R-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

R

r - Variable in class burlap.behavior.singleagent.learning.lspi.SARSData.SARS: The resulting reward received
rand - Variable in class burlap.behavior.singleagent.learnbydemo.mlirl.MultipleIntentionsMLIRL: A random object used for initializing each cluster's RF parameters randomly.
rand - Variable in class burlap.behavior.singleagent.learning.modellearning.Model: Random number generator
rand - Variable in class burlap.behavior.singleagent.options.Option: Random object for following stochastic option policies
rand - Variable in class burlap.behavior.singleagent.planning.commonpolicies.EpsilonGreedy
rand - Variable in class burlap.behavior.singleagent.planning.commonpolicies.GreedyQPolicy
rand - Variable in class burlap.behavior.singleagent.planning.deterministic.uninformed.dfs.DFS: A random object for random walks
rand - Variable in class burlap.behavior.singleagent.planning.stochastic.montecarlo.uct.UCT
rand - Variable in class burlap.behavior.singleagent.Policy.RandomPolicy: The random factory used to randomly select actions.
rand - Variable in class burlap.behavior.singleagent.vfa.cmac.CMACFeatureDatabase: A random object for jittering the tile alignments.
rand - Variable in class burlap.behavior.singleagent.vfa.cmac.FVCMACFeatureDatabase: A random object for jittering the tile alignments.
rand - Variable in class burlap.behavior.stochasticgame.agents.twoplayer.singlestage.equilibriumplayer.EquilibriumPlayingAgent: Random generator for selecting actions according to the solved solution
rand - Variable in class burlap.behavior.stochasticgame.mavaluefunction.policies.EGreedyJointPolicy: A random object used for sampling
rand - Variable in class burlap.behavior.stochasticgame.mavaluefunction.policies.EGreedyMaxWellfare: A random object used for sampling
rand - Variable in class burlap.datastructures.BoltzmannDistribution: The random object to use for sampling.
rand - Variable in class burlap.datastructures.StochasticTree: A random object used for sampling.
rand - Variable in class burlap.domain.singleagent.frostbite.FrostbiteDomain.MovementAction: Random object for sampling distribution
rand - Variable in class burlap.domain.singleagent.graphdefined.GraphDefinedDomain.GraphAction: Random object for sampling the stochastic graph transitions
rand - Variable in class burlap.domain.singleagent.gridworld.GridWorldDomain.MovementAction: Random object for sampling distribution
RandomAgent - Class in burlap.behavior.stochasticgame.agents: Stochastic games agent that chooses actions uniformly randomly.
RandomAgent() - Constructor for class burlap.behavior.stochasticgame.agents.RandomAgent
RandomFactory - Class in burlap.debugtools: Random factory that allows you to logically group various random generators.
RandomFactory() - Constructor for class burlap.debugtools.RandomFactory: Initializes the map structures
randomizeParameters(double, double, Random) - Method in class burlap.behavior.singleagent.learnbydemo.mlirl.differentiableplanners.diffvinit.DifferentiableVInit.ParamedDiffVInit: Randomizes the parameter values using the given random number generator.
randomizeParameters(double[]) - Method in class burlap.behavior.singleagent.learnbydemo.mlirl.MultipleIntentionsMLIRL: Randomizes parameters in the given vector between -1 and 1.
randomizeParameters(double, double, Random) - Method in class burlap.behavior.singleagent.learnbydemo.mlirl.support.DifferentiableRF: Randomizes the parameter values using the given random number generator.
RandomStartStateGenerator - Class in burlap.oomdp.auxiliary.common: This class will return a random state from a set of states that are reachable from a source seed state.
RandomStartStateGenerator(SADomain, State) - Constructor for class burlap.oomdp.auxiliary.common.RandomStartStateGenerator: Will discover the reachable states from which to randomly select.
RandomStartStateGenerator(SADomain, State, StateHashFactory) - Constructor for class burlap.oomdp.auxiliary.common.RandomStartStateGenerator: Will discover reachable states from which to randomly select.
RATTNAME - Static variable in class burlap.domain.singleagent.lunarlander.LunarLanderDomain: Constant for the name of the right boundary attribute for rectangular obstacles and landing pads
RBF - Class in burlap.behavior.singleagent.vfa.rbf: An abstract class for defining RBF units.
RBF(State, DistanceMetric) - Constructor for class burlap.behavior.singleagent.vfa.rbf.RBF: Initializes with a center state for this unit and a distance metric to compare input states to it.
RBFFeatureDatabase - Class in burlap.behavior.singleagent.vfa.rbf: A feature database of RBF units that can be used for linear value function approximation.
RBFFeatureDatabase(boolean) - Constructor for class burlap.behavior.singleagent.vfa.rbf.RBFFeatureDatabase: Initializes with an empty list of RBF units.
rbfs - Variable in class burlap.behavior.singleagent.vfa.rbf.FVRBFFeatureDatabase: The list of RBF units in this database
rbfs - Variable in class burlap.behavior.singleagent.vfa.rbf.RBFFeatureDatabase: The list of RBF units in this database
REALATT - Static variable in class burlap.behavior.singleagent.interfaces.rlglue.RLGlueWrappedDomainGenerator
REALCLASS - Static variable in class burlap.behavior.singleagent.interfaces.rlglue.RLGlueWrappedDomainGenerator
realVal - Variable in class burlap.oomdp.core.values.RealValue: The real value stored as a double.
