- dataset - Variable in class burlap.behavior.singleagent.learning.lspi.LSPI
-
The SARS dataset on which LSPI is performed
- dataset - Variable in class burlap.behavior.singleagent.learning.lspi.SARSData
-
- datasets - Variable in class burlap.behavior.stochasticgames.auxiliary.performance.MultiAgentPerformancePlotter.DatasetsAndTrials
-
The series datasets for this agent
- DatasetsAndTrials(String) - Constructor for class burlap.behavior.stochasticgames.auxiliary.performance.MultiAgentPerformancePlotter.DatasetsAndTrials
-
Initializes for an agent with the given name.
- DDPlannerPolicy - Class in burlap.behavior.singleagent.planning.deterministic
-
This is a dynamic deterministic valueFunction policy, which means
if the source deterministic valueFunction has not already computed
and cached the plan for a query state, then this policy
will first compute a plan using the valueFunction and then return the
answer
- DDPlannerPolicy() - Constructor for class burlap.behavior.singleagent.planning.deterministic.DDPlannerPolicy
-
- DDPlannerPolicy(DeterministicPlanner) - Constructor for class burlap.behavior.singleagent.planning.deterministic.DDPlannerPolicy
-
Initializes with the deterministic valueFunction
- DEBUG_CODE_RF_WEIGHTS - Static variable in class burlap.behavior.singleagent.learnfromdemo.apprenticeship.ApprenticeshipLearning
-
- DEBUG_CODE_SCORE - Static variable in class burlap.behavior.singleagent.learnfromdemo.apprenticeship.ApprenticeshipLearning
-
- debugCode - Variable in class burlap.behavior.singleagent.auxiliary.performance.LearningAlgorithmExperimenter
-
The debug code used for debug printing.
- debugCode - Variable in class burlap.behavior.singleagent.interfaces.rlglue.RLGlueAgent
-
Debug code used for printing debug information.
- debugCode - Variable in class burlap.behavior.singleagent.learnfromdemo.mlirl.MLIRL
-
The debug code used for printing information to the terminal.
- debugCode - Variable in class burlap.behavior.singleagent.learnfromdemo.mlirl.MultipleIntentionsMLIRL
-
The debug code used for printing information to the terminal.
- debugCode - Variable in class burlap.behavior.singleagent.MDPSolver
-
The debug code use for calls to
DPrint
- debugCode - Variable in class burlap.behavior.stochasticgames.auxiliary.performance.MultiAgentExperimenter
-
The debug code used for debug printing.
- debugCode - Variable in class burlap.behavior.stochasticgames.auxiliary.performance.MultiAgentPerformancePlotter
-
The debug code used for debug printing.
- debugCode - Variable in class burlap.behavior.stochasticgames.madynamicprogramming.dpplanners.MAValueIteration
-
The debug code used for printing VI progress.
- DebugFlags - Class in burlap.debugtools
-
A data structure for specifying debug flags that can be accessed and modified from any class
- debugID - Static variable in class burlap.behavior.singleagent.auxiliary.StateReachability
-
The debugID used for making calls to
DPrint.
- debugId - Variable in class burlap.mdp.stochasticgames.tournament.Tournament
-
- debugId - Variable in class burlap.mdp.stochasticgames.world.World
-
- decayConstantShift - Variable in class burlap.behavior.learningrate.SoftTimeInverseDecayLR
-
The division scale offset
- decayRate - Variable in class burlap.behavior.learningrate.ExponentialDecayLR
-
The exponential base by which the learning rate is decayed
- deepCopyActionNameMapArray(SingleStageNormalFormGame.ActionNameMap[]) - Static method in class burlap.domain.stochasticgames.normalform.SingleStageNormalFormGame.ActionNameMap
-
- DeepCopyState - Annotation Type in burlap.mdp.core.state.annotations
-
A marker for
State implementations that indicates that their copy operation is deep.
