- ECorrelatedQJointPolicy - Class in burlap.behavior.stochasticgame.mavaluefunction.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.stochasticgame.mavaluefunction.policies.ECorrelatedQJointPolicy
-
Initializes with the epislon probability of a random joint action.
- ECorrelatedQJointPolicy(CorrelatedEquilibriumSolver.CorrelatedEquilibriumObjective, double) - Constructor for class burlap.behavior.stochasticgame.mavaluefunction.policies.ECorrelatedQJointPolicy
-
Initializes with the correlated equilibrium objective and the epsilon probability of a random joint action.
- EGreedyJointPolicy - Class in burlap.behavior.stochasticgame.mavaluefunction.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.stochasticgame.mavaluefunction.policies.EGreedyJointPolicy
-
Initializes for a given epsilon value.
- EGreedyJointPolicy(MultiAgentQLearning, double) - Constructor for class burlap.behavior.stochasticgame.mavaluefunction.policies.EGreedyJointPolicy
-
Initializes for a multi-agent Q-learning object and epsilon value.
- EGreedyMaxWellfare - Class in burlap.behavior.stochasticgame.mavaluefunction.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.stochasticgame.mavaluefunction.policies.EGreedyMaxWellfare
-
Initializes for a given epsilon value.
- EGreedyMaxWellfare(double, boolean) - Constructor for class burlap.behavior.stochasticgame.mavaluefunction.policies.EGreedyMaxWellfare
-
Initializes for a given epsilon value and whether to break ties randomly.
- EGreedyMaxWellfare(MultiAgentQLearning, double) - Constructor for class burlap.behavior.stochasticgame.mavaluefunction.policies.EGreedyMaxWellfare
-
Initializes for a multi-agent Q-learning object and epsilon value.
- EGreedyMaxWellfare(MultiAgentQLearning, double, boolean) - Constructor for class burlap.behavior.stochasticgame.mavaluefunction.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.stochasticgame.mavaluefunction.policies
-
Class for following a minmax joint policy.
- EMinMaxPolicy(double) - Constructor for class burlap.behavior.stochasticgame.mavaluefunction.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.stochasticgame.mavaluefunction.policies.EMinMaxPolicy
-
Initializes for a given Q-learning agent and epsilon value.
- enableEpisodeRecording(String, String) - Method in class burlap.oomdp.singleagent.explorer.VisualExplorer
-
Enables episodes recording of actions taken.
- enableEpisodeRecording(String, String, RewardFunction) - Method in class burlap.oomdp.singleagent.explorer.VisualExplorer
-
Enables episodes recording of actions taken.
- enableEpisodeRecording(String, String, RewardFunction, String, StateParser) - Method in class burlap.oomdp.singleagent.explorer.VisualExplorer
-
Enables episodes recording of actions taken.
- encodePlanIntoPolicy(SearchNode) - Method in class burlap.behavior.singleagent.planning.deterministic.DeterministicPlanner
-
Encodes a solution path found by the planner 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.stochasticgame.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.stochasticgame.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.stochasticgame.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.stochasticgame.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.DeterminisitcTerminationOption
-
Returns true if the initiation states and termination states of this option are iterable; false if either of them are not.
- 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.environment.DomainEnvironmentWrapper
-
- 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 - Class in burlap.oomdp.singleagent.environment
-
In some cases it may be useful to have agents interact with an external environment that handles the current state, execution of actions, and rewards
and maintains other important external information, rather than use the standard Domain, Action, RewardFunction TerminalFunction paradigm of BURLAP.
- Environment() - Constructor for class burlap.oomdp.singleagent.environment.Environment
-
- Environment.CurStateTerminalTF - Class in burlap.oomdp.singleagent.environment
-
A terminal function that always returns whether the current environment state
is a terminal state, regardless of the state parameter passed to the method.
- Environment.CurStateTerminalTF() - Constructor for class burlap.oomdp.singleagent.environment.Environment.CurStateTerminalTF
-
- Environment.LastRewardRF - Class in burlap.oomdp.singleagent.environment
-
A reward function that returns the last reward returned by the environment, regardless
of the state, action, state parameters passed to the method.