RealValue - Class in burlap.oomdp.core.values: A real-valued value subclass in which real-values are stored as doubles.
RealValue(Attribute) - Constructor for class burlap.oomdp.core.values.RealValue: Initializes this value to be an assignment for Attribute attribute.
RealValue(Value) - Constructor for class burlap.oomdp.core.values.RealValue: Initializes this value as a copy from the source Value object v.
realWorldDomain - Variable in class burlap.behavior.singleagent.learning.modellearning.DomainMappedPolicy: The real world domain containing the real world actions that need to be executed.
receiveSAAction(GroundedAction) - Method in class burlap.behavior.stochasticgame.agents.interfacing.singleagent.SingleAgentInterface: A method that receives calls from the single agent domain actions to inform this stochastic games agent which action to take next when requested by the world.
recomputeReachableStates() - Method in class burlap.behavior.singleagent.learnbydemo.mlirl.differentiableplanners.DifferentiableVI: Calling this method will force the planner to recompute the reachable states when the DifferentiableVI.planFromState(burlap.oomdp.core.State) method is called next.
recomputeReachableStates() - Method in class burlap.behavior.singleagent.planning.stochastic.policyiteration.PolicyIteration: Calling this method will force the planner to recompute the reachable states when the PolicyIteration.planFromState(State) method is called next.
recomputeReachableStates() - Method in class burlap.behavior.singleagent.planning.stochastic.valueiteration.ValueIteration: Calling this method will force the planner to recompute the reachable states when the ValueIteration.planFromState(State) method is called next.
recordedEpisodes - Variable in class burlap.oomdp.singleagent.explorer.VisualExplorer
recordTransitionTo(State, GroundedAction, double) - Method in class burlap.behavior.singleagent.EpisodeAnalysis: Deprecated.
recordTransitionTo(GroundedAction, State, double) - Method in class burlap.behavior.singleagent.EpisodeAnalysis: Records an transition event where the agent applied the usingAction action in the last state in this object's state sequence, transitioned to state nextState, and received reward r,.
recordTransitionTo(JointAction, State, Map<String, Double>) - Method in class burlap.behavior.stochasticgame.GameAnalysis: Records a transition from the last recorded state in this object using the specififed joint action to the specified next state and with the specified joint reward being recieved as a result.
referencesSuccessor(UCTStateNode) - Method in class burlap.behavior.singleagent.planning.stochastic.montecarlo.uct.UCTActionNode: Returns whether this action node has a observed a given successor state node in the past
refreshPriority(T) - Method in class burlap.datastructures.HashIndexedHeap: Calling this method indicates that the priority of the object passed to the method has been modified and that this heap needs to reorder its elements as a result
registerAgent(Agent, AgentType) - Method in class burlap.oomdp.stochasticgames.World: Registers an agent to be a participant in this world.
RelationalValue - Class in burlap.oomdp.core.values: A relational valued value subclass in which values are stored as a single String object for the name of the object instance to which it is linked.
RelationalValue(Attribute) - Constructor for class burlap.oomdp.core.values.RelationalValue: Initializes this value to be an assignment for Attribute attribute.
RelationalValue(Value) - Constructor for class burlap.oomdp.core.values.RelationalValue: Initializes this value as a copy from the source Value object v.
remove(int) - Method in class burlap.behavior.singleagent.learning.lspi.SARSData: Removes the SARSData.SARS tuple at the ith index
remove(T) - Method in class burlap.datastructures.StochasticTree: Removes the given element from the tree.
removeAction(String) - Method in class burlap.behavior.singleagent.Policy.RandomPolicy: Removes an action from consideration.
removeEdge(int, int, int) - Method in class burlap.domain.singleagent.graphdefined.GraphDefinedDomain: Removes a given edge from the transition dynamics.
removeHelper(StochasticTree<T>.STNode) - Method in class burlap.datastructures.StochasticTree: A recursive method for removing a node
removeObject(String) - Method in class burlap.oomdp.core.State: Removes the object instance with the name oname from this state.
removeObject(ObjectInstance) - Method in class burlap.oomdp.core.State: Removes the object instance o from this state.
removeRelationalTarget(String, String) - Method in class burlap.oomdp.core.ObjectInstance: Removes an object target from the specified relational attribute.
removeRelationalTarget(String) - Method in class burlap.oomdp.core.Value: Removes a specific relational target from the relational value in relational attribute.
removeRelationalTarget(String) - Method in class burlap.oomdp.core.values.DiscreteValue
removeRelationalTarget(String) - Method in class burlap.oomdp.core.values.DoubleArrayValue
removeRelationalTarget(String) - Method in class burlap.oomdp.core.values.IntArrayValue
removeRelationalTarget(String) - Method in class burlap.oomdp.core.values.IntValue
removeRelationalTarget(String) - Method in class burlap.oomdp.core.values.MultiTargetRelationalValue
removeRelationalTarget(String) - Method in class burlap.oomdp.core.values.RealValue
removeRelationalTarget(String) - Method in class burlap.oomdp.core.values.RelationalValue
removeRelationalTarget(String) - Method in class burlap.oomdp.core.values.StringValue
removeRenderLayer(int) - Method in class burlap.oomdp.visualizer.MultiLayerRenderer: Removes the render layer at teh specified position.
