- p - Variable in class burlap.mdp.core.StateTransitionProb
-
- p - Variable in class burlap.mdp.singleagent.model.TransitionProb
-
The probability of the transition
- p - Variable in class burlap.mdp.singleagent.pomdp.observations.ObservationProbability
-
The probability of the observation
- p0 - Variable in class burlap.mdp.stochasticgames.tournament.common.AllPairWiseSameTypeMS
-
- p1 - Variable in class burlap.mdp.stochasticgames.tournament.common.AllPairWiseSameTypeMS
-
- pad - Variable in class burlap.domain.singleagent.lunarlander.state.LLState
-
- PadPainter(LunarLanderDomain.LLPhysicsParams) - Constructor for class burlap.domain.singleagent.lunarlander.LLVisualizer.PadPainter
-
- paint(Graphics2D, float, float) - Method in interface burlap.behavior.singleagent.auxiliary.valuefunctionvis.StaticDomainPainter
-
Use to paint general domain information to a 2D graphics context.
- paint(Graphics2D, State, float, float) - Method in class burlap.domain.singleagent.cartpole.CartPoleVisualizer.CartPolePainter
-
- paint(Graphics2D, State, float, float) - Method in class burlap.domain.singleagent.gridworld.GridWorldVisualizer.MapPainter
-
- paint(Graphics2D, State, float, float) - Method in class burlap.domain.singleagent.mountaincar.MountainCarVisualizer.AgentPainter
-
- paint(Graphics2D, State, float, float) - Method in class burlap.domain.singleagent.mountaincar.MountainCarVisualizer.HillPainter
-
- paint(Graphics2D, State, float, float) - Method in class burlap.visualizer.OOStatePainter
-
- paint(Graphics2D, State, float, float) - Method in interface burlap.visualizer.StatePainter
-
Paints general state information not to graphics context g2
- paintComponent(Graphics) - Method in class burlap.visualizer.MultiLayerRenderer
-
- painter - Variable in class burlap.behavior.singleagent.auxiliary.EpisodeSequenceVisualizer
-
- painter - Variable in class burlap.behavior.stochasticgames.auxiliary.GameSequenceVisualizer
-
- painter - Variable in class burlap.mdp.singleagent.common.VisualActionObserver
-
The visualizer that will render states
- painter - Variable in class burlap.mdp.stochasticgames.common.VisualWorldObserver
-
The visualizer that will render states
- painter - Variable in class burlap.shell.visual.SGVisualExplorer
-
- painter - Variable in class burlap.shell.visual.VisualExplorer
-
- paintGlyph(Graphics2D, float, float, float, float) - Method in interface burlap.behavior.singleagent.auxiliary.valuefunctionvis.common.ActionGlyphPainter
-
Called to paint a glyph in the rectangle defined by the top left origin (x,y) with the given width and height.
- paintGlyph(Graphics2D, float, float, float, float) - Method in class burlap.behavior.singleagent.auxiliary.valuefunctionvis.common.ArrowActionGlyph
-
- paintObject(Graphics2D, OOState, ObjectInstance, float, float) - Method in class burlap.domain.singleagent.blockdude.BlockDudeVisualizer.AgentPainter
-
- paintObject(Graphics2D, OOState, ObjectInstance, float, float) - Method in class burlap.domain.singleagent.blockdude.BlockDudeVisualizer.BlockPainter
-
- paintObject(Graphics2D, OOState, ObjectInstance, float, float) - Method in class burlap.domain.singleagent.blockdude.BlockDudeVisualizer.BricksPainter
-
- paintObject(Graphics2D, OOState, ObjectInstance, float, float) - Method in class burlap.domain.singleagent.blockdude.BlockDudeVisualizer.ExitPainter
-
- paintObject(Graphics2D, OOState, ObjectInstance, float, float) - Method in class burlap.domain.singleagent.blocksworld.BlocksWorldVisualizer.BlockPainter
-
- paintObject(Graphics2D, OOState, ObjectInstance, float, float) - Method in class burlap.domain.singleagent.frostbite.FrostbiteVisualizer.AgentPainter
-
- paintObject(Graphics2D, OOState, ObjectInstance, float, float) - Method in class burlap.domain.singleagent.frostbite.FrostbiteVisualizer.IglooPainter
-
- paintObject(Graphics2D, OOState, ObjectInstance, float, float) - Method in class burlap.domain.singleagent.frostbite.FrostbiteVisualizer.PlatformPainter
-
- paintObject(Graphics2D, OOState, ObjectInstance, float, float) - Method in class burlap.domain.singleagent.gridworld.GridWorldVisualizer.CellPainter
-
- paintObject(Graphics2D, OOState, ObjectInstance, float, float) - Method in class burlap.domain.singleagent.gridworld.GridWorldVisualizer.LocationPainter
-
- paintObject(Graphics2D, OOState, ObjectInstance, float, float) - Method in class burlap.domain.singleagent.lunarlander.LLVisualizer.AgentPainter
-
- paintObject(Graphics2D, OOState, ObjectInstance, float, float) - Method in class burlap.domain.singleagent.lunarlander.LLVisualizer.ObstaclePainter
-
- paintObject(Graphics2D, OOState, ObjectInstance, float, float) - Method in class burlap.domain.singleagent.lunarlander.LLVisualizer.PadPainter
-
- paintObject(Graphics2D, OOState, ObjectInstance, float, float) - Method in interface burlap.visualizer.ObjectPainter
-
Paints object instance ob to graphics context g2
- paintStatePolicy(Graphics2D, State, Policy, float, float) - Method in class burlap.behavior.singleagent.auxiliary.valuefunctionvis.common.PolicyGlyphPainter2D
-
- paintStatePolicy(Graphics2D, State, Policy, float, float) - Method in interface burlap.behavior.singleagent.auxiliary.valuefunctionvis.StatePolicyPainter
-
Paints a representation of the given policy for a specific state to a 2D graphics context.
- paintStateValue(Graphics2D, State, double, float, float) - Method in class burlap.behavior.singleagent.auxiliary.valuefunctionvis.common.StateValuePainter2D
-
- paintStateValue(Graphics2D, State, double, float, float) - Method in class burlap.behavior.singleagent.auxiliary.valuefunctionvis.StateValuePainter
-
Paints the representation of a value function for a specific state.
- parameterClasses - Variable in class burlap.mdp.core.oo.propositional.PropositionalFunction
-
- parameterClasses - Variable in class burlap.mdp.singleagent.oo.ObjectParameterizedActionType
-
The object classes each parameter of this action can accept; empty list for a parameter-less action (which is the default)
- parameterId - Variable in class burlap.behavior.functionapproximation.FunctionGradient.PartialDerivative
-
- parameterOrderGroup - Variable in class burlap.mdp.core.oo.propositional.PropositionalFunction
-
- parameterOrderGroup - Variable in class burlap.mdp.singleagent.oo.ObjectParameterizedActionType
-
Specifies the parameter order group each parameter.
