S-Index

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

S

s - Variable in class burlap.behavior.functionapproximation.supervised.SupervisedVFA.SupervisedVFAInstance: The state
s - Variable in class burlap.behavior.singleagent.learnfromdemo.mlirl.support.QGradientTuple: The state
s - Variable in class burlap.behavior.singleagent.learning.actorcritic.CritiqueResult: The source state
s - Variable in class burlap.behavior.singleagent.learning.lspi.SARSData.SARS: The previou state
s - Variable in class burlap.behavior.singleagent.learning.tdmethods.QLearningStateNode: A hashed state entry for which Q-value will be stored.
s - Variable in class burlap.behavior.singleagent.options.model.BFSMarkovOptionModel.OptionScanNode: the state this search node wraps
s - Variable in class burlap.behavior.singleagent.planning.deterministic.SearchNode: The (hashed) state of this node
s - Variable in class burlap.behavior.stochasticgames.madynamicprogramming.JAQValue
s - Variable in class burlap.behavior.valuefunction.QValue: The state with which this Q-value is associated.
s - Variable in class burlap.mdp.core.StateTransitionProb
s - Variable in class burlap.mdp.singleagent.pomdp.beliefstate.EnumerableBeliefState.StateBelief: The MDP state defined by a State instance.
s() - Method in class burlap.mdp.singleagent.pomdp.beliefstate.TabularBeliefState
s() - Method in interface burlap.statehashing.HashableState: Returns the underlying source state that is hashed.
s - Variable in class burlap.statehashing.WrappedHashableState: The source State to be hashed and evaluated by the WrappedHashableState.hashCode() and WrappedHashableState.equals(Object) method.
s() - Method in class burlap.statehashing.WrappedHashableState
saAfterStateRL - Variable in class burlap.visualizer.Visualizer
sActionFeatures - Variable in class burlap.behavior.singleagent.learning.lspi.LSPI.SSFeatures: State-action features
SADomain - Class in burlap.mdp.singleagent: A domain subclass for single agent domains.
SADomain() - Constructor for class burlap.mdp.singleagent.SADomain
saFeatures - Variable in class burlap.behavior.singleagent.learning.lspi.LSPI: The state feature database on which the linear VFA is performed
sample(State, Action) - Method in class burlap.behavior.singleagent.learnfromdemo.CustomRewardModel
sample(State, Action) - Method in class burlap.behavior.singleagent.learning.modellearning.models.TabularModel
sample(State, Action) - Method in class burlap.behavior.singleagent.learning.modellearning.rmax.RMaxModel
sample(State, Action) - Method in class burlap.behavior.singleagent.options.model.BFSMarkovOptionModel
sample() - Method in class burlap.datastructures.BoltzmannDistribution: Samples the output probability distribution.
sample() - Method in class burlap.datastructures.StochasticTree: Samples an element according to a probability defined by the relative weight of objects from the tree and returns it
sample(State, Action) - Method in class burlap.domain.singleagent.blockdude.BlockDudeModel
sample(State, Action) - Method in class burlap.domain.singleagent.blocksworld.BWModel
sample(State, Action) - Method in class burlap.domain.singleagent.cartpole.model.CPClassicModel
sample(State, Action) - Method in class burlap.domain.singleagent.cartpole.model.CPCorrectModel
sample(State, Action) - Method in class burlap.domain.singleagent.cartpole.model.IPModel
sample(State, Action) - Method in class burlap.domain.singleagent.frostbite.FrostbiteModel
sample(State, Action) - Method in class burlap.domain.singleagent.graphdefined.GraphDefinedDomain.GraphStateModel
sample(State, Action) - Method in class burlap.domain.singleagent.gridworld.GridWorldDomain.GridWorldModel
sample(State, Action) - Method in class burlap.domain.singleagent.lunarlander.LunarLanderModel
sample(State, Action) - Method in class burlap.domain.singleagent.mountaincar.MountainCar.MCModel
sample(State, Action) - Method in class burlap.domain.singleagent.pomdp.tiger.TigerModel
sample(State, Action) - Method in class burlap.domain.singleagent.pomdp.tiger.TigerObservations
sample(State, JointAction) - Method in class burlap.domain.stochasticgames.gridgame.GridGameStandardMechanics
sample(State, Action) - Method in class burlap.mdp.singleagent.model.DelegatedModel
sample(State, Action) - Method in class burlap.mdp.singleagent.model.FactoredModel
sample(State, Action) - Method in interface burlap.mdp.singleagent.model.SampleModel: Samples a transition from the transition distribution and returns it.
sample(State, Action) - Method in interface burlap.mdp.singleagent.model.statemodel.SampleStateModel: Samples and returns a State from a state transition function.
sample(State, Action) - Method in class burlap.mdp.singleagent.pomdp.BeliefMDPGenerator.BeliefModel
sample() - Method in interface burlap.mdp.singleagent.pomdp.beliefstate.BeliefState: Samples an MDP state state from this belief distribution.
sample() - Method in class burlap.mdp.singleagent.pomdp.beliefstate.TabularBeliefState
sample(State, Action) - Method in interface burlap.mdp.singleagent.pomdp.observations.ObservationFunction: Samples an observation given the true MDP state and action taken in the previous step that led to the MDP state.
sample(State, JointAction) - Method in class burlap.mdp.stochasticgames.common.StaticRepeatedGameModel
sample(State, JointAction) - Method in interface burlap.mdp.stochasticgames.model.JointModel: Samples the result of performing JointAction ja in State s.
sampleBasicMovement(OOState, GridGameStandardMechanics.Location2, GridGameStandardMechanics.Location2, List<GridGameStandardMechanics.Location2>) - Method in class burlap.domain.stochasticgames.gridgame.GridGameStandardMechanics: Returns a movement result of the agent.
sampleByEnumeration(FullModel, State, Action) - Static method in class burlap.mdp.singleagent.model.FullModel.Helper: Method to implement the SampleModel.sample(State, Action) method when the FullModel.transitions(State, Action) method is implemented.
sampleByEnumeration(FullStateModel, State, Action) - Static method in class burlap.mdp.singleagent.model.statemodel.FullStateModel.Helper: Method to implement the SampleStateModel.sample(State, Action) method when the FullStateModel.stateTransitions(State, Action) method is implemented.
sampleByEnumeration(DiscreteObservationFunction, State, Action) - Static method in class burlap.mdp.singleagent.pomdp.observations.ObservationUtilities: A helper method for easily implementing the ObservationFunction.sample(State, Action) method that samples an observation by first getting all non-zero probability observations, as returned by the DiscreteObservationFunction.probabilities(State, Action) method, and then sampling from the enumerated distribution.
sampledQEstimate(Action, DifferentiableSparseSampling.QAndQGradient) - Method in class burlap.behavior.singleagent.learnfromdemo.mlirl.differentiableplanners.DifferentiableSparseSampling.DiffStateNode
sampledQEstimate(Action) - Method in class burlap.behavior.singleagent.planning.stochastic.sparsesampling.SparseSampling.StateNode: Estimates the Q-value using sampling from the transition dynamics.
sampleFromActionDistribution(EnumerablePolicy, State) - Static method in class burlap.behavior.policy.PolicyUtils: This is a helper method for stochastic policies.
sampleHelper(StochasticTree<T>.STNode, double) - Method in class burlap.datastructures.StochasticTree: A recursive method for performing sampling
SampleModel - Interface in burlap.mdp.singleagent.model: Interface for model that can be used to sample a transition from an input state for a given action and can indicate when a state is terminal or not.
samples - Variable in class burlap.behavior.singleagent.planning.vfa.fittedvi.FittedVI: The set of samples on which to perform value iteration.
SampleStateModel - Interface in burlap.mdp.singleagent.model.statemodel: An interface for a model that can sample a state transition from the state transition function for a given input state and action.
sampleStrategy(double[]) - Method in class burlap.behavior.stochasticgames.agents.twoplayer.singlestage.equilibriumplayer.EquilibriumPlayingSGAgent: Samples an action from a strategy, where a strategy is defined as probability distribution over actions.
sampleWallCollision(GridGameStandardMechanics.Location2, GridGameStandardMechanics.Location2, List<ObjectInstance>, boolean) - Method in class burlap.domain.stochasticgames.gridgame.GridGameStandardMechanics: Return true if the agent is able to move in the desired location; false if the agent moves into a solid wall or if the agent randomly fails to move through a semi-wall that is in the way.
SAObjectParameterizedAction() - Constructor for class burlap.mdp.singleagent.oo.ObjectParameterizedActionType.SAObjectParameterizedAction
SAObjectParameterizedAction(String, String[]) - Constructor for class burlap.mdp.singleagent.oo.ObjectParameterizedActionType.SAObjectParameterizedAction
sarender - Variable in class burlap.visualizer.Visualizer: An optional StateActionRenderLayer so that actions can be visualized on the same screen.
SARS(State, Action, double, State) - Constructor for class burlap.behavior.singleagent.learning.lspi.SARSData.SARS: Initializes.
SarsaLam - Class in burlap.behavior.singleagent.learning.tdmethods: Tabular SARSA(\lambda) implementation [1].
SarsaLam(SADomain, double, HashableStateFactory, double, double, double) - Constructor for class burlap.behavior.singleagent.learning.tdmethods.SarsaLam: Initializes SARSA(\lambda) with 0.1 epsilon greedy policy, the same Q-value initialization everywhere, and places no limit on the number of steps the agent can take in an episode.
SarsaLam(SADomain, double, HashableStateFactory, double, double, int, double) - Constructor for class burlap.behavior.singleagent.learning.tdmethods.SarsaLam: Initializes SARSA(\lambda) with 0.1 epsilon greedy policy, the same Q-value initialization everywhere.
SarsaLam(SADomain, double, HashableStateFactory, double, double, Policy, int, double) - Constructor for class burlap.behavior.singleagent.learning.tdmethods.SarsaLam: Initializes SARSA(\lambda) with the same Q-value initialization everywhere.
SarsaLam(SADomain, double, HashableStateFactory, QFunction, double, Policy, int, double) - Constructor for class burlap.behavior.singleagent.learning.tdmethods.SarsaLam: Initializes SARSA(\lambda).
SarsaLam.EligibilityTrace - Class in burlap.behavior.singleagent.learning.tdmethods: A data structure for maintaining eligibility trace values
sarsalamInit(double) - Method in class burlap.behavior.singleagent.learning.tdmethods.SarsaLam
SARSCollector - Class in burlap.behavior.singleagent.learning.lspi: This object is used to collected SARSData (state-action-reard-state tuples) that can then be used by algorithms like LSPI for learning.
SARSCollector(SADomain) - Constructor for class burlap.behavior.singleagent.learning.lspi.SARSCollector: Initializes the collector's action set using the actions that are part of the domain.
SARSCollector(List<ActionType>) - Constructor for class burlap.behavior.singleagent.learning.lspi.SARSCollector: Initializes this collector's action set to use for collecting data.
SARSCollector.UniformRandomSARSCollector - Class in burlap.behavior.singleagent.learning.lspi: Collects SARS data from source states generated by a StateGenerator by choosing actions uniformly at random.
SARSData - Class in burlap.behavior.singleagent.learning.lspi: Class that provides a wrapper for a List holding a bunch of state-action-reward-state (SARSData.SARS) tuples.
SARSData() - Constructor for class burlap.behavior.singleagent.learning.lspi.SARSData: Initializes with an empty dataset
SARSData(int) - Constructor for class burlap.behavior.singleagent.learning.lspi.SARSData: Initializes with an empty dataset with initial capacity for the given parameter available.
SARSData.SARS - Class in burlap.behavior.singleagent.learning.lspi: State-action-reward-state tuple.
saThread - Variable in class burlap.behavior.stochasticgames.agents.interfacing.singleagent.LearningAgentToSGAgentInterface: The thread that runs the single agent learning algorithm
satisfies(State) - Method in class burlap.mdp.auxiliary.stateconditiontest.SinglePFSCT
satisfies(State) - Method in interface burlap.mdp.auxiliary.stateconditiontest.StateConditionTest
satisfies(State) - Method in class burlap.mdp.auxiliary.stateconditiontest.TFGoalCondition
satisifiesHeap() - Method in class burlap.datastructures.HashIndexedHeap: This method returns whether the data structure stored is in fact a heap (costs linear time).
scale - Variable in class burlap.domain.singleagent.frostbite.FrostbiteDomain: Constant to adjust the scale of the game
scale - Variable in class burlap.domain.singleagent.frostbite.FrostbiteModel: Constant to adjust the scale of the game
scanner - Variable in class burlap.shell.BurlapShell
sdp - Variable in class burlap.behavior.singleagent.auxiliary.valuefunctionvis.ValueFunctionVisualizerGUI: Painter used to visualize general state-independent domain information
SDPlannerPolicy - Class in burlap.behavior.singleagent.planning.deterministic: This is a static deterministic valueFunction policy, which means if the source deterministic valueFunction has not already computed and cached the plan for a query state, then this policy is undefined for that state and will cause the policy to throw a corresponding PolicyUndefinedException exception object.
SDPlannerPolicy() - Constructor for class burlap.behavior.singleagent.planning.deterministic.SDPlannerPolicy
SDPlannerPolicy(DeterministicPlanner) - Constructor for class burlap.behavior.singleagent.planning.deterministic.SDPlannerPolicy
SearchNode - Class in burlap.behavior.singleagent.planning.deterministic: The SearchNode class is used for classic deterministic forward search planners.
SearchNode(HashableState) - Constructor for class burlap.behavior.singleagent.planning.deterministic.SearchNode: Constructs a SearchNode for the input state.