removeTerminals(int...) - Method in class burlap.domain.singleagent.graphdefined.GraphTF: Removes nodes as being marked as terminal states
removeWorldObserver(WorldObserver) - Method in class burlap.oomdp.stochasticgames.World: Removes the specified world observer from this world
removeZeroRows(double[][]) - Static method in class burlap.behavior.stochasticgame.solvers.CorrelatedEquilibriumSolver: Takes an input 2D double matrix and returns a new matrix will all the all zero rows removed.
renameObject(String, String) - Method in class burlap.oomdp.core.State: Renames the identifier for the object instance currently named originalName with the name newName.
renameObject(ObjectInstance, String) - Method in class burlap.oomdp.core.State: Renames the identifier for object instance o in this state to newName.
render(Graphics2D, float, float) - Method in class burlap.behavior.singleagent.auxiliary.valuefunctionvis.PolicyRenderLayer
render(Graphics2D, float, float) - Method in class burlap.behavior.singleagent.auxiliary.valuefunctionvis.ValueFunctionRenderLayer
render(Graphics2D, float, float) - Method in interface burlap.oomdp.visualizer.RenderLayer
render(Graphics2D, float, float) - Method in class burlap.oomdp.visualizer.StateRenderLayer
RenderLayer - Interface in burlap.oomdp.visualizer: A RenderLayer is a 2 dimensional layer that paints to a provided 2D graphics context.
renderLayers - Variable in class burlap.oomdp.visualizer.MultiLayerRenderer: The layers that will be rendered in order from index 0 to n
renderStyle - Variable in class burlap.behavior.singleagent.auxiliary.valuefunctionvis.common.PolicyGlyphPainter2D: The render style to use
renderValueString - Variable in class burlap.behavior.singleagent.auxiliary.valuefunctionvis.common.StateValuePainter2D: Whether the numeric string for the value of the state should be rendered in its cell or not.
replayEpisode(EpisodeAnalysis) - Method in class burlap.oomdp.singleagent.common.VisualActionObserver: Casues the visualizer to replay through the provided EpisodeAnalysis object.
replayGame(GameAnalysis) - Method in class burlap.oomdp.stochasticgames.common.VisualWorldObserver: Causes the visualizer to be replayed for the given GameAnalysis object.
request - Variable in class burlap.behavior.singleagent.learnbydemo.mlirl.MLIRL: The MLRIL request defining the IRL problem.
request - Variable in class burlap.behavior.singleagent.learnbydemo.mlirl.MultipleIntentionsMLIRL: The source problem request defining the problem to be solved.
rerunVI() - Method in class burlap.behavior.singleagent.learning.modellearning.modelplanners.VIModelPlanner: Reruns VI on the new updated model.
rescale(double, double) - Method in interface burlap.behavior.singleagent.auxiliary.valuefunctionvis.common.ColorBlend: Tells this object the minimum value and the maximum value it can receive.
rescale(double, double) - Method in class burlap.behavior.singleagent.auxiliary.valuefunctionvis.common.LandmarkColorBlendInterpolation
rescale(double, double) - Method in class burlap.behavior.singleagent.auxiliary.valuefunctionvis.common.StateValuePainter2D
rescale(double, double) - Method in class burlap.behavior.singleagent.auxiliary.valuefunctionvis.StateValuePainter: Used to tell this painter that it should render state values so that the minimum possible value is lowerValue and the maximum is upperValue.
rescaleRect(float, float, float, float, float) - Method in class burlap.behavior.singleagent.auxiliary.valuefunctionvis.common.PolicyGlyphPainter2D: Takes in a rectangle specification and scales it equally along each direction by a scale factor.
resetAvgs() - Method in class burlap.debugtools.MyTimer: Resets to zero the average and total time recorded over all start-stop calls.
resetData() - Method in class burlap.behavior.singleagent.learning.actorcritic.actor.BoltzmannActor
resetData() - Method in class burlap.behavior.singleagent.learning.actorcritic.Actor: Used to reset any data that was created/modified during learning so that learning can be begin anew.
resetData() - Method in interface burlap.behavior.singleagent.learning.actorcritic.Critic: Used to reset any data that was created/modified during learning so that learning can be begin anew.
resetData() - Method in class burlap.behavior.singleagent.learning.actorcritic.critics.TDLambda
resetData() - Method in class burlap.behavior.singleagent.learning.actorcritic.critics.TimeIndexedTDLambda
resetDecay() - Method in class burlap.behavior.learningrate.ConstantLR
resetDecay() - Method in class burlap.behavior.learningrate.ExponentialDecayLR
resetDecay() - Method in interface burlap.behavior.learningrate.LearningRate: Causes any learnign rate decay to reset to where it started.
