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.singleagent.learnbydemo.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.planning.deterministic.SearchNode: The (hashed) state of this node
s - Variable in class burlap.behavior.singleagent.planning.vfa.fittedvi.SupervisedVFA.SupervisedVFAInstance: The state
s - Variable in class burlap.behavior.singleagent.QValue: The state with which this Q-value is associated.
s - Variable in class burlap.behavior.singleagent.vfa.cmac.Tiling.StateTile: The state the tile is for
s - Variable in class burlap.behavior.statehashing.StateHashTuple
s - Variable in class burlap.behavior.stochasticgame.mavaluefunction.JAQValue
s - Variable in class burlap.oomdp.core.TransitionProbability: The state to which the agent may transition.
saAgent - Variable in class burlap.behavior.stochasticgame.agents.interfacing.singleagent.SingleAgentInterface: The BURLAP single agent learning agent that is being used.
saAgentFactory - Variable in class burlap.behavior.stochasticgame.agents.interfacing.singleagent.SingleAgentInterface: The single agent learning factory
sActionFeatures - Variable in class burlap.behavior.singleagent.learning.lspi.LSPI.SSFeatures: State-action features
saDomain - Variable in class burlap.behavior.stochasticgame.agents.interfacing.singleagent.SingleAgentInterface: The single agent version of the domain
SADomain - Class in burlap.oomdp.singleagent: A domain subclass for single agent domains.
SADomain() - Constructor for class burlap.oomdp.singleagent.SADomain
saInterface - Variable in class burlap.behavior.stochasticgame.agents.interfacing.singleagent.SGToSADomain: The agent interface which single agent action objects will call
SALearningAgentFactoryForSG - Interface in burlap.behavior.stochasticgame.agents.interfacing.singleagent
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
sampleBasicMovement(State, GridGameStandardMechanics.Location2, GridGameStandardMechanics.Location2, List<GridGameStandardMechanics.Location2>) - Method in class burlap.domain.stochasticgames.gridgame.GridGameStandardMechanics: Returns a movement result of the agent.
sampledBellmanQEstimate(GroundedAction, DifferentiableSparseSampling.QAndQGradient) - Method in class burlap.behavior.singleagent.learnbydemo.mlirl.differentiableplanners.DifferentiableSparseSampling.DiffStateNode
sampledBellmanQEstimate(GroundedAction) - Method in class burlap.behavior.singleagent.planning.stochastic.sparsesampling.SparseSampling.StateNode: Estimates the Q-value using sampling from the transition dynamics.
sampleFromActionDistribution(State) - Method in class burlap.behavior.singleagent.Policy: 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(State, GroundedAction) - Method in class burlap.behavior.singleagent.learning.modellearning.Model: A method to sample this model's transition dynamics for the given state and action.
sampleModelHelper(State, GroundedAction) - Method in class burlap.behavior.singleagent.learning.modellearning.Model: A helper method to sample this model's transition dynamics for the given state and action.
sampleModelHelper(State, GroundedAction) - Method in class burlap.behavior.singleagent.learning.modellearning.models.TabularModel
samples - Variable in class burlap.behavior.singleagent.planning.vfa.fittedvi.FittedVI: The set of samples on which to perform value iteration.
sampleStrategy(double[]) - Method in class burlap.behavior.stochasticgame.agents.twoplayer.singlestage.equilibriumplayer.EquilibriumPlayingAgent: Samples an action from a strategy, where a strategy is defined as probability distribution over actions.
sampleTransitionFromTransitionProbabilities(State, GroundedAction) - Method in class burlap.behavior.singleagent.learning.modellearning.Model: Will return a sampled outcome state by calling the Model.getTransitionProbabilities(State, GroundedAction) method and randomly drawing a state according to its distribution
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.
SarsaLam - Class in burlap.behavior.singleagent.learning.tdmethods: Tabular SARSA(\lambda) implementation [1].
SarsaLam(Domain, RewardFunction, TerminalFunction, double, StateHashFactory, 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(Domain, RewardFunction, TerminalFunction, double, StateHashFactory, 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(Domain, RewardFunction, TerminalFunction, double, StateHashFactory, 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(Domain, RewardFunction, TerminalFunction, double, StateHashFactory, ValueFunctionInitialization, 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
SarsaLam.EligibilityTrace(StateHashTuple, QValue, double) - Constructor for class burlap.behavior.singleagent.learning.tdmethods.SarsaLam.EligibilityTrace: Creates a new eligibility trace to track for an episode.
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(Domain) - 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<Action>) - 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.
SARSCollector.UniformRandomSARSCollector(Domain) - Constructor for class burlap.behavior.singleagent.learning.lspi.SARSCollector.UniformRandomSARSCollector: Initializes the collector's action set using the actions that are part of the domain.
SARSCollector.UniformRandomSARSCollector(List<Action>) - Constructor for class burlap.behavior.singleagent.learning.lspi.SARSCollector.UniformRandomSARSCollector: Initializes this collector's action set to use for collecting data.
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.
SARSData.SARS(State, GroundedAction, double, State) - Constructor for class burlap.behavior.singleagent.learning.lspi.SARSData.SARS: Initializes.
saThread - Variable in class burlap.behavior.stochasticgame.agents.interfacing.singleagent.SingleAgentInterface: The thread that runs the single agent learning algorithm
satisfies(State) - Method in class burlap.behavior.singleagent.planning.deterministic.TFGoalCondition
satisfies(State) - Method in class burlap.behavior.singleagent.planning.SinglePFSCT
satisfies(State) - Method in interface burlap.behavior.singleagent.planning.StateConditionTest
satisfiesGoal(State) - Method in class burlap.behavior.singleagent.planning.stochastic.rtdp.BFSRTDP: Returns whether a state is a goal state.
satisifiesHeap() - Method in class burlap.datastructures.HashIndexedHeap: This method returns whether the data structure stored is in fact a heap (costs linear time).
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 planner policy, which means if the source deterministic planner 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 Policy.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(StateHashTuple) - Constructor for class burlap.behavior.singleagent.planning.deterministic.SearchNode: Constructs a SearchNode for the input state.
SearchNode(StateHashTuple, GroundedAction, 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.stochasticgame.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.oomdp.stochasticgames.tournament.Tournament
semiDeepCopy(String...) - Method in class burlap.oomdp.core.State: Performs a semi-deep copy of the state in which only the objects with the names in deepCopyObjectNames are deep copied and the rest of the objects are shallowed copied.
semiDeepCopy(ObjectInstance...) - Method in class burlap.oomdp.core.State: Performs a semi-deep copy of the state in which only the objects in deepCopyObjects are deep copied and the rest of the objects are shallowed copied.
semiDeepCopy(Set<ObjectInstance>) - Method in class burlap.oomdp.core.State: Performs a semi-deep copy of the state in which only the objects in deepCopyObjects are deep copied and the rest of the objects are shallowed copied.
semiWallProb - Variable in class burlap.domain.stochasticgames.gridgame.GridGame: The probability that an agent will pass through a semi-wall.
set(SingleStageNormalFormGame.StrategyProfile, double) - Method in class burlap.domain.stochasticgames.normalform.SingleStageNormalFormGame.AgentPayoutFunction: sets the payout for a given strategy profile
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.
setActingAgentName(String) - Method in class burlap.behavior.stochasticgame.PolicyFromJointPolicy: Sets the acting agents name
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
setActionObserverForAllAction(ActionObserver) - Method in class burlap.oomdp.singleagent.SADomain: Clears all action observers for all actions in this domain and then sets them to have the single action observer provided
setActions(List<Action>) - Method in class burlap.behavior.singleagent.planning.OOMDPPlanner: Sets the action set the planner should use.
setActionShortHand(Map<String, String>) - Method in class burlap.oomdp.singleagent.explorer.TerminalExplorer: Sets teh short hand names to use for actions.
setActionShortHand(Map<String, String>) - Method in class burlap.oomdp.stochasticgames.explorers.SGTerminalExplorer: Sets the action shorthands to use
setAgent(State, int, int, int, boolean) - Static method in class burlap.domain.singleagent.blockdude.BlockDude: Sets the agent object's x, y, direction, and holding attribute to the specified values.
setAgent(State, int, int) - Static method in class burlap.domain.singleagent.frostbite.FrostbiteDomain: Sets the agent s position, with a height of 0 (on the ground)
setAgent(State, int, int, int) - Static method in class burlap.domain.singleagent.frostbite.FrostbiteDomain: Sets the agent s position and height
setAgent(State, int, int) - Static method in class burlap.domain.singleagent.gridworld.GridWorldDomain: Sets the first agent object in s to the specified x and y position.
setAgent(State, double, double, double) - Static method in class burlap.domain.singleagent.lunarlander.LunarLanderDomain: Sets the agent/lander position/orientation and zeros out the lander velocity.
setAgent(State, double, double, double, double, double) - Static method in class burlap.domain.singleagent.lunarlander.LunarLanderDomain: Sets the agent/lander position/orientation and the velocity.
setAgent(State, double, double) - Static method in class burlap.domain.singleagent.mountaincar.MountainCar: Sets the agent position in the provided state to the given position and with the given velocity.
setAgent(State, int, int, int, int) - Static method in class burlap.domain.stochasticgames.gridgame.GridGame: Sets an agent's attribute values
setAgentDefinitions - Variable in class burlap.behavior.stochasticgame.agents.mavf.MultiAgentVFPlanningAgent: Whether the agent definitions for this planner have been set yet.
setAgentDefinitions(Map<String, AgentType>) - Method in class burlap.behavior.stochasticgame.mavaluefunction.MAValueFunctionPlanner: Sets/changes the agent definitions to use in planning.
setAgents(List<MultiAgentQLearning>) - Method in class burlap.behavior.stochasticgame.mavaluefunction.AgentQSourceMap.MAQLControlledQSourceMap: Initializes with a list of agents that each keep their own Q_source.
setAgentsInJointPolicy(Map<String, AgentType>) - Method in class burlap.behavior.stochasticgame.JointPolicy: Sets the agent definitions that define the set of possible joint actions in each state.
setAgentsInJointPolicy(List<Agent>) - Method in class burlap.behavior.stochasticgame.JointPolicy: Sets the agent definitions by querying the agent names and AgentType objects from a list of agents.
setAgentsInJointPolicyFromWorld(World) - Method in class burlap.behavior.stochasticgame.JointPolicy: Sets teh agent definitions by querying the agents that exist in a World object.
