- 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) - 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
- 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) - 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.DeterministicTerminationOption
-
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.oomdp.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.oomdp.singleagent.pomdp.beliefstate
-
A class for specifying the probability mass of an MDP state in a
BeliefState.
- EnumerableBeliefState.StateBelief(State, double) - Constructor for class burlap.oomdp.singleagent.pomdp.beliefstate.EnumerableBeliefState.StateBelief
-
Initializes
- enumeration - Variable in class burlap.behavior.singleagent.auxiliary.StateEnumerator
-
The forward state enumeration map
- enumerator - Variable in class burlap.domain.singleagent.tabularized.TabulatedDomainWrapper
-
The state enumerator used for enumerating (or tabulating) all states
- env - Variable in class burlap.oomdp.singleagent.explorer.VisualExplorer
-
- env - Variable in class burlap.shell.EnvironmentShell
-
- env_cleanup() - Method in class burlap.oomdp.singleagent.interfaces.rlglue.RLGlueEnvironment
-
- env_init() - Method in class burlap.oomdp.singleagent.interfaces.rlglue.RLGlueEnvironment
-
- env_message(String) - Method in class burlap.oomdp.singleagent.interfaces.rlglue.RLGlueEnvironment
-
- env_start() - Method in class burlap.oomdp.singleagent.interfaces.rlglue.RLGlueEnvironment
-
- env_step(Action) - Method in class burlap.oomdp.singleagent.interfaces.rlglue.RLGlueEnvironment
-
- Environment - Interface in burlap.oomdp.singleagent.environment
-
- environment - Variable in class burlap.oomdp.singleagent.pomdp.BeliefAgent
-
The POMDP environment.
- EnvironmentDelegation - Interface in burlap.oomdp.singleagent.environment
-
- EnvironmentDelegation.EnvDelegationTools - Class in burlap.oomdp.singleagent.environment
-
A class that provides tools for working with Environment delegates
- EnvironmentDelegation.EnvDelegationTools() - Constructor for class burlap.oomdp.singleagent.environment.EnvironmentDelegation.EnvDelegationTools
-
- EnvironmentObserver - Interface in burlap.oomdp.singleagent.environment
-
A class that is told of interactions in an environment.
- EnvironmentOptionOutcome - Class in burlap.behavior.singleagent.options.support
-
- EnvironmentOptionOutcome(State, GroundedAction, State, double, boolean, double, int) - Constructor for class burlap.behavior.singleagent.options.support.EnvironmentOptionOutcome
-
Initializes.
- EnvironmentOutcome - Class in burlap.oomdp.singleagent.environment
-
A class for specifying the outcome of executing an action in an
Environment.
- EnvironmentOutcome(State, GroundedAction, State, double, boolean) - Constructor for class burlap.oomdp.singleagent.environment.EnvironmentOutcome
-
Initializes.
- EnvironmentServer - Class in burlap.oomdp.singleagent.environment
-
- EnvironmentServer(Environment, EnvironmentObserver...) - Constructor for class burlap.oomdp.singleagent.environment.EnvironmentServer
-
- EnvironmentServer.StateSettableEnvironmentServer - Class in burlap.oomdp.singleagent.environment
-
- EnvironmentServer.StateSettableEnvironmentServer(StateSettableEnvironment, EnvironmentObserver...) - Constructor for class burlap.oomdp.singleagent.environment.EnvironmentServer.StateSettableEnvironmentServer
-
- EnvironmentServerInterface - Interface in burlap.oomdp.singleagent.environment
-
- environmentSever - Variable in class burlap.behavior.singleagent.auxiliary.performance.LearningAlgorithmExperimenter
-
- EnvironmentShell - Class in burlap.shell
-
- EnvironmentShell(Domain, Environment, InputStream, PrintStream) - Constructor for class burlap.shell.EnvironmentShell
-
- EpisodeAnalysis - Class in burlap.behavior.singleagent
-
This class is used to keep track of all events that occur in an episode.