resetDecay() - Method in class burlap.behavior.learningrate.SoftTimeInverseDecayLR
resetDecay() - Method in class burlap.behavior.singleagent.vfa.fourier.FourierBasisLearningRateWrapper
resetMatchSelections() - Method in class burlap.oomdp.stochasticgames.tournament.common.AllPairWiseSameTypeMS
resetMatchSelections() - Method in interface burlap.oomdp.stochasticgames.tournament.MatchSelector: Resets the match selections and causes the MatchSelector.getNextMatch() method to start from the beginning of matches
resetModel() - Method in class burlap.behavior.singleagent.learning.modellearning.Model: Resets the model data so that learning can begin anew.
resetModel() - Method in class burlap.behavior.singleagent.learning.modellearning.models.TabularModel
resetPlanner() - Method in interface burlap.behavior.singleagent.learning.modellearning.ModelPlanner: Resets planner as if no planning had never been called.
resetPlanner() - Method in class burlap.behavior.singleagent.learning.modellearning.modelplanners.VIModelPlanner
resetPlannerResults() - Method in class burlap.behavior.singleagent.learnbydemo.mlirl.differentiableplanners.DifferentiableSparseSampling
resetPlannerResults() - Method in class burlap.behavior.singleagent.learnbydemo.mlirl.differentiableplanners.DifferentiableVFPlanner
resetPlannerResults() - Method in class burlap.behavior.singleagent.learnbydemo.mlirl.differentiableplanners.DifferentiableVI
resetPlannerResults() - Method in class burlap.behavior.singleagent.learning.actorcritic.ActorCritic
resetPlannerResults() - Method in class burlap.behavior.singleagent.learning.lspi.LSPI
resetPlannerResults() - Method in class burlap.behavior.singleagent.learning.modellearning.artdp.ARTDP
resetPlannerResults() - Method in class burlap.behavior.singleagent.learning.modellearning.rmax.PotentialShapedRMax
resetPlannerResults() - Method in class burlap.behavior.singleagent.learning.tdmethods.QLearning
resetPlannerResults() - Method in class burlap.behavior.singleagent.learning.tdmethods.vfa.GradientDescentSarsaLam
resetPlannerResults() - Method in class burlap.behavior.singleagent.planning.deterministic.DeterministicPlanner
resetPlannerResults() - Method in class burlap.behavior.singleagent.planning.deterministic.uninformed.dfs.DFS
resetPlannerResults() - Method in class burlap.behavior.singleagent.planning.OOMDPPlanner: Use this method to reset all planner results so that planning can be started fresh with a call to OOMDPPlanner.planFromState(State) as if no planning had ever been performed before.
resetPlannerResults() - Method in class burlap.behavior.singleagent.planning.stochastic.montecarlo.uct.UCT
resetPlannerResults() - Method in class burlap.behavior.singleagent.planning.stochastic.policyiteration.PolicyIteration
resetPlannerResults() - Method in class burlap.behavior.singleagent.planning.stochastic.sparsesampling.SparseSampling
resetPlannerResults() - Method in class burlap.behavior.singleagent.planning.stochastic.valueiteration.ValueIteration
resetPlannerResults() - Method in class burlap.behavior.singleagent.planning.ValueFunctionPlanner
resetPlannerResults() - Method in class burlap.behavior.singleagent.planning.vfa.fittedvi.FittedVI
resetTournamentReward() - Method in class burlap.oomdp.stochasticgames.tournament.Tournament: Reset the cumulative reward received by each agent in this tournament.
resetWeights() - Method in class burlap.behavior.singleagent.vfa.common.LinearFVVFA
resetWeights() - Method in class burlap.behavior.singleagent.vfa.common.LinearVFA
resetWeights() - Method in interface burlap.behavior.singleagent.vfa.ValueFunctionApproximation: Resets the weights as is learning had never been performed.
resolveCollisions(List<GridGameStandardMechanics.Location2>, List<GridGameStandardMechanics.Location2>) - Method in class burlap.domain.stochasticgames.gridgame.GridGameStandardMechanics: Resolves collisions that occur when two or more agents try to enter the same cell, in which case only one agent will make it into the position and the rest will stay in place
resolvePositionSwaps(List<GridGameStandardMechanics.Location2>, List<GridGameStandardMechanics.Location2>) - Method in class burlap.domain.stochasticgames.gridgame.GridGameStandardMechanics: Returns the position of each agent after accounting for collisions that are a result of agents trying to move into each others previous locations.
responseFor(double[]) - Method in class burlap.behavior.singleagent.vfa.rbf.functions.FVGaussianRBF
responseFor(State) - Method in class burlap.behavior.singleagent.vfa.rbf.functions.GaussianRBF
responseFor(double[]) - Method in class burlap.behavior.singleagent.vfa.rbf.FVRBF: Returns the RBF response from its center state to the query input state.
responseFor(State) - Method in class burlap.behavior.singleagent.vfa.rbf.RBF: Returns a response value to an input state that is a function of the distance between the input and this unit's center state.
