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

E

e1 - Variable in class burlap.domain.stochasticgames.gridgame.state.GGWall

e2 - Variable in class burlap.domain.stochasticgames.gridgame.state.GGWall

ECorrelatedQJointPolicy - Class in burlap.behavior.stochasticgames.madynamicprogramming.policies

A joint policy that computes the correlated equilibrium using the Q-values of the agents as input and then either follows that policy or returns a random action with probability epsilon.

ECorrelatedQJointPolicy(double) - Constructor for class burlap.behavior.stochasticgames.madynamicprogramming.policies.ECorrelatedQJointPolicy

Initializes with the epislon probability of a random joint action.

ECorrelatedQJointPolicy(CorrelatedEquilibriumSolver.CorrelatedEquilibriumObjective, double) - Constructor for class burlap.behavior.stochasticgames.madynamicprogramming.policies.ECorrelatedQJointPolicy

Initializes with the correlated equilibrium objective and the epsilon probability of a random joint action.

EGreedyJointPolicy - Class in burlap.behavior.stochasticgames.madynamicprogramming.policies

An epsilon greedy joint policy, in which the joint action with the highest Q-value for a given target agent is returned a 1-epsilon fraction of the time, and a random joint action an epsilon fraction of the time.

EGreedyJointPolicy(double) - Constructor for class burlap.behavior.stochasticgames.madynamicprogramming.policies.EGreedyJointPolicy

Initializes for a given epsilon value.

EGreedyJointPolicy(MultiAgentQLearning, double, int) - Constructor for class burlap.behavior.stochasticgames.madynamicprogramming.policies.EGreedyJointPolicy

Initializes for a multi-agent Q-learning object and epsilon value.

EGreedyMaxWellfare - Class in burlap.behavior.stochasticgames.madynamicprogramming.policies

An epsilon greedy joint policy, in which the joint aciton with the highest aggregate Q-values for each agent is returned a 1-epsilon fraction of the time and a random joint action an epsilon fraction of the time.

EGreedyMaxWellfare(double) - Constructor for class burlap.behavior.stochasticgames.madynamicprogramming.policies.EGreedyMaxWellfare

Initializes for a given epsilon value.

EGreedyMaxWellfare(double, boolean) - Constructor for class burlap.behavior.stochasticgames.madynamicprogramming.policies.EGreedyMaxWellfare

Initializes for a given epsilon value and whether to break ties randomly.

EGreedyMaxWellfare(MultiAgentQLearning, double) - Constructor for class burlap.behavior.stochasticgames.madynamicprogramming.policies.EGreedyMaxWellfare

Initializes for a multi-agent Q-learning object and epsilon value.

EGreedyMaxWellfare(MultiAgentQLearning, double, boolean) - Constructor for class burlap.behavior.stochasticgames.madynamicprogramming.policies.EGreedyMaxWellfare

Initializes for a multi-agent Q-learning object and epsilon value.

eligibility - Variable in class burlap.behavior.singleagent.learning.actorcritic.critics.TDLambda.StateEligibilityTrace

The eligibility value

eligibility - Variable in class burlap.behavior.singleagent.learning.tdmethods.SarsaLam.EligibilityTrace

The eligibility value

EligibilityTrace(HashableState, QValue, double) - Constructor for class burlap.behavior.singleagent.learning.tdmethods.SarsaLam.EligibilityTrace

Creates a new eligibility trace to track for an episode.

EligibilityTraceVector(int, double, double) - Constructor for class burlap.behavior.singleagent.learning.tdmethods.vfa.GradientDescentSarsaLam.EligibilityTraceVector

Creates a trace for the given weight with the given eligibility value

eligibilityValue - Variable in class burlap.behavior.singleagent.learning.tdmethods.vfa.GradientDescentSarsaLam.EligibilityTraceVector

The eligibility value

EMinMaxPolicy - Class in burlap.behavior.stochasticgames.madynamicprogramming.policies

Class for following a minmax joint policy.