SearchNode(HashableState, Action, SearchNode) - Constructor for class burlap.behavior.singleagent.planning.deterministic.SearchNode: Constructs a SearchNode for the input state and sets the generating action and back pointer to the provided elements.
seAvgSeries - Variable in class burlap.behavior.singleagent.auxiliary.performance.PerformancePlotter.AgentDatasets: All trial's average steps per episode series data
seAvgSeries - Variable in class burlap.behavior.stochasticgames.auxiliary.performance.MultiAgentPerformancePlotter.AgentDatasets: All trial's average steps per episode series data
seedDefault(long) - Static method in class burlap.debugtools.RandomFactory: Sets the seed of the default random number generator
seedMapped(int, long) - Static method in class burlap.debugtools.RandomFactory: Seeds and returns the random generator with the associated id or creates it if it does not yet exist
seedMapped(String, long) - Static method in class burlap.debugtools.RandomFactory: Seeds and returns the random generator with the associated String id or creates it if it does not yet exist
selectActionNode(UCTStateNode) - Method in class burlap.behavior.singleagent.planning.stochastic.montecarlo.uct.UCT: Selections which action to take.
selectionMode - Variable in class burlap.behavior.singleagent.planning.stochastic.rtdp.BoundedRTDP: Which state selection mode is used.
selector - Variable in class burlap.mdp.stochasticgames.tournament.Tournament
semiWallProb - Variable in class burlap.domain.stochasticgames.gridgame.GridGame: The probability that an agent will pass through a semi-wall.
serialize() - Method in class burlap.behavior.singleagent.Episode
serialize() - Method in class burlap.behavior.stochasticgames.GameEpisode
set(Object, Object) - Method in class burlap.domain.singleagent.blockdude.state.BlockDudeState
set(Object, Object) - Method in class burlap.domain.singleagent.blocksworld.BlocksWorldState
set(Object, Object) - Method in class burlap.domain.singleagent.cartpole.states.CartPoleFullState
set(Object, Object) - Method in class burlap.domain.singleagent.cartpole.states.CartPoleState
set(Object, Object) - Method in class burlap.domain.singleagent.cartpole.states.InvertedPendulumState
set(Object, Object) - Method in class burlap.domain.singleagent.frostbite.state.FrostbiteState
set(Object, Object) - Method in class burlap.domain.singleagent.graphdefined.GraphStateNode
set(Object, Object) - Method in class burlap.domain.singleagent.gridworld.state.GridWorldState
set(Object, Object) - Method in class burlap.domain.singleagent.lunarlander.state.LLState
set(Object, Object) - Method in class burlap.domain.singleagent.mountaincar.MCState
set(Object, Object) - Method in class burlap.domain.singleagent.pomdp.tiger.TigerObservation
set(Object, Object) - Method in class burlap.domain.singleagent.pomdp.tiger.TigerState
set(Object, Object) - Method in class burlap.domain.stochasticgames.gridgame.state.GGAgent
set(Object, Object) - Method in class burlap.domain.stochasticgames.gridgame.state.GGGoal
set(Object, Object) - Method in class burlap.domain.stochasticgames.gridgame.state.GGWall
set(Object, Object) - Method in class burlap.domain.stochasticgames.normalform.NFGameState
set(SingleStageNormalFormGame.StrategyProfile, double) - Method in class burlap.domain.stochasticgames.normalform.SingleStageNormalFormGame.AgentPayoutFunction: sets the payout for a given strategy profile
set(Object, Object) - Method in class burlap.mdp.core.oo.state.generic.DeepOOState
set(Object, Object) - Method in class burlap.mdp.core.oo.state.generic.GenericOOState
set(Object, Object) - Method in interface burlap.mdp.core.state.MutableState: Sets the value for the given variable key.
set(Object, Object) - Method in class burlap.mdp.singleagent.pomdp.beliefstate.TabularBeliefState
set1DEastWall(int, int) - Method in class burlap.domain.singleagent.gridworld.GridWorldDomain: Sets a specified location to have a 1D east wall.
set1DNorthWall(int, int) - Method in class burlap.domain.singleagent.gridworld.GridWorldDomain: Sets a specified location to have a 1D north wall.
setActingAgent(int) - Method in class burlap.behavior.stochasticgames.PolicyFromJointPolicy: Sets the acting agents name
setAction(int, Action) - Method in class burlap.mdp.stochasticgames.JointAction: Sets the action for the specified agent.
setAction(int, Action) - Method in class burlap.shell.command.world.JointActionCommand: Sets the action for a single agent in the joint action this shell command controls
setAction - Variable in class burlap.shell.command.world.ManualAgentsCommands
setActionNameGlyphPainter(String, ActionGlyphPainter) - Method in class burlap.behavior.singleagent.auxiliary.valuefunctionvis.common.PolicyGlyphPainter2D: Sets which glyph painter to use for an action with the given name
setActionOffset(Map<Action, Integer>) - Method in class burlap.behavior.functionapproximation.dense.DenseLinearVFA: Sets the Map of feature index offsets into the full feature vector for each action
setActionOffset(Action, int) - Method in class burlap.behavior.functionapproximation.dense.DenseLinearVFA: Sets the Map of feature index offset into the full feature vector for the given action
setActions(List<Action>) - Method in class burlap.mdp.stochasticgames.JointAction
setActionSequence(List<Action>) - Method in class burlap.behavior.singleagent.options.MacroAction
setActionsTypes(List<ActionType>) - Method in class burlap.behavior.singleagent.learning.modellearning.rmax.RMaxModel
setActionTypes(List<ActionType>) - Method in class burlap.behavior.singleagent.MDPSolver
setActionTypes(List<ActionType>) - Method in interface burlap.behavior.singleagent.MDPSolverInterface: Sets the action set the solver should use.
setActionTypes(ActionType...) - Method in class burlap.mdp.singleagent.SADomain: Sets the ActionTypes for this domain.
setActionTypes(List<ActionType>) - Method in class burlap.mdp.singleagent.SADomain: Sets the ActionTypes for this domain.
SetAgentAction() - Constructor for class burlap.shell.command.world.ManualAgentsCommands.SetAgentAction
setAgentDefinitions - Variable in class burlap.behavior.stochasticgames.agents.madp.MultiAgentDPPlanningAgent: Whether the agent definitions for this valueFunction have been set yet.
setAgentDefinitions(List<SGAgentType>) - Method in class burlap.behavior.stochasticgames.madynamicprogramming.MADynamicProgramming: Sets/changes the agent definitions to use in planning.
setAgentDetails(String, SGAgentType) - Method in class burlap.behavior.stochasticgames.agents.naiveq.history.SGQWActionHistory
setAgentDetails(String, SGAgentType) - Method in class burlap.behavior.stochasticgames.agents.naiveq.SGNaiveQLAgent
setAgentDetails(String, SGAgentType) - Method in class burlap.mdp.stochasticgames.agent.SGAgentBase
setAgents(List<MultiAgentQLearning>) - Method in class burlap.behavior.stochasticgames.madynamicprogramming.AgentQSourceMap.MAQLControlledQSourceMap: Initializes with a list of agents that each keep their own Q_source.
setAgentsInJointPolicy(List<SGAgent>) - Method in class burlap.behavior.stochasticgames.JointPolicy: Sets the agent definitions by querying the agent names and SGAgentType objects from a list of agents.
setAgentsInJointPolicyFromWorld(World) - Method in class burlap.behavior.stochasticgames.JointPolicy: Sets teh agent definitions by querying the agents that exist in a World object.
setAgentTypesInJointPolicy(List<SGAgentType>) - Method in class burlap.behavior.stochasticgames.JointPolicy: Sets the agent definitions that define the set of possible joint actions in each state.
setAlias(String, String) - Method in class burlap.shell.BurlapShell
setAlias(String, String, boolean) - Method in class burlap.shell.BurlapShell
setAllowActionFromTerminalStates(boolean) - Method in class burlap.mdp.singleagent.environment.SimulatedEnvironment: Sets whether the environment will respond to actions from a terminal state.
setAnginc(double) - Method in class burlap.domain.singleagent.lunarlander.LunarLanderDomain.LLPhysicsParams
setAnginc(double) - Method in class burlap.domain.singleagent.lunarlander.LunarLanderDomain: Sets how many radians the agent will rotate from its current orientation when a turn/rotate action is applied
setAngmax(double) - Method in class burlap.domain.singleagent.lunarlander.LunarLanderDomain.LLPhysicsParams
setAngmax(double) - Method in class burlap.domain.singleagent.lunarlander.LunarLanderDomain: Sets the maximum rotate angle (in radians) that the lander can be rotated from the vertical orientation in either clockwise or counterclockwise direction.
setAuxInfoTo(PrioritizedSearchNode) - Method in class burlap.behavior.singleagent.planning.deterministic.informed.PrioritizedSearchNode: This method rewires the generating node information and priority to that specified in a different PrioritizedSearchNode.
setBelief(State, double) - Method in class burlap.mdp.singleagent.pomdp.beliefstate.TabularBeliefState: Sets the probability mass (belief) associated with the underlying MDP state.
setBelief(int, double) - Method in class burlap.mdp.singleagent.pomdp.beliefstate.TabularBeliefState: Sets the probability mass (belief) associated with the underlying MDP state.
setBeliefState(BeliefState) - Method in class burlap.mdp.singleagent.pomdp.BeliefAgent: Sets this agent's current belief
setBeliefValues(Map<Integer, Double>) - Method in class burlap.mdp.singleagent.pomdp.beliefstate.TabularBeliefState
setBeliefVector(double[]) - Method in interface burlap.mdp.singleagent.pomdp.beliefstate.DenseBeliefVector: Sets this belief state to the provided.
setBeliefVector(double[]) - Method in class burlap.mdp.singleagent.pomdp.beliefstate.TabularBeliefState: Sets this belief state to the provided.
setBeta(double) - Method in class burlap.behavior.singleagent.planning.stochastic.dpoperator.SoftmaxOperator
setBgColor(Color) - Method in class burlap.behavior.singleagent.auxiliary.valuefunctionvis.ValueFunctionVisualizerGUI: Sets the canvas background color
setBGColor(Color) - Method in class burlap.visualizer.MultiLayerRenderer: Sets the color that will fill the canvas before rendering begins
setBGColor(Color) - Method in class burlap.visualizer.Visualizer: Sets the background color of the canvas
setBoltzmannBeta(double) - Method in class burlap.behavior.singleagent.learnfromdemo.mlirl.MLIRLRequest
setBoundaryWalls(GenericOOState, int, int) - Static method in class burlap.domain.stochasticgames.gridgame.GridGame: /** Sets boundary walls of a domain.
setBreakTiesRandomly(boolean) - Method in class burlap.behavior.stochasticgames.madynamicprogramming.policies.EGreedyMaxWellfare: Whether to break ties randomly or deterministically.
setC(int) - Method in class burlap.behavior.singleagent.learnfromdemo.mlirl.differentiableplanners.DifferentiableSparseSampling: Sets the number of state transition samples used.
setC(int) - Method in class burlap.behavior.singleagent.planning.stochastic.sparsesampling.SparseSampling: Sets the number of state transition samples used.
setCellWallState(int, int, int) - Method in class burlap.domain.singleagent.gridworld.GridWorldDomain: Sets the map at the specified location to have the specified wall configuration.
setClassName(String) - Method in class burlap.domain.singleagent.blockdude.state.BlockDudeCell
setCoefficientVectors(List<short[]>) - Method in class burlap.behavior.functionapproximation.dense.fourier.FourierBasis: Forces the set of coefficient vectors (and thereby Fourier basis functions) used.
setCollisionReward(double) - Method in class burlap.domain.singleagent.lunarlander.LunarLanderRF
setColorBlend(ColorBlend) - Method in class burlap.behavior.singleagent.auxiliary.valuefunctionvis.common.StateValuePainter2D: Sets the color blending used for the value function.
setColorsForPFs(BlocksWorld.NamedColor...) - Method in class burlap.domain.singleagent.blocksworld.BlocksWorld
setComputeExactValueFunction(boolean) - Method in class burlap.behavior.singleagent.planning.stochastic.sparsesampling.SparseSampling: Sets whether this valueFunction will compute the exact finite horizon value function (using the full transition dynamics) or if sampling to estimate the value function will be used.
setConfig(MaskedConfig) - Method in class burlap.statehashing.masked.MaskedHashableStateFactory
setControlDepth(int) - Method in class burlap.behavior.singleagent.planning.vfa.fittedvi.FittedVI: Sets the Bellman operator depth used for computing Q-values (the FittedVI.qValue(State, Action) and FittedVI.qValue(State, Action) methods).
setCorrectDoorReward(double) - Method in class burlap.domain.singleagent.pomdp.tiger.TigerDomain
setCorrelatedQObjective(CorrelatedEquilibriumSolver.CorrelatedEquilibriumObjective) - Method in class burlap.behavior.stochasticgames.madynamicprogramming.policies.ECorrelatedQJointPolicy: Sets the correlated equilibrium objective to be solved.
setCurObservationTo(State) - Method in class burlap.mdp.singleagent.pomdp.SimulatedPOEnvironment: Overrides the current observation of this environment to the specified value
setCurrentState(State) - Method in class burlap.mdp.stochasticgames.world.World: Sets the world state to the provided state if the a game is not currently running.
setCurStateTo(State) - Method in class burlap.mdp.singleagent.environment.extensions.EnvironmentServer.StateSettableEnvironmentServer
setCurStateTo(State) - Method in interface burlap.mdp.singleagent.environment.extensions.StateSettableEnvironment: Sets the current state of the environment to the specified state.
setCurStateTo(State) - Method in class burlap.mdp.singleagent.environment.SimulatedEnvironment
setCurTime(int) - Method in class burlap.behavior.singleagent.learning.actorcritic.critics.TimeIndexedTDLambda: Sets the time/depth of the current episode.
setDataset(SARSData) - Method in class burlap.behavior.singleagent.learning.lspi.LSPI: Sets the SARS dataset this object will use for LSPI
setDebugCode(int) - Method in class burlap.behavior.singleagent.learnfromdemo.mlirl.differentiableplanners.DifferentiableSparseSampling: Sets the debug code used for logging plan results with DPrint.
setDebugCode(int) - Method in class burlap.behavior.singleagent.learnfromdemo.mlirl.MLIRL: Sets the debug code used for printing to the terminal
setDebugCode(int) - Method in class burlap.behavior.singleagent.learnfromdemo.mlirl.MultipleIntentionsMLIRL: Sets the debug code used for printing to the terminal
setDebugCode(int) - Method in class burlap.behavior.singleagent.MDPSolver
setDebugCode(int) - Method in interface burlap.behavior.singleagent.MDPSolverInterface: Sets the debug code to be used by calls to DPrint
setDebugCode(int) - Method in class burlap.behavior.singleagent.planning.stochastic.sparsesampling.SparseSampling: Sets the debug code used for logging plan results with DPrint.
setDebugId(int) - Method in class burlap.mdp.stochasticgames.world.World: Sets the debug code that is use for printing with DPrint.
setDefaultFloorDiscretizingMultiple(double) - Method in class burlap.statehashing.discretized.DiscConfig: Sets the default multiple to use for continuous values that do not have specific multiples set for them.
setDefaultFloorDiscretizingMultiple(double) - Method in class burlap.statehashing.discretized.DiscretizingHashableStateFactory: Sets the default multiple to use for continuous values that do not have specific multiples set for them.
setDefaultFloorDiscretizingMultiple(double) - Method in class burlap.statehashing.maskeddiscretized.DiscMaskedConfig: Sets the default multiple to use for continuous values that do not have specific multiples set for them.
setDefaultFloorDiscretizingMultiple(double) - Method in class burlap.statehashing.maskeddiscretized.DiscretizingMaskedHashableStateFactory: Sets the default multiple to use for continuous values that do not have specific multiples set for them.
setDefaultReward(double) - Method in class burlap.domain.singleagent.lunarlander.LunarLanderRF
setDefaultReward(double) - Method in class burlap.mdp.singleagent.common.GoalBasedRF
setDefaultValueFunctionAfterARollout(boolean) - Method in class burlap.behavior.singleagent.planning.stochastic.rtdp.BoundedRTDP: Use this method to set which value function--the lower bound or upper bound--to use after a planning rollout is complete.
setDeterministicTransitionDynamics() - Method in class burlap.domain.singleagent.gridworld.GridWorldDomain: Will set the domain to use deterministic action transitions.
setDomain(SADomain) - Method in class burlap.behavior.singleagent.learnfromdemo.IRLRequest
setDomain(SADomain) - Method in class burlap.behavior.singleagent.MDPSolver
setDomain(SADomain) - Method in interface burlap.behavior.singleagent.MDPSolverInterface: Sets the domain of this solver.
setDomain(PODomain) - Method in class burlap.mdp.singleagent.pomdp.beliefstate.TabularBeliefState
setDomain(PODomain) - Method in class burlap.mdp.singleagent.pomdp.beliefstate.TabularBeliefUpdate
setDomain(SGDomain) - Method in class burlap.mdp.stochasticgames.world.World
setDomain(Domain) - Method in class burlap.shell.BurlapShell
setEnv(Environment) - Method in class burlap.shell.EnvironmentShell
setEnvironment(Environment) - Method in class burlap.mdp.singleagent.pomdp.BeliefAgent: Sets the POMDP environment
setEnvironmentDelegate(Environment) - Method in interface burlap.mdp.singleagent.environment.extensions.EnvironmentDelegation: Sets the Environment delegate that handles Environment functionality
setEnvironmentDelegate(Environment) - Method in class burlap.mdp.singleagent.environment.extensions.EnvironmentServer: Sets the Environment delegate that handles all Environment functionality
setEpisodeWeights(double[]) - Method in class burlap.behavior.singleagent.learnfromdemo.mlirl.MLIRLRequest
setEpsilon(double) - Method in class burlap.behavior.policy.EpsilonGreedy: Sets the epsilon value, where epsilon is the probability of taking a random action.