reverseEnumerate - Variable in class burlap.behavior.singleagent.auxiliary.StateEnumerator: The reverse enumeration id to state map
reward(State, GroundedAction, State) - Method in class burlap.behavior.singleagent.interfaces.rlglue.RLGlueAgentShell.RLGlueRF
reward(State, GroundedAction, State) - Method in class burlap.behavior.singleagent.learnbydemo.mlirl.commonrfs.LinearStateActionDifferentiableRF
reward(State, GroundedAction, State) - Method in class burlap.behavior.singleagent.learnbydemo.mlirl.commonrfs.LinearStateDifferentiableRF
reward(State, GroundedAction, State) - Method in class burlap.behavior.singleagent.learnbydemo.mlirl.differentiableplanners.diffvinit.DiffVFRF
reward(State, GroundedAction, State) - Method in class burlap.behavior.singleagent.learnbydemo.mlirl.differentiableplanners.diffvinit.LinearDiffRFVInit
reward(State, GroundedAction, State) - Method in class burlap.behavior.singleagent.learning.GoalBasedRF
reward(State, GroundedAction, State) - Method in class burlap.behavior.singleagent.learning.modellearning.rmax.PotentialShapedRMax.PotentialShapedRMaxRF
reward(State, GroundedAction, State) - Method in class burlap.behavior.singleagent.options.LocalSubgoalRF
reward(State, GroundedAction, State) - Method in class burlap.behavior.singleagent.options.OptionEvaluatingRF
reward(State, GroundedAction, State) - Method in class burlap.behavior.singleagent.planning.deterministic.UniformPlusGoalRF
reward(State, GroundedAction, State) - Method in class burlap.behavior.singleagent.shaping.ShapedRewardFunction
reward(State, GroundedAction, State) - Method in class burlap.behavior.stochasticgame.agents.interfacing.singleagent.SingleAgentInterface.SARFWrapper
reward(State, GroundedAction, State) - Method in class burlap.domain.singleagent.cartpole.CartPoleDomain.CartPoleRewardFunction
reward(State, GroundedAction, State) - Method in class burlap.domain.singleagent.cartpole.InvertedPendulum.InvertedPendulumRewardFunction
reward(State, GroundedAction, State) - Method in class burlap.domain.singleagent.frostbite.FrostbiteRF
reward(State, GroundedAction, State) - Method in class burlap.domain.singleagent.graphdefined.GraphRF
reward(int, int, int) - Method in class burlap.domain.singleagent.graphdefined.GraphRF: Returns the reward for taking action a in state node s and transition to state node sprime.
reward(State, GroundedAction, State) - Method in class burlap.domain.singleagent.gridworld.GridWorldRewardFunction
reward(State, GroundedAction, State) - Method in class burlap.domain.singleagent.gridworld.macro.MacroCellGridWorld.LinearInPFRewardFunction
reward(State, GroundedAction, State) - Method in class burlap.domain.singleagent.lunarlander.LunarLanderRF
reward(State, JointAction, State) - Method in class burlap.domain.stochasticgames.gridgame.GridGame.GGJointRewardFunction
reward(State, JointAction, State) - Method in class burlap.domain.stochasticgames.normalform.SingleStageNormalFormGame.SingleStageNormalFormJointReward
reward(State, GroundedAction, State) - Method in class burlap.oomdp.singleagent.common.NullRewardFunction
reward(State, GroundedAction, State) - Method in class burlap.oomdp.singleagent.common.SingleGoalPFRF
reward(State, GroundedAction, State) - Method in class burlap.oomdp.singleagent.common.UniformCostRF
reward(State, GroundedAction, State) - Method in class burlap.oomdp.singleagent.environment.Environment.LastRewardRF
reward(State, GroundedAction, State) - Method in interface burlap.oomdp.singleagent.RewardFunction: Returns the reward received when action a is executed in state s and the agent transitions to state sprime.
reward(State, JointAction, State) - Method in interface burlap.oomdp.stochasticgames.JointReward: Returns the reward received by each agent specified in the joint action.
rewardFunction - Variable in class burlap.oomdp.singleagent.explorer.TerminalExplorer
RewardFunction - Interface in burlap.oomdp.singleagent: Defines the reward function for a task.
rewardFunction - Variable in class burlap.oomdp.stochasticgames.explorers.SGTerminalExplorer
rewardFunction - Variable in class burlap.oomdp.stochasticgames.explorers.SGVisualExplorer
rewardMap - Variable in class burlap.domain.singleagent.gridworld.macro.MacroCellVisualizer.MacroCellRewardWeightPainter
rewardMatrix - Variable in class burlap.domain.singleagent.gridworld.GridWorldRewardFunction
rewardRange - Variable in class burlap.oomdp.singleagent.interfaces.rlglue.RLGlueEnvironment: The reward function value range
rewards - Variable in class burlap.domain.singleagent.gridworld.macro.MacroCellGridWorld.LinearInPFRewardFunction
rewardSequence - Variable in class burlap.behavior.singleagent.EpisodeAnalysis: The sequence of rewards received.
rf - Variable in class burlap.behavior.singleagent.auxiliary.performance.LearningAlgorithmExperimenter: The reward function used to evaluate performance
rf - Variable in class burlap.behavior.singleagent.auxiliary.performance.PerformancePlotter: The reward funciton used to measure performance.
rf - Variable in class burlap.behavior.singleagent.learnbydemo.mlirl.differentiableplanners.diffvinit.VanillaDiffVinit: The differentiable reward function that defines the parameter space over which this value function initialization must differentiate.