- parameters - Variable in class burlap.behavior.singleagent.learnfromdemo.mlirl.commonrfs.LinearStateActionDifferentiableRF
-
The parameters of this reward function
- parameters - Variable in class burlap.behavior.singleagent.learnfromdemo.mlirl.commonrfs.LinearStateDifferentiableRF
-
The parameters of this reward function
- parameters - Variable in class burlap.behavior.singleagent.learnfromdemo.mlirl.differentiableplanners.diffvinit.LinearDiffRFVInit
-
- parameters - Variable in class burlap.behavior.singleagent.learnfromdemo.mlirl.differentiableplanners.diffvinit.LinearStateDiffVF
-
- ParametricFunction - Interface in burlap.behavior.functionapproximation
-
An interface for defining a parametric function.
- ParametricFunction.ParametricStateActionFunction - Interface in burlap.behavior.functionapproximation
-
A parametric function that operations on state-actions
- ParametricFunction.ParametricStateFunction - Interface in burlap.behavior.functionapproximation
-
A Parametric function that operates on states.
- params - Variable in class burlap.mdp.core.oo.propositional.GroundedProp
-
- params - Variable in class burlap.mdp.singleagent.oo.ObjectParameterizedActionType.SAObjectParameterizedAction
-
- parse(String) - Static method in class burlap.behavior.stochasticgames.GameEpisode
-
- parseEpisode(String) - Static method in class burlap.behavior.singleagent.Episode
-
- parseEpisodeFiles(String) - Method in class burlap.behavior.singleagent.auxiliary.EpisodeSequenceVisualizer
-
- parser - Variable in class burlap.shell.command.env.AddStateObjectCommand
-
- parser - Variable in class burlap.shell.command.env.EpisodeRecordingCommands.EpisodeBrowserCommand
-
- parser - Variable in class burlap.shell.command.env.EpisodeRecordingCommands.RecordCommand
-
- parser - Variable in class burlap.shell.command.env.ExecuteActionCommand
-
- parser - Variable in class burlap.shell.command.env.IsTerminalCommand
-
- parser - Variable in class burlap.shell.command.env.ListActionsCommand
-
- parser - Variable in class burlap.shell.command.env.ListPropFunctions
-
- parser - Variable in class burlap.shell.command.env.ObservationCommand
-
- parser - Variable in class burlap.shell.command.env.RemoveStateObjectCommand
-
- parser - Variable in class burlap.shell.command.env.RewardCommand
-
- parser - Variable in class burlap.shell.command.env.SetVarCommand
-
- parser - Variable in class burlap.shell.command.reserved.AliasCommand
-
- parser - Variable in class burlap.shell.command.reserved.AliasesCommand
-
- parser - Variable in class burlap.shell.command.reserved.CommandsCommand
-
- parser - Variable in class burlap.shell.command.world.AddStateObjectSGCommand
-
- parser - Variable in class burlap.shell.command.world.GameCommand
-
- parser - Variable in class burlap.shell.command.world.GenerateStateCommand
-
- parser - Variable in class burlap.shell.command.world.IsTerminalSGCommand
-
- parser - Variable in class burlap.shell.command.world.JointActionCommand
-
- parser - Variable in class burlap.shell.command.world.LastJointActionCommand
-
- parser - Variable in class burlap.shell.command.world.ManualAgentsCommands.ListManualAgents
-
- parser - Variable in class burlap.shell.command.world.ManualAgentsCommands.LSManualAgentActionsCommands
-
- parser - Variable in class burlap.shell.command.world.ManualAgentsCommands.RegisterAgentCommand
-
- parser - Variable in class burlap.shell.command.world.ManualAgentsCommands.SetAgentAction
-
- parser - Variable in class burlap.shell.command.world.RemoveStateObjectSGCommand
-
- parser - Variable in class burlap.shell.command.world.RewardsCommand
-
- parser - Variable in class burlap.shell.command.world.SetVarSGCommand
-
- parser - Variable in class burlap.shell.command.world.WorldObservationCommand
-
- parser - Variable in class burlap.shell.visual.VisualExplorer.LivePollCommand
-
- PartialDerivative(int, double) - Constructor for class burlap.behavior.functionapproximation.FunctionGradient.PartialDerivative
-
- payouts - Variable in class burlap.domain.stochasticgames.normalform.SingleStageNormalFormGame
-
The pay out function for each player.
- peek() - Method in class burlap.datastructures.HashIndexedHeap
-
Returns a pointer to the head of the heap without removing it
- peekAtLearningRate(State, Action) - Method in class burlap.behavior.functionapproximation.dense.fourier.FourierBasisLearningRateWrapper
-
- peekAtLearningRate(int) - Method in class burlap.behavior.functionapproximation.dense.fourier.FourierBasisLearningRateWrapper
-
- peekAtLearningRate(State, Action) - Method in class burlap.behavior.learningrate.ConstantLR
-
- peekAtLearningRate(int) - Method in class burlap.behavior.learningrate.ConstantLR
-
- peekAtLearningRate(State, Action) - Method in class burlap.behavior.learningrate.ExponentialDecayLR
-
- peekAtLearningRate(int) - Method in class burlap.behavior.learningrate.ExponentialDecayLR
-
- peekAtLearningRate(State, Action) - Method in interface burlap.behavior.learningrate.LearningRate
-
A method for looking at the current learning rate for a state-action pair without having it altered.
- peekAtLearningRate(int) - Method in interface burlap.behavior.learningrate.LearningRate
-
A method for looking at the current learning rate for a state (-action) feature without having it altered.
- peekAtLearningRate(State, Action) - Method in class burlap.behavior.learningrate.SoftTimeInverseDecayLR
-
- peekAtLearningRate(int) - Method in class burlap.behavior.learningrate.SoftTimeInverseDecayLR
-
- peekAtTime() - Method in class burlap.debugtools.MyTimer
-
Returns the current elapsed time since the timer was started.
- percolateWeightChange(StochasticTree<T>.STNode, double) - Method in class burlap.datastructures.StochasticTree
-
A recursive method for percolating a weight change of a node
- PerformanceMetric - Enum in burlap.behavior.singleagent.auxiliary.performance
-
- PerformancePlotter - Class in burlap.behavior.singleagent.auxiliary.performance
-
This class is an action observer used to collect and plot performance data of a learning agent either by itself or against another learning agent.
- PerformancePlotter(String, int, int, int, int, TrialMode, PerformanceMetric...) - Constructor for class burlap.behavior.singleagent.auxiliary.performance.PerformancePlotter
-
Initializes a performance plotter.