- EpisodeAnalysis() - Constructor for class burlap.behavior.singleagent.EpisodeAnalysis
-
Creates a new EpisodeAnalysis object.
- EpisodeAnalysis(State) - Constructor for class burlap.behavior.singleagent.EpisodeAnalysis
-
Initializes a new EpisodeAnalysis object with the initial state in which the episode started.
- 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
- episodeHistory - Variable in class burlap.behavior.singleagent.learning.tdmethods.QLearning
-
the saved previous learning episodes
- episodeHistory - Variable in class burlap.behavior.singleagent.learning.tdmethods.vfa.GradientDescentSarsaLam
-
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.EpisodeBrowserCommand() - Constructor for class burlap.shell.command.env.EpisodeRecordingCommands.EpisodeBrowserCommand
-
- EpisodeRecordingCommands.RecordCommand - Class in burlap.shell.command.env
-
- EpisodeRecordingCommands.RecordCommand() - Constructor for class burlap.shell.command.env.EpisodeRecordingCommands.RecordCommand
-
- 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
EpisodeAnalysis 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<EpisodeAnalysis>) - Constructor for class burlap.behavior.singleagent.auxiliary.EpisodeSequenceVisualizer
-
Initializes the EpisodeSequenceVisualizer with a programatically supplied list of
EpisodeAnalysis objects to view.
- EpisodeSequenceVisualizer(Visualizer, Domain, List<EpisodeAnalysis>, int, int) - Constructor for class burlap.behavior.singleagent.auxiliary.EpisodeSequenceVisualizer
-
Initializes the EpisodeSequenceVisualizer with a programatically supplied list of
EpisodeAnalysis 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.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.singleagent.vfa.rbf.functions.FVGaussianRBF
-
The bandwidth parameter.
- epsilon - Variable in class burlap.behavior.singleagent.vfa.rbf.functions.GaussianRBF
-
The bandwidth parameter.
- 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(QFunction, 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.policy.Policy.GroundedAnnotatedAction
-
- 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.behavior.singleagent.vfa.cmac.FVTiling.FVTile
-
- equals(Object) - Method in class burlap.behavior.singleagent.vfa.cmac.Tiling.ObjectTile
-
- equals(Object) - Method in class burlap.behavior.singleagent.vfa.cmac.Tiling.StateTile
-
- equals(Object) - Method in class burlap.behavior.singleagent.vfa.FunctionGradient.PartialDerivative
-
- equals(Object) - Method in class burlap.behavior.stochasticgames.agents.interfacing.singleagent.SGToSADomain.GroundedSAAActionWrapper
-
- 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.stochasticgames.normalform.SingleStageNormalFormGame.StrategyProfile
-
- equals(Object) - Method in class burlap.oomdp.core.Attribute
-
- equals(Object) - Method in class burlap.oomdp.core.GroundedProp
-
- equals(Object) - Method in class burlap.oomdp.core.objects.ImmutableObjectInstance
-
- equals(Object) - Method in class burlap.oomdp.core.objects.MutableObjectInstance
-
- equals(Object) - Method in class burlap.oomdp.core.PropositionalFunction
-
- equals(Object) - Method in class burlap.oomdp.core.states.FixedSizeImmutableState
-
- equals(Object) - Method in class burlap.oomdp.core.states.ImmutableState
-
- equals(Object) - Method in class burlap.oomdp.core.states.MutableState
-
- equals(Object) - Method in class burlap.oomdp.core.values.DiscreteValue
-
- equals(Object) - Method in class burlap.oomdp.core.values.DoubleArrayValue
-
- equals(Object) - Method in class burlap.oomdp.core.values.IntArrayValue
-
- equals(Object) - Method in class burlap.oomdp.core.values.IntValue
-