EMinMaxPolicy(double) - Constructor for class burlap.behavior.stochasticgames.madynamicprogramming.policies.EMinMaxPolicy

Initializes for a given epsilon value; the fraction of the time a random joint action is selected

EMinMaxPolicy(MultiAgentQLearning, double, int) - Constructor for class burlap.behavior.stochasticgames.madynamicprogramming.policies.EMinMaxPolicy

Initializes for a given Q-learning agent and epsilon value.

encodePlanIntoPolicy(SearchNode) - Method in class burlap.behavior.singleagent.planning.deterministic.DeterministicPlanner

Encodes a solution path found by the valueFunction into this class's internal policy structure.

endAllAgents() - Method in class burlap.behavior.singleagent.auxiliary.performance.PerformancePlotter

Informs the plotter that all data for all agents has been collected.

endAllTrials() - Method in class burlap.behavior.stochasticgames.auxiliary.performance.MultiAgentPerformancePlotter

Specifies that all trials are complete and that the average trial results and error bars should be plotted.

endAllTrialsForAgent(String) - Method in class burlap.behavior.stochasticgames.auxiliary.performance.MultiAgentPerformancePlotter

Ends all the trials, plotting the average trial data for the agent with the given name

endAllTrialsHelper() - Method in class burlap.behavior.stochasticgames.auxiliary.performance.MultiAgentPerformancePlotter

The end all trial methods helper called at the end of a swing update.

endEpisode() - Method in class burlap.behavior.singleagent.auxiliary.performance.PerformancePlotter

Informs the plotter that all data for the last episode has been collected.

endEpisode() - Method in interface burlap.behavior.singleagent.learning.actorcritic.Critic

This method is called whenever a learning episode terminates

endEpisode() - Method in class burlap.behavior.singleagent.learning.actorcritic.critics.TDLambda

endEpisode() - Method in class burlap.behavior.singleagent.learning.actorcritic.critics.TimeIndexedTDLambda

endTrial() - Method in class burlap.behavior.singleagent.auxiliary.performance.PerformancePlotter

Informs the plotter that all data for the current trial as been collected.

endTrial() - Method in class burlap.behavior.stochasticgames.auxiliary.performance.MultiAgentPerformancePlotter

Ends the current trial data and updates the plots accordingly.

endTrialsForCurrentAgent() - Method in class burlap.behavior.singleagent.auxiliary.performance.PerformancePlotter

Informs the plotter that all trials for the current agent have been collected and causes the average plots to be set and displayed.

entrySet() - Method in class burlap.datastructures.HashedAggregator

The entry set for stored keys and values.

enumerable() - Method in class burlap.behavior.singleagent.options.SubgoalOption

Returns true if the initiation states and termination states of this option are iterable; false if either of them are not.

EnumerableBeliefState - Interface in burlap.mdp.singleagent.pomdp.beliefstate

An interface to be used by BeliefState implementations that also can enumerate the set of states that have probability mass.

EnumerableBeliefState.StateBelief - Class in burlap.mdp.singleagent.pomdp.beliefstate

A class for specifying the probability mass of an MDP state in a BeliefState.

EnumerablePolicy - Interface in burlap.behavior.policy

An interface extension to Policy for policies that can enumerate their probability distribution over all actions.

enumeration - Variable in class burlap.behavior.singleagent.auxiliary.StateEnumerator

The forward state enumeration map

env - Variable in class burlap.shell.EnvironmentShell

env - Variable in class burlap.shell.visual.VisualExplorer

env_cleanup() - Method in class burlap.domain.singleagent.rlglue.RLGlueEnvironment

env_init() - Method in class burlap.domain.singleagent.rlglue.RLGlueEnvironment

env_message(String) - Method in class burlap.domain.singleagent.rlglue.RLGlueEnvironment

env_start() - Method in class burlap.domain.singleagent.rlglue.RLGlueEnvironment

env_step(Action) - Method in class burlap.domain.singleagent.rlglue.RLGlueEnvironment

Environment - Interface in burlap.mdp.singleagent.environment

Environments define a current observation represetned with a State and manage state and reward transitions when an action is executed in the environment through the Environment.executeAction(Action) method.

environment - Variable in class burlap.mdp.singleagent.pomdp.BeliefAgent

The POMDP environment.

EnvironmentDelegation - Interface in burlap.mdp.singleagent.environment.extensions

Provides an interface for an Environment that delegates its responsibilities to another Environment

EnvironmentDelegation.Helper - Class in burlap.mdp.singleagent.environment.extensions

A class that provides tools for working with Environment delegates

EnvironmentObserver - Interface in burlap.mdp.singleagent.environment.extensions

A class that is told of interactions in an environment.

EnvironmentOptionOutcome - Class in burlap.behavior.singleagent.options

An EnvironmentOutcome class for reporting the effects of applying an Option in a given Environment.