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.
setAttributes(List<Attribute>) - Method in class burlap.oomdp.core.ObjectClass: Sets the attributes used to define this object class
setAttributesForClass(String, List<Attribute>) - Method in class burlap.behavior.statehashing.DiscreteStateHashFactory: Sets which attributes to use in the hash calculation for the given class.
setAttributesForHashCode(Map<String, List<Attribute>>) - Method in class burlap.behavior.statehashing.DiscreteStateHashFactory: Sets this hashing factory to hash on only the attributes for the specified classes in the provided map
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.
setBase(State) - Method in class burlap.oomdp.singleagent.explorer.StateResetSpecialAction: Sets the base state to reset to
setBaseStateGenerator(StateGenerator) - Method in class burlap.oomdp.singleagent.explorer.StateResetSpecialAction: Sets the state generator to draw from on reset
setBgColor(Color) - Method in class burlap.behavior.singleagent.auxiliary.valuefunctionvis.ValueFunctionVisualizerGUI: Sets the canvas background color
setBGColor(Color) - Method in class burlap.oomdp.visualizer.MultiLayerRenderer: Sets the color that will fill the canvas before rendering begins
setBGColor(Color) - Method in class burlap.oomdp.visualizer.Visualizer: Sets the background color of the canvas
setBlock(State, int, int, int) - Static method in class burlap.domain.singleagent.blockdude.BlockDude: Sets the ith block's x and y position in a state.
setBlock(State, int, String, int, int, String) - Static method in class burlap.domain.singleagent.blocksworld.BlocksWorld: Use this method to quickly set the various values of a block
setBlock(State, String, String, int, int, String) - Static method in class burlap.domain.singleagent.blocksworld.BlocksWorld: Use this method to quickly set the various values of a block
setBlock(ObjectInstance, String, int, int, String) - Static method in class burlap.domain.singleagent.blocksworld.BlocksWorld: Use this method to quickly set the various values of a block
setBlockColor(State, int, String) - Static method in class burlap.domain.singleagent.blocksworld.BlocksWorld: Use this method to quickly set the color of a block
setBoltzmannBeta(double) - Method in class burlap.behavior.singleagent.learnbydemo.mlirl.MLIRLRequest
setBoltzmannBetaParameter(double) - Method in class burlap.behavior.singleagent.learnbydemo.mlirl.differentiableplanners.DifferentiableSparseSampling
setBoltzmannBetaParameter(double) - Method in class burlap.behavior.singleagent.learnbydemo.mlirl.differentiableplanners.DifferentiableVFPlanner
setBoltzmannBetaParameter(double) - Method in interface burlap.behavior.singleagent.learnbydemo.mlirl.support.QGradientPlanner: Sets this planner's Boltzmann beta parameter used to compute gradients.
setBoundaryWalls(State, int, int) - Static method in class burlap.domain.stochasticgames.gridgame.GridGame: Sets boundary walls of a domain.
setBreakTiesRandomly(boolean) - Method in class burlap.behavior.stochasticgame.mavaluefunction.policies.EGreedyMaxWellfare: Whether to break ties randomly or deterministically.
setBrickMap(State, int[][]) - Static method in class burlap.domain.singleagent.blockdude.BlockDude: Sets the state to use the provided brick map.
setBrickValue(State, int, int, int) - Static method in class burlap.domain.singleagent.blockdude.BlockDude: Sets the brick value in grid location x, y.
setC(int) - Method in class burlap.behavior.singleagent.learnbydemo.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.oomdp.core.PropositionalFunction: Sets the class name for this propositional function.
setCoefficientVectors(List<short[]>) - Method in class burlap.behavior.singleagent.vfa.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.
setComputeExactValueFunction(boolean) - Method in class burlap.behavior.singleagent.planning.stochastic.sparsesampling.SparseSampling: Sets whether this planner will compute the exact finite horizon value funciton (using the full transition dynamics) or if sampling to estimate the value function will be used.
setControlDepth(int) - Method in class burlap.behavior.singleagent.planning.vfa.fittedvi.FittedVI: Sets the Bellman operator depth used for computing Q-values (the FittedVI.getQ(burlap.oomdp.core.State, burlap.oomdp.core.AbstractGroundedAction) and FittedVI.getQ(burlap.oomdp.core.State, burlap.oomdp.core.AbstractGroundedAction) methods).
setCorrelatedQObjective(CorrelatedEquilibriumSolver.CorrelatedEquilibriumObjective) - Method in class burlap.behavior.stochasticgame.mavaluefunction.policies.ECorrelatedQJointPolicy: Sets the correlated equilibrium objective to be solved.
setCurStateTo(State) - Method in class burlap.oomdp.singleagent.environment.Environment: Sets the current state of the environment
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.learnbydemo.mlirl.differentiableplanners.DifferentiableSparseSampling: Sets the debug code used for logging plan results with DPrint.
setDebugCode(int) - Method in class burlap.behavior.singleagent.learnbydemo.mlirl.MLIRL: Sets the debug code used for printing to the terminal
setDebugCode(int) - Method in class burlap.behavior.singleagent.learnbydemo.mlirl.MultipleIntentionsMLIRL: Sets the debug code used for printing to the terminal
setDebugCode(int) - Method in class burlap.behavior.singleagent.planning.OOMDPPlanner: 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.
setDebugCode(int) - Method in class burlap.oomdp.core.Domain: Sets the debug code used for printing debug messages.
setDebugId(int) - Method in class burlap.oomdp.stochasticgames.World: Sets the debug code that is use for printing with DPrint.
setDefaultFloorDiscretizingMultiple(double) - Method in class burlap.behavior.statehashing.DiscretizingStateHashFactory: Sets the default multiple to use for continuous attributes that do not have specific multiples set for them.
setDefaultReward(double) - Method in class burlap.domain.singleagent.lunarlander.LunarLanderRF
setDefaultTileWidth(double) - Method in class burlap.behavior.singleagent.interfaces.rlglue.common.RLGlueCMACSarsaLambdaFactory
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.frostbite.FrostbiteDomain: Will set the domain to use deterministic action transitions.
setDeterministicTransitionDynamics() - Method in class burlap.domain.singleagent.gridworld.GridWorldDomain: Will set the domain to use deterministic action transitions.
setDiscValues(List<String>) - Method in class burlap.oomdp.core.Attribute: Sets a discrete attribute's categorical values
setDiscValues(String[]) - Method in class burlap.oomdp.core.Attribute: Sets a discrete attribute's categorical values.
setDiscValuesForRange(int, int, int) - Method in class burlap.oomdp.core.Attribute: Sets the possible range of discrete (@link Attribute.AttributeType.DISC) values for the attribute.
setDomain(Domain) - Method in class burlap.behavior.singleagent.learnbydemo.apprenticeship.ApprenticeshipLearningRequest
setDomain(Domain) - Method in class burlap.behavior.singleagent.learnbydemo.IRLRequest
setDomain(Domain) - Method in class burlap.behavior.singleagent.planning.OOMDPPlanner: Sets the domain of this planner.
setEpisodeWeights(double[]) - Method in class burlap.behavior.singleagent.learnbydemo.mlirl.MLIRLRequest
setEpsilon(double) - Method in class burlap.behavior.singleagent.learnbydemo.apprenticeship.ApprenticeshipLearningRequest
setEpsilon(double) - Method in class burlap.behavior.singleagent.planning.commonpolicies.EpsilonGreedy: Sets the epsilon value, where epsilon is the probability of taking a random action.
setEpsilon(double) - Method in class burlap.behavior.stochasticgame.agents.naiveq.history.SGQWActionHistoryFactory: Sets the epislon parmaeter (for epsilon greedy policy).
setExernalTermination(TerminalFunction) - Method in class burlap.behavior.singleagent.options.Option: Sets what the external MDPs terminal function is that will cause this option to terminate if it enters those terminal states.
setExit(State, int, int) - Static method in class burlap.domain.singleagent.blockdude.BlockDude: Sets the x and y position of the first exit object in the state.
setExpectationCalculationProbabilityCutoff(double) - Method in class burlap.behavior.singleagent.options.Option: Sets the minimum probability of reaching a terminal state for it to be included in the options computed transition dynamics distribution.
setExpectationHashingFactory(StateHashFactory) - Method in class burlap.behavior.singleagent.options.Option: Sets the option to use the provided hashing factory for caching transition probability results.
setExpertEpisodes(List<EpisodeAnalysis>) - Method in class burlap.behavior.singleagent.learnbydemo.apprenticeship.ApprenticeshipLearningRequest
setExpertEpisodes(List<EpisodeAnalysis>) - Method in class burlap.behavior.singleagent.learnbydemo.IRLRequest
setFd(FeatureDatabase) - Method in class burlap.behavior.singleagent.vfa.common.FDFeatureVectorGenerator
setFeatureDatabase(FeatureDatabase) - Method in class burlap.behavior.singleagent.learning.lspi.LSPI: Sets the feature datbase defining state features
setFeatureGenerator(StateToFeatureVectorGenerator) - Method in class burlap.behavior.singleagent.learnbydemo.apprenticeship.ApprenticeshipLearningRequest
setFeaturesAreForNextState(boolean) - Method in class burlap.behavior.singleagent.learnbydemo.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.learnbydemo.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 forgetten or resued in subsequent planning.
setFrameDelay(long) - Method in class burlap.oomdp.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.oomdp.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.learnbydemo.apprenticeship.ApprenticeshipLearningRequest
setGamma(double) - Method in class burlap.behavior.singleagent.learnbydemo.IRLRequest
setGamma(double) - Method in class burlap.behavior.singleagent.planning.OOMDPPlanner: Sets gamma, the discount factor used by this planner
setGoal(State, int, int, int, int) - Static method in class burlap.domain.stochasticgames.gridgame.GridGame: Sets a goal objects attribute values
setGoalCondition(StateConditionTest) - Method in class burlap.behavior.singleagent.planning.stochastic.rtdp.BFSRTDP: Sets the goal state that causes the BFS-like pass to stop expanding when found.
setGoalReward(double) - Method in class burlap.domain.singleagent.lunarlander.LunarLanderRF
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.learnbydemo.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.