- equals(Object) - Method in class burlap.oomdp.core.values.MultiTargetRelationalValue
-
- equals(Object) - Method in class burlap.oomdp.core.values.RealValue
-
- equals(Object) - Method in class burlap.oomdp.core.values.RelationalValue
-
- equals(Object) - Method in class burlap.oomdp.core.values.StringValue
-
- equals(Object) - Method in class burlap.oomdp.singleagent.Action
-
- equals(Object) - Method in class burlap.oomdp.singleagent.GroundedAction
-
- equals(Object) - Method in class burlap.oomdp.singleagent.ObjectParameterizedAction.ObjectParameterizedGroundedAction
-
- equals(Object) - Method in class burlap.oomdp.singleagent.pomdp.beliefstate.tabular.HashableTabularBeliefStateFactory.HashableTabularBeliefState
-
- equals(Object) - Method in class burlap.oomdp.singleagent.pomdp.beliefstate.tabular.TabularBeliefState
-
- equals(Object) - Method in class burlap.oomdp.statehashing.HashableState
-
- equals(Object) - Method in class burlap.oomdp.statehashing.ImmutableStateHashableStateFactory.ImmutableHashableState
-
- equals(Object) - Method in class burlap.oomdp.statehashing.SimpleHashableStateFactory.SimpleCachedHashableState
-
- equals(Object) - Method in class burlap.oomdp.statehashing.SimpleHashableStateFactory.SimpleHashableState
-
- equals(Object) - Method in class burlap.oomdp.stochasticgames.agentactions.GroundedSGAgentAction
-
- equals(Object) - Method in class burlap.oomdp.stochasticgames.agentactions.ObParamSGAgentAction.GroundedObParamSGAgentAction
-
- equals(Object) - Method in class burlap.oomdp.stochasticgames.agentactions.SGAgentAction
-
- equals(Object) - Method in class burlap.oomdp.stochasticgames.JointAction
-
- equals(Object) - Method in class burlap.oomdp.stochasticgames.SGAgentType
-
- 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.
- EquilibriumPlayingSGAgent.BimatrixTuple(int, int) - Constructor for class burlap.behavior.stochasticgames.agents.twoplayer.singlestage.equilibriumplayer.EquilibriumPlayingSGAgent.BimatrixTuple
-
Initializes the payoff matrices for a bimatrix of the given row and column dimensionality
- EquilibriumPlayingSGAgent.BimatrixTuple(double[][], double[][]) - Constructor for class burlap.behavior.stochasticgames.agents.twoplayer.singlestage.equilibriumplayer.EquilibriumPlayingSGAgent.BimatrixTuple
-
Initializes with a given row and column player payoffs.
- 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(EpisodeAnalysis, StateToFeatureVectorGenerator, 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<EpisodeAnalysis>, StateToFeatureVectorGenerator, 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.singleagent.vfa.rbf.metrics
-
- EuclideanDistance(StateToFeatureVectorGenerator) - Constructor for class burlap.behavior.singleagent.vfa.rbf.metrics.EuclideanDistance
-
- evaluate(State, AbstractGroundedAction) - Method in class burlap.behavior.singleagent.vfa.common.LinearFVVFA
-
- evaluate(State) - Method in class burlap.behavior.singleagent.vfa.common.LinearFVVFA
-
- evaluate(State, AbstractGroundedAction) - Method in class burlap.behavior.singleagent.vfa.common.LinearVFA
-
- evaluate(State) - Method in class burlap.behavior.singleagent.vfa.common.LinearVFA
-
- evaluate(State, AbstractGroundedAction) - Method in interface burlap.behavior.singleagent.vfa.ParametricFunction.ParametricStateActionFunction
-
- evaluate(State) - Method in interface burlap.behavior.singleagent.vfa.ParametricFunction.ParametricStateFunction
-
Sets the input of this function to the given
State and returns
the value of it.
- evaluateBehavior(State, RewardFunction, TerminalFunction) - Method in class burlap.behavior.policy.Policy
-
This method will return the an episode that results from following this policy from state s.