EnvironmentOptionOutcome(State, Action, State, double, boolean, double, Episode) - Constructor for class burlap.behavior.singleagent.options.EnvironmentOptionOutcome

Initializes.

EnvironmentOutcome - Class in burlap.mdp.singleagent.environment

A tuple for specifying the outcome of executing an action in an Environment.

EnvironmentOutcome(State, Action, State, double, boolean) - Constructor for class burlap.mdp.singleagent.environment.EnvironmentOutcome

Initializes.

EnvironmentServer - Class in burlap.mdp.singleagent.environment.extensions

A EnvironmentServerInterface implementation that delegates all Environment interactions and request to a provided Environment delegate.

EnvironmentServer(Environment, EnvironmentObserver...) - Constructor for class burlap.mdp.singleagent.environment.extensions.EnvironmentServer

EnvironmentServer.StateSettableEnvironmentServer - Class in burlap.mdp.singleagent.environment.extensions

EnvironmentServerInterface - Interface in burlap.mdp.singleagent.environment.extensions

An interface for Environment implementations that also can serve EnvironmentObserver instances about the interactions in the Environment.

environmentSever - Variable in class burlap.behavior.singleagent.auxiliary.performance.LearningAlgorithmExperimenter

The EnvironmentServer that wraps the test Environment and tells a PerformancePlotter about the individual interactions.

EnvironmentShell - Class in burlap.shell

This is a subclass of BurlapShell for a shell with shell commands that manipulate or read an Environment.

EnvironmentShell(Domain, Environment, InputStream, PrintStream) - Constructor for class burlap.shell.EnvironmentShell

EnvironmentShell(Domain, Environment) - Constructor for class burlap.shell.EnvironmentShell

Creates shell for std in and std out.

EnvironmentShell(SADomain, State) - Constructor for class burlap.shell.EnvironmentShell

Creates a shell for a SimulatedEnvironment rooted at the input state using std in and std out.

eo - Variable in class burlap.mdp.singleagent.model.TransitionProb

The transition

Episode - Class in burlap.behavior.singleagent

This class is used to keep track of all events that occur in an episode.

Episode() - Constructor for class burlap.behavior.singleagent.Episode

Creates a new EpisodeAnalysis object.

Episode(State) - Constructor for class burlap.behavior.singleagent.Episode

Initializes a new EpisodeAnalysis object with the initial state in which the episode started.

episode - Variable in class burlap.behavior.singleagent.options.EnvironmentOptionOutcome

The executed episode from this execution

episode - Variable in class burlap.behavior.singleagent.options.model.BFSNonMarkovOptionModel.NonMarkovOptionScanNode

EpisodeBrowserCommand() - Constructor for class burlap.shell.command.env.EpisodeRecordingCommands.EpisodeBrowserCommand

episodeFiles - Variable in class burlap.behavior.singleagent.auxiliary.EpisodeSequenceVisualizer

episodeFiles - Variable in class burlap.behavior.stochasticgames.auxiliary.GameSequenceVisualizer

episodeHistory - Variable in class burlap.behavior.singleagent.learning.actorcritic.ActorCritic

The saved and most recent learning episodes this agent has performed.

episodeHistory - Variable in class burlap.behavior.singleagent.learning.lspi.LSPI

the saved previous learning episodes

episodeHistory - Variable in class burlap.behavior.singleagent.learning.modellearning.artdp.ARTDP

the saved previous learning episodes

episodeHistory - Variable in class burlap.behavior.singleagent.learning.modellearning.rmax.PotentialShapedRMax

the saved previous learning episodes

episodeList - Variable in class burlap.behavior.singleagent.auxiliary.EpisodeSequenceVisualizer

episodeList - Variable in class burlap.behavior.stochasticgames.auxiliary.GameSequenceVisualizer

EpisodeRecordingCommands - Class in burlap.shell.command.env

Two ShellCommands, rec and episode, for recording and browsing episodes of behavior that take place in the Environment.