setEpsilon(double) - Method in class burlap.behavior.singleagent.learnfromdemo.apprenticeship.ApprenticeshipLearningRequest
setEpsilon(double) - Method in class burlap.behavior.stochasticgames.agents.naiveq.history.SGQWActionHistoryFactory: Sets the epislon parmaeter (for epsilon greedy policy).
setExpertEpisodes(List<Episode>) - Method in class burlap.behavior.singleagent.learnfromdemo.apprenticeship.ApprenticeshipLearningRequest
setExpertEpisodes(List<Episode>) - Method in class burlap.behavior.singleagent.learnfromdemo.IRLRequest
setFeatureGenerator(DenseStateFeatures) - Method in class burlap.behavior.singleagent.learnfromdemo.apprenticeship.ApprenticeshipLearningRequest
setFeaturesAreForNextState(boolean) - Method in class burlap.behavior.singleagent.learnfromdemo.mlirl.commonrfs.LinearStateDifferentiableRF: Sets whether features for the reward function are generated from the next state or previous state.
setFlag(int, int) - Static method in class burlap.debugtools.DebugFlags: Creates/sets a debug flag
setForgetPreviousPlanResults(boolean) - Method in class burlap.behavior.singleagent.learnfromdemo.mlirl.differentiableplanners.DifferentiableSparseSampling: Sets whether previous planning results should be forgetten or resued in subsequent planning.
setForgetPreviousPlanResults(boolean) - Method in class burlap.behavior.singleagent.planning.stochastic.sparsesampling.SparseSampling: Sets whether previous planning results should be forgotten or reused in subsequent planning.
setFrameDelay(long) - Method in class burlap.mdp.singleagent.common.VisualActionObserver: Sets how long to wait in ms for a state to be rendered before returning control the agent.
setFrameDelay(long) - Method in class burlap.mdp.stochasticgames.common.VisualWorldObserver: Sets how long to wait in ms for a state to be rendered before returning control the world.
setGamma(double) - Method in class burlap.behavior.singleagent.learnfromdemo.IRLRequest
setGamma(double) - Method in class burlap.behavior.singleagent.learning.modellearning.rmax.RMaxModel
setGamma(double) - Method in class burlap.behavior.singleagent.MDPSolver
setGamma(double) - Method in interface burlap.behavior.singleagent.MDPSolverInterface: Sets gamma, the discount factor used by this solver
setGoalReward(double) - Method in class burlap.domain.singleagent.lunarlander.LunarLanderRF
setGoalReward(double) - Method in class burlap.mdp.singleagent.common.GoalBasedRF
setGravity(double) - Method in class burlap.domain.singleagent.lunarlander.LunarLanderDomain.LLPhysicsParams
setGravity(double) - Method in class burlap.domain.singleagent.lunarlander.LunarLanderDomain: Sets the gravity of the domain
setH(int) - Method in class burlap.behavior.singleagent.learnfromdemo.mlirl.differentiableplanners.DifferentiableSparseSampling: Sets the height of the tree.
setH(int) - Method in class burlap.behavior.singleagent.planning.stochastic.sparsesampling.SparseSampling: Sets the height of the tree.
setHalfTrackLength(double) - Method in class burlap.domain.singleagent.cartpole.CartPoleDomain.CartPoleRewardFunction
setHalfTrackLength(double) - Method in class burlap.domain.singleagent.cartpole.CartPoleDomain.CartPoleTerminalFunction
setHAndCByMDPError(double, double, int) - Method in class burlap.behavior.singleagent.planning.stochastic.sparsesampling.SparseSampling: Sets the height and number of transition dynamics samples in a way that ensure epsilon optimality.
setHashingFactory(HashableStateFactory) - Method in class burlap.behavior.singleagent.MDPSolver
setHashingFactory(HashableStateFactory) - Method in interface burlap.behavior.singleagent.MDPSolverInterface: Sets the HashableStateFactory used to hash states for tabular solvers.
setHelpText(String) - Method in class burlap.shell.BurlapShell
setId(int) - Method in class burlap.domain.singleagent.graphdefined.GraphStateNode
setIdentityScalar(double) - Method in class burlap.behavior.singleagent.learning.lspi.LSPI: Sets the initial LSPI identity matrix scalar used.
setIncludeDoNothing(boolean) - Method in class burlap.domain.singleagent.pomdp.tiger.TigerDomain
setInitiationTest(StateConditionTest) - Method in class burlap.behavior.singleagent.options.SubgoalOption
setInternalRewardFunction(JointRewardFunction) - Method in class burlap.mdp.stochasticgames.agent.SGAgentBase
setIs(InputStream) - Method in class burlap.shell.BurlapShell
setIterationListData() - Method in class burlap.behavior.singleagent.auxiliary.EpisodeSequenceVisualizer
setJointActionModel(JointModel) - Method in class burlap.mdp.stochasticgames.SGDomain: Sets the joint action model associated with this domain.
setJointPolicy(JointPolicy) - Method in class burlap.behavior.stochasticgames.PolicyFromJointPolicy: Sets the underlying joint policy
setK(int) - Method in class burlap.behavior.singleagent.learnfromdemo.mlirl.MultipleIntentionsMLIRLRequest: Sets the number of clusters
setLearningPolicy(Policy) - Method in class burlap.behavior.singleagent.learning.lspi.LSPI: Sets the learning policy followed by the LSPI.runLearningEpisode(burlap.mdp.singleagent.environment.Environment) and LSPI.runLearningEpisode(burlap.mdp.singleagent.environment.Environment, int) methods.
setLearningPolicy(Policy) - Method in class burlap.behavior.singleagent.learning.tdmethods.QLearning: Sets which policy this agent should use for learning.
setLearningPolicy(Policy) - Method in class burlap.behavior.singleagent.learning.tdmethods.vfa.GradientDescentSarsaLam: Sets which policy this agent should use for learning.
setLearningPolicy(PolicyFromJointPolicy) - Method in class burlap.behavior.stochasticgames.agents.maql.MultiAgentQLearning: Sets the learning policy to be followed by the agent.
setLearningRate(LearningRate) - Method in class burlap.behavior.singleagent.learning.actorcritic.actor.BoltzmannActor: Sets the learning rate function to use.
setLearningRate(LearningRate) - Method in class burlap.behavior.singleagent.learning.actorcritic.critics.TDLambda: Sets the learning rate function to use.
setLearningRate(LearningRate) - Method in class burlap.behavior.singleagent.learning.tdmethods.vfa.GradientDescentSarsaLam: Sets the learning rate function to use.
setLearningRate(LearningRate) - Method in class burlap.behavior.stochasticgames.agents.naiveq.SGNaiveQLAgent
setLearningRateFunction(LearningRate) - Method in class burlap.behavior.singleagent.learning.tdmethods.QLearning: Sets the learning rate function to use
setListenAccuracy(double) - Method in class burlap.domain.singleagent.pomdp.tiger.TigerDomain
setListenReward(double) - Method in class burlap.domain.singleagent.pomdp.tiger.TigerDomain
setManualAgent(String, ManualAgentsCommands.ManualSGAgent) - Method in class burlap.shell.command.world.ManualAgentsCommands
setManualAgents(Map<String, ManualAgentsCommands.ManualSGAgent>) - Method in class burlap.shell.command.world.ManualAgentsCommands
setMap(int[][]) - Method in class burlap.domain.singleagent.gridworld.GridWorldDomain: Set the map of the world.
setMapToFourRooms() - Method in class burlap.domain.singleagent.gridworld.GridWorldDomain: Will set the map of the world to the classic Four Rooms map used the original options work (Sutton, R.S.
setMaxAbsoluteAngle(double) - Method in class burlap.domain.singleagent.cartpole.CartPoleDomain.CartPoleRewardFunction
setMaxAbsoluteAngle(double) - Method in class burlap.domain.singleagent.cartpole.CartPoleDomain.CartPoleTerminalFunction
setMaxChange(double) - Method in class burlap.behavior.singleagent.learning.lspi.LSPI: Sets the maximum change in weights required to terminate policy iteration when called from the LSPI.planFromState(State), LSPI.runLearningEpisode(burlap.mdp.singleagent.environment.Environment) or LSPI.runLearningEpisode(burlap.mdp.singleagent.environment.Environment, int) methods.
setMaxDelta(double) - Method in class burlap.behavior.singleagent.planning.stochastic.rtdp.RTDP: Sets the maximum delta state value update in a rollout that will cause planning to terminate
setMaxDifference(double) - Method in class burlap.behavior.singleagent.planning.stochastic.rtdp.BoundedRTDP: Sets the max permitted difference in value function margin to permit planning termination.
setMaxDim(int) - Method in class burlap.domain.stochasticgames.gridgame.GridGame: Sets the maximum dimension of the world; it's width and height.
setMaxDynamicDepth(int) - Method in class burlap.behavior.singleagent.planning.stochastic.rtdp.RTDP: Sets the maximum depth of a rollout to use until it is prematurely temrinated to update the value function.
setMaxGT(int) - Method in class burlap.domain.stochasticgames.gridgame.GridGame: Sets the maximum goal types
setMaximumEpisodesForPlanning(int) - Method in class burlap.behavior.singleagent.learning.tdmethods.QLearning: Sets the maximum number of episodes that will be performed when the QLearning.planFromState(State) method is called.
setMaximumEpisodesForPlanning(int) - Method in class burlap.behavior.singleagent.learning.tdmethods.vfa.GradientDescentSarsaLam: Sets the maximum number of episodes that will be performed when the GradientDescentSarsaLam.planFromState(State) method is called.
setMaxIterations(int) - Method in class burlap.behavior.singleagent.learnfromdemo.apprenticeship.ApprenticeshipLearningRequest
setMaxLearningSteps(int) - Method in class burlap.behavior.singleagent.learning.lspi.LSPI: Sets the maximum number of learning steps permitted by the LSPI.runLearningEpisode(burlap.mdp.singleagent.environment.Environment) method.
setMaxNumberOfRollouts(int) - Method in class burlap.behavior.singleagent.planning.stochastic.rtdp.BoundedRTDP: Sets the maximum number of rollouts permitted before planning is forced to terminate.
setMaxNumPlanningIterations(int) - Method in class burlap.behavior.singleagent.learning.lspi.LSPI: Sets the maximum number of policy iterations that will be used by the LSPI.planFromState(State) method.
setMaxPlyrs(int) - Method in class burlap.domain.stochasticgames.gridgame.GridGame: Sets the max number of players
setMaxQChangeForPlanningTerminaiton(double) - Method in class burlap.behavior.singleagent.learning.tdmethods.QLearning: Sets a max change in the Q-function threshold that will cause the QLearning.planFromState(State) to stop planning when it is achieved.
setMaxRolloutDepth(int) - Method in class burlap.behavior.singleagent.planning.stochastic.rtdp.BoundedRTDP: Sets the maximum rollout depth of any rollout.
setMaxVFAWeightChangeForPlanningTerminaiton(double) - Method in class burlap.behavior.singleagent.learning.tdmethods.vfa.GradientDescentSarsaLam: Sets a max change in the VFA weight threshold that will cause the GradientDescentSarsaLam.planFromState(State) to stop planning when it is achieved.
setMaxWT(int) - Method in class burlap.domain.stochasticgames.gridgame.GridGame: Sets the maximum number of wall types
setMaxx(int) - Method in class burlap.domain.singleagent.blockdude.BlockDude
setMaxy(int) - Method in class burlap.domain.singleagent.blockdude.BlockDude
setMinNewStepsForLearningPI(int) - Method in class burlap.behavior.singleagent.learning.lspi.LSPI: Sets the minimum number of new learning observations before policy iteration is run again.
setMinNumRolloutsWithSmallValueChange(int) - Method in class burlap.behavior.singleagent.planning.stochastic.rtdp.RTDP: Sets the minimum number of consecutive rollsouts with a value function change less than the maxDelta value that will cause RTDP to stop.
setMinProb(double) - Method in class burlap.behavior.singleagent.options.model.BFSMarkovOptionModel
setModel(SampleModel) - Method in class burlap.behavior.singleagent.MDPSolver
setModel(SampleModel) - Method in interface burlap.behavior.singleagent.MDPSolverInterface: Sets the model to use for this solver
setModel(SampleModel) - Method in class burlap.mdp.singleagent.SADomain: Sets the SampleModel associated with this domain.
setName(String) - Method in class burlap.behavior.singleagent.options.MacroAction
setName(String) - Method in class burlap.behavior.singleagent.options.SubgoalOption
setName(String) - Method in class burlap.domain.singleagent.blockdude.state.BlockDudeCell
setName(String) - Method in class burlap.domain.singleagent.blocksworld.BlocksWorldBlock
setName(String) - Method in class burlap.domain.singleagent.frostbite.state.FrostbitePlatform
setName(String) - Method in class burlap.domain.singleagent.gridworld.state.GridAgent
setName(String) - Method in class burlap.domain.singleagent.gridworld.state.GridLocation
setName(String) - Method in class burlap.domain.singleagent.lunarlander.state.LLBlock
setName(String) - Method in class burlap.domain.stochasticgames.gridgame.state.GGAgent
setName(String) - Method in class burlap.domain.stochasticgames.gridgame.state.GGGoal
setName(String) - Method in class burlap.domain.stochasticgames.gridgame.state.GGWall
setName(String) - Method in class burlap.mdp.core.action.SimpleAction
setNextAction(Action) - Method in class burlap.shell.command.world.ManualAgentsCommands.ManualSGAgent
setNothingReward(double) - Method in class burlap.domain.singleagent.pomdp.tiger.TigerDomain
setNumActions(int) - Method in class burlap.behavior.functionapproximation.dense.DenseCrossProductFeatures
setNumberOfLocationTypes(int) - Method in class burlap.domain.singleagent.gridworld.GridWorldDomain: Sets the number of possible location types to which a location object can belong.
setNumberPlatformCol(int) - Method in class burlap.domain.singleagent.frostbite.FrostbiteModel
setNumEpisodesToStore(int) - Method in class burlap.behavior.singleagent.learning.actorcritic.ActorCritic
setNumEpisodesToStore(int) - Method in class burlap.behavior.singleagent.learning.lspi.LSPI
setNumEpisodesToStore(int) - Method in class burlap.behavior.singleagent.learning.modellearning.artdp.ARTDP
setNumEpisodesToStore(int) - Method in class burlap.behavior.singleagent.learning.modellearning.rmax.PotentialShapedRMax
setNumPasses(int) - Method in class burlap.behavior.singleagent.planning.stochastic.rtdp.RTDP: Sets the number of rollouts to perform when planning is started (unless the value function delta is small enough).
setNumSamplesForPlanning(int) - Method in class burlap.behavior.singleagent.learning.lspi.LSPI: Sets the number of SARS samples that will be gathered by the LSPI.planFromState(State) method.
setNumXCells(int) - Method in class burlap.behavior.singleagent.auxiliary.valuefunctionvis.common.StateValuePainter2D: Sets the number of states that will be rendered along a row
setNumYCells(int) - Method in class burlap.behavior.singleagent.auxiliary.valuefunctionvis.common.StateValuePainter2D: Sets the number of states that will be rendered along a row
setObjectParameters(String[]) - Method in interface burlap.mdp.core.oo.ObjectParameterizedAction: Sets the object parameters for this Action.
setObjectParameters(String[]) - Method in class burlap.mdp.singleagent.oo.ObjectParameterizedActionType.SAObjectParameterizedAction
setObjectsByClass(Map<String, List<ObjectInstance>>) - Method in class burlap.mdp.core.oo.state.generic.GenericOOState: Setter method for underlying data to support serialization
setObjectsMap(Map<String, ObjectInstance>) - Method in class burlap.mdp.core.oo.state.generic.GenericOOState: Setter method for underlying data to support serialization
setObs(Observation) - Method in class burlap.behavior.singleagent.interfaces.rlglue.RLGlueState
setObservationFunction(ObservationFunction) - Method in class burlap.mdp.singleagent.pomdp.PODomain: Sets the ObservationFunction used by the domain.