- Environment.LastRewardRF() - Constructor for class burlap.oomdp.singleagent.environment.Environment.LastRewardRF
-
- 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.EpisodeSequenceVisualizer
-
- episodeFiles - Variable in class burlap.behavior.stochasticgame.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.LearningAgent.LearningAgentBookKeeping
-
The history of learning episodes
- 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.EpisodeSequenceVisualizer
-
- episodeList - Variable in class burlap.behavior.stochasticgame.GameSequenceVisualizer
-
- episodeScroller - Variable in class burlap.behavior.singleagent.EpisodeSequenceVisualizer
-
- episodeScroller - Variable in class burlap.behavior.stochasticgame.GameSequenceVisualizer
-
- EpisodeSequenceVisualizer - Class in burlap.behavior.singleagent
-
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, StateParser, String) - Constructor for class burlap.behavior.singleagent.EpisodeSequenceVisualizer
-
Initializes the EpisodeSequenceVisualizer.
- EpisodeSequenceVisualizer(Visualizer, Domain, StateParser, String, int, int) - Constructor for class burlap.behavior.singleagent.EpisodeSequenceVisualizer
-
Initializes the EpisodeSequenceVisualizer.
- EpisodeSequenceVisualizer(Visualizer, Domain, List<EpisodeAnalysis>) - Constructor for class burlap.behavior.singleagent.EpisodeSequenceVisualizer
-
Initializes the EpisodeSequenceVisualizer with a programmatically supplied list of
EpisodeAnalysis objects to view.
- EpisodeSequenceVisualizer(Visualizer, Domain, List<EpisodeAnalysis>, int, int) - Constructor for class burlap.behavior.singleagent.EpisodeSequenceVisualizer
-
Initializes the EpisodeSequenceVisualizer with a programmatically supplied list of
EpisodeAnalysis objects to view.
- episodesListModel - Variable in class burlap.behavior.singleagent.EpisodeSequenceVisualizer
-
- episodesListModel - Variable in class burlap.behavior.stochasticgame.GameSequenceVisualizer
-
- episodeWeights - Variable in class burlap.behavior.singleagent.learnbydemo.mlirl.MLIRLRequest
-
The weight assigned to each episode.
- epsilon - Variable in class burlap.behavior.singleagent.learnbydemo.apprenticeship.ApprenticeshipLearningRequest
-
The maximum feature score to cause termination of Apprenticeship learning
- epsilon - Variable in class burlap.behavior.singleagent.planning.commonpolicies.EpsilonGreedy
-
- 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.stochasticgame.agents.naiveq.history.SGQWActionHistoryFactory
-
The epislon value for epislon greedy policy.
- epsilon - Variable in class burlap.behavior.stochasticgame.mavaluefunction.policies.ECorrelatedQJointPolicy
-
The epsilon parameter specifying how often random joint actions are returned
- epsilon - Variable in class burlap.behavior.stochasticgame.mavaluefunction.policies.EGreedyJointPolicy
-
The epsilon parameter specifying how often random joint actions are returned
- epsilon - Variable in class burlap.behavior.stochasticgame.mavaluefunction.policies.EGreedyMaxWellfare
-
The epsilon parameter specifying how often random joint actions are returned
- epsilon - Variable in class burlap.behavior.stochasticgame.mavaluefunction.policies.EMinMaxPolicy
-
The epsilon parameter specifying how often random joint actions are returned
- EpsilonGreedy - Class in burlap.behavior.singleagent.planning.commonpolicies
-
This class defines a an epsilon-greedy policy over Q-values and requires a QComputable planner to be specified.
- EpsilonGreedy(double) - Constructor for class burlap.behavior.singleagent.planning.commonpolicies.EpsilonGreedy
-
Initializes with the value of epsilon, where epsilon is the probability of taking a random action.
- EpsilonGreedy(QComputablePlanner, double) - Constructor for class burlap.behavior.singleagent.planning.commonpolicies.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.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.statehashing.DiscreteMaskHashingFactory.DiscreteMaskHashTuple
-
- equals(Object) - Method in class burlap.behavior.statehashing.DiscretizingStateHashFactory.DiscretizedStateHashTuple
-
- equals(Object) - Method in class burlap.behavior.statehashing.NameDependentStateHashFactory.NameDependentStateHashTuple
-
- equals(Object) - Method in class burlap.behavior.statehashing.StateHashTuple
-
- equals(Object) - Method in class burlap.domain.singleagent.graphdefined.GraphDefinedDomain.NodeTransitionProbibility
-
- 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.ObjectInstance
-
- equals(Object) - Method in class burlap.oomdp.core.PropositionalFunction
-
- equals(Object) - Method in class burlap.oomdp.core.State
-
- 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.stochasticgames.AgentType
-
- equals(Object) - Method in class burlap.oomdp.stochasticgames.GroundedSingleAction
-
- equals(Object) - Method in class burlap.oomdp.stochasticgames.JointAction
-
- equals(Object) - Method in class burlap.oomdp.stochasticgames.SingleAction
-
- EquilibriumPlayingAgent - Class in burlap.behavior.stochasticgame.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.
