- 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(Domain, RewardFunction, TerminalFunction, double, HashableStateFactory, int, int, int) - Constructor for class burlap.behavior.singleagent.planning.stochastic.montecarlo.uct.UCT
-
Initializes UCT
- UCTActionNode - Class in burlap.behavior.singleagent.planning.stochastic.montecarlo.uct
-
UCT Action node that stores relevant action statics necessary for UCT.
- UCTActionNode(GroundedAction) - 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.
- UCTActionNode.UCTActionConstructor() - Constructor for class burlap.behavior.singleagent.planning.stochastic.montecarlo.uct.UCTActionNode.UCTActionConstructor
-
- UCTInit(Domain, RewardFunction, TerminalFunction, double, HashableStateFactory, int, int, int) - Method in class burlap.behavior.singleagent.planning.stochastic.montecarlo.uct.UCT
-
- 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<Action>, 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
- UCTStateNode.UCTStateConstructor() - Constructor for class burlap.behavior.singleagent.planning.stochastic.montecarlo.uct.UCTStateNode.UCTStateConstructor
-
- 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.oomdp.singleagent.common
-
Defines a reward function that always returns -1.
- UniformCostRF() - Constructor for class burlap.oomdp.singleagent.common.UniformCostRF
-
- 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
-
- 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
- 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.
- UnmodeledFavoredPolicy - Class in burlap.behavior.singleagent.learning.modellearning.rmax
-
- UnmodeledFavoredPolicy(Policy, Model, List<Action>) - Constructor for class burlap.behavior.singleagent.learning.modellearning.rmax.UnmodeledFavoredPolicy
-
- unpackPOMDPAction(AbstractGroundedAction) - Method in class burlap.behavior.policy.BeliefPolicyToPOMDPPolicy
-
- UNSET - Static variable in class burlap.oomdp.core.values.DiscreteValue
-
- unsetAttributes() - Method in class burlap.oomdp.core.objects.ImmutableObjectInstance
-
- unsetAttributes() - Method in class burlap.oomdp.core.objects.MutableObjectInstance
-
Returns a list of the names of
Attribute
s that have unset values
- unsetAttributes() - Method in interface burlap.oomdp.core.objects.ObjectInstance
-
Returns a list of the names of
Attribute
s that have unset values
- unsetAttributes() - Method in class burlap.oomdp.statehashing.HashableObject
-
- UnsetValueException - Exception in burlap.oomdp.core.values
-
A class for indicating that a OO-MDP object instance value is unset.
- UnsetValueException() - Constructor for exception burlap.oomdp.core.values.UnsetValueException
-
- update(double) - Method in class burlap.behavior.singleagent.planning.stochastic.montecarlo.uct.UCTActionNode
-
Updates the node statistics with a sample return
- 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.oomdp.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(EpisodeAnalysis) - Method in class burlap.behavior.singleagent.learning.lspi.LSPI
-
Updates this object's
SARSData
to include the results of a learning episode.
- updateFromCritqique(CritiqueResult) - Method in class burlap.behavior.singleagent.learning.actorcritic.actor.BoltzmannActor
-
- updateFromCritqique(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 class burlap.behavior.singleagent.learning.modellearning.Model
-
- updateModel(State, GroundedAction, State, double, boolean) - Method in class burlap.behavior.singleagent.learning.modellearning.Model
-
Causes this model to be updated with a new interaction with the world.
- updateModel(State, GroundedAction, State, double, boolean) - Method in class burlap.behavior.singleagent.learning.modellearning.models.TabularModel
-
- updateMostRecentSeriesHelper() - Method in class burlap.behavior.stochasticgames.auxiliary.performance.MultiAgentPerformancePlotter
-
Updates the series data for the most recent trial plots.
- updateMotion(State, double, LunarLanderDomain.LLPhysicsParams) - Static method in class burlap.domain.singleagent.lunarlander.LunarLanderDomain
-
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.oomdp.singleagent.explorer.VisualExplorer
-
Updates the propositional function evaluation text display for the given state.