- evaluateBehavior(State, RewardFunction, TerminalFunction, int) - Method in class burlap.behavior.policy.Policy
-
This method will return the an episode that results from following this policy from state s.
- evaluateBehavior(State, RewardFunction, int) - Method in class burlap.behavior.policy.Policy
-
This method will return the an episode that results from following this policy from state s.
- evaluateBehavior(Environment) - Method in class burlap.behavior.policy.Policy
-
- evaluateBehavior(Environment, int) - Method in class burlap.behavior.policy.Policy
-
- evaluateDecomposesOptions - Variable in class burlap.behavior.policy.Policy
-
- evaluateEpisode(EpisodeAnalysis) - Method in class burlap.testing.TestPlanning
-
- evaluateEpisode(EpisodeAnalysis, Boolean) - Method in class burlap.testing.TestPlanning
-
- evaluateMethodsShouldAnnotateOptionDecomposition(boolean) - Method in class burlap.behavior.policy.Policy
-
Sets whether options that are decomposed into primitives will have the option that produced them and listed.
- evaluateMethodsShouldDecomposeOption(boolean) - Method in class burlap.behavior.policy.Policy
-
Sets whether the primitive actions taken during an options will be included as steps in produced EpisodeAnalysis objects.
- evaluatePolicy(Policy, 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(Policy) - 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
- ExampleGridWorld - Class in burlap.tutorials.bd
-
- ExampleGridWorld() - Constructor for class burlap.tutorials.bd.ExampleGridWorld
-
- ExampleGridWorld.AgentPainter - Class in burlap.tutorials.bd
-
- ExampleGridWorld.AgentPainter() - Constructor for class burlap.tutorials.bd.ExampleGridWorld.AgentPainter
-
- ExampleGridWorld.AtLocation - Class in burlap.tutorials.bd
-
- ExampleGridWorld.AtLocation(Domain) - Constructor for class burlap.tutorials.bd.ExampleGridWorld.AtLocation
-
- ExampleGridWorld.ExampleRF - Class in burlap.tutorials.bd
-
- ExampleGridWorld.ExampleRF(int, int) - Constructor for class burlap.tutorials.bd.ExampleGridWorld.ExampleRF
-
- ExampleGridWorld.ExampleTF - Class in burlap.tutorials.bd
-
- ExampleGridWorld.ExampleTF(int, int) - Constructor for class burlap.tutorials.bd.ExampleGridWorld.ExampleTF
-
- ExampleGridWorld.LocationPainter - Class in burlap.tutorials.bd
-
- ExampleGridWorld.LocationPainter() - Constructor for class burlap.tutorials.bd.ExampleGridWorld.LocationPainter
-
- ExampleGridWorld.Movement - Class in burlap.tutorials.bd
-
- ExampleGridWorld.Movement(String, Domain, int) - Constructor for class burlap.tutorials.bd.ExampleGridWorld.Movement
-
- ExampleGridWorld.WallPainter - Class in burlap.tutorials.bd
-
- ExampleGridWorld.WallPainter() - Constructor for class burlap.tutorials.bd.ExampleGridWorld.WallPainter
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- executeAction(GroundedAction) - Method in class burlap.behavior.singleagent.interfaces.rlglue.RLGlueEnvironmentInterface
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- executeAction(GroundedAction) - Method in class burlap.behavior.stochasticgames.agents.interfacing.singleagent.LearningAgentToSGAgentInterface
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- executeAction(GroundedAction) - Method in interface burlap.oomdp.singleagent.environment.Environment
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Executes the specified action in this environment
- executeAction(GroundedAction) - Method in class burlap.oomdp.singleagent.environment.EnvironmentServer
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- executeAction(GroundedAction) - Method in class burlap.oomdp.singleagent.environment.SimulatedEnvironment
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- executeAction(String[]) - Method in class burlap.oomdp.singleagent.explorer.VisualExplorer
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Executes the action defined in string array with the first component being the action name and the rest the parameters.