rf - Variable in class burlap.behavior.singleagent.learnbydemo.mlirl.MLIRLRequest: The differentiable reward function model that will be estimated by MLRIL.
rf - Variable in class burlap.behavior.singleagent.learning.actorcritic.critics.TDLambda: The reward function used for learning.
rf - Variable in class burlap.behavior.singleagent.learning.modellearning.modelplanners.VIModelPlanner: The model reward function
rf - Variable in class burlap.behavior.singleagent.options.Option: reward function for keeping track of the cumulative reward during an execution
rf - Variable in class burlap.behavior.singleagent.planning.OOMDPPlanner: The reward function used for planning
rf - Variable in class burlap.oomdp.singleagent.interfaces.rlglue.RLGlueEnvironment: The reward function
rfDim - Variable in class burlap.behavior.singleagent.learnbydemo.mlirl.differentiableplanners.DifferentiableSparseSampling: The dimensionality of the differentiable reward function
rfDim - Variable in class burlap.behavior.singleagent.learnbydemo.mlirl.differentiableplanners.diffvinit.LinearDiffRFVInit: The dimensionality of the reward function parameters
rfFeaturesAreForNextState - Variable in class burlap.behavior.singleagent.learnbydemo.mlirl.differentiableplanners.diffvinit.LinearDiffRFVInit: Whether features are based on the next state or previous state.
rfFvGen - Variable in class burlap.behavior.singleagent.learnbydemo.mlirl.differentiableplanners.diffvinit.LinearDiffRFVInit: The state feature vector generator.
RLGlueAgentShell - Class in burlap.behavior.singleagent.interfaces.rlglue: This class is used to to serve as an interface between a BURLAP learning agent and an RLGlue hosted environment and experiment.
RLGlueAgentShell(RLGlueLearningAgentFactory) - Constructor for class burlap.behavior.singleagent.interfaces.rlglue.RLGlueAgentShell: Initializes an RLGlue agent to use a BURLAP agent that will be generated from the specified factory.
RLGlueAgentShell.MutableInt - Class in burlap.behavior.singleagent.interfaces.rlglue: A mutable int wrapper
RLGlueAgentShell.MutableInt() - Constructor for class burlap.behavior.singleagent.interfaces.rlglue.RLGlueAgentShell.MutableInt
RLGlueAgentShell.MutableState - Class in burlap.behavior.singleagent.interfaces.rlglue: A mutable OO-MDP state wrapper
RLGlueAgentShell.MutableState() - Constructor for class burlap.behavior.singleagent.interfaces.rlglue.RLGlueAgentShell.MutableState
RLGlueAgentShell.RLGlueRF - Class in burlap.behavior.singleagent.interfaces.rlglue: A reward function for returning the last RLGlue reward.
RLGlueAgentShell.RLGlueRF() - Constructor for class burlap.behavior.singleagent.interfaces.rlglue.RLGlueAgentShell.RLGlueRF
RLGlueAgentShell.RLGlueTF - Class in burlap.behavior.singleagent.interfaces.rlglue: A termianl function that returns true when the last RLGlue state was terminal.
RLGlueAgentShell.RLGlueTF() - Constructor for class burlap.behavior.singleagent.interfaces.rlglue.RLGlueAgentShell.RLGlueTF
RLGlueCMACSarsaLambdaFactory - Class in burlap.behavior.singleagent.interfaces.rlglue.common: This class is used to setup a BURLAP gradient SARSA-lambda algorithm with CMAC value function approximation for RLGlue.
RLGlueCMACSarsaLambdaFactory() - Constructor for class burlap.behavior.singleagent.interfaces.rlglue.common.RLGlueCMACSarsaLambdaFactory
RLGlueEnvironment - Class in burlap.oomdp.singleagent.interfaces.rlglue: This class can be used to take a BURLAP domain and task and turn it into an RLGlue environment with which other RLGlue agents can interact.
RLGlueEnvironment(Domain, StateGenerator, RewardFunction, TerminalFunction, DoubleRange, boolean, double) - Constructor for class burlap.oomdp.singleagent.interfaces.rlglue.RLGlueEnvironment: Constructs with all the BURLAP information necessary for generating an RLGlue Environment.
RLGlueEnvironment.ActionIndexParameterization - Class in burlap.oomdp.singleagent.interfaces.rlglue: A class that represents an action parameterization in terms of the object index in a state, rather than object name.
RLGlueEnvironment.ActionIndexParameterization(GroundedAction, State) - Constructor for class burlap.oomdp.singleagent.interfaces.rlglue.RLGlueEnvironment.ActionIndexParameterization: Constructs from a grounded action
RLGlueLearningAgentFactory - Interface in burlap.behavior.singleagent.interfaces.rlglue: An interface for defining a learning agent factory that can take as inptu an RLGlue generated domain, reward function, terminal function, and discount factor and generated a relevant BURLAP learning agent.
RLGLueQlearningFactory - Class in burlap.behavior.singleagent.interfaces.rlglue.common: This class is used to setup a BURLAP Q-learning algorithm with RLGlue.