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.
setHorizontalWall(State, int, int, int, int, int) - Static method in class burlap.domain.stochasticgames.gridgame.GridGame: Sets the attribute values for a horizontal wall
setIdentityScalar(double) - Method in class burlap.behavior.singleagent.learning.lspi.LSPI: Sets the initial LSPI identity matrix scalar used.
setIgloo(State, int) - Static method in class burlap.domain.singleagent.frostbite.FrostbiteDomain: Sets the igloo building status
setInitialFunctionWeight(double) - Method in class burlap.behavior.singleagent.interfaces.rlglue.common.RLGlueCMACSarsaLambdaFactory
setInternalRewardFunction(JointReward) - Method in class burlap.oomdp.stochasticgames.Agent: Internal reward functions are optional, but can be useful for purposes like reward shaping.
setIterationListData() - Method in class burlap.behavior.singleagent.EpisodeSequenceVisualizer
setJAC(String) - Method in class burlap.oomdp.stochasticgames.explorers.SGVisualExplorer: Sets the joint action model to use
setJointActionModel(JointActionModel) - Method in class burlap.oomdp.stochasticgames.SGDomain: Sets the joint action model associated with this domain.
setJointPolicy(JointPolicy) - Method in class burlap.behavior.stochasticgame.PolicyFromJointPolicy: Sets the underlying joint policy
setK(int) - Method in class burlap.behavior.singleagent.learnbydemo.mlirl.MultipleIntentionsMLIRLRequest: Sets the number of clusters
setLambda(double) - Method in class burlap.behavior.singleagent.interfaces.rlglue.common.RLGlueCMACSarsaLambdaFactory
setLambda(double) - Method in class burlap.behavior.singleagent.interfaces.rlglue.common.RLGlueSARSALambdaFactory
setLearningPolicy(Policy) - Method in class burlap.behavior.singleagent.interfaces.rlglue.common.RLGlueCMACSarsaLambdaFactory
setLearningPolicy(Policy) - Method in class burlap.behavior.singleagent.interfaces.rlglue.common.RLGLueQlearningFactory
setLearningPolicy(Policy) - Method in class burlap.behavior.singleagent.learning.lspi.LSPI: Sets the learning policy followed by the LSPI.runLearningEpisodeFrom(State) and LSPI.runLearningEpisodeFrom(State, 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.stochasticgame.agents.maql.MultiAgentQLearning: Sets the learning policy to be followed by the agent.
setLearningRate(LearningRate) - Method in class burlap.behavior.singleagent.interfaces.rlglue.common.RLGlueCMACSarsaLambdaFactory
setLearningRate(LearningRate) - Method in class burlap.behavior.singleagent.interfaces.rlglue.common.RLGLueQlearningFactory
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.stochasticgame.agents.naiveq.SGNaiveQLAgent
setLearningRateFunction(LearningRate) - Method in class burlap.behavior.singleagent.learning.tdmethods.QLearning: Sets the learning rate function to use
setLims(double, double) - Method in class burlap.oomdp.core.Attribute: Sets the upper and lower bound limits for a bounded real attribute.
setLocation(State, int, int, int) - Static method in class burlap.domain.singleagent.gridworld.GridWorldDomain: Sets the i'th location object to the specified x and y position.
setLocation(State, int, int, int, int) - Static method in class burlap.domain.singleagent.gridworld.GridWorldDomain: Sets the i'th location object to the specified x and y position and location type.
setMacroCellHorizontalCount(int) - Method in class burlap.domain.singleagent.gridworld.macro.MacroCellGridWorld: Sets the number of coumns of macro-cells (cells across the x-axis)
setMacroCellVerticalCount(int) - Method in class burlap.domain.singleagent.gridworld.macro.MacroCellGridWorld: Sets the number of rows of macro-cells (cells across the y-axis)
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.
setMaxCartSpeedToMaxWithMovementFromOneSideToOther() - Method in class burlap.domain.singleagent.cartpole.CartPoleDomain: Given the current action force, track length and masses, sets the max cart speed to an upperbound of what is possible from moving from one side of the track to another.
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.runLearningEpisodeFrom(State) or LSPI.runLearningEpisodeFrom(State, 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.learnbydemo.apprenticeship.ApprenticeshipLearningRequest
setMaxLearningSteps(int) - Method in class burlap.behavior.singleagent.learning.lspi.LSPI: Sets the maximum number of learning steps permitted by the LSPI.runLearningEpisodeFrom(State) 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.
setName(String) - Method in class burlap.oomdp.core.ObjectInstance: Sets the name of this object instance.
setNormalizeValues(boolean) - Method in class burlap.behavior.singleagent.vfa.common.ConcatenatedObjectFeatureVectorGenerator: Sets whether the object values are normalized in the returned feature vector
setnTiles(int) - Method in class burlap.behavior.singleagent.interfaces.rlglue.common.RLGlueCMACSarsaLambdaFactory
setNumberOfLocationTypes(int) - Method in class burlap.domain.singleagent.gridworld.GridWorldDomain: Sets the number of possible location types to which a location object can belong.
setNumEpisodesToStore(int) - Method in class burlap.behavior.singleagent.learning.actorcritic.ActorCritic
setNumEpisodesToStore(int) - Method in class burlap.behavior.singleagent.learning.LearningAgent.LearningAgentBookKeeping: Tells the agent how many EpisodeAnalysis objects representing learning episodes to internally store.
setNumEpisodesToStore(int) - Method in interface burlap.behavior.singleagent.learning.LearningAgent: Tells the agent how many EpisodeAnalysis objects representing learning episodes to internally store.
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
setNumEpisodesToStore(int) - Method in class burlap.behavior.singleagent.learning.tdmethods.QLearning
setNumEpisodesToStore(int) - Method in class burlap.behavior.singleagent.learning.tdmethods.vfa.GradientDescentSarsaLam
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.PolicyGlyphPainter2D: Sets the number of states that will be rendered along a row
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.PolicyGlyphPainter2D: 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
setObjectClassAttributesToTile(String, StateGridder.AttributeSpecification...) - Method in class burlap.behavior.singleagent.auxiliary.StateGridder: Sets the attribute specifications to use for a single ObjectClass
setObjectIdentiferDependence(boolean) - Method in class burlap.oomdp.core.Domain: Sets whether this domain's states are object identifier (name) dependent.
setObservability(boolean) - Method in class burlap.oomdp.core.Value: Sets whether this value is observable to the agent or not.
setObstacle(State, int, double, double, double, double) - Static method in class burlap.domain.singleagent.lunarlander.LunarLanderDomain: Sets an obstacles boundaries/position
setObstacleInCell(int, int) - Method in class burlap.domain.singleagent.gridworld.GridWorldDomain: Sets a complete cell obstacle in the designated location.
setOptionsFirst() - Method in class burlap.behavior.singleagent.planning.deterministic.uninformed.dfs.DFS: Sets the planner to explore nodes generated by options first.
setPad(State, double, double, double, double) - Static method in class burlap.domain.singleagent.lunarlander.LunarLanderDomain: Sets the first landing pad's boundaries/position
setPad(State, int, double, double, double, double) - Static method in class burlap.domain.singleagent.lunarlander.LunarLanderDomain: Sets a landing pad's boundaries/position
setParameter(int, double) - Method in class burlap.behavior.singleagent.learnbydemo.mlirl.differentiableplanners.diffvinit.DifferentiableVInit.ParamedDiffVInit: Sets the value of a given parameter.
setParameter(int, double) - Method in class burlap.behavior.singleagent.learnbydemo.mlirl.support.DifferentiableRF: Sets the value of a given parameter.
setParameters(double[]) - Method in class burlap.behavior.singleagent.learnbydemo.mlirl.differentiableplanners.diffvinit.DifferentiableVInit.ParamedDiffVInit: Sets the parameters of this differentiable value function initialization
setParameters(double[]) - Method in class burlap.behavior.singleagent.learnbydemo.mlirl.differentiableplanners.diffvinit.DiffVFRF
setParameters(double[]) - Method in class burlap.behavior.singleagent.learnbydemo.mlirl.support.DifferentiableRF
setPayoff(int, int, double, double) - Method in class burlap.behavior.stochasticgame.agents.twoplayer.singlestage.equilibriumplayer.EquilibriumPlayingAgent.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.ActionIdle
setPhysParams(LunarLanderDomain.LLPhysicsParams) - Method in class burlap.domain.singleagent.lunarlander.LunarLanderDomain.ActionThrust
setPhysParams(LunarLanderDomain.LLPhysicsParams) - Method in class burlap.domain.singleagent.lunarlander.LunarLanderDomain.ActionTurn
setPhysParams(LunarLanderDomain.LLPhysicsParams) - Method in class burlap.domain.singleagent.lunarlander.LunarLanderDomain
setPlanner(OOMDPPlanner) - Method in class burlap.behavior.singleagent.learnbydemo.apprenticeship.ApprenticeshipLearningRequest
setPlanner(OOMDPPlanner) - Method in class burlap.behavior.singleagent.learnbydemo.IRLRequest
setPlanner(OOMDPPlanner) - Method in class burlap.behavior.singleagent.learnbydemo.mlirl.MLIRLRequest
setPlanner(OOMDPPlanner) - Method in class burlap.behavior.singleagent.planning.commonpolicies.BoltzmannQPolicy
setPlanner(OOMDPPlanner) - Method in class burlap.behavior.singleagent.planning.commonpolicies.EpsilonGreedy
setPlanner(OOMDPPlanner) - Method in class burlap.behavior.singleagent.planning.commonpolicies.GreedyDeterministicQPolicy
setPlanner(OOMDPPlanner) - Method in class burlap.behavior.singleagent.planning.commonpolicies.GreedyQPolicy
setPlanner(OOMDPPlanner) - Method in class burlap.behavior.singleagent.planning.deterministic.DDPlannerPolicy
setPlanner(OOMDPPlanner) - Method in class burlap.behavior.singleagent.planning.deterministic.SDPlannerPolicy
setPlanner(OOMDPPlanner) - Method in interface burlap.behavior.singleagent.planning.PlannerDerivedPolicy: Sets the planner whose results affect this policy.