setObstacleInCell(int, int) - Method in class burlap.domain.singleagent.gridworld.GridWorldDomain: Sets a complete cell obstacle in the designated location.
setOperator(DPOperator) - Method in class burlap.behavior.singleagent.learnfromdemo.mlirl.differentiableplanners.DifferentiableDP
setOperator(DifferentiableDPOperator) - Method in class burlap.behavior.singleagent.learnfromdemo.mlirl.differentiableplanners.DifferentiableSparseSampling
setOperator(DPOperator) - Method in class burlap.behavior.singleagent.planning.stochastic.DynamicProgramming: Sets the dynamic programming operator use.
setOperator(DPOperator) - Method in class burlap.behavior.singleagent.planning.stochastic.rtdp.BoundedRTDP
setOperator(DPOperator) - Method in class burlap.behavior.singleagent.planning.stochastic.sparsesampling.SparseSampling
setOptionsFirst() - Method in class burlap.behavior.singleagent.planning.deterministic.uninformed.dfs.DFS: Sets the valueFunction to explore nodes generated by options first.
setOs(PrintStream) - Method in class burlap.shell.BurlapShell
setPainter(Visualizer) - Method in class burlap.mdp.singleagent.common.VisualActionObserver
setParameter(int, double) - Method in class burlap.behavior.functionapproximation.dense.DenseLinearVFA
setParameter(int, double) - Method in class burlap.behavior.functionapproximation.dense.DenseStateActionLinearVFA
setParameter(int, double) - Method in interface burlap.behavior.functionapproximation.ParametricFunction: Sets the value of the ith parameter to given value
setParameter(int, double) - Method in class burlap.behavior.functionapproximation.sparse.LinearVFA
setParameter(int, double) - Method in class burlap.behavior.singleagent.learnfromdemo.mlirl.commonrfs.LinearStateActionDifferentiableRF
setParameter(int, double) - Method in class burlap.behavior.singleagent.learnfromdemo.mlirl.commonrfs.LinearStateDifferentiableRF
setParameter(int, double) - Method in class burlap.behavior.singleagent.learnfromdemo.mlirl.differentiableplanners.diffvinit.DiffVFRF
setParameter(int, double) - Method in class burlap.behavior.singleagent.learnfromdemo.mlirl.differentiableplanners.diffvinit.LinearDiffRFVInit
setParameter(int, double) - Method in class burlap.behavior.singleagent.learnfromdemo.mlirl.differentiableplanners.diffvinit.LinearStateDiffVF
setParameter(int, double) - Method in class burlap.behavior.singleagent.learnfromdemo.mlirl.differentiableplanners.diffvinit.VanillaDiffVinit
setPayoff(int, int, double, double) - Method in class burlap.behavior.stochasticgames.agents.twoplayer.singlestage.equilibriumplayer.EquilibriumPlayingSGAgent.BimatrixTuple: Sets the payoffs for a given row and column.
setPayout(int, double, String...) - Method in class burlap.domain.stochasticgames.normalform.SingleStageNormalFormGame: Sets the pay out that player number playerNumber receives for a given strategy profile
setPayout(int, double, int...) - Method in class burlap.domain.stochasticgames.normalform.SingleStageNormalFormGame: Sets the pay out that player number playerNumber receives for a given strategy profile
setPhysParams(LunarLanderDomain.LLPhysicsParams) - Method in class burlap.domain.singleagent.lunarlander.LunarLanderDomain
setPlanner(Planner) - Method in class burlap.behavior.singleagent.learnfromdemo.IRLRequest
setPlanner(Planner) - Method in class burlap.behavior.singleagent.learnfromdemo.mlirl.MLIRLRequest
setPlannerFactory(QGradientPlannerFactory) - Method in class burlap.behavior.singleagent.learnfromdemo.mlirl.MultipleIntentionsMLIRLRequest: Sets the QGradientPlannerFactory to use and also sets this request object's valueFunction instance to a valueFunction generated from it, if it has not already been set.
setPlannerReference(MADynamicProgramming) - Method in class burlap.behavior.stochasticgames.agents.madp.MADPPlannerFactory.ConstantMADPPlannerFactory: Changes the valueFunction reference
setPlanningAndControlDepth(int) - Method in class burlap.behavior.singleagent.planning.vfa.fittedvi.FittedVI: Sets the Bellman operator depth used during planning for computing Q-values (the FittedVI.qValue(State, Action) and FittedVI.qValue(State, Action) methods).
setPlanningCollector(SARSCollector) - Method in class burlap.behavior.singleagent.learning.lspi.LSPI: Sets the SARSCollector used by the LSPI.planFromState(State) method for collecting data.
setPlanningDepth(int) - Method in class burlap.behavior.singleagent.planning.vfa.fittedvi.FittedVI: Sets the Bellman operator depth used during planning.
setPlotCISignificance(double) - Method in class burlap.behavior.singleagent.auxiliary.performance.LearningAlgorithmExperimenter: Sets the significance used for confidence intervals.
setPlotCISignificance(double) - Method in class burlap.behavior.stochasticgames.auxiliary.performance.MultiAgentExperimenter: Sets the significance used for confidence intervals.
setPlotRefreshDelay(int) - Method in class burlap.behavior.singleagent.auxiliary.performance.LearningAlgorithmExperimenter: Sets the delay in milliseconds between automatic plot refreshes
setPlotRefreshDelay(int) - Method in class burlap.behavior.stochasticgames.auxiliary.performance.MultiAgentExperimenter: Sets the delay in milliseconds between automatic plot refreshes
setPolicy(Policy) - Method in class burlap.behavior.singleagent.auxiliary.valuefunctionvis.PolicyRenderLayer: Sets the policy to render
setPolicy(Policy) - Method in class burlap.behavior.singleagent.auxiliary.valuefunctionvis.ValueFunctionVisualizerGUI: Sets the policy to render
setPolicy(SolverDerivedPolicy) - Method in class burlap.behavior.singleagent.learning.modellearning.artdp.ARTDP: Sets the policy to the provided one.
setPolicy(Policy) - Method in class burlap.behavior.singleagent.options.SubgoalOption
setPolicy(PolicyFromJointPolicy) - Method in class burlap.behavior.stochasticgames.agents.madp.MultiAgentDPPlanningAgent: Sets the policy derived from this agents valueFunction to follow.
setPolicyCount(int) - Method in class burlap.behavior.singleagent.learnfromdemo.apprenticeship.ApprenticeshipLearningRequest
setPolicyToEvaluate(EnumerablePolicy) - Method in class burlap.behavior.singleagent.planning.stochastic.policyiteration.PolicyIteration: Sets the initial policy that will be evaluated when planning with policy iteration begins.
setPolynomialDegree(double) - Method in class burlap.behavior.singleagent.auxiliary.valuefunctionvis.common.LandmarkColorBlendInterpolation: Sets the color blend to raise the normalized distance of values to the given degree.
setPotentialFunction(PotentialFunction) - Method in class burlap.behavior.singleagent.learning.modellearning.rmax.RMaxModel
setPreference(int, double) - Method in class burlap.datastructures.BoltzmannDistribution: Sets the preference for the ith elemnt
setPreferences(double[]) - Method in class burlap.datastructures.BoltzmannDistribution: Sets the input preferences
setProbSucceedTransitionDynamics(double) - Method in class burlap.domain.singleagent.gridworld.GridWorldDomain: Sets the domain to use probabilistic transitions.
setQInitFunction(QFunction) - Method in class burlap.behavior.singleagent.learning.tdmethods.QLearning: Sets how to initialize Q-values for previously unexperienced state-action pairs.
setQSourceMap(Map<Integer, QSourceForSingleAgent>) - Method in class burlap.behavior.stochasticgames.madynamicprogramming.AgentQSourceMap.HashMapAgentQSourceMap: Sets the Q-source hash map to be used.
setQSourceProvider(MultiAgentQSourceProvider) - Method in class burlap.behavior.stochasticgames.madynamicprogramming.MAQSourcePolicy: Sets the MultiAgentQSourceProvider that will be used to define this object's joint policy.
setQSourceProvider(MultiAgentQSourceProvider) - Method in class burlap.behavior.stochasticgames.madynamicprogramming.policies.ECorrelatedQJointPolicy
setQSourceProvider(MultiAgentQSourceProvider) - Method in class burlap.behavior.stochasticgames.madynamicprogramming.policies.EGreedyJointPolicy
setQSourceProvider(MultiAgentQSourceProvider) - Method in class burlap.behavior.stochasticgames.madynamicprogramming.policies.EGreedyMaxWellfare
setQSourceProvider(MultiAgentQSourceProvider) - Method in class burlap.behavior.stochasticgames.madynamicprogramming.policies.EMinMaxPolicy
setQValueInitializer(QFunction) - Method in class burlap.behavior.stochasticgames.agents.naiveq.history.SGQWActionHistoryFactory: Sets the Q-value initialization function that will be used by the agent.
setQValueInitializer(QFunction) - Method in class burlap.behavior.stochasticgames.agents.naiveq.SGNaiveQLAgent
setRandom(Random) - Method in class burlap.datastructures.StochasticTree: Sets the tree to use a specific random object when performing sampling
setRandomGenerator(Random) - Method in class burlap.behavior.policy.RandomPolicy: Sets the random generator used for action selection.
setRandomObject(Random) - Method in class burlap.domain.singleagent.mountaincar.MCRandomStateGenerator: Sets the random object used for generating states
setRefreshDelay(int) - Method in class burlap.behavior.singleagent.auxiliary.performance.PerformancePlotter: sets the delay in milliseconds between automatic refreshes of the plots
setRefreshDelay(int) - Method in class burlap.behavior.stochasticgames.auxiliary.performance.MultiAgentPerformancePlotter: sets the delay in milliseconds between automatic refreshes of the plots
setRenderStyle(PolicyGlyphPainter2D.PolicyGlyphRenderStyle) - Method in class burlap.behavior.singleagent.auxiliary.valuefunctionvis.common.PolicyGlyphPainter2D: Sets the rendering style
setRepaintOnActionInitiation(boolean) - Method in class burlap.mdp.singleagent.common.VisualActionObserver: Sets whether the state-action should be updated when an action is initiated in an Environment via the VisualActionObserver.observeEnvironmentActionInitiation(State, Action) method.
setRepaintStateOnEnvironmentInteraction(boolean) - Method in class burlap.mdp.singleagent.common.VisualActionObserver: Sets whether the state should be updated on environment interactions events (the VisualActionObserver.observeEnvironmentInteraction(burlap.mdp.singleagent.environment.EnvironmentOutcome) or only with state-actions in the VisualActionObserver.observeEnvironmentActionInitiation(State, Action).
setRequest(MLIRLRequest) - Method in class burlap.behavior.singleagent.learnfromdemo.mlirl.MLIRL: Sets the MLIRLRequest object defining the IRL problem.
setReward(int, int, double) - Method in class burlap.domain.singleagent.gridworld.GridWorldRewardFunction: Sets the reward the agent will receive to transitioning to position x, y
setRf(DifferentiableRF) - Method in class burlap.behavior.singleagent.learnfromdemo.mlirl.MLIRLRequest
setRf(RewardFunction) - Method in class burlap.domain.singleagent.blockdude.BlockDude
setRf(RewardFunction) - Method in class burlap.domain.singleagent.blocksworld.BlocksWorld
setRf(RewardFunction) - Method in class burlap.domain.singleagent.cartpole.CartPoleDomain
setRf(RewardFunction) - Method in class burlap.domain.singleagent.cartpole.InvertedPendulum
setRf(RewardFunction) - Method in class burlap.domain.singleagent.frostbite.FrostbiteDomain
setRf(RewardFunction) - Method in class burlap.domain.singleagent.graphdefined.GraphDefinedDomain
setRf(RewardFunction) - Method in class burlap.domain.singleagent.gridworld.GridWorldDomain
setRf(RewardFunction) - Method in class burlap.domain.singleagent.lunarlander.LunarLanderDomain
setRf(RewardFunction) - Method in class burlap.domain.singleagent.mountaincar.MountainCar
setRf(RewardFunction) - Method in class burlap.mdp.singleagent.model.FactoredModel
setRfDim(int) - Method in class burlap.behavior.singleagent.learnfromdemo.mlirl.differentiableplanners.diffvinit.LinearDiffRFVInit
setRfFeaturesAreForNextState(boolean) - Method in class burlap.behavior.singleagent.learnfromdemo.mlirl.differentiableplanners.diffvinit.LinearDiffRFVInit
setRfFvGen(DenseStateFeatures) - Method in class burlap.behavior.singleagent.learnfromdemo.mlirl.differentiableplanners.diffvinit.LinearDiffRFVInit
setRollOutPolicy(Policy) - Method in class burlap.behavior.singleagent.planning.stochastic.rtdp.RTDP: Sets the rollout policy to use.
setRunRolloutsInRevere(boolean) - Method in class burlap.behavior.singleagent.planning.stochastic.rtdp.BoundedRTDP: Sets whether each rollout should be run in reverse after completion.
setS(State) - Method in class burlap.statehashing.WrappedHashableState: Setter for Java Bean serialization purposes.
setSaFeatures(DenseStateActionFeatures) - Method in class burlap.behavior.singleagent.learning.lspi.LSPI: Sets the state-action features to used
setSamples(List<State>) - Method in class burlap.behavior.singleagent.planning.vfa.fittedvi.FittedVI: Sets the state samples to which the value function will be fit.
setScale(int) - Method in class burlap.domain.singleagent.frostbite.FrostbiteDomain
setScale(int) - Method in class burlap.domain.singleagent.frostbite.FrostbiteModel
setSemiWallPassableProbability(double) - Method in class burlap.domain.stochasticgames.gridgame.GridGame: Sets the probability that an agent can pass through a semi-wall.
setSetRenderLayer(StateRenderLayer) - Method in class burlap.visualizer.Visualizer
setSignificanceForCI(double) - Method in class burlap.behavior.singleagent.auxiliary.performance.PerformancePlotter: Sets the significance used for confidence intervals.
setSignificanceForCI(double) - Method in class burlap.behavior.stochasticgames.auxiliary.performance.MultiAgentPerformancePlotter: Sets the significance used for confidence intervals.
setSoftTieRenderStyleDelta(double) - Method in class burlap.behavior.singleagent.auxiliary.valuefunctionvis.common.PolicyGlyphPainter2D: Sets the soft difference between max actions to determine ties when the MAXACTIONSOFSOFTTIE render style is used.
setSolver(MDPSolverInterface) - Method in class burlap.behavior.policy.BoltzmannQPolicy
setSolver(MDPSolverInterface) - Method in class burlap.behavior.policy.EpsilonGreedy
setSolver(MDPSolverInterface) - Method in class burlap.behavior.policy.GreedyDeterministicQPolicy
setSolver(MDPSolverInterface) - Method in class burlap.behavior.policy.GreedyQPolicy
setSolver(MDPSolverInterface) - Method in interface burlap.behavior.policy.SolverDerivedPolicy: Sets the valueFunction whose results affect this policy.