- DeepOOState - Class in burlap.mdp.core.oo.state.generic
-
- DeepOOState() - Constructor for class burlap.mdp.core.oo.state.generic.DeepOOState
-
- DeepOOState(OOState) - Constructor for class burlap.mdp.core.oo.state.generic.DeepOOState
-
- DeepOOState(ObjectInstance...) - Constructor for class burlap.mdp.core.oo.state.generic.DeepOOState
-
- deepTouchLocations() - Method in class burlap.domain.singleagent.gridworld.state.GridWorldState
-
- deepTouchObstacles() - Method in class burlap.domain.singleagent.lunarlander.state.LLState
-
- deepTouchPlatforms() - Method in class burlap.domain.singleagent.frostbite.state.FrostbiteState
-
- DEFAULT_EPSILON - Static variable in class burlap.behavior.singleagent.learnfromdemo.apprenticeship.ApprenticeshipLearningRequest
-
- DEFAULT_MAXITERATIONS - Static variable in class burlap.behavior.singleagent.learnfromdemo.apprenticeship.ApprenticeshipLearningRequest
-
- DEFAULT_POLICYCOUNT - Static variable in class burlap.behavior.singleagent.learnfromdemo.apprenticeship.ApprenticeshipLearningRequest
-
- DEFAULT_USEMAXMARGIN - Static variable in class burlap.behavior.singleagent.learnfromdemo.apprenticeship.ApprenticeshipLearningRequest
-
- defaultCost(int, JointAction) - Method in class burlap.domain.stochasticgames.gridgame.GridGame.GGJointRewardFunction
-
Returns a default cost for an agent assuming the agent didn't transition to a goal state.
- defaultMultiple - Variable in class burlap.statehashing.discretized.DiscConfig
-
The default multiple to use for any continuous attributes that have not been specifically set.
- defaultMultiple - Variable in class burlap.statehashing.maskeddiscretized.DiscMaskedConfig
-
The default multiple to use for any continuous attributes that have not been specifically set.
- defaultQ - Variable in class burlap.behavior.stochasticgames.agents.naiveq.SGNaiveQFactory
-
The default Q-value to which Q-values will be initialized
- defaultReward - Variable in class burlap.domain.singleagent.frostbite.FrostbiteRF
-
- defaultReward - Variable in class burlap.domain.singleagent.lunarlander.LunarLanderRF
-
The default reward received for moving through the air
- defaultReward - Variable in class burlap.mdp.singleagent.common.GoalBasedRF
-
- defaultToLowerValueAfterPlanning - Variable in class burlap.behavior.singleagent.planning.stochastic.rtdp.BoundedRTDP
-
Sets what the
DynamicProgramming valueFunction reference points to (the lower bound or upperbound) once a planning rollout is complete.
- defaultWeight - Variable in class burlap.behavior.functionapproximation.dense.DenseLinearVFA
-
A default weight value for the functions weights.
- defaultWeight - Variable in class burlap.behavior.functionapproximation.dense.DenseStateActionLinearVFA
-
A default weight value for the functions weights.
- defaultWeight - Variable in class burlap.behavior.functionapproximation.sparse.LinearVFA
-
A default weight for the functions
- definedFor(State) - Method in class burlap.behavior.policy.BoltzmannQPolicy
-
- definedFor(State) - Method in class burlap.behavior.policy.CachedPolicy
-
- definedFor(State) - Method in class burlap.behavior.policy.EpsilonGreedy
-
- definedFor(State) - Method in class burlap.behavior.policy.GreedyDeterministicQPolicy
-
- definedFor(State) - Method in class burlap.behavior.policy.GreedyQPolicy
-
- definedFor(State) - Method in interface burlap.behavior.policy.Policy
-
Specifies whether this policy is defined for the input state.
- definedFor(State) - Method in class burlap.behavior.policy.RandomPolicy
-
- definedFor(State) - Method in class burlap.behavior.singleagent.learnfromdemo.apprenticeship.ApprenticeshipLearning.StationaryRandomDistributionPolicy
-
- definedFor(State) - Method in class burlap.behavior.singleagent.learning.actorcritic.actor.BoltzmannActor
-
- definedFor(State) - Method in class burlap.behavior.singleagent.learning.modellearning.rmax.UnmodeledFavoredPolicy
-
- definedFor(State) - Method in class burlap.behavior.singleagent.planning.deterministic.DDPlannerPolicy
-
- definedFor(State) - Method in class burlap.behavior.singleagent.planning.deterministic.SDPlannerPolicy
-
- definedFor(State) - Method in class burlap.behavior.singleagent.planning.stochastic.montecarlo.uct.UCTTreeWalkPolicy
-
- definedFor(State) - Method in class burlap.behavior.stochasticgames.madynamicprogramming.policies.ECorrelatedQJointPolicy
-
- definedFor(State) - Method in class burlap.behavior.stochasticgames.madynamicprogramming.policies.EGreedyJointPolicy
-
- definedFor(State) - Method in class burlap.behavior.stochasticgames.madynamicprogramming.policies.EGreedyMaxWellfare
-
- definedFor(State) - Method in class burlap.behavior.stochasticgames.madynamicprogramming.policies.EMinMaxPolicy
-
- definedFor(State) - Method in class burlap.behavior.stochasticgames.PolicyFromJointPolicy
-
- delay - Variable in class burlap.behavior.singleagent.auxiliary.performance.PerformancePlotter
-
the delay in milliseconds between which the charts are updated automatically
- delay - Variable in class burlap.behavior.stochasticgames.auxiliary.performance.MultiAgentPerformancePlotter
-
the delay in milliseconds between which the charts are updated automatically
- delegate - Variable in class burlap.mdp.singleagent.environment.extensions.EnvironmentServer
-
- DelegatedModel - Class in burlap.mdp.singleagent.model
-
An implementation of
FullModel that will delegate transition estimates for different actions to different
SampleModel or
FullModel implementations.