setPlanner(OOMDPPlanner) - Method in class burlap.behavior.singleagent.planning.stochastic.montecarlo.uct.UCTTreeWalkPolicy
setPlannerFactory(QGradientPlannerFactory) - Method in class burlap.behavior.singleagent.learnbydemo.mlirl.MultipleIntentionsMLIRLRequest: Sets the QGradientPlannerFactory to use and also sets this request object's planner instance to a planner generated from it, if it has not already been set.
setPlannerReference(MAValueFunctionPlanner) - Method in class burlap.behavior.stochasticgame.agents.mavf.MAVFPlannerFactory.ConstantMAVFPlannerFactory: Changes the planner 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.getQ(burlap.oomdp.core.State, burlap.oomdp.core.AbstractGroundedAction) and FittedVI.getQ(burlap.oomdp.core.State, burlap.oomdp.core.AbstractGroundedAction) 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.
setPlatform(State, int, int, int, int, boolean) - Static method in class burlap.domain.singleagent.frostbite.FrostbiteDomain: Sets a platform position, size and status
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.stochasticgame.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.stochasticgame.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(PlannerDerivedPolicy) - Method in class burlap.behavior.singleagent.learning.modellearning.artdp.ARTDP: Sets the policy to the provided one.
setPolicy(PolicyFromJointPolicy) - Method in class burlap.behavior.stochasticgame.agents.mavf.MultiAgentVFPlanningAgent: Sets the policy derived from this agents planner to follow.
setPolicyClassToEvaluate(PlannerDerivedPolicy) - Method in class burlap.behavior.singleagent.planning.stochastic.policyiteration.PolicyIteration: Sets which kind of policy to use whenever the policy is updated.
setPolicyCount(int) - Method in class burlap.behavior.singleagent.learnbydemo.apprenticeship.ApprenticeshipLearningRequest
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.
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(ValueFunctionInitialization) - Method in class burlap.behavior.singleagent.interfaces.rlglue.common.RLGLueQlearningFactory
setQInitFunction(ValueFunctionInitialization) - Method in class burlap.behavior.singleagent.learning.tdmethods.QLearning: Sets how to initialize Q-values for previously unexperienced state-action pairs.
setQSourceMap(Map<String, QSourceForSingleAgent>) - Method in class burlap.behavior.stochasticgame.mavaluefunction.AgentQSourceMap.HashMapAgentQSourceMap: Sets the Q-source hash map to be used.
setQSourceProvider(MultiAgentQSourceProvider) - Method in class burlap.behavior.stochasticgame.mavaluefunction.MAQSourcePolicy: Sets the MultiAgentQSourceProvider that will be used to define this object's joint policy.
setQSourceProvider(MultiAgentQSourceProvider) - Method in class burlap.behavior.stochasticgame.mavaluefunction.policies.ECorrelatedQJointPolicy
setQSourceProvider(MultiAgentQSourceProvider) - Method in class burlap.behavior.stochasticgame.mavaluefunction.policies.EGreedyJointPolicy
setQSourceProvider(MultiAgentQSourceProvider) - Method in class burlap.behavior.stochasticgame.mavaluefunction.policies.EGreedyMaxWellfare
setQSourceProvider(MultiAgentQSourceProvider) - Method in class burlap.behavior.stochasticgame.mavaluefunction.policies.EMinMaxPolicy
setQValueInitializer(ValueFunctionInitialization) - Method in class burlap.behavior.stochasticgame.agents.naiveq.history.SGQWActionHistoryFactory: Sets the Q-value initialization function that will be used by the agent.
setQValueInitializer(ValueFunctionInitialization) - Method in class burlap.behavior.stochasticgame.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.singleagent.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.stochasticgame.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
setRequest(MLIRLRequest) - Method in class burlap.behavior.singleagent.learnbydemo.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
setRewardFunction(RewardFunction) - Method in class burlap.behavior.singleagent.learning.actorcritic.critics.TDLambda: Sets the reward function to use.
setRewardFunction(RewardFunction) - Method in class burlap.oomdp.singleagent.explorer.TerminalExplorer
setRewardFunction(JointReward) - Method in class burlap.oomdp.stochasticgames.explorers.SGTerminalExplorer: Allows the explorer to keep track of the reward received that will be printed to the output.
setRewardFunction(JointReward) - Method in class burlap.oomdp.stochasticgames.explorers.SGVisualExplorer
setRf(DifferentiableRF) - Method in class burlap.behavior.singleagent.learnbydemo.mlirl.MLIRLRequest
setRf(RewardFunction) - Method in class burlap.behavior.singleagent.planning.OOMDPPlanner: Sets the reward function used by this planner
setRfDim(int) - Method in class burlap.behavior.singleagent.learnbydemo.mlirl.differentiableplanners.diffvinit.LinearDiffRFVInit
setRfFeaturesAreForNextState(boolean) - Method in class burlap.behavior.singleagent.learnbydemo.mlirl.differentiableplanners.diffvinit.LinearDiffRFVInit
setRfFvGen(StateToFeatureVectorGenerator) - Method in class burlap.behavior.singleagent.learnbydemo.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.
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.
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.oomdp.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.stochasticgame.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.
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.learnbydemo.apprenticeship.ApprenticeshipLearningRequest
setStateContext(State) - Method in interface burlap.behavior.singleagent.planning.StateConditionTestIterable
setStateMapping(StateMapping) - Method in class burlap.behavior.singleagent.options.Option: Sets this option to use a state mapping that maps from the source MDP states to another state representation that will be used by this option for making action selections.
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.ValueFunctionRenderLayer: Sets the states to visualize
setStateValuesToVisualize(Collection<State>) - Method in class burlap.behavior.singleagent.auxiliary.valuefunctionvis.PolicyRenderLayer: Sets the states to visualize
setStoredAbstraction(StateAbstraction) - Method in class burlap.behavior.stochasticgame.agents.naiveq.SGNaiveQFactory: Sets the factory to provide Q-learning algorithms with the given state abstraction.
setStoredMapAbstraction(StateAbstraction) - Method in class burlap.behavior.stochasticgame.agents.naiveq.SGNaiveQLAgent: Sets the state abstraction that this agent will use
setStrategy(Policy) - Method in class burlap.behavior.stochasticgame.agents.naiveq.SGNaiveQLAgent: Sets the Q-learning policy that this agent will use (e.g., epsilon greedy)
SetStrategyAgent - Class in burlap.behavior.stochasticgame.agents: A class for an agent who makes decisions by following a specified strategy and does not respond to the other player's actions.
SetStrategyAgent(SGDomain, Policy) - Constructor for class burlap.behavior.stochasticgame.agents.SetStrategyAgent: Initializes for the given domain in which the agent will play and the strategy that they will follow.
SetStrategyAgent.SetStrategyAgentFactory - Class in burlap.behavior.stochasticgame.agents
SetStrategyAgent.SetStrategyAgentFactory(SGDomain, Policy) - Constructor for class burlap.behavior.stochasticgame.agents.SetStrategyAgent.SetStrategyAgentFactory
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.stochasticgame.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(String) - Method in class burlap.behavior.stochasticgame.JointPolicy: Sets the target privledged agent from which this joint policy is defined.
setTargetAgent(String) - Method in class burlap.behavior.stochasticgame.mavaluefunction.policies.ECorrelatedQJointPolicy
setTargetAgent(String) - Method in class burlap.behavior.stochasticgame.mavaluefunction.policies.EGreedyJointPolicy
setTargetAgent(String) - Method in class burlap.behavior.stochasticgame.mavaluefunction.policies.EGreedyMaxWellfare
setTargetAgent(String) - Method in class burlap.behavior.stochasticgame.mavaluefunction.policies.EMinMaxPolicy
setTemperature(double) - Method in class burlap.datastructures.BoltzmannDistribution: Sets the temperature value to use.
setTerminalFunction(TerminalFunction) - Method in class burlap.oomdp.singleagent.explorer.VisualExplorer
setTerminalFunction(TerminalFunction) - Method in class burlap.oomdp.stochasticgames.explorers.SGTerminalExplorer
setTerminalFunction(TerminalFunction) - Method in class burlap.oomdp.stochasticgames.explorers.SGVisualExplorer
setTerminalFunctionf(TerminalFunction) - Method in class burlap.oomdp.singleagent.explorer.TerminalExplorer
setTerminalStates(Set<Integer>) - Method in class burlap.domain.singleagent.graphdefined.GraphTF
setTerminateMapper(DirectOptionTerminateMapper) - Method in class burlap.behavior.singleagent.options.Option: Sets this option to determine its execution results using a direct terminal state mapping rather than actually executing each action selcted by the option step by step.
setTerminateOnTrue(boolean) - Method in class burlap.oomdp.singleagent.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.
setTf(TerminalFunction) - Method in class burlap.behavior.singleagent.planning.deterministic.TFGoalCondition: Sets the TerminalFunction used to specify the goal condition.
setTf(TerminalFunction) - Method in class burlap.behavior.singleagent.planning.OOMDPPlanner: Sets the terminal state function used by this planner
setTHistory(double[]) - Method in class burlap.behavior.singleagent.learnbydemo.apprenticeship.ApprenticeshipLearningRequest
setThrustValue(double) - Method in class burlap.domain.singleagent.lunarlander.LunarLanderDomain.ActionThrust
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.