RLGLueQlearningFactory() - Constructor for class burlap.behavior.singleagent.interfaces.rlglue.common.RLGLueQlearningFactory: Constructs the factory with default constant learning rate of 0.1, q-value initialization of 0, and epsilong greedy policy of 0.1.
RLGlueSARSALambdaFactory - Class in burlap.behavior.singleagent.interfaces.rlglue.common: This class is used to setup a BURLAP SARSA-lambda algorithm with RLGlue.
RLGlueSARSALambdaFactory() - Constructor for class burlap.behavior.singleagent.interfaces.rlglue.common.RLGlueSARSALambdaFactory
RLGlueWrappedDomainGenerator - Class in burlap.behavior.singleagent.interfaces.rlglue: This class is used to generate a domain object from an RLGlue task specification.
RLGlueWrappedDomainGenerator(RLGlueAgentShell, TaskSpec) - Constructor for class burlap.behavior.singleagent.interfaces.rlglue.RLGlueWrappedDomainGenerator: Constructs the domain.
RLGlueWrappedDomainGenerator.RLGlueActionWrapper - Class in burlap.behavior.singleagent.interfaces.rlglue: A BURLAP Action class that has an associated RLGlue action index and will make calls to the BURLAP-RLGlue interface (RLGlueAgentShell).
RLGlueWrappedDomainGenerator.RLGlueActionWrapper(Domain, int) - Constructor for class burlap.behavior.singleagent.interfaces.rlglue.RLGlueWrappedDomainGenerator.RLGlueActionWrapper: Constructs for the given BURLAP domain and RLGlue action index.
RMAXFICTIOUSSTATEACTIONNAME - Static variable in class burlap.behavior.singleagent.learning.modellearning.ModeledDomainGenerator: Name of the only action that can be taken from a fictitious RMAx state.
RMAXFICTIOUSSTATENAME - Static variable in class burlap.behavior.singleagent.learning.modellearning.ModeledDomainGenerator: Name of both the object class and single binary attribute used to indicate a RMax fictitious state.
RMaxState - Variable in class burlap.behavior.singleagent.learning.modellearning.ModeledDomainGenerator.ModeledAction: The fictitious RMax state this action will transition to for unknown transitions.
rolloutJointPolicy(JointPolicy, int) - Method in class burlap.oomdp.stochasticgames.World: Rollsout a joint policy until a terminate state is reached for a maximum number of stages.
rolloutJointPolicyFromState(JointPolicy, State, int) - Method in class burlap.oomdp.stochasticgames.World: Rollsout a joint policy from a given state until a terminate state is reached for a maximum number of stages.
rolloutOneStageOfJointPolicy(JointPolicy) - Method in class burlap.oomdp.stochasticgames.World: Runs a single stage following a joint policy for the current world state
rollOutPolicy - Variable in class burlap.behavior.singleagent.planning.stochastic.rtdp.RTDP: The policy to use for episode rollouts
root - Variable in class burlap.behavior.singleagent.planning.stochastic.montecarlo.uct.UCT
root - Variable in class burlap.datastructures.StochasticTree: Root node of the stochastic tree
rootLevelQValues - Variable in class burlap.behavior.singleagent.learnbydemo.mlirl.differentiableplanners.DifferentiableSparseSampling: The root state node Q-values that have been estimated by previous planning calls.
rootLevelQValues - Variable in class burlap.behavior.singleagent.planning.stochastic.sparsesampling.SparseSampling: The root state node Q-values that have been estimated by previous planning calls.
roundNegativesToZero(double[]) - Static method in class burlap.behavior.stochasticgame.solvers.CorrelatedEquilibriumSolver: Creates a new 1D double array with all negative values rounded to 0.
rowCol(int, int) - Static method in class burlap.behavior.stochasticgame.solvers.CorrelatedEquilibriumSolver: Returns the 2D row column index in a matrix of a given number of columns for a given 1D array index.
rowPayoffs - Variable in class burlap.behavior.stochasticgame.agents.twoplayer.singlestage.equilibriumplayer.EquilibriumPlayingAgent.BimatrixTuple: The row player's payoffs.
RTDP - Class in burlap.behavior.singleagent.planning.stochastic.rtdp: Implementation of Real-time dynamic programming [1].
RTDP(Domain, RewardFunction, TerminalFunction, double, StateHashFactory, double, int, double, int) - Constructor for class burlap.behavior.singleagent.planning.stochastic.rtdp.RTDP: Initializes the planner.