- DelegatedModel(SampleModel) - Constructor for class burlap.mdp.singleagent.model.DelegatedModel
-
- delta - Static variable in class burlap.testing.TestPlanning
-
- DenseBeliefVector - Interface in burlap.mdp.singleagent.pomdp.beliefstate
-
An interface to be used in conjunction with
BeliefState instances
for belief states that can generate a dense belief vector representation.
- DenseCrossProductFeatures - Class in burlap.behavior.functionapproximation.dense
-
Class that generates state-action features as cross product of underlying state-features with the action set.
- DenseCrossProductFeatures(DenseStateFeatures, int) - Constructor for class burlap.behavior.functionapproximation.dense.DenseCrossProductFeatures
-
- DenseCrossProductFeatures(DenseStateFeatures, int, Map<Action, Integer>) - Constructor for class burlap.behavior.functionapproximation.dense.DenseCrossProductFeatures
-
- DenseLinearVFA - Class in burlap.behavior.functionapproximation.dense
-
This class can be used to perform linear value function approximation, either for a states or state-actions (Q-values).
- DenseLinearVFA(DenseStateFeatures, double) - Constructor for class burlap.behavior.functionapproximation.dense.DenseLinearVFA
-
Initializes.
- DenseStateActionFeatures - Interface in burlap.behavior.functionapproximation.dense
-
- DenseStateActionLinearVFA - Class in burlap.behavior.functionapproximation.dense
-
- DenseStateActionLinearVFA(DenseStateActionFeatures, double) - Constructor for class burlap.behavior.functionapproximation.dense.DenseStateActionLinearVFA
-
- DenseStateActionLinearVFA(DenseStateActionFeatures, double[], double) - Constructor for class burlap.behavior.functionapproximation.dense.DenseStateActionLinearVFA
-
- DenseStateFeatures - Interface in burlap.behavior.functionapproximation.dense
-
Many functions approximation techniques require a fixed feature vector to work and in many cases, using abstract features from
the state attributes is useful.
- depth - Variable in class burlap.behavior.singleagent.planning.stochastic.montecarlo.uct.UCTStateNode
-
The depth the UCT tree
- depthMap - Variable in class burlap.behavior.singleagent.planning.deterministic.informed.astar.DynamicWeightedAStar
-
Data structure for storing the depth of explored states
- DeterministicPlanner - Class in burlap.behavior.singleagent.planning.deterministic
-
This class extends the OOMDPlanner to provide the interface and common mechanisms for classic deterministic forward search planners.
- DeterministicPlanner() - Constructor for class burlap.behavior.singleagent.planning.deterministic.DeterministicPlanner
-
- DeterministicPlanner.PlanningFailedException - Exception in burlap.behavior.singleagent.planning.deterministic
-
Exception class for indicating that a solution failed to be found by the planning algorithm.
- deterministicPlannerInit(SADomain, StateConditionTest, HashableStateFactory) - Method in class burlap.behavior.singleagent.planning.deterministic.DeterministicPlanner
-
Initializes the valueFunction.
- deterministicPolicyDistribution(Policy, State) - Static method in class burlap.behavior.policy.PolicyUtils
-
A helper method for defining deterministic policies.
- deterministicTransition(SampleModel, State, Action) - Static method in class burlap.mdp.singleagent.model.FullModel.Helper
-
- deterministicTransition(SampleStateModel, State, Action) - Static method in class burlap.mdp.singleagent.model.statemodel.FullStateModel.Helper
-
- deterministicTransition(JointModel, State, JointAction) - Static method in class burlap.mdp.stochasticgames.model.FullJointModel.Helper
-
A helper method for deterministic transition dynamics.
- DFS - Class in burlap.behavior.singleagent.planning.deterministic.uninformed.dfs
-
Implements depth-first search.