- PerformancePlotter.AgentDatasets - Class in burlap.behavior.singleagent.auxiliary.performance
-
A datastructure for maintain the plot series data in the current agent
- PerformancePlotter.MutableBoolean - Class in burlap.behavior.singleagent.auxiliary.performance
-
A class for a mutable boolean
- PerformancePlotter.Trial - Class in burlap.behavior.singleagent.auxiliary.performance
-
A datastructure for maintaining all the metric stats for a single trial.
- performBackup(State, int, List<SGAgentType>, AgentQSourceMap) - Method in class burlap.behavior.stochasticgames.madynamicprogramming.backupOperators.CoCoQ
-
- performBackup(State, int, List<SGAgentType>, AgentQSourceMap) - Method in class burlap.behavior.stochasticgames.madynamicprogramming.backupOperators.CorrelatedQ
-
- performBackup(State, int, List<SGAgentType>, AgentQSourceMap) - Method in class burlap.behavior.stochasticgames.madynamicprogramming.backupOperators.MaxQ
-
- performBackup(State, int, List<SGAgentType>, AgentQSourceMap) - Method in class burlap.behavior.stochasticgames.madynamicprogramming.backupOperators.MinMaxQ
-
- performBackup(State, int, List<SGAgentType>, AgentQSourceMap) - Method in interface burlap.behavior.stochasticgames.madynamicprogramming.SGBackupOperator
-
- performBellmanUpdateOn(State) - Method in class burlap.behavior.singleagent.planning.stochastic.DynamicProgramming
-
Performs a Bellman value function update on the provided state.
- performBellmanUpdateOn(HashableState) - Method in class burlap.behavior.singleagent.planning.stochastic.DynamicProgramming
-
Performs a Bellman value function update on the provided (hashed) state.
- performDPValueGradientUpdateOn(HashableState) - Method in class burlap.behavior.singleagent.learnfromdemo.mlirl.differentiableplanners.DifferentiableDP
-
Performs the Boltzmann value function gradient backup for the given
HashableState.
- performFixedPolicyBellmanUpdateOn(State, EnumerablePolicy) - Method in class burlap.behavior.singleagent.planning.stochastic.DynamicProgramming
-
Performs a fixed-policy Bellman value function update (i.e., policy evaluation) on the provided state.
- performFixedPolicyBellmanUpdateOn(HashableState, EnumerablePolicy) - Method in class burlap.behavior.singleagent.planning.stochastic.DynamicProgramming
-
Performs a fixed-policy Bellman value function update (i.e., policy evaluation) on the provided state.
- performIRL() - Method in class burlap.behavior.singleagent.learnfromdemo.mlirl.MLIRL
-
Runs gradient ascent.
- performIRL() - Method in class burlap.behavior.singleagent.learnfromdemo.mlirl.MultipleIntentionsMLIRL
-
Performs multiple intention inverse reinforcement learning.
- performOrderedBellmanUpdates(List<HashableState>) - Method in class burlap.behavior.singleagent.planning.stochastic.rtdp.RTDP
-
Performs ordered Bellman updates on the list of (hashed) states provided to it.
- performReachabilityFrom(State) - Method in class burlap.behavior.singleagent.learnfromdemo.mlirl.differentiableplanners.DifferentiableVI
-
This method will find all reachable states that will be used by the
DifferentiableVI.runVI() method and will cache all the transition dynamics.
- performReachabilityFrom(State) - Method in class burlap.behavior.singleagent.planning.stochastic.policyiteration.PolicyEvaluation
-
This method will find all reachable states that will be used when computing the value function.
- performReachabilityFrom(State) - Method in class burlap.behavior.singleagent.planning.stochastic.policyiteration.PolicyIteration
-
This method will find all reachable states that will be used when computing the value function.
- performReachabilityFrom(State) - Method in class burlap.behavior.singleagent.planning.stochastic.valueiteration.PrioritizedSweeping
-
- performReachabilityFrom(State) - Method in class burlap.behavior.singleagent.planning.stochastic.valueiteration.ValueIteration
-
This method will find all reachable states that will be used by the
ValueIteration.runVI() method and will cache all the transition dynamics.
- performStateReachabilityFrom(State) - Method in class burlap.behavior.stochasticgames.madynamicprogramming.dpplanners.MAValueIteration
-
Finds and stores all states that are reachable from input state s.
- pf - Variable in class burlap.mdp.core.oo.propositional.GroundedProp
-
- PF_AT_EXIT - Static variable in class burlap.domain.singleagent.blockdude.BlockDude
-
Name for the propositional function that tests whether the agent is at an exit
- PF_AT_LOCATION - Static variable in class burlap.domain.singleagent.gridworld.GridWorldDomain
-
Constant for the name of the at location propositional function
- PF_CLEAR - Static variable in class burlap.domain.singleagent.blocksworld.BlocksWorld
-
Constant for the propositional function "clear" name
- PF_HOLDING_BLOCK - Static variable in class burlap.domain.singleagent.blockdude.BlockDude
-
Name for the propositional function that tests whether the agent is holding a block.
- PF_IGLOO_BUILT - Static variable in class burlap.domain.singleagent.frostbite.FrostbiteDomain
-
Constant for the name of the propositional function "igloo is built"
- PF_IN_P_GOAL - Static variable in class burlap.domain.stochasticgames.gridgame.GridGame
-
A constant for the name of a propositional function that evaluates whether an agent is in a personal goal location for just them.
- PF_IN_U_GOAL - Static variable in class burlap.domain.stochasticgames.gridgame.GridGame
-
A constant for the name of a propositional function that evaluates whether an agent is in a universal goal location.
- PF_IN_WATER - Static variable in class burlap.domain.singleagent.frostbite.FrostbiteDomain
-
Constant for the name of the propositional function "agent is in water"
- PF_ON_BLOCK - Static variable in class burlap.domain.singleagent.blocksworld.BlocksWorld
-
Constant for the propositional function "on" name
- PF_ON_GROUND - Static variable in class burlap.domain.singleagent.lunarlander.LunarLanderDomain
-
Constant for the name of the propositional function that indicates whether the agent/lander is on the ground
- PF_ON_ICE - Static variable in class burlap.domain.singleagent.frostbite.FrostbiteDomain
-
Constant for the name of the propositional function "agent is on ice"
- PF_ON_PAD - Static variable in class burlap.domain.singleagent.lunarlander.LunarLanderDomain
-
Constant for the name of the propositional function that indicates whether the agent/lander is on a landing pad
- PF_ON_PLATFORM - Static variable in class burlap.domain.singleagent.frostbite.FrostbiteDomain
-
Constant for the name of the propositional function "agent is on platform"
- PF_ON_TABLE - Static variable in class burlap.domain.singleagent.blocksworld.BlocksWorld
-
Constant for the propositional function "on table" name
- PF_PLATFORM_ACTIVE - Static variable in class burlap.domain.singleagent.frostbite.FrostbiteDomain
-
Constant for the name of the propositional function "platform is active"
- PF_TOUCH_SURFACE - Static variable in class burlap.domain.singleagent.lunarlander.LunarLanderDomain
-
Constant for the name of the propositional function that indicates whether the agent/lander is touching
an obstacle surface.