gravity = -0.2
xmin = 0
xmax = 100
ymin = 0
ymax = 50
max velocity component speed = 4
maximum angle of rotation = pi/4
change in angle from turning = pi/20
thrust1 force = 0.32
thrust2 force = 0.2 (opposite gravity)
setTrackingRewardFunction(RewardFunction) - Method in class burlap.oomdp.singleagent.explorer.VisualExplorer
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.NodeTransitionProbibility>>>) - Method in class burlap.domain.singleagent.graphdefined.GraphDefinedDomain.GraphAction
setTransitionDynamics(double[][]) - Method in class burlap.domain.singleagent.gridworld.GridWorldDomain: Will set the movement direction probabilities based on the action chosen.
setType(Attribute.AttributeType) - Method in class burlap.oomdp.core.Attribute: Sets the type for this attribute.
setup() - Method in class burlap.testing.TestGridWorld
setup() - Method in class burlap.testing.TestPlanning
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.stochasticgame.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.stochasticgame.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.
setUseSemiDeep(boolean) - Method in class burlap.domain.singleagent.blockdude.BlockDude: Sets whether generated domain's actions use semi-deep state copies or full deep copies.
setUseVariableCSize(boolean) - Method in class burlap.behavior.singleagent.learnbydemo.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.learnbydemo.apprenticeship.ApprenticeshipLearningRequest
setV(DifferentiableSparseSampling.QAndQGradient) - Method in class burlap.behavior.singleagent.learnbydemo.mlirl.differentiableplanners.DifferentiableSparseSampling.DiffStateNode
setValue(StateHashTuple, double) - Method in class burlap.behavior.stochasticgame.mavaluefunction.MAValueFunctionPlanner.BackupBasedQSource: Sets the value of the state in this objects value function map.
setValue(String, String) - Method in class burlap.oomdp.core.ObjectInstance: Sets the value of the attribute named attName for this object instance.
setValue(String, double) - Method in class burlap.oomdp.core.ObjectInstance: Sets the value of the attribute named attName for this object instance.
setValue(String, int) - Method in class burlap.oomdp.core.ObjectInstance: Sets the value of the attribute named attName for this object instance.
setValue(String, boolean) - Method in class burlap.oomdp.core.ObjectInstance: Sets the value of the attribute named attName for this object instance.
setValue(String, int[]) - Method in class burlap.oomdp.core.ObjectInstance: Sets the value of the attribute named attName for this object instance.
setValue(String, double[]) - Method in class burlap.oomdp.core.ObjectInstance: Sets the value of the attribute named attName for this object instance.
setValue(int) - Method in class burlap.oomdp.core.Value: Sets the internal value representation using an int value
setValue(double) - Method in class burlap.oomdp.core.Value: Sets the internal value representation using a double value
setValue(String) - Method in class burlap.oomdp.core.Value: Sets the internal value representation using a string value
setValue(boolean) - Method in class burlap.oomdp.core.Value: Sets the internalvalue representation using a boolean value
setValue(int[]) - Method in class burlap.oomdp.core.Value: Sets the int array value.
setValue(double[]) - Method in class burlap.oomdp.core.Value: Sets the double array value.
setValue(int) - Method in class burlap.oomdp.core.values.DiscreteValue
setValue(double) - Method in class burlap.oomdp.core.values.DiscreteValue
setValue(boolean) - Method in class burlap.oomdp.core.values.DiscreteValue
setValue(String) - Method in class burlap.oomdp.core.values.DiscreteValue
setValue(int[]) - Method in class burlap.oomdp.core.values.DiscreteValue
setValue(double[]) - Method in class burlap.oomdp.core.values.DiscreteValue
setValue(int) - Method in class burlap.oomdp.core.values.DoubleArrayValue
setValue(double) - Method in class burlap.oomdp.core.values.DoubleArrayValue
setValue(String) - Method in class burlap.oomdp.core.values.DoubleArrayValue
setValue(int[]) - Method in class burlap.oomdp.core.values.DoubleArrayValue
setValue(double[]) - Method in class burlap.oomdp.core.values.DoubleArrayValue
setValue(boolean) - Method in class burlap.oomdp.core.values.DoubleArrayValue
setValue(int) - Method in class burlap.oomdp.core.values.IntArrayValue
setValue(double) - Method in class burlap.oomdp.core.values.IntArrayValue
setValue(String) - Method in class burlap.oomdp.core.values.IntArrayValue
setValue(int[]) - Method in class burlap.oomdp.core.values.IntArrayValue
setValue(double[]) - Method in class burlap.oomdp.core.values.IntArrayValue
setValue(boolean) - Method in class burlap.oomdp.core.values.IntArrayValue
setValue(int) - Method in class burlap.oomdp.core.values.IntValue
setValue(double) - Method in class burlap.oomdp.core.values.IntValue
setValue(String) - Method in class burlap.oomdp.core.values.IntValue
setValue(boolean) - Method in class burlap.oomdp.core.values.IntValue
setValue(int[]) - Method in class burlap.oomdp.core.values.IntValue
setValue(double[]) - Method in class burlap.oomdp.core.values.IntValue
setValue(int) - Method in class burlap.oomdp.core.values.MultiTargetRelationalValue
setValue(double) - Method in class burlap.oomdp.core.values.MultiTargetRelationalValue
setValue(String) - Method in class burlap.oomdp.core.values.MultiTargetRelationalValue
setValue(boolean) - Method in class burlap.oomdp.core.values.MultiTargetRelationalValue
setValue(int[]) - Method in class burlap.oomdp.core.values.MultiTargetRelationalValue
setValue(double[]) - Method in class burlap.oomdp.core.values.MultiTargetRelationalValue
setValue(int) - Method in class burlap.oomdp.core.values.RealValue
setValue(double) - Method in class burlap.oomdp.core.values.RealValue
setValue(String) - Method in class burlap.oomdp.core.values.RealValue
setValue(boolean) - Method in class burlap.oomdp.core.values.RealValue
setValue(int[]) - Method in class burlap.oomdp.core.values.RealValue
setValue(double[]) - Method in class burlap.oomdp.core.values.RealValue
setValue(int) - Method in class burlap.oomdp.core.values.RelationalValue
setValue(double) - Method in class burlap.oomdp.core.values.RelationalValue
setValue(boolean) - Method in class burlap.oomdp.core.values.RelationalValue
setValue(String) - Method in class burlap.oomdp.core.values.RelationalValue
setValue(int[]) - Method in class burlap.oomdp.core.values.RelationalValue
setValue(double[]) - Method in class burlap.oomdp.core.values.RelationalValue
setValue(int) - Method in class burlap.oomdp.core.values.StringValue
setValue(double) - Method in class burlap.oomdp.core.values.StringValue
setValue(String) - Method in class burlap.oomdp.core.values.StringValue
setValue(boolean) - Method in class burlap.oomdp.core.values.StringValue
setValue(int[]) - Method in class burlap.oomdp.core.values.StringValue
setValue(double[]) - Method in class burlap.oomdp.core.values.StringValue
setValueForLeafNodes(ValueFunctionInitialization) - Method in class burlap.behavior.singleagent.learnbydemo.mlirl.differentiableplanners.DifferentiableSparseSampling: Sets the ValueFunctionInitialization object to use for settting the value of leaf nodes.
setValueForLeafNodes(ValueFunctionInitialization) - Method in class burlap.behavior.singleagent.planning.stochastic.sparsesampling.SparseSampling: Sets the ValueFunctionInitialization object to use for settting the value of leaf nodes.
setValueFunctionInitialization(ValueFunctionInitialization) - Method in class burlap.behavior.singleagent.planning.ValueFunctionPlanner: 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.
setVerticalWall(State, int, int, int, int, int) - Static method in class burlap.domain.stochasticgames.gridgame.GridGame: Sets the attribute values for a vertical wall
setVGrad(DifferentiableSparseSampling.QAndQGradient) - Method in class burlap.behavior.singleagent.learnbydemo.mlirl.differentiableplanners.DifferentiableSparseSampling.DiffStateNode
setVInit(ValueFunctionInitialization) - 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.learnbydemo.mlirl.differentiableplanners.diffvinit.LinearDiffRFVInit
setVinitFvGen(StateToFeatureVectorGenerator) - Method in class burlap.behavior.singleagent.learnbydemo.mlirl.differentiableplanners.diffvinit.LinearDiffRFVInit
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.
setWallInstance(ObjectInstance, int, int, int, int) - Static method in class burlap.domain.stochasticgames.gridgame.GridGame: Sets the attribute values for a wall instance
setWeight(int, double) - Method in class burlap.behavior.singleagent.vfa.common.LinearFVVFA
setWeight(int, double) - Method in class burlap.behavior.singleagent.vfa.common.LinearVFA
setWeight(double) - Method in class burlap.behavior.singleagent.vfa.FunctionWeight: Sets the weight
setWeight(int, double) - Method in interface burlap.behavior.singleagent.vfa.ValueFunctionApproximation: Sets the weight for a features
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.
setXYAttByObjectClass(String, String, String, String) - Method in class burlap.behavior.singleagent.auxiliary.valuefunctionvis.common.PolicyGlyphPainter2D: Will set the x-y attributes to use for cell rendering to the x y attributes of the first object in the state of the designated classes.
setXYAttByObjectClass(String, String, String, String) - Method in class burlap.behavior.singleagent.auxiliary.valuefunctionvis.common.StateValuePainter2D: Will set the x-y attributes to use for cell rendering to the x y attributes of the first object in the state of the designated classes.
setXYAttByObjectReference(String, String, String, String) - Method in class burlap.behavior.singleagent.auxiliary.valuefunctionvis.common.PolicyGlyphPainter2D: Will set the x-y attributes to use for cell rendering to the x y attributes of the designated object references.
setXYAttByObjectReference(String, String, String, String) - Method in class burlap.behavior.singleagent.auxiliary.valuefunctionvis.common.StateValuePainter2D: Will set the x-y attributes to use for cell rendering to the x y attributes of the designated object references.
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)
sg - Variable in class burlap.behavior.singleagent.auxiliary.performance.LearningAlgorithmExperimenter: The state generated used to generate states at the beginning of each episode
SGBackupOperator - Interface in burlap.behavior.stochasticgame.mavaluefunction: A stochastic games backup operator to be used in multi-agent Q-learning or value function planning.