- executeAction(GroundedAction) - Method in class burlap.oomdp.singleagent.explorer.VisualExplorer
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Executes the provided
GroundedAction in the explorer's environment and records
the result if episodes are being recorded.
- executeAction(GroundedAction) - Method in class burlap.oomdp.singleagent.pomdp.SimulatedPOEnvironment
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- ExecuteActionCommand - Class in burlap.shell.command.env
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- ExecuteActionCommand(Domain) - Constructor for class burlap.shell.command.env.ExecuteActionCommand
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- executeCommand(String) - Method in class burlap.shell.BurlapShell
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- executeIn(Environment) - Method in class burlap.behavior.policy.Policy.GroundedAnnotatedAction
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- executeIn(State) - Method in class burlap.behavior.policy.Policy.GroundedAnnotatedAction
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- executeIn(Environment) - Method in class burlap.oomdp.singleagent.GroundedAction
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Executes this grounded action in the specified
Environment.
- executeIn(State) - Method in class burlap.oomdp.singleagent.GroundedAction
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Executes the grounded action on a given state
- executeJointAction(JointAction) - Method in class burlap.oomdp.stochasticgames.World
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Manually attempts to execute a joint action in the current world state, if a game is currently not running.
- expandStateActionWeights(int) - Method in class burlap.behavior.singleagent.vfa.common.LinearFVVFA
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Expands the state-action function weight vector by a fixed sized and initializes their value
to the default weight value set for this object.
- expectationSearchCutoffProb - Variable in class burlap.behavior.singleagent.options.Option
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The minimum probability a possible terminal state being reached to be included in the computed transition dynamics
- expectationStateHashingFactory - Variable in class burlap.behavior.singleagent.options.Option
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State hash factory used to cache the transition probabilities so that they only need to be computed once for each state
- expectedDepth - Variable in class burlap.behavior.singleagent.planning.deterministic.informed.astar.DynamicWeightedAStar
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The expected depth required for a plan
- expectedGap - Variable in class burlap.behavior.singleagent.planning.stochastic.rtdp.BoundedRTDP.StateSelectionAndExpectedGap
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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
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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
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- ExperimentalEnvironment - Interface in burlap.behavior.singleagent.auxiliary.performance
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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.
- experimentAndPlotter() - Method in class burlap.tutorials.bpl.BasicBehavior
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- expertEpisodes - Variable in class burlap.behavior.singleagent.learnfromdemo.IRLRequest
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The input trajectories/episodes that are to be modeled.
- explorationBias - Variable in class burlap.behavior.singleagent.planning.stochastic.montecarlo.uct.UCT
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- explorationQBoost(int, int) - Method in class burlap.behavior.singleagent.planning.stochastic.montecarlo.uct.UCT
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Returns the extra value added to the average sample Q-value that is sued to produce the upper confidence Q-value.
- explore() - Method in class burlap.oomdp.singleagent.explorer.TerminalExplorer
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Deprecated.
Starts the shell.
- explore() - Method in class burlap.oomdp.stochasticgames.explorers.SGTerminalExplorer
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Deprecated.
- ExponentialDecayLR - Class in burlap.behavior.learningrate
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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
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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
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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
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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
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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
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A class for storing a mutable double value object
- ExponentialDecayLR.MutableDouble(double) - Constructor for class burlap.behavior.learningrate.ExponentialDecayLR.MutableDouble
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- ExponentialDecayLR.StateWiseLearningRate - Class in burlap.behavior.learningrate
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A class for storing a learning rate for a state, or a learning rate for each action for a given state
- ExponentialDecayLR.StateWiseLearningRate() - Constructor for class burlap.behavior.learningrate.ExponentialDecayLR.StateWiseLearningRate
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- externalTerminalFunction - Variable in class burlap.behavior.singleagent.options.Option
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the terminal function of the MDP in which this option is to be executed.