- PF_TOUTCH_PAD - Static variable in class burlap.domain.singleagent.lunarlander.LunarLanderDomain
-
Constant for the name of the propositional function that indicates whether the agent/lander is *touching* a landing pad.
- PF_WALL_EAST - Static variable in class burlap.domain.singleagent.gridworld.GridWorldDomain
-
Constant for the name of the wall to east propositional function
- PF_WALL_NORTH - Static variable in class burlap.domain.singleagent.gridworld.GridWorldDomain
-
Constant for the name of the wall to north propositional function
- PF_WALL_SOUTH - Static variable in class burlap.domain.singleagent.gridworld.GridWorldDomain
-
Constant for the name of the wall to south propositional function
- PF_WALL_WEST - Static variable in class burlap.domain.singleagent.gridworld.GridWorldDomain
-
Constant for the name of the wall to west propositional function
- PFFeatures - Class in burlap.behavior.functionapproximation.dense
-
- PFFeatures(OODomain) - Constructor for class burlap.behavior.functionapproximation.dense.PFFeatures
-
Initializes using all propositional functions that belong to the domain
- PFFeatures(List<PropositionalFunction>) - Constructor for class burlap.behavior.functionapproximation.dense.PFFeatures
-
Initializes using the list of given propositional functions.
- PFFeatures(PropositionalFunction[]) - Constructor for class burlap.behavior.functionapproximation.dense.PFFeatures
-
Initializes using the array of given propositional functions.
- pfsToUse - Variable in class burlap.behavior.functionapproximation.dense.PFFeatures
-
- phiConstructor(double[], int) - Method in class burlap.behavior.singleagent.learning.lspi.LSPI
-
Constructs the state-action feature vector as a SimpleMatrix.
- physParams - Variable in class burlap.domain.singleagent.cartpole.CartPoleDomain
-
An object specifying the physics parameters for the cart pole domain.
- physParams - Variable in class burlap.domain.singleagent.cartpole.InvertedPendulum
-
- physParams - Variable in class burlap.domain.singleagent.cartpole.model.CPClassicModel
-
- physParams - Variable in class burlap.domain.singleagent.cartpole.model.CPCorrectModel
-
- physParams - Variable in class burlap.domain.singleagent.lunarlander.LunarLanderDomain
-
An object for holding the physics parameters of this domain.
- physParams - Variable in class burlap.domain.singleagent.mountaincar.MountainCar.MCModel
-
- physParams - Variable in class burlap.domain.singleagent.mountaincar.MountainCar
-
The physics parameters for mountain car.
- pickupBlock(BlockDudeState) - Method in class burlap.domain.singleagent.blockdude.BlockDudeModel
-
Modifies state s to be the result of the pick up action.
- planContainsOption(SearchNode) - Method in class burlap.behavior.singleagent.planning.deterministic.DeterministicPlanner
-
Returns true if a solution path uses an option in its solution.
- planEndNode(SearchNode) - Method in class burlap.behavior.singleagent.planning.deterministic.informed.astar.IDAStar
-
Returns true if the search node wraps a goal state.
- planFromState(State) - Method in class burlap.behavior.singleagent.learnfromdemo.mlirl.differentiableplanners.DifferentiableSparseSampling
-
Plans from the input state and returns a
BoltzmannQPolicy following the
Boltzmann parameter used for value Botlzmann value backups in this planner.
- planFromState(State) - Method in class burlap.behavior.singleagent.learnfromdemo.mlirl.differentiableplanners.DifferentiableVI
-
Plans from the input state and returns a
BoltzmannQPolicy following the
Boltzmann parameter used for value Botlzmann value backups in this planner.
- planFromState(State) - Method in class burlap.behavior.singleagent.learning.actorcritic.ActorCritic
-
- planFromState(State) - Method in class burlap.behavior.singleagent.learning.lspi.LSPI
-
Plans from the input state and then returns a
GreedyQPolicy that greedily
selects the action with the highest Q-value and breaks ties uniformly randomly.
- planFromState(State) - Method in class burlap.behavior.singleagent.learning.tdmethods.QLearning
-
Plans from the input state and then returns a
GreedyQPolicy that greedily
selects the action with the highest Q-value and breaks ties uniformly randomly.
- planFromState(State) - Method in class burlap.behavior.singleagent.learning.tdmethods.vfa.GradientDescentSarsaLam
-
Plans from the input state and then returns a
GreedyQPolicy that greedily
selects the action with the highest Q-value and breaks ties uniformly randomly.
- planFromState(State) - Method in class burlap.behavior.singleagent.planning.deterministic.informed.astar.DynamicWeightedAStar
-
- planFromState(State) - Method in class burlap.behavior.singleagent.planning.deterministic.informed.astar.IDAStar
-
- planFromState(State) - Method in class burlap.behavior.singleagent.planning.deterministic.informed.BestFirst
-
- planFromState(State) - Method in class burlap.behavior.singleagent.planning.deterministic.uninformed.bfs.BFS
-
- planFromState(State) - Method in class burlap.behavior.singleagent.planning.deterministic.uninformed.dfs.DFS
-
- planFromState(State) - Method in class burlap.behavior.singleagent.planning.deterministic.uninformed.dfs.LimitedMemoryDFS
-
- planFromState(State) - Method in interface burlap.behavior.singleagent.planning.Planner
-
This method will cause the
Planner to begin planning from the specified initial
State.
- planFromState(State) - Method in class burlap.behavior.singleagent.planning.stochastic.montecarlo.uct.UCT
-
Plans from the input state and then returns a
GreedyQPolicy that greedily
selects the action with the highest Q-value and breaks ties uniformly randomly.
- planFromState(State) - Method in class burlap.behavior.singleagent.planning.stochastic.policyiteration.PolicyIteration
-
Plans from the input state and then returns a
GreedyQPolicy that greedily
selects the action with the highest Q-value and breaks ties uniformly randomly.
- planFromState(State) - Method in class burlap.behavior.singleagent.planning.stochastic.rtdp.BoundedRTDP
-
Plans from the input state and then returns a
GreedyQPolicy that greedily
selects the action with the highest Q-value and breaks ties uniformly randomly.
- planFromState(State) - Method in class burlap.behavior.singleagent.planning.stochastic.rtdp.RTDP
-
Plans from the input state and then returns a
GreedyQPolicy that greedily
selects the action with the highest Q-value and breaks ties uniformly randomly.
- planFromState(State) - Method in class burlap.behavior.singleagent.planning.stochastic.sparsesampling.SparseSampling
-
Plans from the input state and then returns a
GreedyQPolicy that greedily
selects the action with the highest Q-value and breaks ties uniformly randomly.
