlotter

Updates all the most recent trial time series with the latest data

upper - Variable in class burlap.mdp.core.state.vardomain.VariableDomain

The upper value of the domain

upperBoundV - Variable in class burlap.behavior.singleagent.planning.stochastic.rtdp.BoundedRTDP

The upperbound value function

upperVal - Variable in class burlap.behavior.singleagent.auxiliary.gridset.VariableGridSpec

The upper value of the variable on the grid

upperVInit - Variable in class burlap.behavior.singleagent.planning.stochastic.rtdp.BoundedRTDP

The upperbound value function initialization

useAllDomains(StateDomain) - Method in class burlap.behavior.functionapproximation.dense.NormalizedVariableFeatures

Goes through the state and sets the ranges for all variables that have a VariableDomain set.

useBatch - Variable in class burlap.behavior.singleagent.planning.stochastic.rtdp.RTDP

If set to use batch mode; Bellman updates will be stalled until a rollout is complete and then run in reverse.

useCorrectModel - Variable in class burlap.domain.singleagent.cartpole.CartPoleDomain.CPPhysicsParams

Specifies whether the correct Cart Pole physical model should be used or the classic, but incorrect, Barto Sutton and Anderson model [1].

usedConstructorState - Variable in class burlap.domain.singleagent.rlglue.RLGlueEnvironment

Whether the state generated from the state generator to gather auxiliary information (like the number of objects of each class) has yet be used as a starting state for an RLGlue episode.

useFeatureWiseLearningRate - Variable in class burlap.behavior.singleagent.learning.tdmethods.vfa.GradientDescentSarsaLam

Whether the learning rate polls should be based on the VFA state features or OO-MDP state.

useGoalConditionStopCriteria(StateConditionTest) - Method in class burlap.behavior.singleagent.planning.stochastic.montecarlo.uct.UCT

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

useMaxMargin - Variable in class burlap.behavior.singleagent.learnfromdemo.apprenticeship.ApprenticeshipLearningRequest

If true, use the full max margin method (expensive); if false, use the cheaper projection method

useReplacingTraces - Variable in class burlap.behavior.singleagent.learning.tdmethods.vfa.GradientDescentSarsaLam

Whether to use accumulating or replacing eligibility traces.

useRewardFunction(RewardFunction) - Method in class burlap.mdp.singleagent.model.FactoredModel

useRewardFunction(RewardFunction) - Method in interface burlap.mdp.singleagent.model.TaskFactoredModel

Tells this model to use the corresponding RewardFunction

useStateActionWise - Variable in class burlap.behavior.learningrate.ExponentialDecayLR

Whether the learning rate is dependent on state-actions

useStateActionWise - Variable in class burlap.behavior.learningrate.SoftTimeInverseDecayLR

Whether the learning rate is dependent on state-actions

useStateWise - Variable in class burlap.behavior.learningrate.ExponentialDecayLR

Whether the learning rate is dependent on the state

useStateWise - Variable in class burlap.behavior.learningrate.SoftTimeInverseDecayLR

Whether the learning rate is dependent on the state

useTerminalFunction(TerminalFunction) - Method in class burlap.mdp.singleagent.model.FactoredModel

useTerminalFunction(TerminalFunction) - Method in interface burlap.mdp.singleagent.model.TaskFactoredModel

Tells this model to use the corresponding TerminalFunction

useValueRescaling(boolean) - Method in class burlap.behavior.singleagent.auxiliary.valuefunctionvis.StateValuePainter

Enabling value rescaling allows the painter to adjust to the minimum and maximum values passed to it.

useVariableC - Variable in class burlap.behavior.singleagent.learnfromdemo.mlirl.differentiableplanners.DifferentiableSparseSampling

Whether the number of transition dynamic samples should scale with the depth of the node.

useVariableC - Variable in class burlap.behavior.singleagent.planning.stochastic.sparsesampling.SparseSampling

Whether the number of transition dyanmic samples should scale with the depth of the node.

usingOptionModel - Variable in class burlap.behavior.singleagent.MDPSolver

Utilitarian - Class in burlap.behavior.stochasticgames.agents.twoplayer.singlestage.equilibriumplayer.equilibriumsolvers

Finds the maximum utilitarian value joint action and retuns a detemrinistic strategy respecting it.

Utilitarian() - Constructor for class burlap.behavior.stochasticgames.agents.twoplayer.singlestage.equilibriumplayer.equilibriumsolvers.Utilitarian

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