SGDomain - Class in burlap.oomdp.stochasticgames: This class is used to define Stochastic Games Domains.
SGDomain() - Constructor for class burlap.oomdp.stochasticgames.SGDomain
SGNaiveQFactory - Class in burlap.behavior.stochasticgame.agents.naiveq: An agent factory that produces SGNaiveQLAgents.
SGNaiveQFactory(SGDomain, double, double, double, StateHashFactory) - Constructor for class burlap.behavior.stochasticgame.agents.naiveq.SGNaiveQFactory: Initializes the factory.
SGNaiveQFactory(SGDomain, double, double, double, StateHashFactory, StateAbstraction) - Constructor for class burlap.behavior.stochasticgame.agents.naiveq.SGNaiveQFactory: Initializes the factory.
SGNaiveQLAgent - Class in burlap.behavior.stochasticgame.agents.naiveq: A Tabular Q-learning [1] algorithm for stochastic games formalisms.
SGNaiveQLAgent(SGDomain, double, double, StateHashFactory) - Constructor for class burlap.behavior.stochasticgame.agents.naiveq.SGNaiveQLAgent: Initializes with a default Q-value of 0 and a 0.1 epsilon greedy policy/strategy
SGNaiveQLAgent(SGDomain, double, double, double, StateHashFactory) - Constructor for class burlap.behavior.stochasticgame.agents.naiveq.SGNaiveQLAgent: Initializes with a default 0.1 epsilon greedy policy/strategy
SGNaiveQLAgent(SGDomain, double, double, ValueFunctionInitialization, StateHashFactory) - Constructor for class burlap.behavior.stochasticgame.agents.naiveq.SGNaiveQLAgent: Initializes with a default 0.1 epsilon greedy policy/strategy
SGQWActionHistory - Class in burlap.behavior.stochasticgame.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, StateHashFactory, int, int, ActionIdMap) - Constructor for class burlap.behavior.stochasticgame.agents.naiveq.history.SGQWActionHistory: Initializes the learning algorithm using 0.1 epsilon greedy learning strategy/policy
SGQWActionHistory(SGDomain, double, double, StateHashFactory, int) - Constructor for class burlap.behavior.stochasticgame.agents.naiveq.history.SGQWActionHistory: Initializes the learning algorithm using 0.1 epsilon greedy learning strategy/policy
SGQWActionHistoryFactory - Class in burlap.behavior.stochasticgame.agents.naiveq.history: An agent factory for Q-learning with history agents.
SGQWActionHistoryFactory(SGDomain, double, double, StateHashFactory, int, int, ActionIdMap) - Constructor for class burlap.behavior.stochasticgame.agents.naiveq.history.SGQWActionHistoryFactory: Initializes the factory
SGQWActionHistoryFactory(SGDomain, double, double, StateHashFactory, int) - Constructor for class burlap.behavior.stochasticgame.agents.naiveq.history.SGQWActionHistoryFactory: Initializes the factory
SGStateGenerator - Class in burlap.oomdp.stochasticgames: An abstract class defining the interface and common mechanism for generating State objects specifically for stochastic games domains.
SGStateGenerator() - Constructor for class burlap.oomdp.stochasticgames.SGStateGenerator
SGTerminalExplorer - Class in burlap.oomdp.stochasticgames.explorers: This class allows you act as all of the agents in a domain by choosing actions for each of them to take in specific states.
SGTerminalExplorer(SGDomain, JointActionModel) - Constructor for class burlap.oomdp.stochasticgames.explorers.SGTerminalExplorer: Deprecated.
SGTerminalExplorer(SGDomain) - Constructor for class burlap.oomdp.stochasticgames.explorers.SGTerminalExplorer: Initializes the explorer with a domain and action model
SGTerminalExplorer(SGDomain, JointActionModel, Map<String, String>) - Constructor for class burlap.oomdp.stochasticgames.explorers.SGTerminalExplorer: Deprecated.
SGTerminalExplorer(SGDomain, Map<String, String>) - Constructor for class burlap.oomdp.stochasticgames.explorers.SGTerminalExplorer: Initializes the explorer with a domain and action model and shorthand names for actions
SGToSADomain - Class in burlap.behavior.stochasticgame.agents.interfacing.singleagent: This domain generator is used to produce single agent domain version of a stochastic games domain for an agent of a given type (specified by an AgentType object or for a given list of stochastic games single actions (SingleAction).
SGToSADomain(SGDomain, AgentType, SingleAgentInterface) - Constructor for class burlap.behavior.stochasticgame.agents.interfacing.singleagent.SGToSADomain: Initializes.
SGToSADomain(SGDomain, List<SingleAction>, SingleAgentInterface) - Constructor for class burlap.behavior.stochasticgame.agents.interfacing.singleagent.SGToSADomain: Initializes.
SGToSADomain.SAActionWrapper - Class in burlap.behavior.stochasticgame.agents.interfacing.singleagent: A single agent action wrapper for a stochastic game action.
SGToSADomain.SAActionWrapper(SingleAction) - Constructor for class burlap.behavior.stochasticgame.agents.interfacing.singleagent.SGToSADomain.SAActionWrapper: Initializes for a given stochastic games action.
SGVisualExplorer - Class in burlap.oomdp.stochasticgames.explorers: This class allows you act as all of the agents in a stochastic game by choosing actions for each of them to take in specific states.
SGVisualExplorer(SGDomain, Visualizer, State, JointActionModel) - Constructor for class burlap.oomdp.stochasticgames.explorers.SGVisualExplorer: Deprecated.
SGVisualExplorer(SGDomain, Visualizer, State) - Constructor for class burlap.oomdp.stochasticgames.explorers.SGVisualExplorer: Initializes the data members for the visual explorer.
SGVisualExplorer(SGDomain, Visualizer, State, JointActionModel, int, int) - Constructor for class burlap.oomdp.stochasticgames.explorers.SGVisualExplorer: Deprecated.
SGVisualExplorer(SGDomain, Visualizer, State, int, int) - Constructor for class burlap.oomdp.stochasticgames.explorers.SGVisualExplorer: Initializes the data members for the visual explorer.
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.HashedTransitionProbability
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
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.
shouldAnnotateExecution - Variable in class burlap.behavior.singleagent.options.Option: Boolean indicating whether the last option execution recording annotates the selected actions with this option's name
shouldAnnotateOptions - Variable in class burlap.behavior.singleagent.learning.tdmethods.QLearning: Whether decomposed options should have their primitive actions annotated with the options name in the returned EpisodeAnalysis objects.
shouldAnnotateOptions - Variable in class burlap.behavior.singleagent.learning.tdmethods.vfa.GradientDescentSarsaLam: Whether decomposed options should have their primitive actions annotated with the options name in the returned EpisodeAnalysis objects.
shouldDecomposeOptions - Variable in class burlap.behavior.singleagent.learning.tdmethods.QLearning: Whether options should be decomposed into actions in the returned EpisodeAnalysis objects.
shouldDecomposeOptions - Variable in class burlap.behavior.singleagent.learning.tdmethods.vfa.GradientDescentSarsaLam: Whether options should be decomposed into actions in the returned EpisodeAnalysis objects.
shouldRecordResults - Variable in class burlap.behavior.singleagent.options.Option: Boolean indicating whether the last option execution result should be saved
shouldRereunPolicyIteration(EpisodeAnalysis) - 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<GroundedAction>, 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.stochasticgame.auxiliary.performance.MultiAgentPerformancePlotter: the significance level used for confidence intervals.
SingleAction - Class in burlap.oomdp.stochasticgames: A single action is an action specification for individual agents in a stochastic game.
SingleAction(SGDomain, String) - Constructor for class burlap.oomdp.stochasticgames.SingleAction: Initializes this single action to be for the given domain and with the given name.
SingleAction(SGDomain, String, String[]) - Constructor for class burlap.oomdp.stochasticgames.SingleAction: Initializes this single action to be for the given domain, with the given name, and with the given parameter class types.
SingleAction(SGDomain, String, String[], String[]) - Constructor for class burlap.oomdp.stochasticgames.SingleAction: Initializes this single action to be for the given domain, with the given name, with the given parameter class types, and with the given parameter order groups.
singleActionMap - Variable in class burlap.oomdp.stochasticgames.SGDomain: A map from action names to their corresponding SingleAction
singleActions - Variable in class burlap.oomdp.stochasticgames.SGDomain: The full set of actions that could be taken by any agent.
SingleAgentInterface - Class in burlap.behavior.stochasticgame.agents.interfacing.singleagent: For a number of reasons outside the scope of this class description, BURLAP single agent learning algorithms use a different interface for interacting with the world than stochastic games agents do.
SingleAgentInterface(SGDomain, SALearningAgentFactoryForSG) - Constructor for class burlap.behavior.stochasticgame.agents.interfacing.singleagent.SingleAgentInterface: Initializes for a given stochastic games domain and a factory to produce the single agent learning object
SingleAgentInterface.MutableGroundedSingleAction - Class in burlap.behavior.stochasticgame.agents.interfacing.singleagent: A mutable grounded singled action
SingleAgentInterface.MutableGroundedSingleAction() - Constructor for class burlap.behavior.stochasticgame.agents.interfacing.singleagent.SingleAgentInterface.MutableGroundedSingleAction
SingleAgentInterface.MutableState - Class in burlap.behavior.stochasticgame.agents.interfacing.singleagent: A mutable OO-MDP state wrapper
SingleAgentInterface.MutableState() - Constructor for class burlap.behavior.stochasticgame.agents.interfacing.singleagent.SingleAgentInterface.MutableState
SingleAgentInterface.SARFWrapper - Class in burlap.behavior.stochasticgame.agents.interfacing.singleagent: A reward function for returning the last RLGlue reward.
SingleAgentInterface.SARFWrapper() - Constructor for class burlap.behavior.stochasticgame.agents.interfacing.singleagent.SingleAgentInterface.SARFWrapper
SingleAgentInterface.SATFWrapper - Class in burlap.behavior.stochasticgame.agents.interfacing.singleagent: A termianl function that returns true when the last RLGlue state was terminal.