- planFromState(State) - Method in class burlap.behavior.singleagent.planning.stochastic.valueiteration.ValueIteration
-
Plans from the input state and then returns a
GreedyQPolicy that greedily
selects the action with the highest Q-value and breaks ties uniformly randomly.
- planFromState(State) - Method in class burlap.behavior.singleagent.planning.vfa.fittedvi.FittedVI
-
Plans from the input state and then returns a
GreedyQPolicy that greedily
selects the action with the highest Q-value and breaks ties uniformly randomly.
- planFromState(State) - Method in class burlap.behavior.singleagent.pomdp.qmdp.QMDP
-
- planFromState(State) - Method in class burlap.behavior.singleagent.pomdp.wrappedmdpalgs.BeliefSparseSampling
-
- planFromState(State) - Method in class burlap.behavior.stochasticgames.madynamicprogramming.dpplanners.MAValueIteration
-
- planFromState(State) - Method in class burlap.behavior.stochasticgames.madynamicprogramming.MADynamicProgramming
-
Calling this method causes planning to be performed from State s.
- planHasDupilicateStates(SearchNode) - Method in class burlap.behavior.singleagent.planning.deterministic.DeterministicPlanner
-
Returns true if a solution path visits the same state multiple times.
- planner - Variable in class burlap.behavior.singleagent.learnfromdemo.IRLRequest
-
The planning algorithm used to compute the policy for a given reward function
- Planner - Interface in burlap.behavior.singleagent.planning
-
- planner - Variable in class burlap.behavior.stochasticgames.agents.madp.MultiAgentDPPlanningAgent
-
The valueFunction this agent will use to estiamte the value function and thereby determine its policy.
- plannerFactory - Variable in class burlap.behavior.singleagent.learnfromdemo.mlirl.MultipleIntentionsMLIRLRequest
-
- plannerFactory - Variable in class burlap.behavior.stochasticgames.agents.madp.MADPPlanAgentFactory
-
- plannerReferece - Variable in class burlap.behavior.stochasticgames.agents.madp.MADPPlannerFactory.ConstantMADPPlannerFactory
-
- planningCollector - Variable in class burlap.behavior.singleagent.learning.lspi.LSPI
-
- planningDepth - Variable in class burlap.behavior.singleagent.planning.vfa.fittedvi.FittedVI
-
The
SparseSampling planning depth used
for computing Bellman operators during value iteration.
- PlanningFailedException() - Constructor for exception burlap.behavior.singleagent.planning.deterministic.DeterministicPlanner.PlanningFailedException
-
- planningStarted - Variable in class burlap.behavior.stochasticgames.madynamicprogramming.MADynamicProgramming
-
Whether planning has begun or not.
- PlatformActivePF(String) - Constructor for class burlap.domain.singleagent.frostbite.FrostbiteDomain.PlatformActivePF
-
Initializes to be evaluated on an agent object and platform object.
- PlatformPainter(int) - Constructor for class burlap.domain.singleagent.frostbite.FrostbiteVisualizer.PlatformPainter
-
- platforms - Variable in class burlap.domain.singleagent.frostbite.state.FrostbiteState
-
- platformSpeed - Variable in class burlap.domain.singleagent.frostbite.FrostbiteModel
-
- pLayer - Variable in class burlap.behavior.singleagent.auxiliary.valuefunctionvis.ValueFunctionVisualizerGUI
-
The policy renderer
- player - Variable in class burlap.domain.stochasticgames.gridgame.state.GGAgent
-
- playerIndex(String) - Method in class burlap.domain.stochasticgames.normalform.NFGameState
-
- players - Variable in class burlap.domain.stochasticgames.normalform.NFGameState
-
- plotCISignificance - Variable in class burlap.behavior.singleagent.auxiliary.performance.LearningAlgorithmExperimenter
-
The signficance value for the confidence interval in the plots.
- plotCISignificance - Variable in class burlap.behavior.stochasticgames.auxiliary.performance.MultiAgentExperimenter
-
The signficance value for the confidence interval in the plots.
- plotRefresh - Variable in class burlap.behavior.singleagent.auxiliary.performance.LearningAlgorithmExperimenter
-
The delay in milliseconds between autmatic refreshes of the plots
- plotRefresh - Variable in class burlap.behavior.stochasticgames.auxiliary.performance.MultiAgentExperimenter
-
The delay in milliseconds between autmatic refreshes of the plots
- plotter - Variable in class burlap.behavior.singleagent.auxiliary.performance.LearningAlgorithmExperimenter
-
The PerformancePlotter used to collect and plot results
- plotter - Variable in class burlap.behavior.stochasticgames.auxiliary.performance.MultiAgentExperimenter
-
The performance plotter object
- poDomain - Variable in class burlap.mdp.singleagent.pomdp.BeliefAgent
-
The POMDP Domain defining the environment mechanics.
- poDomain - Variable in class burlap.mdp.singleagent.pomdp.BeliefMDPGenerator.BeliefModel
-
- podomain - Variable in class burlap.mdp.singleagent.pomdp.BeliefMDPGenerator
-
The input POMDP domain
- PODomain - Class in burlap.mdp.singleagent.pomdp
-
A class for defining POMDP domains.
- PODomain() - Constructor for class burlap.mdp.singleagent.pomdp.PODomain
-
- poDomain - Variable in class burlap.mdp.singleagent.pomdp.SimulatedPOEnvironment
-
- poleFriction - Variable in class burlap.domain.singleagent.cartpole.CartPoleDomain.CPPhysicsParams
-
The friction between the pole and the joint on the cart.
- poleLength - Variable in class burlap.domain.singleagent.cartpole.InvertedPendulum.IPPhysicsParams
-
The length of the pole
- poleMass - Variable in class burlap.domain.singleagent.cartpole.CartPoleDomain.CPPhysicsParams
-
The mass of the pole.
- poleMass - Variable in class burlap.domain.singleagent.cartpole.InvertedPendulum.IPPhysicsParams
-
The mass of the pole.
- Policy - Interface in burlap.behavior.policy
-
An interface for defining a
Policy.
- policy - Variable in class burlap.behavior.singleagent.auxiliary.valuefunctionvis.PolicyRenderLayer
-
The policy to use for visualizing the policy
- policy - Variable in class burlap.behavior.singleagent.learning.modellearning.artdp.ARTDP
-
the policy to follow
- policy(State, Episode) - Method in class burlap.behavior.singleagent.options.MacroAction
-
- policy(State, Episode) - Method in interface burlap.behavior.singleagent.options.Option
-
- policy - Variable in class burlap.behavior.singleagent.options.SubgoalOption
-
The policy of the options
- policy(State, Episode) - Method in class burlap.behavior.singleagent.options.SubgoalOption
-
- policy - Variable in class burlap.behavior.singleagent.pomdp.BeliefPolicyAgent
-
The policy that the agent will follow.