setSolver(MDPSolverInterface) - Method in class burlap.behavior.singleagent.planning.deterministic.DDPlannerPolicy
setSolver(MDPSolverInterface) - Method in class burlap.behavior.singleagent.planning.deterministic.SDPlannerPolicy
setSolver(MDPSolverInterface) - Method in class burlap.behavior.singleagent.planning.stochastic.montecarlo.uct.UCTTreeWalkPolicy
setSourceModel(KWIKModel) - Method in class burlap.behavior.singleagent.learning.modellearning.rmax.RMaxModel
setSparseStateFeatures(SparseStateFeatures) - Method in class burlap.behavior.functionapproximation.dense.SparseToDenseFeatures
setSpp(StatePolicyPainter) - Method in class burlap.behavior.singleagent.auxiliary.valuefunctionvis.PolicyRenderLayer: Sets the state-wise policy painter
setSpp(StatePolicyPainter) - Method in class burlap.behavior.singleagent.auxiliary.valuefunctionvis.ValueFunctionVisualizerGUI: Sets the state-wise policy painter
setStartStateGenerator(StateGenerator) - Method in class burlap.behavior.singleagent.learnfromdemo.apprenticeship.ApprenticeshipLearningRequest
setStateActionRenderLayer(StateActionRenderLayer, boolean) - Method in class burlap.visualizer.Visualizer: Adds a StateActionRenderLayer to this Visualizer.
setStateContext(State) - Method in interface burlap.mdp.auxiliary.stateconditiontest.StateConditionTestIterable
setStateEnumerator(StateEnumerator) - Method in class burlap.mdp.singleagent.pomdp.beliefstate.TabularBeliefState
setStateEnumerator(StateEnumerator) - Method in class burlap.mdp.singleagent.pomdp.beliefstate.TabularBeliefUpdate
setStateEnumerator(StateEnumerator) - Method in class burlap.mdp.singleagent.pomdp.PODomain: Sets the StateEnumerator used by this domain to enumerate all underlying MDP states.
setStateFeatures(DenseStateFeatures) - Method in class burlap.behavior.functionapproximation.dense.DenseCrossProductFeatures
setStateGenerator(StateGenerator) - Method in class burlap.mdp.singleagent.environment.SimulatedEnvironment
setStateModel(SampleStateModel) - Method in class burlap.mdp.singleagent.model.FactoredModel
setStateSelectionMode(BoundedRTDP.StateSelectionMode) - Method in class burlap.behavior.singleagent.planning.stochastic.rtdp.BoundedRTDP: Sets the state selection mode used when choosing next states to expand.
setStatesToVisualize(Collection<State>) - Method in class burlap.behavior.singleagent.auxiliary.valuefunctionvis.PolicyRenderLayer: Sets the states to visualize
setStatesToVisualize(Collection<State>) - Method in class burlap.behavior.singleagent.auxiliary.valuefunctionvis.ValueFunctionRenderLayer: Sets the states to visualize
setStoredAbstraction(StateMapping) - Method in class burlap.behavior.stochasticgames.agents.naiveq.SGNaiveQFactory: Sets the factory to provide Q-learning algorithms with the given state abstraction.
setStoredMapAbstraction(StateMapping) - Method in class burlap.behavior.stochasticgames.agents.naiveq.SGNaiveQLAgent: Sets the state abstraction that this agent will use
setStrategy(Policy) - Method in class burlap.behavior.stochasticgames.agents.naiveq.SGNaiveQLAgent: Sets the Q-learning policy that this agent will use (e.g., epsilon greedy)
SetStrategyAgentFactory(SGDomain, Policy) - Constructor for class burlap.behavior.stochasticgames.agents.SetStrategySGAgent.SetStrategyAgentFactory
SetStrategySGAgent - Class in burlap.behavior.stochasticgames.agents: A class for an agent who makes decisions by following a specified strategy and does not respond to the other player's actions.
SetStrategySGAgent(SGDomain, Policy, String, SGAgentType) - Constructor for class burlap.behavior.stochasticgames.agents.SetStrategySGAgent: Initializes for the given domain in which the agent will play and the strategy that they will follow.
SetStrategySGAgent.SetStrategyAgentFactory - Class in burlap.behavior.stochasticgames.agents
setSvp(StateValuePainter) - Method in class burlap.behavior.singleagent.auxiliary.valuefunctionvis.ValueFunctionRenderLayer: Sets the state-wise value function painter
setSvp(StateValuePainter) - Method in class burlap.behavior.singleagent.auxiliary.valuefunctionvis.ValueFunctionVisualizerGUI: Sets the state-wise value function painter
setSynchronizeJointActionSelectionAmongAgents(boolean) - Method in class burlap.behavior.stochasticgames.PolicyFromJointPolicy: Sets whether actions selection of this agent's policy should be synchronized with the action selection of other agents following the same underlying joint policy.
setTargetAgent(int) - Method in class burlap.behavior.stochasticgames.JointPolicy: Sets the target privileged agent from which this joint policy is defined.
setTargetAgent(int) - Method in class burlap.behavior.stochasticgames.madynamicprogramming.policies.ECorrelatedQJointPolicy
setTargetAgent(int) - Method in class burlap.behavior.stochasticgames.madynamicprogramming.policies.EGreedyJointPolicy
setTargetAgent(int) - Method in class burlap.behavior.stochasticgames.madynamicprogramming.policies.EGreedyMaxWellfare
setTargetAgent(int) - Method in class burlap.behavior.stochasticgames.madynamicprogramming.policies.EMinMaxPolicy
setTemperature(double) - Method in class burlap.datastructures.BoltzmannDistribution: Sets the temperature value to use.
setTerminalStates(Set<Integer>) - Method in class burlap.domain.singleagent.graphdefined.GraphTF
setTerminateOnTrue(boolean) - Method in class burlap.mdp.auxiliary.common.SinglePFTF: Sets whether to be terminal state it is required for there to be a true grounded version of this class' propositional function or whether it is required for there to be a false grounded version.
setTerminationStates(StateConditionTest) - Method in class burlap.behavior.singleagent.options.SubgoalOption
setTf(TerminalFunction) - Method in class burlap.domain.singleagent.blockdude.BlockDude
setTf(TerminalFunction) - Method in class burlap.domain.singleagent.blocksworld.BlocksWorld
setTf(TerminalFunction) - Method in class burlap.domain.singleagent.cartpole.CartPoleDomain
setTf(TerminalFunction) - Method in class burlap.domain.singleagent.cartpole.InvertedPendulum
setTf(TerminalFunction) - Method in class burlap.domain.singleagent.frostbite.FrostbiteDomain
setTf(TerminalFunction) - Method in class burlap.domain.singleagent.graphdefined.GraphDefinedDomain
setTf(TerminalFunction) - Method in class burlap.domain.singleagent.gridworld.GridWorldDomain
setTf(TerminalFunction) - Method in class burlap.domain.singleagent.lunarlander.LunarLanderDomain
setTf(TerminalFunction) - Method in class burlap.domain.singleagent.mountaincar.MountainCar
setTf(TerminalFunction) - Method in class burlap.mdp.auxiliary.stateconditiontest.TFGoalCondition: Sets the TerminalFunction used to specify the goal condition.
setTf(TerminalFunction) - Method in class burlap.mdp.singleagent.model.FactoredModel
setTheTaskSpec(TaskSpec) - Method in class burlap.behavior.singleagent.interfaces.rlglue.RLGlueDomain
setTHistory(double[]) - Method in class burlap.behavior.singleagent.learnfromdemo.apprenticeship.ApprenticeshipLearningRequest
setToCorrectModel() - Method in class burlap.domain.singleagent.cartpole.CartPoleDomain: Sets to use the correct physics model by Florian.
setToIncorrectClassicModel() - Method in class burlap.domain.singleagent.cartpole.CartPoleDomain: Sets to the use the classic model by Barto, Sutton, and Anderson, which has incorrect friction forces and gravity in the wrong direction
setToIncorrectClassicModelWithCorrectGravity() - Method in class burlap.domain.singleagent.cartpole.CartPoleDomain: Sets to use the classic model by Barto, Sutton, and Anderson which has incorrect friction forces, but will use correct gravity.
setToStandardLunarLander() - Method in class burlap.domain.singleagent.lunarlander.LunarLanderDomain: Sets the domain to use a standard set of physics and with a standard set of two thrust actions.
setTransition(int, int, int, double) - Method in class burlap.domain.singleagent.graphdefined.GraphDefinedDomain: Sets the probability p for transitioning to state node tNode after taking action number action in state node srcNode.
setTransitionDynamics(Map<Integer, Map<Integer, Set<GraphDefinedDomain.NodeTransitionProbability>>>) - Method in class burlap.domain.singleagent.graphdefined.GraphDefinedDomain.GraphStateModel
setTransitionDynamics(double[][]) - Method in class burlap.domain.singleagent.gridworld.GridWorldDomain: Will set the movement direction probabilities based on the action chosen.
setup() - Method in class burlap.testing.TestBlockDude
setup() - Method in class burlap.testing.TestGridWorld
setup() - Method in class burlap.testing.TestHashing
setup() - Method in class burlap.testing.TestPlanning
setUpdater(BeliefUpdate) - Method in class burlap.mdp.singleagent.pomdp.BeliefAgent
setupForNewEpisode() - Method in class burlap.behavior.singleagent.auxiliary.performance.PerformancePlotter.Trial: Completes the last episode and sets up the datastructures for the next episode
setupForNewEpisode() - Method in class burlap.behavior.stochasticgames.auxiliary.performance.MultiAgentPerformancePlotter.Trial: Completes the last episode and sets up the datastructures for the next episode
setUpPlottingConfiguration(int, int, int, int, TrialMode, PerformanceMetric...) - Method in class burlap.behavior.singleagent.auxiliary.performance.LearningAlgorithmExperimenter: Setsup the plotting confiruation.
setUpPlottingConfiguration(int, int, int, int, TrialMode, PerformanceMetric...) - Method in class burlap.behavior.stochasticgames.auxiliary.performance.MultiAgentExperimenter: Setsup the plotting confiruation.
setUseFeatureWiseLearningRate(boolean) - Method in class burlap.behavior.singleagent.learning.tdmethods.vfa.GradientDescentSarsaLam: Sets whether learning rate polls should be based on the VFA state feature ids, or the OO-MDP state.
setUseMaxHeap(boolean) - Method in class burlap.datastructures.HashIndexedHeap: Sets whether this heap is a max heap or a min heap
setUseReplaceTraces(boolean) - Method in class burlap.behavior.singleagent.learning.tdmethods.vfa.GradientDescentSarsaLam: Sets whether to use replacing eligibility traces rather than accumulating traces.
setUseVariableCSize(boolean) - Method in class burlap.behavior.singleagent.learnfromdemo.mlirl.differentiableplanners.DifferentiableSparseSampling: Sets whether the number of state transition samples (C) should be variable with respect to the depth of the node.
setUseVariableCSize(boolean) - Method in class burlap.behavior.singleagent.planning.stochastic.sparsesampling.SparseSampling: Sets whether the number of state transition samples (C) should be variable with respect to the depth of the node.
setUsingMaxMargin(boolean) - Method in class burlap.behavior.singleagent.learnfromdemo.apprenticeship.ApprenticeshipLearningRequest
setV(DifferentiableSparseSampling.QAndQGradient) - Method in class burlap.behavior.singleagent.learnfromdemo.mlirl.differentiableplanners.DifferentiableSparseSampling.DiffStateNode
setValue(HashableState, double) - Method in class burlap.behavior.stochasticgames.madynamicprogramming.MADynamicProgramming.BackupBasedQSource: Sets the value of the state in this objects value function map.
setValueForLeafNodes(ValueFunction) - Method in class burlap.behavior.singleagent.learnfromdemo.mlirl.differentiableplanners.DifferentiableSparseSampling: Sets the ValueFunction object to use for settting the value of leaf nodes.
setValueForLeafNodes(ValueFunction) - Method in class burlap.behavior.singleagent.planning.stochastic.sparsesampling.SparseSampling: Sets the ValueFunction object to use for settting the value of leaf nodes.
setValueFunctionInitialization(ValueFunction) - Method in class burlap.behavior.singleagent.planning.stochastic.DynamicProgramming: Sets the value function initialization to use.
setValueFunctionToLowerBound() - Method in class burlap.behavior.singleagent.planning.stochastic.rtdp.BoundedRTDP: Sets the value function to use to be the lower bound.
setValueFunctionToUpperBound() - Method in class burlap.behavior.singleagent.planning.stochastic.rtdp.BoundedRTDP: Sets the value function to use to be the upper bound.
setValueStringRenderingFormat(int, Color, int, float, float) - Method in class burlap.behavior.singleagent.auxiliary.valuefunctionvis.common.StateValuePainter2D: Sets the rendering format of the string displaying the value of each state.
SetVarCommand - Class in burlap.shell.command.env: A ShellCommand for setting state variables values for the current Environment State.
SetVarCommand() - Constructor for class burlap.shell.command.env.SetVarCommand
SetVarSGCommand - Class in burlap.shell.command.world: A ShellCommand for setting state variables values for the current World State.
SetVarSGCommand() - Constructor for class burlap.shell.command.world.SetVarSGCommand
setVGrad(DifferentiableSparseSampling.QAndQGradient) - Method in class burlap.behavior.singleagent.learnfromdemo.mlirl.differentiableplanners.DifferentiableSparseSampling.DiffStateNode
setVInit(ValueFunction) - Method in class burlap.behavior.singleagent.planning.vfa.fittedvi.FittedVI: Sets the value function initialization used at the start of planning.
setVinitDim(int) - Method in class burlap.behavior.singleagent.learnfromdemo.mlirl.differentiableplanners.diffvinit.LinearDiffRFVInit
setVinitFvGen(DenseStateFeatures) - Method in class burlap.behavior.singleagent.learnfromdemo.mlirl.differentiableplanners.diffvinit.LinearDiffRFVInit
setVisualizer(Visualizer) - Method in class burlap.shell.BurlapShell
setVmax(double) - Method in class burlap.domain.singleagent.lunarlander.LunarLanderDomain.LLPhysicsParams
setVmax(double) - Method in class burlap.domain.singleagent.lunarlander.LunarLanderDomain: Sets the maximum velocity of the agent (the agent cannot move faster than this value).
setVmax(double) - Method in class burlap.domain.singleagent.mountaincar.MCRandomStateGenerator: Sets the maximum velocity that a generated state can have.
setVmin(double) - Method in class burlap.domain.singleagent.mountaincar.MCRandomStateGenerator: Sets the minimum velocity that a generated state can have.
setVRange(double, double) - Method in class burlap.domain.singleagent.mountaincar.MCRandomStateGenerator: Sets the random velocity range that a generated state can have.
setW(World) - Method in class burlap.shell.visual.SGVisualExplorer: Sets the World associated with this visual explorer and shell.
setWelcomeMessage(String) - Method in class burlap.shell.BurlapShell
setWorld(World) - Method in class burlap.behavior.stochasticgames.auxiliary.performance.MultiAgentPerformancePlotter
setWorld(World) - Method in class burlap.shell.SGWorldShell
setWrongDoorReward(double) - Method in class burlap.domain.singleagent.pomdp.tiger.TigerDomain
setXmax(double) - Method in class burlap.domain.singleagent.lunarlander.LunarLanderDomain.LLPhysicsParams
setXmax(double) - Method in class burlap.domain.singleagent.lunarlander.LunarLanderDomain: Sets the maximum x position of the lander (the agent cannot cross this boundary)
setXmax(double) - Method in class burlap.domain.singleagent.mountaincar.MCRandomStateGenerator: Sets the maximum x-value that a generated state can have.
setXmin(double) - Method in class burlap.domain.singleagent.lunarlander.LunarLanderDomain.LLPhysicsParams
setXmin(double) - Method in class burlap.domain.singleagent.lunarlander.LunarLanderDomain: Sets the minimum x position of the lander (the agent cannot cross this boundary)
setXmin(double) - Method in class burlap.domain.singleagent.mountaincar.MCRandomStateGenerator: Sets the minimum x-value that a generated state can have.
setXRange(double, double) - Method in class burlap.domain.singleagent.mountaincar.MCRandomStateGenerator: Sets the random x-value range that a generated state can have.
setXY(int, int) - Method in class burlap.domain.singleagent.blockdude.state.BlockDudeCell
setXYKeys(Object, Object, VariableDomain, VariableDomain, double, double) - Method in class burlap.behavior.singleagent.auxiliary.valuefunctionvis.common.PolicyGlyphPainter2D: Sets the variable keys for the x and y variables in the state and the width of cells along those domains.
setXYKeys(Object, Object, VariableDomain, VariableDomain, double, double) - Method in class burlap.behavior.singleagent.auxiliary.valuefunctionvis.common.StateValuePainter2D: Sets the variable keys for the x and y variables in the state and the width of cells along those domains.
setYmax(double) - Method in class burlap.domain.singleagent.lunarlander.LunarLanderDomain.LLPhysicsParams
setYmax(double) - Method in class burlap.domain.singleagent.lunarlander.LunarLanderDomain: Sets the maximum y position of the lander (the agent cannot cross this boundary)
setYmin(double) - Method in class burlap.domain.singleagent.lunarlander.LunarLanderDomain.LLPhysicsParams
setYmin(double) - Method in class burlap.domain.singleagent.lunarlander.LunarLanderDomain: Sets the minimum y position of the lander (the agent cannot cross this boundary)
sFeatures - Variable in class burlap.behavior.functionapproximation.sparse.SparseCrossProductFeatures
sg - Variable in class burlap.domain.singleagent.pomdp.tiger.TigerModel
SGAgent - Interface in burlap.mdp.stochasticgames.agent: This abstract class defines the the shell code and interface for creating agents that can make decisions in mutli-agent stochastic game worlds.