EpisodeRecordingCommands() - Constructor for class burlap.shell.command.env.EpisodeRecordingCommands

EpisodeRecordingCommands.EpisodeBrowserCommand - Class in burlap.shell.command.env

EpisodeRecordingCommands.RecordCommand - Class in burlap.shell.command.env

episodes - Variable in class burlap.shell.command.env.EpisodeRecordingCommands

episodeScroller - Variable in class burlap.behavior.singleagent.auxiliary.EpisodeSequenceVisualizer

episodeScroller - Variable in class burlap.behavior.stochasticgames.auxiliary.GameSequenceVisualizer

EpisodeSequenceVisualizer - Class in burlap.behavior.singleagent.auxiliary

This class is used to visualize a set of episodes that have been saved to files in a common directory or which are provided to the object as a list of Episode objects.

EpisodeSequenceVisualizer(Visualizer, Domain, String) - Constructor for class burlap.behavior.singleagent.auxiliary.EpisodeSequenceVisualizer

Initializes the EpisodeSequenceVisualizer.

EpisodeSequenceVisualizer(Visualizer, Domain, String, int, int) - Constructor for class burlap.behavior.singleagent.auxiliary.EpisodeSequenceVisualizer

Initializes the EpisodeSequenceVisualizer.

EpisodeSequenceVisualizer(Visualizer, Domain, List<Episode>) - Constructor for class burlap.behavior.singleagent.auxiliary.EpisodeSequenceVisualizer

Initializes the EpisodeSequenceVisualizer with a programatically supplied list of Episode objects to view.

EpisodeSequenceVisualizer(Visualizer, Domain, List<Episode>, int, int) - Constructor for class burlap.behavior.singleagent.auxiliary.EpisodeSequenceVisualizer

Initializes the EpisodeSequenceVisualizer with a programatically supplied list of Episode objects to view.

episodesListModel - Variable in class burlap.behavior.singleagent.auxiliary.EpisodeSequenceVisualizer

episodesListModel - Variable in class burlap.behavior.stochasticgames.auxiliary.GameSequenceVisualizer

episodeWeights - Variable in class burlap.behavior.singleagent.learnfromdemo.mlirl.MLIRLRequest

The weight assigned to each episode.

epsilon - Variable in class burlap.behavior.functionapproximation.dense.rbf.functions.GaussianRBF

The bandwidth parameter.

epsilon - Variable in class burlap.behavior.policy.EpsilonGreedy

epsilon - Variable in class burlap.behavior.singleagent.learnfromdemo.apprenticeship.ApprenticeshipLearningRequest

The maximum feature score to cause termination of Apprenticeship learning

epsilon - Variable in class burlap.behavior.singleagent.planning.deterministic.informed.astar.DynamicWeightedAStar

parameter > 1 indicating the maximum amount of greediness; the larger the more greedy.

epsilon - Variable in class burlap.behavior.stochasticgames.agents.naiveq.history.SGQWActionHistoryFactory

The epislon value for epislon greedy policy.

epsilon - Variable in class burlap.behavior.stochasticgames.madynamicprogramming.policies.ECorrelatedQJointPolicy

The epsilon parameter specifying how often random joint actions are returned

epsilon - Variable in class burlap.behavior.stochasticgames.madynamicprogramming.policies.EGreedyJointPolicy

The epsilon parameter specifying how often random joint actions are returned

epsilon - Variable in class burlap.behavior.stochasticgames.madynamicprogramming.policies.EGreedyMaxWellfare

The epsilon parameter specifying how often random joint actions are returned

epsilon - Variable in class burlap.behavior.stochasticgames.madynamicprogramming.policies.EMinMaxPolicy

The epsilon parameter specifying how often random joint actions are returned

EpsilonGreedy - Class in burlap.behavior.policy

This class defines a an epsilon-greedy policy over Q-values and requires a QComputable valueFunction to be specified.

EpsilonGreedy(double) - Constructor for class burlap.behavior.policy.EpsilonGreedy

Initializes with the value of epsilon, where epsilon is the probability of taking a random action.

EpsilonGreedy(QProvider, double) - Constructor for class burlap.behavior.policy.EpsilonGreedy

Initializes with the QComputablePlanner to use and the value of epsilon to use, where epsilon is the probability of taking a random action.

epsilonP1 - Variable in class burlap.behavior.singleagent.planning.deterministic.informed.astar.StaticWeightedAStar

The > 1 epsilon parameter.

epsilonWeight(int) - Method in class burlap.behavior.singleagent.planning.deterministic.informed.astar.DynamicWeightedAStar