- policy - Variable in class burlap.behavior.stochasticgames.agents.madp.MADPPlanAgentFactory
-
- policy - Variable in class burlap.behavior.stochasticgames.agents.madp.MultiAgentDPPlanningAgent
-
The policy dervied from a joint policy derived from the valueFunction's value function estimate that this agent will follow.
- policy - Variable in class burlap.behavior.stochasticgames.agents.naiveq.SGNaiveQLAgent
-
The policy this agent follows
- policy - Variable in class burlap.behavior.stochasticgames.agents.SetStrategySGAgent
-
The policy encoding the strategy this agent will follow
- policy - Variable in class burlap.behavior.stochasticgames.agents.SetStrategySGAgent.SetStrategyAgentFactory
-
The strategy this agent will follow
- policyCount - Variable in class burlap.behavior.singleagent.learnfromdemo.apprenticeship.ApprenticeshipLearningRequest
-
The maximum number of times a policy is rolled out and evaluated
- policyDistribution(State) - Method in class burlap.behavior.policy.BoltzmannQPolicy
-
- policyDistribution(State) - Method in class burlap.behavior.policy.CachedPolicy
-
- policyDistribution(State) - Method in interface burlap.behavior.policy.EnumerablePolicy
-
This method will return action probability distribution defined by the policy.
- policyDistribution(State) - Method in class burlap.behavior.policy.EpsilonGreedy
-
- policyDistribution(State) - Method in class burlap.behavior.policy.GreedyDeterministicQPolicy
-
- policyDistribution(State) - Method in class burlap.behavior.policy.GreedyQPolicy
-
- policyDistribution(State) - Method in class burlap.behavior.policy.RandomPolicy
-
- policyDistribution(State) - Method in class burlap.behavior.singleagent.learnfromdemo.apprenticeship.ApprenticeshipLearning.StationaryRandomDistributionPolicy
-
- policyDistribution(State) - Method in class burlap.behavior.singleagent.learning.actorcritic.actor.BoltzmannActor
-
- policyDistribution(State) - Method in class burlap.behavior.singleagent.learning.modellearning.rmax.UnmodeledFavoredPolicy
-
- policyDistribution(State, Episode) - Method in class burlap.behavior.singleagent.options.MacroAction
-
- policyDistribution(State, Episode) - Method in interface burlap.behavior.singleagent.options.Option
-
- policyDistribution(State, Episode) - Method in class burlap.behavior.singleagent.options.SubgoalOption
-
- policyDistribution(State) - Method in class burlap.behavior.singleagent.planning.deterministic.DDPlannerPolicy
-
- policyDistribution(State) - Method in class burlap.behavior.singleagent.planning.deterministic.SDPlannerPolicy
-
- policyDistribution(State) - Method in class burlap.behavior.singleagent.planning.stochastic.montecarlo.uct.UCTTreeWalkPolicy
-
- policyDistribution(State) - Method in class burlap.behavior.stochasticgames.madynamicprogramming.policies.ECorrelatedQJointPolicy
-
- policyDistribution(State) - Method in class burlap.behavior.stochasticgames.madynamicprogramming.policies.EGreedyJointPolicy
-
- policyDistribution(State) - Method in class burlap.behavior.stochasticgames.madynamicprogramming.policies.EGreedyMaxWellfare
-
- policyDistribution(State) - Method in class burlap.behavior.stochasticgames.madynamicprogramming.policies.EMinMaxPolicy
-
- policyDistribution(State) - Method in class burlap.behavior.stochasticgames.PolicyFromJointPolicy
-
- PolicyEvaluation - Class in burlap.behavior.singleagent.planning.stochastic.policyiteration
-
This class is used to compute the value function under some specified policy.
- PolicyEvaluation(SADomain, double, HashableStateFactory, double, double) - Constructor for class burlap.behavior.singleagent.planning.stochastic.policyiteration.PolicyEvaluation
-
Initializes.
- PolicyFromJointPolicy - Class in burlap.behavior.stochasticgames
-
This class defines a single agent's policy that is derived from a joint policy.
- PolicyFromJointPolicy(JointPolicy) - Constructor for class burlap.behavior.stochasticgames.PolicyFromJointPolicy
-
Initializes with the underlying joint polciy
- PolicyFromJointPolicy(JointPolicy, boolean) - Constructor for class burlap.behavior.stochasticgames.PolicyFromJointPolicy
-
Initializes with the underlying joint polciy and whether actions should be synchronized with other agents following the same underlying joint policy.
- PolicyFromJointPolicy(int, JointPolicy) - Constructor for class burlap.behavior.stochasticgames.PolicyFromJointPolicy
-
Initializes with the acting agent name whose actions from the underlying joint policy will be returned.
- PolicyFromJointPolicy(int, JointPolicy, boolean) - Constructor for class burlap.behavior.stochasticgames.PolicyFromJointPolicy
-
Initializes with the acting agent name whose actions from the underlying joint policy will be returned and
whether actions should be synchronized with other agents following the same underlying joint policy.
- PolicyGlyphPainter2D - Class in burlap.behavior.singleagent.auxiliary.valuefunctionvis.common
-
An class for rendering the policy for states by painting different glyphs for different actions.
- PolicyGlyphPainter2D() - Constructor for class burlap.behavior.singleagent.auxiliary.valuefunctionvis.common.PolicyGlyphPainter2D
-
- PolicyGlyphPainter2D.PolicyGlyphRenderStyle - Enum in burlap.behavior.singleagent.auxiliary.valuefunctionvis.common
-
MAXACTION paints only glphys for only those actions that have the highest likelihood
- PolicyIteration - Class in burlap.behavior.singleagent.planning.stochastic.policyiteration
-
- PolicyIteration(SADomain, double, HashableStateFactory, double, int, int) - Constructor for class burlap.behavior.singleagent.planning.stochastic.policyiteration.PolicyIteration
-
Initializes the valueFunction.
- PolicyIteration(SADomain, double, HashableStateFactory, double, double, int, int) - Constructor for class burlap.behavior.singleagent.planning.stochastic.policyiteration.PolicyIteration
-
Initializes the valueFunction.
- PolicyRenderLayer - Class in burlap.behavior.singleagent.auxiliary.valuefunctionvis
-
- PolicyRenderLayer(Collection<State>, StatePolicyPainter, Policy) - Constructor for class burlap.behavior.singleagent.auxiliary.valuefunctionvis.PolicyRenderLayer
-
- PolicyUndefinedException - Exception in burlap.behavior.policy.support
-
RuntimeException to be thrown when a Policy is queried for a state in which the policy is undefined.
- PolicyUndefinedException() - Constructor for exception burlap.behavior.policy.support.PolicyUndefinedException
-
- PolicyUtils - Class in burlap.behavior.policy
-
- policyValue(QProvider, State, EnumerablePolicy) - Static method in class burlap.behavior.valuefunction.QProvider.Helper
-
Returns the state value under a given policy for a state and
QProvider.