- EquilibriumPlayingAgent() - Constructor for class burlap.behavior.stochasticgame.agents.twoplayer.singlestage.equilibriumplayer.EquilibriumPlayingAgent
-
Initializes with the
MaxMax solution concept.
- EquilibriumPlayingAgent(BimatrixEquilibriumSolver) - Constructor for class burlap.behavior.stochasticgame.agents.twoplayer.singlestage.equilibriumplayer.EquilibriumPlayingAgent
-
Initializes with strategies formed usign the solution concept generated by the given solver.
- EquilibriumPlayingAgent.BimatrixTuple - Class in burlap.behavior.stochasticgame.agents.twoplayer.singlestage.equilibriumplayer
-
A Bimatrix tuple.
- EquilibriumPlayingAgent.BimatrixTuple(int, int) - Constructor for class burlap.behavior.stochasticgame.agents.twoplayer.singlestage.equilibriumplayer.EquilibriumPlayingAgent.BimatrixTuple
-
Initializes the payoff matrices for a bimatrix of the given row and column dimensionality
- EquilibriumPlayingAgent.BimatrixTuple(double[][], double[][]) - Constructor for class burlap.behavior.stochasticgame.agents.twoplayer.singlestage.equilibriumplayer.EquilibriumPlayingAgent.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.learnbydemo.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.learnbydemo.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.learnbydemo.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.learnbydemo.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
-
- evaluateBehavior(State, RewardFunction, TerminalFunction) - Method in class burlap.behavior.singleagent.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.singleagent.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.singleagent.Policy
-
This method will return the an episode that results from following this policy from state s.
- evaluateDecomposesOptions - Variable in class burlap.behavior.singleagent.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.singleagent.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.singleagent.Policy
-
Sets whether the primitive actions taken during an options will be included as steps in produced EpisodeAnalysis objects.
- 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
- executeAction(GroundedAction) - Method in class burlap.oomdp.singleagent.environment.Environment
-
This method is used to pass a
GroundedAction to be executed in this environment.
- executeAction(String, String[]) - Method in class burlap.oomdp.singleagent.environment.Environment
-
Tells the environment to execute the action with the given name and with the given parameters.
- executeAction(String[]) - Method in class burlap.oomdp.singleagent.explorer.VisualExplorer
-
Executes the action defined in string array with the first component being the action name and the rest the parameters.
- executeAction() - Method in class burlap.oomdp.stochasticgames.explorers.SGVisualExplorer
-
- executeIn(State) - Method in class burlap.oomdp.core.AbstractGroundedAction
-
Executes the grounded action on a given state
- executeIn(State) - Method in class burlap.oomdp.singleagent.GroundedAction
-
Executes the grounded action on a given state
- executeIn(State) - Method in class burlap.oomdp.stochasticgames.GroundedSingleAction
-
- executeIn(State) - Method in class burlap.oomdp.stochasticgames.JointAction
-
- expandStateActionWeights(int) - Method in class burlap.behavior.singleagent.vfa.common.LinearFVVFA
-
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
-
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
-
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
-
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.stochasticgame.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.stochasticgame.solvers.GeneralBimatrixSolverTools
-
- expertEpisodes - Variable in class burlap.behavior.singleagent.learnbydemo.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.
- exploreFromState(State) - Method in class burlap.oomdp.singleagent.explorer.TerminalExplorer
-
Starts the explorer to run from state s
- exploreFromState(State) - Method in class burlap.oomdp.stochasticgames.explorers.SGTerminalExplorer
-
Causes the explorer to begin from the given input state
- 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, StateHashFactory, 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, StateHashFactory, 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.MutableDouble(double) - Constructor for class burlap.behavior.learningrate.ExponentialDecayLR.MutableDouble
-
- 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
- ExponentialDecayLR.StateWiseLearningRate() - Constructor for class burlap.behavior.learningrate.ExponentialDecayLR.StateWiseLearningRate
-
- externalTerminalFunction - Variable in class burlap.behavior.singleagent.options.Option
-
the terminal function of the MDP in which this option is to be executed.
- extractApproximationForAction(List<ActionApproximationResult>, GroundedAction) - Static method in class burlap.behavior.singleagent.vfa.ActionApproximationResult
-