Returns the weighted epsilon value at the given search depth

equals(Object) - Method in class burlap.behavior.functionapproximation.FunctionGradient.PartialDerivative

equals(Object) - Method in class burlap.behavior.functionapproximation.sparse.tilecoding.Tiling.FVTile

equals(Object) - Method in class burlap.behavior.policy.support.AnnotatedAction

equals(Object) - Method in class burlap.behavior.singleagent.interfaces.rlglue.RLGlueDomain.RLGlueActionType.RLGLueAction

equals(Object) - Method in class burlap.behavior.singleagent.interfaces.rlglue.RLGlueState.RLGlueVarKey

equals(Object) - Method in class burlap.behavior.singleagent.options.MacroAction

equals(Object) - Method in class burlap.behavior.singleagent.options.SubgoalOption

equals(Object) - Method in class burlap.behavior.singleagent.planning.deterministic.informed.PrioritizedSearchNode

equals(Object) - Method in class burlap.behavior.singleagent.planning.deterministic.SearchNode

equals(Object) - Method in class burlap.behavior.singleagent.planning.stochastic.montecarlo.uct.UCTStateNode

equals(Object) - Method in class burlap.behavior.singleagent.planning.stochastic.sparsesampling.SparseSampling.HashedHeightState

equals(Object) - Method in class burlap.behavior.singleagent.planning.stochastic.valueiteration.PrioritizedSweeping.BPTRNode

equals(Object) - Method in class burlap.domain.singleagent.graphdefined.GraphDefinedDomain.GraphActionType.GraphAction

equals(Object) - Method in class burlap.domain.singleagent.graphdefined.GraphDefinedDomain.NodeTransitionProbability

equals(Object) - Method in class burlap.domain.singleagent.gridworld.GridWorldTerminalFunction.IntPair

equals(Object) - Method in class burlap.domain.singleagent.lunarlander.LunarLanderDomain.ThrustType.ThrustAction

equals(Object) - Method in class burlap.domain.singleagent.pomdp.tiger.TigerState

equals(Object) - Method in class burlap.domain.stochasticgames.normalform.SingleStageNormalFormGame.MatrixAction

equals(Object) - Method in class burlap.domain.stochasticgames.normalform.SingleStageNormalFormGame.StrategyProfile

equals(Object) - Method in class burlap.mdp.core.action.SimpleAction

equals(Object) - Method in class burlap.mdp.core.oo.propositional.GroundedProp

equals(Object) - Method in class burlap.mdp.core.oo.propositional.PropositionalFunction

equals(Object) - Method in class burlap.mdp.core.oo.state.OOVariableKey

equals(Object) - Method in class burlap.mdp.core.state.NullState

equals(Object) - Method in class burlap.mdp.singleagent.oo.ObjectParameterizedActionType.SAObjectParameterizedAction

equals(Object) - Method in class burlap.mdp.singleagent.pomdp.beliefstate.TabularBeliefState

equals(Object) - Method in class burlap.mdp.stochasticgames.agent.SGAgentType

equals(Object) - Method in class burlap.mdp.stochasticgames.JointAction

equals(Object) - Method in class burlap.statehashing.simple.IDSimpleHashableState

equals(Object) - Method in class burlap.statehashing.simple.IISimpleHashableState

equals(Object) - Method in class burlap.statehashing.WrappedHashableState

EquilibriumPlayingSGAgent - Class in burlap.behavior.stochasticgames.agents.twoplayer.singlestage.equilibriumplayer

This agent plays an equilibrium solution for two player games based on the immediate joint rewards received for the given state, as if it is a single stage game.

EquilibriumPlayingSGAgent() - Constructor for class burlap.behavior.stochasticgames.agents.twoplayer.singlestage.equilibriumplayer.EquilibriumPlayingSGAgent

Initializes with the MaxMax solution concept.

EquilibriumPlayingSGAgent(BimatrixEquilibriumSolver) - Constructor for class burlap.behavior.stochasticgames.agents.twoplayer.singlestage.equilibriumplayer.EquilibriumPlayingSGAgent

Initializes with strategies formed usign the solution concept generated by the given solver.