- poll() - Method in class burlap.datastructures.HashIndexedHeap
-
Returns a pointer to the head of the heap and removes it
- poll() - Method in class burlap.datastructures.StochasticTree
-
Samples an element according to a probability defined by the relative weight of objects, removes it from the tree, and returns it.
- pollInterval - Variable in class burlap.shell.visual.VisualExplorer
-
- pollLearningRate(int, State, Action) - Method in class burlap.behavior.functionapproximation.dense.fourier.FourierBasisLearningRateWrapper
-
- pollLearningRate(int, int) - Method in class burlap.behavior.functionapproximation.dense.fourier.FourierBasisLearningRateWrapper
-
- pollLearningRate(int, State, Action) - Method in class burlap.behavior.learningrate.ConstantLR
-
- pollLearningRate(int, int) - Method in class burlap.behavior.learningrate.ConstantLR
-
- pollLearningRate(int, State, Action) - Method in class burlap.behavior.learningrate.ExponentialDecayLR
-
- pollLearningRate(int, int) - Method in class burlap.behavior.learningrate.ExponentialDecayLR
-
- pollLearningRate(int, State, Action) - Method in interface burlap.behavior.learningrate.LearningRate
-
A method for returning the learning rate for a given state action pair and then decaying the learning rate as defined by this class.
- pollLearningRate(int, int) - Method in interface burlap.behavior.learningrate.LearningRate
-
A method for returning the learning rate for a given state (-action) feature and then decaying the learning rate as defined by this class.
- pollLearningRate(int, State, Action) - Method in class burlap.behavior.learningrate.SoftTimeInverseDecayLR
-
- pollLearningRate(int, int) - Method in class burlap.behavior.learningrate.SoftTimeInverseDecayLR
-
- polyDegree - Variable in class burlap.behavior.singleagent.auxiliary.valuefunctionvis.common.LandmarkColorBlendInterpolation
-
The power to raise the normalized distance
- pos - Variable in class burlap.domain.stochasticgames.gridgame.state.GGWall
-
- postPlanPrep() - Method in class burlap.behavior.singleagent.planning.deterministic.informed.astar.AStar
-
- postPlanPrep() - Method in class burlap.behavior.singleagent.planning.deterministic.informed.astar.DynamicWeightedAStar
-
- postPlanPrep() - Method in class burlap.behavior.singleagent.planning.deterministic.informed.BestFirst
-
This method is called at the end of the
BestFirst.planFromState(State) method and can be used clean up any special
data structures needed by the subclass.
- potentialFunction - Variable in class burlap.behavior.singleagent.learning.modellearning.rmax.RMaxModel
-
- PotentialFunction - Interface in burlap.behavior.singleagent.shaping.potential
-
Defines an interface for reward potential functions.
- potentialFunction - Variable in class burlap.behavior.singleagent.shaping.potential.PotentialShapedRF
-
The potential function that can be used to return the potential reward from input states.
- PotentialShapedRF - Class in burlap.behavior.singleagent.shaping.potential
-
This class is used to implement Potential-based reward shaping [1] which is guaranteed to preserve the optimal policy.
- PotentialShapedRF(RewardFunction, PotentialFunction, double) - Constructor for class burlap.behavior.singleagent.shaping.potential.PotentialShapedRF
-
Initializes the shaping with the objective reward function, the potential function, and the discount of the MDP.
- PotentialShapedRMax - Class in burlap.behavior.singleagent.learning.modellearning.rmax
-
Potential Shaped RMax [1] is a generalization of RMax in which a potential-shaped reward function is used to provide less (but still admissible)
optimistic views of unknown state transitions.
- PotentialShapedRMax(SADomain, double, HashableStateFactory, double, int, double, int) - Constructor for class burlap.behavior.singleagent.learning.modellearning.rmax.PotentialShapedRMax
-
Initializes for a tabular model, VI valueFunction, and standard RMax paradigm
- PotentialShapedRMax(SADomain, double, HashableStateFactory, PotentialFunction, int, double, int) - Constructor for class burlap.behavior.singleagent.learning.modellearning.rmax.PotentialShapedRMax
-
Initializes for a tabular model, VI valueFunction, and potential shaped function.
- PotentialShapedRMax(SADomain, HashableStateFactory, PotentialFunction, KWIKModel, ModelLearningPlanner) - Constructor for class burlap.behavior.singleagent.learning.modellearning.rmax.PotentialShapedRMax
-
Initializes for a given model, model learning planner, and potential shaped function.
- PotentialShapedRMax.RMaxPotential - Class in burlap.behavior.singleagent.learning.modellearning.rmax
-
A potential function for vanilla RMax; all states have a potential value of R_max/(1-gamma)
- potentialValue(State) - Method in class burlap.behavior.singleagent.learning.modellearning.rmax.PotentialShapedRMax.RMaxPotential
-
- potentialValue(State) - Method in interface burlap.behavior.singleagent.shaping.potential.PotentialFunction
-
Returns the reward potential from the given state.
- preferenceLength() - Method in class burlap.datastructures.BoltzmannDistribution
-
Returns the number of elements on which there are preferences
- preferences - Variable in class burlap.behavior.singleagent.learning.actorcritic.actor.BoltzmannActor
-
A map from (hashed) states to Policy nodes; the latter of which contains the action preferences
for each applicable action in the state.
- preferences - Variable in class burlap.datastructures.BoltzmannDistribution
-
The preference values to turn into probabilities
- prePlanPrep() - Method in class burlap.behavior.singleagent.planning.deterministic.informed.astar.AStar
-
- prePlanPrep() - Method in class burlap.behavior.singleagent.planning.deterministic.informed.astar.DynamicWeightedAStar
-
- prePlanPrep() - Method in class burlap.behavior.singleagent.planning.deterministic.informed.BestFirst
-
This method is called at the start of the
BestFirst.planFromState(State) method and can be used initialize any special
data structures needed by the subclass.
- printDebug - Variable in class burlap.behavior.singleagent.interfaces.rlglue.RLGlueAgent
-
Whether to print debug statements.
- printOutResults() - Method in class burlap.mdp.stochasticgames.tournament.Tournament
-
Prints the tournament results by agent index and their cumulative reward received in the tournament.
- PrioritizedSearchNode - Class in burlap.behavior.singleagent.planning.deterministic.informed
-
An extension of the
SearchNode class that includes
a priority value.
- PrioritizedSearchNode(HashableState, double) - Constructor for class burlap.behavior.singleagent.planning.deterministic.informed.PrioritizedSearchNode
-
Initializes a PrioritizedSearchNode for a given (hashed) input state and priority value.
- PrioritizedSearchNode(HashableState, Action, SearchNode, double) - Constructor for class burlap.behavior.singleagent.planning.deterministic.informed.PrioritizedSearchNode
-
Constructs a SearchNode for the input state and priority and sets the generating action and back pointer to the provided elements.