SGAgentBase - Class in burlap.mdp.stochasticgames.agent
SGAgentBase() - Constructor for class burlap.mdp.stochasticgames.agent.SGAgentBase
SGAgentType - Class in burlap.mdp.stochasticgames.agent: This class specifies the type of agent a stochastic games agent can be.
SGAgentType(String, List<ActionType>) - Constructor for class burlap.mdp.stochasticgames.agent.SGAgentType: Creates a new agent type with a given name, and actions available to the agent.
SGBackupOperator - Interface in burlap.behavior.stochasticgames.madynamicprogramming: A stochastic games backup operator to be used in multi-agent Q-learning or value function planning.
SGDomain - Class in burlap.mdp.stochasticgames: This class is used to define Stochastic Games Domains.
SGDomain() - Constructor for class burlap.mdp.stochasticgames.SGDomain
SGNaiveQFactory - Class in burlap.behavior.stochasticgames.agents.naiveq: An agent factory that produces SGNaiveQLAgents.
SGNaiveQFactory(SGDomain, double, double, double, HashableStateFactory) - Constructor for class burlap.behavior.stochasticgames.agents.naiveq.SGNaiveQFactory: Initializes the factory.
SGNaiveQFactory(SGDomain, double, double, double, HashableStateFactory, StateMapping) - Constructor for class burlap.behavior.stochasticgames.agents.naiveq.SGNaiveQFactory: Initializes the factory.
SGNaiveQLAgent - Class in burlap.behavior.stochasticgames.agents.naiveq: A Tabular Q-learning [1] algorithm for stochastic games formalisms.
SGNaiveQLAgent(SGDomain, double, double, HashableStateFactory) - Constructor for class burlap.behavior.stochasticgames.agents.naiveq.SGNaiveQLAgent: Initializes with a default Q-value of 0 and a 0.1 epsilon greedy policy/strategy
SGNaiveQLAgent(SGDomain, double, double, double, HashableStateFactory) - Constructor for class burlap.behavior.stochasticgames.agents.naiveq.SGNaiveQLAgent: Initializes with a default 0.1 epsilon greedy policy/strategy
SGNaiveQLAgent(SGDomain, double, double, QFunction, HashableStateFactory) - Constructor for class burlap.behavior.stochasticgames.agents.naiveq.SGNaiveQLAgent: Initializes with a default 0.1 epsilon greedy policy/strategy
SGQWActionHistory - Class in burlap.behavior.stochasticgames.agents.naiveq.history: A Tabular Q-learning [1] algorithm for stochastic games formalisms that augments states with the actions each agent took in n previous time steps.
SGQWActionHistory(SGDomain, double, double, HashableStateFactory, int) - Constructor for class burlap.behavior.stochasticgames.agents.naiveq.history.SGQWActionHistory: Initializes the learning algorithm using 0.1 epsilon greedy learning strategy/policy
SGQWActionHistoryFactory - Class in burlap.behavior.stochasticgames.agents.naiveq.history: An agent factory for Q-learning with history agents.
SGQWActionHistoryFactory(SGDomain, double, double, HashableStateFactory, int) - Constructor for class burlap.behavior.stochasticgames.agents.naiveq.history.SGQWActionHistoryFactory: Initializes the factory
SGVisualExplorer - Class in burlap.shell.visual: This class allows you act as all of the agents in a stochastic game (controlled by a World object) by choosing actions for each of them to take in specific states.
SGVisualExplorer(SGDomain, Visualizer, State) - Constructor for class burlap.shell.visual.SGVisualExplorer: Initializes the data members for the visual explorer.
SGVisualExplorer(SGDomain, Visualizer, State, int, int) - Constructor for class burlap.shell.visual.SGVisualExplorer: Initializes the data members for the visual explorer.
SGVisualExplorer(SGDomain, World, Visualizer, int, int) - Constructor for class burlap.shell.visual.SGVisualExplorer: Initializes the data members for the visual explorer.
SGWorldShell - Class in burlap.shell
SGWorldShell(Domain, InputStream, PrintStream, World) - Constructor for class burlap.shell.SGWorldShell
SGWorldShell(Domain, World) - Constructor for class burlap.shell.SGWorldShell: Creates a SGWorldShell for std in and std out
SGWorldShell(SGDomain, State) - Constructor for class burlap.shell.SGWorldShell: Creates s SGWorldShell with a new world using the domain and using std in and std out.
sh - Variable in class burlap.behavior.singleagent.learning.actorcritic.critics.TDLambda.StateEligibilityTrace: The hashed state with which the eligibility value is associated.
sh - Variable in class burlap.behavior.singleagent.learning.tdmethods.SarsaLam.EligibilityTrace: The state for this trace
sh - Variable in class burlap.behavior.singleagent.planning.stochastic.rtdp.BoundedRTDP.StateSelectionAndExpectedGap: The selected state
sh - Variable in class burlap.behavior.singleagent.planning.stochastic.sparsesampling.SparseSampling.HashedHeightState: The hashed state
sh - Variable in class burlap.behavior.singleagent.planning.stochastic.valueiteration.PrioritizedSweeping.BPTRNode
ShallowCopyState - Annotation Type in burlap.mdp.core.state.annotations: A marker for State implementations that indicates that their copy operation is shallow.
ShallowIdentityStateMapping - Class in burlap.mdp.auxiliary.common: A StateAbstraction class the input state without copying it.
ShallowIdentityStateMapping() - Constructor for class burlap.mdp.auxiliary.common.ShallowIdentityStateMapping
shape - Variable in class burlap.domain.singleagent.gridworld.GridWorldVisualizer.CellPainter
ShapedRewardFunction - Class in burlap.behavior.singleagent.shaping: This abstract class is used to define shaped reward functions.
ShapedRewardFunction(RewardFunction) - Constructor for class burlap.behavior.singleagent.shaping.ShapedRewardFunction: Initializes with the base objective task reward function.
shell - Variable in class burlap.shell.visual.SGVisualExplorer
shell - Variable in class burlap.shell.visual.VisualExplorer
ShellCommand - Interface in burlap.shell.command: An interface for implementing shell commands.
ShellCommandEvent(String, ShellCommand, int) - Constructor for class burlap.shell.ShellObserver.ShellCommandEvent: Initializes.
ShellObserver - Interface in burlap.shell: An interface that allows an object to receive messages about BurlapShell ShellCommand execution completion.
ShellObserver.ShellCommandEvent - Class in burlap.shell: Stores information about a command event in various public data members.
shouldDecomposeOptions - Variable in class burlap.behavior.singleagent.learning.tdmethods.QLearning: Whether options should be decomposed into actions in the returned Episode objects.
shouldDecomposeOptions - Variable in class burlap.behavior.singleagent.learning.tdmethods.vfa.GradientDescentSarsaLam: Whether options should be decomposed into actions in the returned Episode objects.
shouldRereunPolicyIteration(Episode) - Method in class burlap.behavior.singleagent.learning.lspi.LSPI: Returns whether LSPI should be rereun given the latest learning episode results.
shouldRescaleValues - Variable in class burlap.behavior.singleagent.auxiliary.valuefunctionvis.StateValuePainter: Indicates whether this painter should scale its rendering of values to whatever it is told the minimum and maximum values are.
showPolicy - Variable in class burlap.behavior.singleagent.auxiliary.valuefunctionvis.ValueFunctionVisualizerGUI: The button to enable the visualization of the policy
shuffleGroundedActions(List<Action>, int, int) - Method in class burlap.behavior.singleagent.planning.deterministic.uninformed.dfs.DFS: Shuffles the order of actions on the index range [s, e)
significance - Variable in class burlap.behavior.singleagent.auxiliary.performance.PerformancePlotter: the significance level used for confidence intervals.
significance - Variable in class burlap.behavior.stochasticgames.auxiliary.performance.MultiAgentPerformancePlotter: the significance level used for confidence intervals.
SimpleAction - Class in burlap.mdp.core.action: A simple implementation of Action for unparameterized actions.
SimpleAction() - Constructor for class burlap.mdp.core.action.SimpleAction
SimpleAction(String) - Constructor for class burlap.mdp.core.action.SimpleAction
SimpleHashableStateFactory - Class in burlap.statehashing.simple: A straightforward factory for creating HashableState objects from State instances.
SimpleHashableStateFactory() - Constructor for class burlap.statehashing.simple.SimpleHashableStateFactory: Default constructor: object identifier independent and no hash code caching.
SimpleHashableStateFactory(boolean) - Constructor for class burlap.statehashing.simple.SimpleHashableStateFactory: Initializes with no hash code caching.
SimulatedEnvironment - Class in burlap.mdp.singleagent.environment: An Environment that simulates interactions using a SampleModel that is provided in an input domain.
SimulatedEnvironment(SADomain) - Constructor for class burlap.mdp.singleagent.environment.SimulatedEnvironment
SimulatedEnvironment(SADomain, State) - Constructor for class burlap.mdp.singleagent.environment.SimulatedEnvironment
SimulatedEnvironment(SADomain, StateGenerator) - Constructor for class burlap.mdp.singleagent.environment.SimulatedEnvironment
SimulatedEnvironment(SampleModel) - Constructor for class burlap.mdp.singleagent.environment.SimulatedEnvironment
SimulatedEnvironment(SampleModel, State) - Constructor for class burlap.mdp.singleagent.environment.SimulatedEnvironment
SimulatedEnvironment(SampleModel, StateGenerator) - Constructor for class burlap.mdp.singleagent.environment.SimulatedEnvironment
SimulatedPOEnvironment - Class in burlap.mdp.singleagent.pomdp: An Environment specifically for simulating interaction with a POMDP environments (PODomain).
SimulatedPOEnvironment(PODomain) - Constructor for class burlap.mdp.singleagent.pomdp.SimulatedPOEnvironment
SimulatedPOEnvironment(PODomain, State) - Constructor for class burlap.mdp.singleagent.pomdp.SimulatedPOEnvironment
SimulatedPOEnvironment(PODomain, StateGenerator) - Constructor for class burlap.mdp.singleagent.pomdp.SimulatedPOEnvironment
sIndex - Variable in class burlap.behavior.stochasticgames.agents.naiveq.history.HistoryState
SingleGoalPFRF - Class in burlap.mdp.singleagent.common: This class defines a reward function that returns a goal reward when any grounded form of a propositional function is true in the resulting state and a default non-goal reward otherwise.
SingleGoalPFRF(PropositionalFunction) - Constructor for class burlap.mdp.singleagent.common.SingleGoalPFRF: Initializes the reward function to return 1 when any grounded from of pf is true in the resulting state.
SingleGoalPFRF(PropositionalFunction, double, double) - Constructor for class burlap.mdp.singleagent.common.SingleGoalPFRF: Initializes the reward function to return the specified goal reward when any grounded from of pf is true in the resulting state and the specified non-goal reward otherwise.
SinglePFSCT - Class in burlap.mdp.auxiliary.stateconditiontest: A state condition class that returns true when ever any grounded version of a specified propositional function is true in a state.
SinglePFSCT(PropositionalFunction) - Constructor for class burlap.mdp.auxiliary.stateconditiontest.SinglePFSCT: Initializes with the propositional function that is checked for state satisfaction
SinglePFTF - Class in burlap.mdp.auxiliary.common: This class defines a terminal function that terminates in states where there exists a grounded version of a specified propositional function that is true in the state or alternatively, when there is a grounded version that is false in the state.
SinglePFTF(PropositionalFunction) - Constructor for class burlap.mdp.auxiliary.common.SinglePFTF: Initializes the propositional function that will cause the state to be terminal when any Grounded version of pf is true.
SinglePFTF(PropositionalFunction, boolean) - Constructor for class burlap.mdp.auxiliary.common.SinglePFTF: Initializes the propositional function that will cause the state to be terminal when any Grounded version of pf is true or alternatively false.
SingleStageNormalFormGame - Class in burlap.domain.stochasticgames.normalform: This stochastic game domain generator provides methods to create N-player single stage games.
SingleStageNormalFormGame(String[][], double[][][]) - Constructor for class burlap.domain.stochasticgames.normalform.SingleStageNormalFormGame: A constructor for bimatrix games with specified action names.
SingleStageNormalFormGame(double[][], double[][]) - Constructor for class burlap.domain.stochasticgames.normalform.SingleStageNormalFormGame: A constructor for a bimatrix game where the row player payoffs and column player payoffs are provided in two different 2D double matrices.
SingleStageNormalFormGame(double[][]) - Constructor for class burlap.domain.stochasticgames.normalform.SingleStageNormalFormGame: A constructor for a bimatrix zero sum game.
SingleStageNormalFormGame(String[][]) - Constructor for class burlap.domain.stochasticgames.normalform.SingleStageNormalFormGame: A constructor for games with a symmetric number of actions for each player.
SingleStageNormalFormGame(List<List<String>>) - Constructor for class burlap.domain.stochasticgames.normalform.SingleStageNormalFormGame: A constructor for games with an asymmetric number of actions for each player.
SingleStageNormalFormGame.ActionNameMap - Class in burlap.domain.stochasticgames.normalform: A wrapper for a HashMap from strings to ints used to map action names to their action index.
SingleStageNormalFormGame.AgentPayoutFunction - Class in burlap.domain.stochasticgames.normalform: A class for defining a payout function for a single agent for each possible strategy profile.
SingleStageNormalFormGame.MatrixAction - Class in burlap.domain.stochasticgames.normalform
SingleStageNormalFormGame.SingleStageNormalFormJointRewardFunction - Class in burlap.domain.stochasticgames.normalform: A Joint Reward Function class that uses the parent domain generators payout matrix to determine payouts for any given strategy profile.
SingleStageNormalFormGame.StrategyProfile - Class in burlap.domain.stochasticgames.normalform: A strategy profile represented as an array of action indices that is hashable.