RTDP(Domain, RewardFunction, TerminalFunction, double, StateHashFactory, ValueFunctionInitialization, int, double, int) - Constructor for class burlap.behavior.singleagent.planning.stochastic.rtdp.RTDP: Initializes the planner.
runEpisodeBoundTrial(LearningAgentFactory) - Method in class burlap.behavior.singleagent.auxiliary.performance.LearningAlgorithmExperimenter: Runs a trial for an agent generated by the given factory when interpreting trial length as a number of episodes.
runEpisodewiseTrial(World) - Method in class burlap.behavior.stochasticgame.auxiliary.performance.MultiAgentExperimenter: Runs a trial where trial length is interpreted as the number of episodes in a trial.
runGame() - Method in class burlap.oomdp.stochasticgames.World: Runs a game until a terminal state is hit.
runGame(int) - Method in class burlap.oomdp.stochasticgames.World: Runs a game until a terminal state is hit for maxStages have occurred
runIteration() - Method in class burlap.behavior.singleagent.planning.vfa.fittedvi.FittedVI: Runs a single iteration of value iteration.
runLearningEpisodeFrom(State) - Method in class burlap.behavior.singleagent.learning.actorcritic.ActorCritic
runLearningEpisodeFrom(State, int) - Method in class burlap.behavior.singleagent.learning.actorcritic.ActorCritic
runLearningEpisodeFrom(State) - Method in interface burlap.behavior.singleagent.learning.LearningAgent: Causes the agent to perform a learning episode starting in the given initial state.
runLearningEpisodeFrom(State, int) - Method in interface burlap.behavior.singleagent.learning.LearningAgent: Causes the agent to perform a learning episode starting in the given initial state.
runLearningEpisodeFrom(State) - Method in class burlap.behavior.singleagent.learning.lspi.LSPI
runLearningEpisodeFrom(State, int) - Method in class burlap.behavior.singleagent.learning.lspi.LSPI
runLearningEpisodeFrom(State) - Method in class burlap.behavior.singleagent.learning.modellearning.artdp.ARTDP
runLearningEpisodeFrom(State, int) - Method in class burlap.behavior.singleagent.learning.modellearning.artdp.ARTDP
runLearningEpisodeFrom(State) - Method in class burlap.behavior.singleagent.learning.modellearning.rmax.PotentialShapedRMax
runLearningEpisodeFrom(State, int) - Method in class burlap.behavior.singleagent.learning.modellearning.rmax.PotentialShapedRMax
runLearningEpisodeFrom(State) - Method in class burlap.behavior.singleagent.learning.tdmethods.QLearning
runLearningEpisodeFrom(State, int) - Method in class burlap.behavior.singleagent.learning.tdmethods.QLearning
runLearningEpisodeFrom(State, int) - Method in class burlap.behavior.singleagent.learning.tdmethods.SarsaLam
runLearningEpisodeFrom(State) - Method in class burlap.behavior.singleagent.learning.tdmethods.vfa.GradientDescentSarsaLam
runLearningEpisodeFrom(State, int) - Method in class burlap.behavior.singleagent.learning.tdmethods.vfa.GradientDescentSarsaLam
runLPAndGetJointActionProbs(LinearProgram, int, int) - Static method in class burlap.behavior.stochasticgame.solvers.CorrelatedEquilibriumSolver: Helper method for running the linear program optimization (after its constraints have already been set) and returning the result in the form of the 2D double matrix joint strategy.
runPlannerForAllInitStates(OOMDPPlanner, StateConditionTestIterable) - Static method in class burlap.behavior.singleagent.planning.deterministic.MultiStatePrePlanner: Runs a planning algorithm from multiple initial states to ensure that an adequate plan/policy exist for of the states.
runPlannerForAllInitStates(OOMDPPlanner, Collection<State>) - Static method in class burlap.behavior.singleagent.planning.deterministic.MultiStatePrePlanner: Runs a planning algorithm from multiple initial states to ensure that an adequate plan/policy exist for of the states.
runPolicyIteration(int, double) - Method in class burlap.behavior.singleagent.learning.lspi.LSPI: Runs LSPI for either numIterations or until the change in the weight matrix is no greater than maxChange.
runRollout(State) - Method in class burlap.behavior.singleagent.planning.stochastic.rtdp.BoundedRTDP: Runs a planning rollout from the provided state.
runRolloutsInReverse - Variable in class burlap.behavior.singleagent.planning.stochastic.rtdp.BoundedRTDP: Whether each rollout should be run in reverse after completion.
runStage() - Method in class burlap.oomdp.stochasticgames.World: Runs a single stage of this game.
runStepBoundTrial(LearningAgentFactory) - Method in class burlap.behavior.singleagent.auxiliary.performance.LearningAlgorithmExperimenter: Runs a trial for an agent generated by the given factor when interpreting trial length as a number of total steps.
runStepwiseTrial(World) - Method in class burlap.behavior.stochasticgame.auxiliary.performance.MultiAgentExperimenter: Runs a trial where the trial lenght is interpreted as the number of total steps taken.
runTournament() - Method in class burlap.oomdp.stochasticgames.tournament.Tournament: Runs the tournament
runVI() - Method in class burlap.behavior.singleagent.learnbydemo.mlirl.differentiableplanners.DifferentiableVI: Runs VI until the specified termination conditions are met.
runVI() - Method in class burlap.behavior.singleagent.planning.stochastic.valueiteration.PrioritizedSweeping
runVI() - Method in class burlap.behavior.singleagent.planning.stochastic.valueiteration.ValueIteration: Runs VI until the specified termination conditions are met.
runVI() - Method in class burlap.behavior.singleagent.planning.vfa.fittedvi.FittedVI: Runs value iteration.
runVI() - Method in class burlap.behavior.stochasticgame.mavaluefunction.vfplanners.MAValueIteration: Runs Value Iteration over the set of states that have been discovered.

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