EquilibriumPlayingSGAgent.BimatrixTuple - Class in burlap.behavior.stochasticgames.agents.twoplayer.singlestage.equilibriumplayer

A Bimatrix tuple.

eStepCounter - Variable in class burlap.behavior.singleagent.learning.tdmethods.QLearning

A counter for counting the number of steps in an episode that have been taken thus far

eStepCounter - Variable in class burlap.behavior.singleagent.learning.tdmethods.vfa.GradientDescentSarsaLam

A counter for counting the number of steps in an episode that have been taken thus far

estimateFeatureExpectation(Episode, DenseStateFeatures, Double) - Static method in class burlap.behavior.singleagent.learnfromdemo.apprenticeship.ApprenticeshipLearning

Calculates the Feature Expectations given one demonstration, a feature mapping and a discount factor gamma

estimateFeatureExpectation(List<Episode>, DenseStateFeatures, Double) - Static method in class burlap.behavior.singleagent.learnfromdemo.apprenticeship.ApprenticeshipLearning

Calculates the Feature Expectations given a list of demonstrations, a feature mapping and a discount factor gamma

estimateQs() - Method in class burlap.behavior.singleagent.learnfromdemo.mlirl.differentiableplanners.DifferentiableSparseSampling.DiffStateNode

estimateQs() - Method in class burlap.behavior.singleagent.planning.stochastic.sparsesampling.SparseSampling.StateNode

Estimates and returns the Q-values for this node.

estimateV() - Method in class burlap.behavior.singleagent.learnfromdemo.mlirl.differentiableplanners.DifferentiableSparseSampling.DiffStateNode

estimateV() - Method in class burlap.behavior.singleagent.planning.stochastic.sparsesampling.SparseSampling.StateNode

Returns the estimated Q-value if this node is closed, or estimates it and closes it otherwise.

EuclideanDistance - Class in burlap.behavior.functionapproximation.dense.rbf.metrics

A distance metric; returns sqrt( sum_i (x_i - y_i)^2 )

EuclideanDistance() - Constructor for class burlap.behavior.functionapproximation.dense.rbf.metrics.EuclideanDistance

evaluate(State, Action) - Method in class burlap.behavior.functionapproximation.dense.DenseLinearVFA

evaluate(State) - Method in class burlap.behavior.functionapproximation.dense.DenseLinearVFA

evaluate(State, Action) - Method in class burlap.behavior.functionapproximation.dense.DenseStateActionLinearVFA

evaluate(State, Action) - Method in interface burlap.behavior.functionapproximation.ParametricFunction.ParametricStateActionFunction

Sets the input of this function to the given State and Action and returns the value of it.

evaluate(State) - Method in interface burlap.behavior.functionapproximation.ParametricFunction.ParametricStateFunction

Sets the input of this function to the given State and returns the value of it.

evaluate(State, Action) - Method in class burlap.behavior.functionapproximation.sparse.LinearVFA

evaluate(State) - Method in class burlap.behavior.functionapproximation.sparse.LinearVFA

evaluateEpisode(Episode) - Method in class burlap.testing.TestPlanning

evaluateEpisode(Episode, Boolean) - Method in class burlap.testing.TestPlanning

evaluatePolicy(EnumerablePolicy, State) - Method in class burlap.behavior.singleagent.planning.stochastic.policyiteration.PolicyEvaluation

Computes the value function for the given policy after finding all reachable states from seed state s

evaluatePolicy(EnumerablePolicy) - Method in class burlap.behavior.singleagent.planning.stochastic.policyiteration.PolicyEvaluation

Computes the value function for the given policy over the states that have been discovered

evaluatePolicy() - Method in class burlap.behavior.singleagent.planning.stochastic.policyiteration.PolicyIteration

Computes the value function under following the current evaluative policy.

evaluativePolicy - Variable in class burlap.behavior.singleagent.planning.stochastic.policyiteration.PolicyIteration

The current policy to be evaluated

exactQEstimate(Action, DifferentiableSparseSampling.QAndQGradient) - Method in class burlap.behavior.singleagent.learnfromdemo.mlirl.differentiableplanners.DifferentiableSparseSampling.DiffStateNode

exactQValue(Action) - Method in class burlap.behavior.singleagent.planning.stochastic.sparsesampling.SparseSampling.StateNode

Computes the exact Q-value using full Bellman update with the actual transition dynamics.

executeAction(Action) - Method in class burlap.behavior.singleagent.interfaces.rlglue.RLGlueAgent

executeAction(Action) - Method in class burlap.behavior.stochasticgames.agents.interfacing.singleagent.LearningAgentToSGAgentInterface

executeAction(Action) - Method in interface burlap.mdp.singleagent.environment.Environment

Executes the specified action in this environment

executeAction(Action) - Method in class burlap.mdp.singleagent.environment.extensions.EnvironmentServer

executeAction(Action) - Method in class burlap.mdp.singleagent.environment.SimulatedEnvironment

executeAction(Action) - Method in class burlap.mdp.singleagent.pomdp.SimulatedPOEnvironment

executeAction(Action) - Method in class burlap.shell.visual.VisualExplorer

Executes the provided Action in the explorer's environment and records the result if episodes are being recorded.