SingleStageNormalFormJointRewardFunction(int, SingleStageNormalFormGame.ActionNameMap[], SingleStageNormalFormGame.AgentPayoutFunction[]) - Constructor for class burlap.domain.stochasticgames.normalform.SingleStageNormalFormGame.SingleStageNormalFormJointRewardFunction
size() - Method in class burlap.behavior.singleagent.learning.lspi.SARSData: The number of SARS tuples stored.
size() - Method in class burlap.datastructures.HashedAggregator: Returns the number of keys stored.
size - Variable in class burlap.datastructures.HashIndexedHeap: Number of objects in the heap
size() - Method in class burlap.datastructures.HashIndexedHeap: Returns the size of the heap
size() - Method in class burlap.datastructures.StochasticTree: Returns the number of objects in this tree
size - Variable in class burlap.domain.singleagent.frostbite.state.FrostbitePlatform
size() - Method in class burlap.domain.stochasticgames.normalform.SingleStageNormalFormGame.ActionNameMap
size() - Method in class burlap.mdp.stochasticgames.JointAction: Returns the number of actions in this joint action.
SoftmaxOperator - Class in burlap.behavior.singleagent.planning.stochastic.dpoperator: A softmax/Boltzmann operator.
SoftmaxOperator() - Constructor for class burlap.behavior.singleagent.planning.stochastic.dpoperator.SoftmaxOperator: Initializes with beta = 1.0
SoftmaxOperator(double) - Constructor for class burlap.behavior.singleagent.planning.stochastic.dpoperator.SoftmaxOperator: Initializes.
softTieDelta - Variable in class burlap.behavior.singleagent.auxiliary.valuefunctionvis.common.PolicyGlyphPainter2D: The max probability difference from the most likely action for which an action that is not the most likely will still be rendered under the MAXACTIONSOFTTIE rendering style.
SoftTimeInverseDecayLR - Class in burlap.behavior.learningrate: Implements a learning rate decay schedule where the learning rate at time t is alpha_0 * (n_0 + 1) / (n_0 + t), where alpha_0 is the initial learning rate and n_0 is a parameter.
SoftTimeInverseDecayLR(double, double) - Constructor for class burlap.behavior.learningrate.SoftTimeInverseDecayLR: Initializes with an initial learning rate and decay constant shift for a state independent learning rate.
SoftTimeInverseDecayLR(double, double, double) - Constructor for class burlap.behavior.learningrate.SoftTimeInverseDecayLR: Initializes with an initial learning rate and decay constant shift (n_0) for a state independent learning rate that will decay to a value no smaller than minimumLearningRate
SoftTimeInverseDecayLR(double, double, HashableStateFactory, boolean) - Constructor for class burlap.behavior.learningrate.SoftTimeInverseDecayLR: Initializes with an initial learning rate and decay constant shift (n_0) for a state or state-action (or state feature-action) dependent learning rate.
SoftTimeInverseDecayLR(double, double, double, HashableStateFactory, boolean) - Constructor for class burlap.behavior.learningrate.SoftTimeInverseDecayLR: Initializes with an initial learning rate and decay constant shift (n_0) for a state or state-action (or state feature-action) dependent learning rate that will decay to a value no smaller than minimumLearningRate If this learning rate function is to be used for state state features, rather than states, then the hashing factory can be null;
SoftTimeInverseDecayLR.MutableInt - Class in burlap.behavior.learningrate: A class for storing a mutable int value object
SoftTimeInverseDecayLR.StateWiseTimeIndex - Class in burlap.behavior.learningrate: A class for storing a time index for a state, or a time index for each action for a given state
solve(double[][], double[][]) - Method in class burlap.behavior.stochasticgames.agents.twoplayer.singlestage.equilibriumplayer.BimatrixEquilibriumSolver: Solves and caches the solution for the given bimatrix.
solver - Variable in class burlap.behavior.stochasticgames.agents.twoplayer.singlestage.equilibriumplayer.EquilibriumPlayingSGAgent: The solution concept to be solved for the immediate rewards.
SolverDerivedPolicy - Interface in burlap.behavior.policy: An interface for defining policies that refer to a MDPSolverInterface objects to defined the policy.
solverInit(SADomain, double, HashableStateFactory) - Method in class burlap.behavior.singleagent.MDPSolver
solverInit(SADomain, double, HashableStateFactory) - Method in interface burlap.behavior.singleagent.MDPSolverInterface: Initializes the solver with the common elements.
someGroundingIsTrue(OOState) - Method in class burlap.mdp.core.oo.propositional.PropositionalFunction: Returns true if there existing a GroundedProp for the provided State that is in true in the State.
sortActionsWithOptionsFirst() - Method in class burlap.behavior.singleagent.planning.deterministic.uninformed.dfs.DFS: Reorders the planners action list so that options are in the front of the list.
sourceDomain - Variable in class burlap.behavior.singleagent.learning.modellearning.models.TabularModel: The source actual domain object for which actions will be modeled.
sourceLearningRateFunction - Variable in class burlap.behavior.functionapproximation.dense.fourier.FourierBasisLearningRateWrapper: The source LearningRate function that is queried.
sourceModel - Variable in class burlap.behavior.singleagent.learning.modellearning.rmax.RMaxModel
sourcePolicy - Variable in class burlap.behavior.policy.CachedPolicy: The source policy that gets cached
sourcePolicy - Variable in class burlap.behavior.singleagent.learning.modellearning.rmax.UnmodeledFavoredPolicy
sp - Variable in class burlap.behavior.singleagent.learning.lspi.SARSData.SARS: The next state
span() - Method in class burlap.mdp.core.state.vardomain.VariableDomain: Returns the spanning size of the domain; that is, upper - lower
SparseCrossProductFeatures - Class in burlap.behavior.functionapproximation.sparse: A SparseStateActionFeatures implementation that takes as input a SparseStateFeatures object, and turns it into state-action features taking the cross product of the features with the action set.
SparseCrossProductFeatures(SparseStateFeatures) - Constructor for class burlap.behavior.functionapproximation.sparse.SparseCrossProductFeatures
SparseCrossProductFeatures(SparseStateFeatures, Map<Action, SparseCrossProductFeatures.FeaturesMap>, int) - Constructor for class burlap.behavior.functionapproximation.sparse.SparseCrossProductFeatures
SparseCrossProductFeatures.FeaturesMap - Class in burlap.behavior.functionapproximation.sparse
SparseGradient() - Constructor for class burlap.behavior.functionapproximation.FunctionGradient.SparseGradient: Initializes with the gradient unspecified for any weights.
SparseGradient(int) - Constructor for class burlap.behavior.functionapproximation.FunctionGradient.SparseGradient: Initializes with the gradient unspecified, but reserves space for the given capacity
SparseSampling - Class in burlap.behavior.singleagent.planning.stochastic.sparsesampling: An implementation of the Sparse Sampling (SS) [1] planning algorithm.
SparseSampling(SADomain, double, HashableStateFactory, int, int) - Constructor for class burlap.behavior.singleagent.planning.stochastic.sparsesampling.SparseSampling: Initializes.
SparseSampling.HashedHeightState - Class in burlap.behavior.singleagent.planning.stochastic.sparsesampling: Tuple for a state and its height in a tree that can be hashed for quick retrieval.
SparseSampling.StateNode - Class in burlap.behavior.singleagent.planning.stochastic.sparsesampling: A class for state nodes.
SparseStateActionFeatures - Interface in burlap.behavior.functionapproximation.sparse
sparseStateFeatures - Variable in class burlap.behavior.functionapproximation.dense.SparseToDenseFeatures
sparseStateFeatures - Variable in class burlap.behavior.functionapproximation.sparse.LinearVFA: The state features
SparseStateFeatures - Interface in burlap.behavior.functionapproximation.sparse: An interface for defining a database of state features that can be returned for any given input state or input state-action pair.
SparseToDenseFeatures - Class in burlap.behavior.functionapproximation.dense: A wrapper for turning the features from a SparseStateFeatures into a DenseStateFeatures.
SparseToDenseFeatures(SparseStateFeatures) - Constructor for class burlap.behavior.functionapproximation.dense.SparseToDenseFeatures: Initializes.
specificObjectPainters - Variable in class burlap.visualizer.OOStatePainter: Map of painters that define how to paint specific objects; if an object it appears in both specific and general lists, the specific painter is used
specs() - Method in class burlap.behavior.singleagent.auxiliary.gridset.FlatStateGridder: Returns the set of all grid specs defined.
spp - Variable in class burlap.behavior.singleagent.auxiliary.valuefunctionvis.PolicyRenderLayer: Painter used to visualize the policy
spp - Variable in class burlap.behavior.singleagent.auxiliary.valuefunctionvis.ValueFunctionVisualizerGUI: Painter used to visualize the policy
sprime - Variable in class burlap.behavior.singleagent.learning.actorcritic.CritiqueResult: The state to which the agent transitioned for when it took action a in state s.
sPrimeActionFeatures - Variable in class burlap.behavior.singleagent.learning.lspi.LSPI.SSFeatures: Next state-action features.
src - Variable in class burlap.mdp.auxiliary.common.ConstantStateGenerator
srcAction - Variable in class burlap.behavior.policy.support.AnnotatedAction
srcTerminateStates - Variable in class burlap.behavior.singleagent.options.model.BFSMarkovOptionModel
srender - Variable in class burlap.visualizer.Visualizer: The StateRenderLayer instance for visualizing OO-MDP states.
SSFeatures(double[], double[]) - Constructor for class burlap.behavior.singleagent.learning.lspi.LSPI.SSFeatures: Initializes.
stack(BlocksWorldState, ObjectParameterizedAction) - Method in class burlap.domain.singleagent.blocksworld.BWModel
StackActionType(String) - Constructor for class burlap.domain.singleagent.blocksworld.BlocksWorld.StackActionType
start() - Method in class burlap.debugtools.MyTimer: Starts the timer if it is not running.
start() - Method in class burlap.shell.BurlapShell
startExperiment() - Method in class burlap.behavior.singleagent.auxiliary.performance.LearningAlgorithmExperimenter: Starts the experiment and runs all trails for all agents.
startExperiment() - Method in class burlap.behavior.stochasticgames.auxiliary.performance.MultiAgentExperimenter: Starts the experiment and runs all trails for all agents.
startGUI() - Method in class burlap.behavior.singleagent.auxiliary.performance.PerformancePlotter: Launches the GUI and automatic refresh thread.
startGUI() - Method in class burlap.behavior.stochasticgames.auxiliary.performance.MultiAgentPerformancePlotter: Launches the GUI and automatic refresh thread.
startLiveStatePolling(int) - Method in class burlap.shell.visual.VisualExplorer: Starts a thread that polls this explorer's Environment every msPollDelay milliseconds for its current state and updates the visualizer to that state.
startNewAgent(String) - Method in class burlap.behavior.singleagent.auxiliary.performance.PerformancePlotter: Informs the plotter that data collecton for a new agent should begin.
startNewExperiment() - Method in interface burlap.behavior.singleagent.auxiliary.performance.ExperimentalEnvironment: Tells this Environment that an experiment with a new LearningAgent has begun.
startNewTrial() - Method in class burlap.behavior.singleagent.auxiliary.performance.PerformancePlotter: Informs the plotter that a new trial of the current agent is beginning.
startNewTrial() - Method in class burlap.behavior.stochasticgames.auxiliary.performance.MultiAgentPerformancePlotter.DatasetsAndTrials: Creates a new trial object and adds it to the end of the list of trials.
startNewTrial() - Method in class burlap.behavior.stochasticgames.auxiliary.performance.MultiAgentPerformancePlotter: Initializes the datastructures for a new trial.
startStateGenerator - Variable in class burlap.behavior.singleagent.learnfromdemo.apprenticeship.ApprenticeshipLearningRequest: The initial state generator that models the initial states from which the expert trajectories were drawn
state(int) - Method in class burlap.behavior.singleagent.Episode: Returns the state observed at time step t.
state - Variable in class burlap.behavior.singleagent.planning.stochastic.montecarlo.uct.UCTStateNode: The (hashed) state this node wraps
state(int) - Method in class burlap.behavior.stochasticgames.GameEpisode: Returns the state stored at time step t where t=0 refers to the initial state
State - Interface in burlap.mdp.core.state: A State instance is used to define the state of an environment or an observation from the environment.
stateActionFeatures - Variable in class burlap.behavior.functionapproximation.sparse.LinearVFA: The State-action features based on the cross product of state features and actions
StateActionRenderLayer - Class in burlap.visualizer: A class for rendering state-action events.
StateActionRenderLayer() - Constructor for class burlap.visualizer.StateActionRenderLayer
stateActionWeights - Variable in class burlap.behavior.functionapproximation.dense.DenseLinearVFA: The function weights when performing Q-value function approximation.
stateActionWeights - Variable in class burlap.behavior.functionapproximation.dense.DenseStateActionLinearVFA: The function weights when performing Q-value function approximation.
StateBelief(State, double) - Constructor for class burlap.mdp.singleagent.pomdp.beliefstate.EnumerableBeliefState.StateBelief: Initializes
stateClass(String) - Method in interface burlap.mdp.core.oo.OODomain: Returns the Java class used to define an OO-MDP object class with the given name.
stateClass(String) - Method in class burlap.mdp.singleagent.oo.OOSADomain
stateClass(String) - Method in class burlap.mdp.stochasticgames.oo.OOSGDomain
stateClasses() - Method in interface burlap.mdp.core.oo.OODomain: Returns the Java classes used to define OO-MDP object classes.
stateClasses() - Method in class burlap.mdp.singleagent.oo.OOSADomain
stateClasses() - Method in class burlap.mdp.stochasticgames.oo.OOSGDomain
stateClassesMap - Variable in class burlap.mdp.singleagent.oo.OOSADomain
stateClassesMap - Variable in class burlap.mdp.stochasticgames.oo.OOSGDomain
StateConditionTest - Interface in burlap.mdp.auxiliary.stateconditiontest: And interface for defining classes that check for certain conditions in states.
StateConditionTestIterable - Interface in burlap.mdp.auxiliary.stateconditiontest: An extension of the StateConditionTest that is iterable.
stateConsole - Variable in class burlap.shell.visual.SGVisualExplorer
stateConsole - Variable in class burlap.shell.visual.VisualExplorer
stateDepthIndex - Variable in class burlap.behavior.singleagent.planning.stochastic.montecarlo.uct.UCT
StateDomain - Interface in burlap.mdp.core.state.vardomain: An interface extension for when a State can specify the numeric domain of one or more of its variables.
StateEligibilityTrace(HashableState, double, TDLambda.VValue) - Constructor for class burlap.behavior.singleagent.learning.actorcritic.critics.TDLambda.StateEligibilityTrace: Initializes with hashed state, eligibility value and the value function value associated with the state.
StateEnumerator - Class in burlap.behavior.singleagent.auxiliary: For some algorithms, it is useful to have an explicit unique state identifier for each possible state and the hashcode of a state cannot reliably give a unique number.
StateEnumerator(Domain, HashableStateFactory) - Constructor for class burlap.behavior.singleagent.auxiliary.StateEnumerator: Constructs
stateEnumerator - Variable in class burlap.mdp.singleagent.pomdp.beliefstate.TabularBeliefState: A state enumerator for determining the index of MDP states in the belief vector.
stateEnumerator - Variable in class burlap.mdp.singleagent.pomdp.beliefstate.TabularBeliefUpdate
stateEnumerator - Variable in class burlap.mdp.singleagent.pomdp.PODomain: The underlying MDP state enumerator
StateFeature - Class in burlap.behavior.functionapproximation.sparse: A class for associating a state feature identifier with a value of that state feature
StateFeature(int, double) - Constructor for class burlap.behavior.functionapproximation.sparse.StateFeature: Initializes.
stateFeatures - Variable in class burlap.behavior.functionapproximation.dense.DenseCrossProductFeatures: The state features
stateFeatures - Variable in class burlap.behavior.functionapproximation.dense.DenseLinearVFA: The state feature vector generator used for linear value function approximation.
stateFlattener - Variable in class burlap.domain.singleagent.rlglue.RLGlueEnvironment: Used to flatten states into a vector representation
stateForId(int) - Method in class burlap.mdp.singleagent.pomdp.beliefstate.TabularBeliefState: Returns the corresponding MDP state for the provided unique identifier.
stateFromObservation(Observation) - Static method in class burlap.behavior.singleagent.interfaces.rlglue.RLGlueDomain: Creates a BURLAP State from a RLGlue Observation.
stateGenerator - Variable in class burlap.domain.singleagent.rlglue.RLGlueEnvironment: The state generator for generating states for each episode
StateGenerator - Interface in burlap.mdp.auxiliary: An interface for generating State objects.
stateGenerator - Variable in class burlap.mdp.singleagent.environment.SimulatedEnvironment: The state generator used to generate new states when the environment is reset with SimulatedEnvironment.resetEnvironment();
stateHash(State) - Method in class burlap.behavior.singleagent.MDPSolver: A shorthand method for hashing a state.
stateHash - Variable in class burlap.behavior.stochasticgames.agents.naiveq.history.SGQWActionHistoryFactory: The state hashing factory the Q-learning algorithm will use
stateHash - Variable in class burlap.behavior.stochasticgames.agents.naiveq.SGNaiveQFactory: The state hashing factory the Q-learning algorithm will use
stateHash(State) - Method in class burlap.behavior.stochasticgames.agents.naiveq.SGNaiveQLAgent: First abstracts state s, and then returns the HashableState object for the abstracted state.