- updatePropTextArea(State) - Method in class burlap.oomdp.stochasticgames.explorers.SGVisualExplorer
-
- updateRenderedStateAction(State, AbstractGroundedAction) - Method in class burlap.oomdp.visualizer.StateActionRenderLayer
-
- 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, InvertedPendulum.IPPhysicsParams) - Static method in class burlap.domain.singleagent.cartpole.InvertedPendulum
-
Updates the given state object given the control force.
- updateState(State) - Method in class burlap.oomdp.singleagent.explorer.VisualExplorer
-
Updates the currently visualized state to the input state.
- updateState(State) - Method in class burlap.oomdp.stochasticgames.explorers.SGVisualExplorer
-
Updates the currently visualized state to the input state.
- updateState(State) - Method in class burlap.oomdp.visualizer.StateRenderLayer
-
Updates the state that needs to be painted
- updateState(State) - Method in class burlap.oomdp.visualizer.Visualizer
-
Updates the state that needs to be painted and repaints.
- updateStateAction(State, AbstractGroundedAction) - Method in class burlap.oomdp.visualizer.Visualizer
-
- 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
- upperBoundV - Variable in class burlap.behavior.singleagent.planning.stochastic.rtdp.BoundedRTDP
-
The upperbound value function
- upperLim - Variable in class burlap.oomdp.core.Attribute
-
highest value for a non-relational attribute
- upperVal - Variable in class burlap.behavior.singleagent.auxiliary.StateGridder.AttributeSpecification
-
The upper value of the attribute on the grid
- upperVInit - Variable in class burlap.behavior.singleagent.planning.stochastic.rtdp.BoundedRTDP
-
The upperbound value function initialization
- 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.
- useCached - Variable in class burlap.oomdp.statehashing.SimpleHashableStateFactory
-
- useCachedTransitions - Variable in class burlap.behavior.singleagent.planning.stochastic.DynamicProgramming
-
A boolean toggle to indicate whether the transition dynamics should cached in a hashed data structure for quicker access,
or computed as needed by the Action methods.
- 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.oomdp.singleagent.interfaces.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.
- usesDeterministicPolicy() - Method in class burlap.behavior.singleagent.options.DeterministicTerminationOption
-
- usesDeterministicPolicy() - Method in class burlap.behavior.singleagent.options.MacroAction
-
- usesDeterministicPolicy() - Method in class burlap.behavior.singleagent.options.Option
-
Returns whether this option's policy is deterministic or stochastic
- usesDeterministicPolicy() - Method in class burlap.behavior.singleagent.options.PolicyDefinedSubgoalOption
-
- usesDeterministicTermination() - Method in class burlap.behavior.singleagent.options.DeterministicTerminationOption
-
- usesDeterministicTermination() - Method in class burlap.behavior.singleagent.options.MacroAction
-
- usesDeterministicTermination() - Method in class burlap.behavior.singleagent.options.Option
-
Returns whether this option's termination conditions are deterministic or stochastic
- usesDeterministicTermination() - Method in class burlap.behavior.singleagent.options.PolicyDefinedSubgoalOption
-
- useSemiDeep - Variable in class burlap.domain.singleagent.blockdude.BlockDude.MoveAction
-
- useSemiDeep - Variable in class burlap.domain.singleagent.blockdude.BlockDude.MoveUpAction
-
- useSemiDeep - Variable in class burlap.domain.singleagent.blockdude.BlockDude.PickupAction
-
- useSemiDeep - Variable in class burlap.domain.singleagent.blockdude.BlockDude.PutdownAction
-
- useSemiDeep - Variable in class burlap.domain.singleagent.blockdude.BlockDude
-
Domain parameter specifying whether actions create semi-deep copies of states or fully deep copies of states.
- 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
- useThreshold - Variable in class burlap.domain.singleagent.mountaincar.MountainCar.ClassicMCTF
-
- 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.
- 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
-