ExecuteActionCommand - Class in burlap.shell.command.env

A ShellCommand for executing an action in the Environment Use the -h option for help information.

ExecuteActionCommand(Domain) - Constructor for class burlap.shell.command.env.ExecuteActionCommand

executeCommand(String) - Method in class burlap.shell.BurlapShell

executeJointAction(JointAction) - Method in class burlap.mdp.stochasticgames.world.World

Manually attempts to execute a joint action in the current world state, if a game is currently not running.

exit(int, int) - Static method in class burlap.domain.singleagent.blockdude.state.BlockDudeCell

exit - Variable in class burlap.domain.singleagent.blockdude.state.BlockDudeState

ExitPainter(int, int) - Constructor for class burlap.domain.singleagent.blockdude.BlockDudeVisualizer.ExitPainter

Initializes.

expandStateActionWeights(int) - Method in class burlap.behavior.functionapproximation.dense.DenseLinearVFA

Expands the state-action function weight vector by a fixed sized and initializes their value to the default weight value set for this object.

expectedDepth - Variable in class burlap.behavior.singleagent.planning.deterministic.informed.astar.DynamicWeightedAStar

The expected depth required for a plan

expectedGap - Variable in class burlap.behavior.singleagent.planning.stochastic.rtdp.BoundedRTDP.StateSelectionAndExpectedGap

The expected margin/gap of the value function from the source transition

expectedPayoffs(double[][], double[][], double[], double[]) - Static method in class burlap.behavior.stochasticgames.solvers.GeneralBimatrixSolverTools

Computes the expected payoff for each player in a bimatrix game according to their strategies.

expectedPayoffs(double[][], double[][], double[][]) - Static method in class burlap.behavior.stochasticgames.solvers.GeneralBimatrixSolverTools

ExperimentalEnvironment - Interface in burlap.behavior.singleagent.auxiliary.performance

An interface to be used in conjunction with Environment implementations that can accept a message informing the environment that a new experiment for a LearningAgent has started.

expertEpisodes - Variable in class burlap.behavior.singleagent.learnfromdemo.IRLRequest

The input trajectories/episodes that are to be modeled.

explorationBias - Variable in class burlap.behavior.singleagent.planning.stochastic.montecarlo.uct.UCT

explorationQBoost(int, int) - Method in class burlap.behavior.singleagent.planning.stochastic.montecarlo.uct.UCT

Returns the extra value added to the average sample Q-value that is sued to produce the upper confidence Q-value.

ExponentialDecayLR - Class in burlap.behavior.learningrate

This class provides a learning rate that decays exponentially with time according to r^t, where r is in [0,1] and t is the time step, from an initial learning rate.

ExponentialDecayLR(double, double) - Constructor for class burlap.behavior.learningrate.ExponentialDecayLR

Initializes with an initial learning rate and decay rate for a state independent learning rate.

ExponentialDecayLR(double, double, double) - Constructor for class burlap.behavior.learningrate.ExponentialDecayLR

Initializes with an initial learning rate and decay rate for a state independent learning rate that will decay to a value no smaller than minimumLearningRate

ExponentialDecayLR(double, double, HashableStateFactory, boolean) - Constructor for class burlap.behavior.learningrate.ExponentialDecayLR

Initializes with an initial learning rate and decay rate for a state or state-action (or state feature-action) dependent learning rate.

ExponentialDecayLR(double, double, double, HashableStateFactory, boolean) - Constructor for class burlap.behavior.learningrate.ExponentialDecayLR

Initializes with an initial learning rate and decay rate for a state or state-action (or state feature-action) dependent learning rate that will decay to a value no smaller than minimumLearningRate If this learning rate function is to be used for state state features, rather than states, then the hashing factory can be null;

ExponentialDecayLR.MutableDouble - Class in burlap.behavior.learningrate

A class for storing a mutable double value object

ExponentialDecayLR.StateWiseLearningRate - Class in burlap.behavior.learningrate

A class for storing a learning rate for a state, or a learning rate for each action for a given state

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