StateMapping - Interface in burlap.mdp.auxiliary: A state mapping interface that maps one state into another state.
stateModel - Variable in class burlap.mdp.singleagent.model.FactoredModel
StateNode(HashableState, int) - Constructor for class burlap.behavior.singleagent.planning.stochastic.sparsesampling.SparseSampling.StateNode: Creates a node for the given hased state at the given height
stateNodeConstructor - Variable in class burlap.behavior.singleagent.planning.stochastic.montecarlo.uct.UCT
stateNodes - Variable in class burlap.behavior.singleagent.learning.modellearning.models.TabularModel: A mapping from (hashed) states to state nodes that store transition statistics
StatePainter - Interface in burlap.visualizer: This class paints general properties of a state/domain that may not be represented by any specific object instance data.
statePainters - Variable in class burlap.visualizer.StateRenderLayer: list of static painters that pain static non-object defined properties of the domain
StatePolicyPainter - Interface in burlap.behavior.singleagent.auxiliary.valuefunctionvis: An interface for painting a representation of the policy for a specific state onto a 2D Graphics context.
StateReachability - Class in burlap.behavior.singleagent.auxiliary: This class provides methods for finding the set of reachable states from a source state.
StateReference() - Constructor for class burlap.behavior.singleagent.interfaces.rlglue.RLGlueAgent.StateReference
StateReference() - Constructor for class burlap.behavior.stochasticgames.agents.interfacing.singleagent.LearningAgentToSGAgentInterface.StateReference
StateRenderLayer - Class in burlap.visualizer: This class provides 2D visualization of states by being provided a set of state painters to iteratively call to paint ono the canvas.
StateRenderLayer() - Constructor for class burlap.visualizer.StateRenderLayer
stateRepresentations - Variable in class burlap.behavior.stochasticgames.agents.naiveq.SGNaiveQLAgent: A map from hashed states to the internal state representation for the states stored in the q-table.
states - Variable in class burlap.behavior.stochasticgames.GameEpisode: The sequence of states
states - Variable in class burlap.behavior.stochasticgames.madynamicprogramming.dpplanners.MAValueIteration: The set of states that have been found
StateSelectionAndExpectedGap(HashableState, double) - Constructor for class burlap.behavior.singleagent.planning.stochastic.rtdp.BoundedRTDP.StateSelectionAndExpectedGap: Initializes.
statesEqual(State, State) - Method in class burlap.statehashing.simple.IDSimpleHashableState: Returns true if the two input states are equal.
statesEqual(State, State) - Method in class burlap.statehashing.simple.IISimpleHashableState: Returns true if the two input states are equal.
stateSequence - Variable in class burlap.behavior.singleagent.Episode: The sequence of states observed
StateSettableEnvironment - Interface in burlap.mdp.singleagent.environment.extensions: An interface to be used with Environment instances that allows the environment to have its set set to a client specified state.
StateSettableEnvironmentServer(StateSettableEnvironment, EnvironmentObserver...) - Constructor for class burlap.mdp.singleagent.environment.extensions.EnvironmentServer.StateSettableEnvironmentServer
statesToStateNodes - Variable in class burlap.behavior.singleagent.planning.stochastic.montecarlo.uct.UCT
statesToVisualize - Variable in class burlap.behavior.singleagent.auxiliary.valuefunctionvis.PolicyRenderLayer: The states to visualize
statesToVisualize - Variable in class burlap.behavior.singleagent.auxiliary.valuefunctionvis.ValueFunctionRenderLayer: The states to visualize
statesToVisualize - Variable in class burlap.behavior.singleagent.auxiliary.valuefunctionvis.ValueFunctionVisualizerGUI
StateTimeElibilityTrace(HashableState, int, double, TDLambda.VValue) - Constructor for class burlap.behavior.singleagent.learning.actorcritic.critics.TimeIndexedTDLambda.StateTimeElibilityTrace: Initializes with hashed state, eligibility value, time/depth of the state, and the value function value associated with the state.
stateToString(State) - Static method in class burlap.mdp.core.state.StateUtilities: A standard method for turning an arbitrary State into a String representation.
StateTransitionProb - Class in burlap.mdp.core: A tuple for a State and a double specifying the probability of transitioning to that state.
StateTransitionProb() - Constructor for class burlap.mdp.core.StateTransitionProb
StateTransitionProb(State, double) - Constructor for class burlap.mdp.core.StateTransitionProb
stateTransitions(State, Action) - Method in class burlap.domain.singleagent.blockdude.BlockDudeModel
stateTransitions(State, Action) - Method in class burlap.domain.singleagent.blocksworld.BWModel
stateTransitions(State, Action) - Method in class burlap.domain.singleagent.cartpole.model.CPClassicModel
stateTransitions(State, Action) - Method in class burlap.domain.singleagent.cartpole.model.CPCorrectModel
stateTransitions(State, Action) - Method in class burlap.domain.singleagent.cartpole.model.IPModel
stateTransitions(State, Action) - Method in class burlap.domain.singleagent.frostbite.FrostbiteModel
stateTransitions(State, Action) - Method in class burlap.domain.singleagent.graphdefined.GraphDefinedDomain.GraphStateModel
stateTransitions(State, Action) - Method in class burlap.domain.singleagent.gridworld.GridWorldDomain.GridWorldModel
stateTransitions(State, Action) - Method in class burlap.domain.singleagent.lunarlander.LunarLanderModel
stateTransitions(State, Action) - Method in class burlap.domain.singleagent.mountaincar.MountainCar.MCModel
stateTransitions(State, JointAction) - Method in class burlap.domain.stochasticgames.gridgame.GridGameStandardMechanics
stateTransitions(State, Action) - Method in interface burlap.mdp.singleagent.model.statemodel.FullStateModel: Returns the set of possible transitions when Action is applied in State s.
stateTransitions(State, JointAction) - Method in class burlap.mdp.stochasticgames.common.StaticRepeatedGameModel
stateTransitions(State, JointAction) - Method in interface burlap.mdp.stochasticgames.model.FullJointModel: Returns the transition probabilities for applying the provided JointAction action in the given state.
stateTransitionsModeled(KWIKModel, List<ActionType>, State) - Static method in class burlap.behavior.singleagent.learning.modellearning.KWIKModel.Helper
StateUtilities - Class in burlap.mdp.core.state: A class with static methods for common tasks with states.
StateValuePainter - Class in burlap.behavior.singleagent.auxiliary.valuefunctionvis: An abstract class for defining the interface and common methods to paint the representation of the value function for a specific state onto a 2D graphics context.
StateValuePainter() - Constructor for class burlap.behavior.singleagent.auxiliary.valuefunctionvis.StateValuePainter
StateValuePainter2D - Class in burlap.behavior.singleagent.auxiliary.valuefunctionvis.common: A class for rendering the value of states as colored 2D cells on the canvas.
StateValuePainter2D() - Constructor for class burlap.behavior.singleagent.auxiliary.valuefunctionvis.common.StateValuePainter2D: Initializes using a LandmarkColorBlendInterpolation object that mixes from red (lowest value) to blue (highest value).
StateValuePainter2D(ColorBlend) - Constructor for class burlap.behavior.singleagent.auxiliary.valuefunctionvis.common.StateValuePainter2D: Initializes the value painter.
stateWeights - Variable in class burlap.behavior.functionapproximation.dense.DenseLinearVFA: The function weights when performing state value function approximation.
StateWiseLearningRate() - Constructor for class burlap.behavior.learningrate.ExponentialDecayLR.StateWiseLearningRate
stateWiseMap - Variable in class burlap.behavior.learningrate.ExponentialDecayLR: The state dependent or state-action dependent learning rates
stateWiseMap - Variable in class burlap.behavior.learningrate.SoftTimeInverseDecayLR: The state dependent or state-action dependent learning rate time indices
StateWiseTimeIndex() - Constructor for class burlap.behavior.learningrate.SoftTimeInverseDecayLR.StateWiseTimeIndex
StaticDomainPainter - Interface in burlap.behavior.singleagent.auxiliary.valuefunctionvis: An interface for painting general domain information to a 2D graphics context.
StaticRepeatedGameModel - Class in burlap.mdp.stochasticgames.common: This action model can be used to take a single stage game, and cause it to repeat itself.
StaticRepeatedGameModel() - Constructor for class burlap.mdp.stochasticgames.common.StaticRepeatedGameModel
StaticWeightedAStar - Class in burlap.behavior.singleagent.planning.deterministic.informed.astar: Statically weighted A* [1] implementation.
StaticWeightedAStar(SADomain, StateConditionTest, HashableStateFactory, Heuristic, double) - Constructor for class burlap.behavior.singleagent.planning.deterministic.informed.astar.StaticWeightedAStar: Initializes.
stepEpisode - Variable in class burlap.behavior.singleagent.auxiliary.performance.PerformancePlotter.Trial: Stores the steps by episode
stepEpisode - Variable in class burlap.behavior.stochasticgames.auxiliary.performance.MultiAgentPerformancePlotter.Trial: Stores the steps by episode
stepEpisodeSeries - Variable in class burlap.behavior.singleagent.auxiliary.performance.PerformancePlotter.AgentDatasets: Most recent trial's steps per step episode data
stepEpisodeSeries - Variable in class burlap.behavior.stochasticgames.auxiliary.performance.MultiAgentPerformancePlotter.AgentDatasets: Most recent trial's steps per step episode data
stepIncrement(double) - Method in class burlap.behavior.singleagent.auxiliary.performance.PerformancePlotter.Trial: Updates all datastructures with the reward received from the last step
stepIncrement(double) - Method in class burlap.behavior.stochasticgames.auxiliary.performance.MultiAgentPerformancePlotter.Trial: Updates all datastructures with the reward received from the last step
stepSize - Variable in class burlap.domain.singleagent.frostbite.FrostbiteModel
STNode(T, double, StochasticTree<T>.STNode) - Constructor for class burlap.datastructures.StochasticTree.STNode: Initializes a leaf node with the given weight and parent
STNode(double) - Constructor for class burlap.datastructures.StochasticTree.STNode: Initializes a node with a weight only
STNode(double, StochasticTree<T>.STNode) - Constructor for class burlap.datastructures.StochasticTree.STNode: Initializes a node with a given weight and parent node
StochasticTree<T> - Class in burlap.datastructures: A class for performing sampling of a set of objects at O(lg(n)) time.
StochasticTree() - Constructor for class burlap.datastructures.StochasticTree: Initializes with an empty tree.
StochasticTree(List<Double>, List<T>) - Constructor for class burlap.datastructures.StochasticTree: Initializes a tree for objects with the given weights
StochasticTree.STNode - Class in burlap.datastructures: A class for storing a stochastic tree node.
stop() - Method in class burlap.debugtools.MyTimer: Stops the timer.
stopLivePolling() - Method in class burlap.shell.visual.VisualExplorer: Stops this class from live polling this explorer's Environment.
stopPlanning() - Method in class burlap.behavior.singleagent.planning.stochastic.montecarlo.uct.UCT: Returns true if rollouts and planning should cease.
stopReachabilityFromTerminalStates - Variable in class burlap.behavior.singleagent.learnfromdemo.mlirl.differentiableplanners.DifferentiableVI: When the reachability analysis to find the state space is performed, a breadth first search-like pass (spreading over all stochastic transitions) is performed.
stopReachabilityFromTerminalStates - Variable in class burlap.behavior.singleagent.planning.stochastic.valueiteration.ValueIteration: When the reachability analysis to find the state space is performed, a breadth first search-like pass (spreading over all stochastic transitions) is performed.
storage - Variable in class burlap.datastructures.HashedAggregator: The backing hash map
storedAbstraction - Variable in class burlap.behavior.stochasticgames.agents.naiveq.SGNaiveQFactory: The state abstract the Q-learning algorithm will use
storedMapAbstraction - Variable in class burlap.behavior.stochasticgames.agents.naiveq.SGNaiveQLAgent: A state abstraction to use.
storedQ(State, Action) - Method in class burlap.behavior.stochasticgames.agents.naiveq.SGNaiveQLAgent
StrategyProfile(int...) - Constructor for class burlap.domain.stochasticgames.normalform.SingleStageNormalFormGame.StrategyProfile
stringOrBoolean(Object) - Static method in class burlap.mdp.core.state.StateUtilities: Takes an input object, typically value to which a variable should be set, that is either a String representation of a boolean, or a Boolean, and returns the corresponding Boolean.
stringOrNumber(Object) - Static method in class burlap.mdp.core.state.StateUtilities: Takes an input object, typically value to which a variable should be set, that is either a String representation of a number, or a Number, and returns the corresponding Number.
SubDifferentiableMaxOperator - Class in burlap.behavior.singleagent.learnfromdemo.mlirl.differentiableplanners.dpoperator: Provides the sub gradient of the BellmanOperator max operator.
SubDifferentiableMaxOperator() - Constructor for class burlap.behavior.singleagent.learnfromdemo.mlirl.differentiableplanners.dpoperator.SubDifferentiableMaxOperator
SubgoalOption - Class in burlap.behavior.singleagent.options: A class for a classic subgoal Markov option.
SubgoalOption() - Constructor for class burlap.behavior.singleagent.options.SubgoalOption: A default constructor for serialization purposes.
SubgoalOption(String, Policy, StateConditionTest, StateConditionTest) - Constructor for class burlap.behavior.singleagent.options.SubgoalOption: Initializes.
successorStates - Variable in class burlap.behavior.singleagent.planning.stochastic.montecarlo.uct.UCTActionNode: The possible successor states.
sumReturn - Variable in class burlap.behavior.singleagent.planning.stochastic.montecarlo.uct.UCTActionNode: The sum return observed for this action node
SupervisedVFA - Interface in burlap.behavior.functionapproximation.supervised: An interface for learning value function approximation via a supervised learning algorithm.
SupervisedVFA.SupervisedVFAInstance - Class in burlap.behavior.functionapproximation.supervised: A pair for a state and it's target value function value.
SupervisedVFAInstance(State, double) - Constructor for class burlap.behavior.functionapproximation.supervised.SupervisedVFA.SupervisedVFAInstance: Initializes
svp - Variable in class burlap.behavior.singleagent.auxiliary.valuefunctionvis.ValueFunctionRenderLayer: Painter used to visualize the value function
svp - Variable in class burlap.behavior.singleagent.auxiliary.valuefunctionvis.ValueFunctionVisualizerGUI: Painter used to visualize the value function
synchronizeJointActionSelectionAmongAgents - Variable in class burlap.behavior.stochasticgames.PolicyFromJointPolicy

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