SingleAgentInterface.SATFWrapper() - Constructor for class burlap.behavior.stochasticgame.agents.interfacing.singleagent.SingleAgentInterface.SATFWrapper
SingleGoalPFRF - Class in burlap.oomdp.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.oomdp.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.oomdp.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.behavior.singleagent.planning: 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.behavior.singleagent.planning.SinglePFSCT: Initializes with the propositional function that is checked for state satisfaction
SinglePFTF - Class in burlap.oomdp.singleagent.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.oomdp.singleagent.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.oomdp.singleagent.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 construtor for a bimatrix game where the row player payoffs and colum 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.ActionNameMap() - Constructor for class burlap.domain.stochasticgames.normalform.SingleStageNormalFormGame.ActionNameMap
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.AgentPayoutFunction() - Constructor for class burlap.domain.stochasticgames.normalform.SingleStageNormalFormGame.AgentPayoutFunction
SingleStageNormalFormGame.NFGSingleAction - Class in burlap.domain.stochasticgames.normalform: A SingleAction class that uses the parent domain generator to determine which agent can take which actions and enforces that in the preconditions.
SingleStageNormalFormGame.NFGSingleAction(SGDomain, String, SingleStageNormalFormGame.ActionNameMap[]) - Constructor for class burlap.domain.stochasticgames.normalform.SingleStageNormalFormGame.NFGSingleAction
SingleStageNormalFormGame.SingleStageNormalFormJointReward - 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.SingleStageNormalFormJointReward(int, SingleStageNormalFormGame.ActionNameMap[], SingleStageNormalFormGame.AgentPayoutFunction[]) - Constructor for class burlap.domain.stochasticgames.normalform.SingleStageNormalFormGame.SingleStageNormalFormJointReward
SingleStageNormalFormGame.StrategyProfile - Class in burlap.domain.stochasticgames.normalform: A strategy profile represented as an array of action indices that is hashable.
SingleStageNormalFormGame.StrategyProfile(int...) - Constructor for class burlap.domain.stochasticgames.normalform.SingleStageNormalFormGame.StrategyProfile
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() - Method in class burlap.domain.stochasticgames.normalform.SingleStageNormalFormGame.ActionNameMap
size() - Method in class burlap.oomdp.stochasticgames.JointAction: Returns the number of actions in this joint action.
SIZEATTNAME - Static variable in class burlap.domain.singleagent.frostbite.FrostbiteDomain: Constant for the name of the size of a frozen platform
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, StateHashFactory, 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, StateHashFactory, 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.MutableInt(int) - Constructor for class burlap.behavior.learningrate.SoftTimeInverseDecayLR.MutableInt
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
SoftTimeInverseDecayLR.StateWiseTimeIndex() - Constructor for class burlap.behavior.learningrate.SoftTimeInverseDecayLR.StateWiseTimeIndex
solve(double[][], double[][]) - Method in class burlap.behavior.stochasticgame.agents.twoplayer.singlestage.equilibriumplayer.BimatrixEquilibriumSolver: Solves and caches the solution for the given bimatrix.
solver - Variable in class burlap.behavior.stochasticgame.agents.twoplayer.singlestage.equilibriumplayer.EquilibriumPlayingAgent: The solution concept to be solved for the immediate rewards.
somePFGroundingIsTrue(State) - Method in class burlap.oomdp.core.PropositionalFunction: Returns true if there existing a GroundedProp for the provided State that is in true in the State.
somePFGroundingIsTrue(PropositionalFunction) - Method in class burlap.oomdp.core.State: Deprecated.
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.
sourceAction - Variable in class burlap.behavior.singleagent.learning.modellearning.ModeledDomainGenerator.ModeledAction: The source action this action models
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.singleagent.vfa.fourier.FourierBasisLearningRateWrapper: The source LearningRate function that is queried.
sourcePolicy - Variable in class burlap.behavior.singleagent.learning.modellearning.rmax.UnmodeledFavoredPolicy
sourcePolicy - Variable in class burlap.behavior.singleagent.planning.commonpolicies.CachedPolicy: The source policy that gets cached
sourceRF - Variable in class burlap.behavior.singleagent.learning.modellearning.rmax.PotentialShapedRMax.PotentialShapedRMaxRF: The source reward function
sp - Variable in class burlap.behavior.singleagent.EpisodeSequenceVisualizer
sp - Variable in class burlap.behavior.singleagent.learning.lspi.SARSData.SARS: The next state
sp - Variable in class burlap.behavior.stochasticgame.GameSequenceVisualizer
sp - Variable in class burlap.oomdp.singleagent.explorer.VisualExplorer.SaveEpisodeAction: The State parser used to save episodes
SparseSampling - Class in burlap.behavior.singleagent.planning.stochastic.sparsesampling: An implementation of the Sparse Sampling (SS) [1] planning algorithm.
SparseSampling(Domain, RewardFunction, TerminalFunction, double, StateHashFactory, 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.HashedHeightState(StateHashTuple, int) - Constructor for class burlap.behavior.singleagent.planning.stochastic.sparsesampling.SparseSampling.HashedHeightState: Initializes.
SparseSampling.StateNode - Class in burlap.behavior.singleagent.planning.stochastic.sparsesampling: A class for state nodes.
SparseSampling.StateNode(StateHashTuple, int) - Constructor for class burlap.behavior.singleagent.planning.stochastic.sparsesampling.SparseSampling.StateNode: Creates a node for the given hased state at the given height
SpecialExplorerAction - Interface in burlap.oomdp.singleagent.explorer: An interface for defining special non-domain actions to take in a visual explorer.
specification - Variable in class burlap.behavior.singleagent.vfa.cmac.Tiling: A map from object class names to attribute tile specifications for attributes of that class
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.oomdp.auxiliary.common.ConstantStateGenerator
srcAction - Variable in class burlap.behavior.singleagent.options.PrimitiveOption: The primitive action this option wraps.
srcAction - Variable in class burlap.domain.singleagent.tabularized.TabulatedDomainWrapper.ActionWrapper
srcDomain - Variable in class burlap.oomdp.singleagent.environment.DomainEnvironmentWrapper
srcState - Variable in class burlap.oomdp.stochasticgames.common.ConstantSGStateGenerator: The source state that will be copied and returned by the ConstantSGStateGenerator.generateState(List) method.
srender - Variable in class burlap.oomdp.visualizer.Visualizer: The StateRenderLayer instance for visualizing OO-MDP states.
start() - Method in class burlap.debugtools.MyTimer: Starts the timer.
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.stochasticgame.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.stochasticgame.auxiliary.performance.MultiAgentPerformancePlotter: Launches the GUI and automatic refresh thread.
startNewAgent(String) - Method in class burlap.behavior.singleagent.auxiliary.performance.PerformancePlotter: Informs the plotter that data collecton for a new agent should begin.
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.stochasticgame.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.stochasticgame.auxiliary.performance.MultiAgentPerformancePlotter: Initializes the datastructures for a new trial.
startStateGenerator - Variable in class burlap.behavior.singleagent.learnbydemo.apprenticeship.ApprenticeshipLearningRequest: The initial state generator that models the initial states from which the expert trajectories were drawn
state - Variable in class burlap.behavior.singleagent.planning.stochastic.montecarlo.uct.UCTStateNode: The (hashed) state this node wraps
State - Class in burlap.oomdp.core: State objects are a collection of Object Instances.
State() - Constructor for class burlap.oomdp.core.State
State(State) - Constructor for class burlap.oomdp.core.State: Initializes this state as a deep copy of the object instances in the provided source state s
StateAbstraction - Interface in burlap.oomdp.auxiliary: An interface for taking an input state and returning a simpler abstracted state representation.
stateActionWeights - Variable in class burlap.behavior.singleagent.vfa.common.LinearFVVFA: The function weights when performing Q-value function approximation.
StateConditionTest - Interface in burlap.behavior.singleagent.planning: And interface for defining classes that check for certain conditions in states.
StateConditionTestIterable - Interface in burlap.behavior.singleagent.planning: An extension of the StateConditionTest that is iterable.
stateConsole - Variable in class burlap.oomdp.singleagent.explorer.VisualExplorer
stateConsole - Variable in class burlap.oomdp.stochasticgames.explorers.SGVisualExplorer
stateDepthIndex - Variable in class burlap.behavior.singleagent.planning.stochastic.montecarlo.uct.UCT
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, StateHashFactory) - Constructor for class burlap.behavior.singleagent.auxiliary.StateEnumerator: Constructs
StateFeature - Class in burlap.behavior.singleagent.vfa: A class for associating a state feature identifier with a value of that state feature
StateFeature(int, double) - Constructor for class burlap.behavior.singleagent.vfa.StateFeature: Initializes.
stateFeatures - Variable in class burlap.behavior.singleagent.vfa.ApproximationResult: The state features used to produce the predicted value.
stateFromObservation(Observation) - Method in class burlap.behavior.singleagent.interfaces.rlglue.RLGlueWrappedDomainGenerator: Returns a state object for the domain of this generator that is a result of the RLGlue observation.
StateGenerator - Interface in burlap.oomdp.auxiliary: An interface for generating State objects.
stateGenerator - Variable in class burlap.oomdp.singleagent.interfaces.rlglue.RLGlueEnvironment: The state generator for generating states for each episode
StateGridder - Class in burlap.behavior.singleagent.auxiliary: This class is used for creating a grid of states over a state space domain.
StateGridder() - Constructor for class burlap.behavior.singleagent.auxiliary.StateGridder
StateGridder.AttributeSpecification - Class in burlap.behavior.singleagent.auxiliary: Class for specifying the grid along a single attribute.
StateGridder.AttributeSpecification(String, double, double, int) - Constructor for class burlap.behavior.singleagent.auxiliary.StateGridder.AttributeSpecification: Initializes.