- PrioritizedSearchNode.PSNComparator - Class in burlap.behavior.singleagent.planning.deterministic.informed
-
A class for comparing the priority of two PrioritizedSearchNodes.
- PrioritizedSweeping - Class in burlap.behavior.singleagent.planning.stochastic.valueiteration
-
An implementation of Prioritized Sweeping as DP planning algorithm as described by Li and Littman [1].
- PrioritizedSweeping(SADomain, double, HashableStateFactory, double, int) - Constructor for class burlap.behavior.singleagent.planning.stochastic.valueiteration.PrioritizedSweeping
-
Initializes
- PrioritizedSweeping.BPTR - Class in burlap.behavior.singleagent.planning.stochastic.valueiteration
-
A back pointer and its max action probability of transition.
- PrioritizedSweeping.BPTRNode - Class in burlap.behavior.singleagent.planning.stochastic.valueiteration
-
A node for state thar contains a list of its back pointers, their max probability of transition to this state, and the priority of this nodes state.
- PrioritizedSweeping.BPTRNodeComparator - Class in burlap.behavior.singleagent.planning.stochastic.valueiteration
-
Comparator for the the priority of BPTRNodes
- priority - Variable in class burlap.behavior.singleagent.planning.deterministic.informed.PrioritizedSearchNode
-
The priority of the node used to order it for expansion.
- priority - Variable in class burlap.behavior.singleagent.planning.stochastic.valueiteration.PrioritizedSweeping.BPTRNode
-
- priorityCompare - Variable in class burlap.datastructures.HashIndexedHeap
-
A comparator to compare objects
- priorityNodes - Variable in class burlap.behavior.singleagent.planning.stochastic.valueiteration.PrioritizedSweeping
-
The priority queue of states
- probabilities(State, Action) - Method in class burlap.domain.singleagent.pomdp.tiger.TigerObservations
-
- probabilities(State, Action) - Method in interface burlap.mdp.singleagent.pomdp.observations.DiscreteObservationFunction
-
Returns the observation probability mass/density function for all observations that have non-zero mass/density conditioned on the true MDP state and previous action taken that led to the state.
- probabilitiesByEnumeration(DiscreteObservationFunction, State, Action) - Static method in class burlap.mdp.singleagent.pomdp.observations.ObservationUtilities
-
- probability - Variable in class burlap.behavior.singleagent.options.model.BFSMarkovOptionModel.OptionScanNode
-
the *un*-discounted probability of reaching this search node
- probability - Variable in class burlap.domain.singleagent.graphdefined.GraphDefinedDomain.NodeTransitionProbability
-
The probability of transitioning to the resulting state
- probability(State, State, Action) - Method in class burlap.domain.singleagent.pomdp.tiger.TigerObservations
-
- probability(State, State, Action) - Method in interface burlap.mdp.singleagent.pomdp.observations.ObservationFunction
-
Returns the probability that an observation will be observed conditioned on the MDP state and previous action taken that led to the state.
- probabilityOfTermination(State, Episode) - Method in class burlap.behavior.singleagent.options.MacroAction
-
- probabilityOfTermination(State, Episode) - Method in interface burlap.behavior.singleagent.options.Option
-
- probabilityOfTermination(State, Episode) - Method in class burlap.behavior.singleagent.options.SubgoalOption
-
- probs - Variable in class burlap.datastructures.BoltzmannDistribution
-
The output probabilities
- produceRandomOffset(boolean[], double[]) - Method in class burlap.behavior.functionapproximation.sparse.tilecoding.TileCodingFeatures
-
Creates and returns a random tiling offset for the given widths and required dimensions.
- produceUniformTilingsOffset(boolean[], double[], int, int) - Method in class burlap.behavior.functionapproximation.sparse.tilecoding.TileCodingFeatures
-
Creates and returns an offset that is uniformly spaced from other tilings.
- projectionType - Variable in class burlap.behavior.singleagent.learnfromdemo.RewardValueProjection
-
- propFunction(String) - Method in interface burlap.mdp.core.oo.OODomain
-
- propFunction(String) - Method in class burlap.mdp.singleagent.oo.OOSADomain
-
- propFunction(String) - Method in class burlap.mdp.stochasticgames.oo.OOSGDomain
-
- propFunctionMap - Variable in class burlap.mdp.singleagent.oo.OOSADomain
-
- propFunctionMap - Variable in class burlap.mdp.stochasticgames.oo.OOSGDomain
-
- propFunctions() - Method in interface burlap.mdp.core.oo.OODomain
-
Returns a list of the propositional functions that define this domain.
- propFunctions() - Method in class burlap.mdp.singleagent.oo.OOSADomain
-
- propFunctions() - Method in class burlap.mdp.stochasticgames.oo.OOSGDomain
-
- PropositionalFunction - Class in burlap.mdp.core.oo.propositional
-
The propositional function class defines evaluations of object instances in an OO-MDP state and are part of the definition for an OO-MDP domain.
- PropositionalFunction(String, String[]) - Constructor for class burlap.mdp.core.oo.propositional.PropositionalFunction
-
Initializes a propositional function with the given name and parameter object classes.
- PropositionalFunction(String, String[], String[]) - Constructor for class burlap.mdp.core.oo.propositional.PropositionalFunction
-
Initializes a propositional function with the given name, parameter object classes, and the parameter order groups of the parameters.
- propViewer - Variable in class burlap.behavior.singleagent.auxiliary.EpisodeSequenceVisualizer
-
- propViewer - Variable in class burlap.behavior.stochasticgames.auxiliary.GameSequenceVisualizer
-
- propViewer - Variable in class burlap.shell.visual.SGVisualExplorer
-
- propViewer - Variable in class burlap.shell.visual.VisualExplorer
-
- providesStateEnumerator() - Method in class burlap.mdp.singleagent.pomdp.PODomain
-
- pSelection - Variable in class burlap.behavior.policy.support.ActionProb
-
The probability of the action being selected.
- PSNComparator() - Constructor for class burlap.behavior.singleagent.planning.deterministic.informed.PrioritizedSearchNode.PSNComparator
-
- put(int, double) - Method in interface burlap.behavior.functionapproximation.FunctionGradient
-
Adds the partial derivative for a given weight
- put(int, double) - Method in class burlap.behavior.functionapproximation.FunctionGradient.SparseGradient
-
- put(int, int) - Method in class burlap.behavior.functionapproximation.sparse.SparseCrossProductFeatures.FeaturesMap
-
- put(String, int) - Method in class burlap.domain.stochasticgames.normalform.SingleStageNormalFormGame.ActionNameMap
-
- putdownBlock(BlockDudeState) - Method in class burlap.domain.singleagent.blockdude.BlockDudeModel
-
Modifies state s to put down the block the agent is holding.