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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(Domain, RewardFunction, TerminalFunction, double, StateHashFactory, 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, StateHashFactory, 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(StateHashTuple, 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 planner
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
 
UniformPlusGoalRF - Class in burlap.behavior.singleagent.planning.deterministic
This Reward function returns a uniform cost (-1) for all transitions that do not transition to a goal state and 0 on transitions to the goal state.
UniformPlusGoalRF(StateConditionTest) - Constructor for class burlap.behavior.singleagent.planning.deterministic.UniformPlusGoalRF
Sets the reward function to return 0 when transition to states that satisfy gc, and -1 otherwise
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
UniversalSingleAction - Class in burlap.oomdp.stochasticgames.common
This SingleAction definition defines an action with a given name and parameters that can be executed in every state by every agent.
UniversalSingleAction(SGDomain, String) - Constructor for class burlap.oomdp.stochasticgames.common.UniversalSingleAction
Initializes this single action to be for the given domain and with the given name.
UniversalSingleAction(SGDomain, String, String[]) - Constructor for class burlap.oomdp.stochasticgames.common.UniversalSingleAction
Initializes this single action to be for the given domain, with the given name, and with the given parameter class types.
UniversalSingleAction(SGDomain, String, String[], String[]) - Constructor for class burlap.oomdp.stochasticgames.common.UniversalSingleAction
Initializes this single action to be for the given domain, with the given name, with the given parameter class types, and with the given parameter order groups.
UniversalStateParser - Class in burlap.oomdp.auxiliary.common
A StateParser class that can convert states for any possible input domain.
UniversalStateParser(Domain) - Constructor for class burlap.oomdp.auxiliary.common.UniversalStateParser
This parser only requires that the source domain for the states is provided.
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
 
unsetAttributes() - Method in class burlap.oomdp.core.ObjectInstance
Returns a list of the names of Attributes that have unset values
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.stochasticgame.auxiliary.performance.MultiAgentPerformancePlotter
Updates the average reward by episode series.
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.stochasticgame.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.stochasticgame.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.stochasticgame.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.stochasticgame.auxiliary.performance.MultiAgentPerformancePlotter
Increments the x-y position of a constraint to the next position.
updateLatestQValue() - Method in class burlap.behavior.stochasticgame.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.stochasticgame.auxiliary.performance.MultiAgentPerformancePlotter
Updates the median reward by episode series.
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.stochasticgame.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.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
 
updateSESeries() - Method in class burlap.behavior.singleagent.auxiliary.performance.PerformancePlotter
Updates the steps by episode series.
updateSESeries(MultiAgentPerformancePlotter.DatasetsAndTrials) - Method in class burlap.behavior.stochasticgame.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.
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.stochasticgame.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.
useCachedTransitions - Variable in class burlap.behavior.singleagent.planning.ValueFunctionPlanner
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 auxiliariy 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 planner to stop planning if a goal state is ever found.
useMaxMargin - Variable in class burlap.behavior.singleagent.learnbydemo.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.
useRMax - Variable in class burlap.behavior.singleagent.learning.modellearning.ModeledDomainGenerator.ModeledAction
Whether this action follows the RMax paradigm of transition to a fictious RMax state when the model is not known
usesDeterministicPolicy() - Method in class burlap.behavior.singleagent.options.DeterminisitcTerminationOption
 
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
 
usesDeterministicPolicy() - Method in class burlap.behavior.singleagent.options.PrimitiveOption
 
usesDeterministicTermination() - Method in class burlap.behavior.singleagent.options.DeterminisitcTerminationOption
 
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
 
usesDeterministicTermination() - Method in class burlap.behavior.singleagent.options.PrimitiveOption
 
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.learnbydemo.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.stochasticgame.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.stochasticgame.agents.twoplayer.singlestage.equilibriumplayer.equilibriumsolvers.Utilitarian
 
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