StateGridder.AttributeSpecification(Attribute, int) - Constructor for class burlap.behavior.singleagent.auxiliary.StateGridder.AttributeSpecification: Initializes with the lower and upper values of the grid being set to the Attribute objects' full domain (i.e., its Attribute.lowerLim and Attribute.upperLim data members.
stateHash(State) - Method in class burlap.behavior.singleagent.planning.OOMDPPlanner: A shorthand method for hashing a state.
stateHash(State) - Method in class burlap.behavior.stochasticgame.agents.naiveq.history.SGQWActionHistory
stateHash - Variable in class burlap.behavior.stochasticgame.agents.naiveq.history.SGQWActionHistoryFactory: The state hashing factory the Q-learning algorithm will use
stateHash - Variable in class burlap.behavior.stochasticgame.agents.naiveq.SGNaiveQFactory: The state hashing factory the Q-learning algorithm will use
stateHash(State) - Method in class burlap.behavior.stochasticgame.agents.naiveq.SGNaiveQLAgent: First abstracts state s, and then returns the StateHashTuple object for the abstracted state.
StateHashFactory - Interface in burlap.behavior.statehashing: This interface is to be used by classes that can produce StateHashTuple objects, object that provide a hash values for State objects.
StateHashTuple - Class in burlap.behavior.statehashing: This class provides a hash value for State objects.
StateHashTuple(State) - Constructor for class burlap.behavior.statehashing.StateHashTuple: Initializes the StateHashTuple with the given State object.
StateJSONParser - Class in burlap.oomdp.auxiliary.common: A StateParser class that uses the JSON file format and can can convert states to JSON strings (and back from them) for any possible input domain.
StateJSONParser(Domain) - Constructor for class burlap.oomdp.auxiliary.common.StateJSONParser: Initializes with a given domain object.
stateMapping - Variable in class burlap.behavior.singleagent.options.Option: An option state mapping to use to map from a source MDP state representation to a representation that this option will use for action selection.
StateMapping - Interface in burlap.behavior.singleagent.planning: A state mapping interface that maps one state into another state.
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
StateParser - Interface in burlap.oomdp.auxiliary: This interface is used to converting states to parsable string representations and parsing those string representations back into states.
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.
StateReachability() - Constructor for class burlap.behavior.singleagent.auxiliary.StateReachability
StateRenderLayer - Class in burlap.oomdp.visualizer: This class provides 2D visualization of states by being provided a set of classes that can paint ObjectInstnaces to the canvas as well as classes that can paint general domain information.
StateRenderLayer() - Constructor for class burlap.oomdp.visualizer.StateRenderLayer
stateRepresentations - Variable in class burlap.behavior.stochasticgame.agents.naiveq.SGNaiveQLAgent: A map from hashed states to the internal state representation for the states stored in the q-table.
StateResetSpecialAction - Class in burlap.oomdp.singleagent.explorer: A special non-domain action that causes a visual explorer to rest the state to a specified base state or to a state drawn from a state generator.
StateResetSpecialAction(State) - Constructor for class burlap.oomdp.singleagent.explorer.StateResetSpecialAction: Initializes which base state to reset to
StateResetSpecialAction(StateGenerator) - Constructor for class burlap.oomdp.singleagent.explorer.StateResetSpecialAction: Initializes with a state generator to draw from on reset
states - Variable in class burlap.behavior.stochasticgame.GameAnalysis: The sequence of states
states - Variable in class burlap.behavior.stochasticgame.mavaluefunction.vfplanners.MAValueIteration: The set of states that have been found
stateSequence - Variable in class burlap.behavior.singleagent.EpisodeAnalysis: The sequence of states observed
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
stateTilings - Variable in class burlap.behavior.singleagent.vfa.cmac.CMACFeatureDatabase: For each tiling, a map from state tiles to an integer representing their feature identifier
StateToFeatureVectorGenerator - Interface in burlap.behavior.singleagent.vfa: Many functions approximation techniques require a fixed feature vector to work and in many cases, using abstract features from the state attributes is useful.
stateToString(State) - Method in class burlap.domain.singleagent.cartpole.CartPoleStateParser
stateToString(State) - Method in class burlap.domain.singleagent.cartpole.InvertedPendulumStateParser
stateToString(State) - Method in class burlap.domain.singleagent.frostbite.FrostbiteStateParser
stateToString(State) - Method in class burlap.domain.singleagent.gridworld.GridWorldStateParser
stateToString(State) - Method in class burlap.domain.singleagent.lunarlander.LLStateParser
stateToString(State) - Method in class burlap.domain.singleagent.mountaincar.MountainCarStateParser
stateToString(State) - Method in class burlap.oomdp.auxiliary.common.StateJSONParser
stateToString(State) - Method in class burlap.oomdp.auxiliary.common.StateYAMLParser
stateToString(State) - Method in class burlap.oomdp.auxiliary.common.UniversalStateParser
stateToString(State) - Method in interface burlap.oomdp.auxiliary.StateParser: Converts state s into a parsable string representation.
stateTransitionsAreModeled(State) - Method in class burlap.behavior.singleagent.learning.modellearning.Model: Indicates whether this model "knows" the transition dynamics from the given input state for all applicable actions.
stateTransitionsAreModeled(State) - Method in class burlap.behavior.singleagent.learning.modellearning.models.TabularModel
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.singleagent.vfa.common.LinearFVVFA: The function weights when performing state value function approximation.
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
StateYAMLParser - Class in burlap.oomdp.auxiliary.common: A StateParser class that uses the YAML file format and can can convert states to YAML strings (and back from them) for any possible input domain.
StateYAMLParser(Domain) - Constructor for class burlap.oomdp.auxiliary.common.StateYAMLParser: Initializes with a a given domain object.
StaticDomainPainter - Interface in burlap.behavior.singleagent.auxiliary.valuefunctionvis: An interface for painting general domain information to a 2D graphics context.
StaticPainter - Interface in burlap.oomdp.visualizer: This class paints general properties of a state/domain that may not be represented by any specific object instance data.
StaticRepeatedGameActionModel - Class in burlap.oomdp.stochasticgames.common: This action model can be used to take a single stage game, and cause it to repeat itself.
StaticRepeatedGameActionModel() - Constructor for class burlap.oomdp.stochasticgames.common.StaticRepeatedGameActionModel
StaticWeightedAStar - Class in burlap.behavior.singleagent.planning.deterministic.informed.astar: Statically weighted A* [1] implementation.
StaticWeightedAStar(Domain, RewardFunction, StateConditionTest, StateHashFactory, Heuristic, double) - Constructor for class burlap.behavior.singleagent.planning.deterministic.informed.astar.StaticWeightedAStar: Initializes the planner.
stepEpisode - Variable in class burlap.behavior.singleagent.auxiliary.performance.PerformancePlotter.Trial: Stores the steps by episode
stepEpisode - Variable in class burlap.behavior.stochasticgame.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.stochasticgame.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.stochasticgame.auxiliary.performance.MultiAgentPerformancePlotter.Trial: Updates all datastructures with the reward received from the last step
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.
StochasticTree.STNode(T, double, StochasticTree<T>.STNode) - Constructor for class burlap.datastructures.StochasticTree.STNode: Initializes a leaf node with the given weight and parent
StochasticTree.STNode(double) - Constructor for class burlap.datastructures.StochasticTree.STNode: Initializes a node with a weight only
StochasticTree.STNode(double, StochasticTree<T>.STNode) - Constructor for class burlap.datastructures.StochasticTree.STNode: Initializes a node with a given weight and parent node
stop() - Method in class burlap.debugtools.MyTimer: Stops the timer.
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.learnbydemo.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.stochasticgame.agents.naiveq.SGNaiveQFactory: The state abstract the Q-learning algorithm will use
storedMapAbstraction - Variable in class burlap.behavior.stochasticgame.agents.naiveq.SGNaiveQLAgent: A state abstraction to use.
stringToState(String) - Method in class burlap.domain.singleagent.cartpole.CartPoleStateParser
stringToState(String) - Method in class burlap.domain.singleagent.cartpole.InvertedPendulumStateParser
stringToState(String) - Method in class burlap.domain.singleagent.frostbite.FrostbiteStateParser
stringToState(String) - Method in class burlap.domain.singleagent.gridworld.GridWorldStateParser
stringToState(String) - Method in class burlap.domain.singleagent.lunarlander.LLStateParser
stringToState(String) - Method in class burlap.domain.singleagent.mountaincar.MountainCarStateParser
stringToState(String) - Method in class burlap.oomdp.auxiliary.common.StateJSONParser
stringToState(String) - Method in class burlap.oomdp.auxiliary.common.StateYAMLParser
stringToState(String) - Method in class burlap.oomdp.auxiliary.common.UniversalStateParser
stringToState(String) - Method in interface burlap.oomdp.auxiliary.StateParser: Converts a string into a State object assuming the string representation was produced using this state parser.
stringVal - Variable in class burlap.oomdp.core.values.StringValue: The string value
StringValue - Class in burlap.oomdp.core.values: This class provides a value for a string.
StringValue(Attribute) - Constructor for class burlap.oomdp.core.values.StringValue: Initializes for a given attribute.
StringValue(Value) - Constructor for class burlap.oomdp.core.values.StringValue: Initializes from an existing value.
subgoalReward - Variable in class burlap.behavior.singleagent.options.LocalSubgoalRF: Defines the reward returned for transitions to subgoal states; default 0.
subgoalStateTest - Variable in class burlap.behavior.singleagent.options.LocalSubgoalRF: Defines he set of subgoal states for the option
subgoalStateTest - Variable in class burlap.behavior.singleagent.options.LocalSubgoalTF: Defines he set of subgoal states for the option
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.singleagent.planning.vfa.fittedvi: An interface for learning value function approximation via a supervised learning algorithm.
SupervisedVFA.SupervisedVFAInstance - Class in burlap.behavior.singleagent.planning.vfa.fittedvi: A pair for a state and it's target value function value.
SupervisedVFA.SupervisedVFAInstance(State, double) - Constructor for class burlap.behavior.singleagent.planning.vfa.fittedvi.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.stochasticgame.PolicyFromJointPolicy

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