- DFS(SADomain, StateConditionTest, HashableStateFactory) - Constructor for class burlap.behavior.singleagent.planning.deterministic.uninformed.dfs.DFS
-
Basic constructor for standard DFS without a depth limit
- DFS(SADomain, StateConditionTest, HashableStateFactory, int) - Constructor for class burlap.behavior.singleagent.planning.deterministic.uninformed.dfs.DFS
-
Basic constructor for standard DFS with a depth limit
- DFS(SADomain, StateConditionTest, HashableStateFactory, int, boolean) - Constructor for class burlap.behavior.singleagent.planning.deterministic.uninformed.dfs.DFS
-
Constructor of DFS with specification of depth limit and whether to maintain a closed list that affects exploration.
- DFS(SADomain, StateConditionTest, HashableStateFactory, int, boolean, boolean) - Constructor for class burlap.behavior.singleagent.planning.deterministic.uninformed.dfs.DFS
-
Constructor of DFS with specification of depth limit, whether to maintain a closed list that affects exploration, and whether paths
generated by options should be explored first.
- dfs(SearchNode, int, Set<HashableState>) - Method in class burlap.behavior.singleagent.planning.deterministic.uninformed.dfs.DFS
-
Runs DFS from a given search node, keeping track of its current depth.
- dfs(SearchNode, int, Set<HashableState>) - Method in class burlap.behavior.singleagent.planning.deterministic.uninformed.dfs.LimitedMemoryDFS
-
Runs DFS from a given search node, keeping track of its current depth.
- DFSInit(SADomain, StateConditionTest, HashableStateFactory, int, boolean, boolean) - Method in class burlap.behavior.singleagent.planning.deterministic.uninformed.dfs.DFS
-
Constructor of DFS with specification of depth limit, whether to maintain a closed list that affects exploration, and whether paths
generated by options should be explored first.
- dheight - Variable in class burlap.domain.singleagent.gridworld.GridWorldVisualizer.CellPainter
-
- dheight - Variable in class burlap.domain.singleagent.gridworld.GridWorldVisualizer.LocationPainter
-
- dheight - Variable in class burlap.domain.singleagent.gridworld.GridWorldVisualizer.MapPainter
-
- DifferentiableDP - Class in burlap.behavior.singleagent.learnfromdemo.mlirl.differentiableplanners
-
A class for performing dynamic programming with a differentiable value backup operator.
- DifferentiableDP() - Constructor for class burlap.behavior.singleagent.learnfromdemo.mlirl.differentiableplanners.DifferentiableDP
-
- DifferentiableDPOperator - Interface in burlap.behavior.singleagent.learnfromdemo.mlirl.differentiableplanners.dpoperator
-
- DifferentiableQFunction - Interface in burlap.behavior.singleagent.learnfromdemo.mlirl.support
-
An interface for a valueFunction that can produce Q-value gradients.
- DifferentiableRF - Interface in burlap.behavior.singleagent.learnfromdemo.mlirl.support
-
An interface for defining differentiable reward functions.
- DifferentiableSoftmaxOperator - Class in burlap.behavior.singleagent.learnfromdemo.mlirl.differentiableplanners.dpoperator
-
- DifferentiableSoftmaxOperator() - Constructor for class burlap.behavior.singleagent.learnfromdemo.mlirl.differentiableplanners.dpoperator.DifferentiableSoftmaxOperator
-
- DifferentiableSoftmaxOperator(double) - Constructor for class burlap.behavior.singleagent.learnfromdemo.mlirl.differentiableplanners.dpoperator.DifferentiableSoftmaxOperator
-
- DifferentiableSparseSampling - Class in burlap.behavior.singleagent.learnfromdemo.mlirl.differentiableplanners
-
A Differentiable finite horizon valueFunction that can also use sparse sampling over the transition dynamics when the
transition function is very large or infinite.
- DifferentiableSparseSampling(SADomain, DifferentiableRF, double, HashableStateFactory, int, int, double) - Constructor for class burlap.behavior.singleagent.learnfromdemo.mlirl.differentiableplanners.DifferentiableSparseSampling
-
Initializes.
- DifferentiableSparseSampling.DiffStateNode - Class in burlap.behavior.singleagent.learnfromdemo.mlirl.differentiableplanners
-
A class for value differentiable state nodes.
- DifferentiableSparseSampling.QAndQGradient - Class in burlap.behavior.singleagent.learnfromdemo.mlirl.differentiableplanners
-
A tuple for storing Q-values and their gradients.
- DifferentiableSparseSampling.VAndVGradient - Class in burlap.behavior.singleagent.learnfromdemo.mlirl.differentiableplanners
-
A tuple for storing a state value and its gradient.
- DifferentiableStateActionValue - Interface in burlap.behavior.functionapproximation
-
- DifferentiableStateValue - Interface in burlap.behavior.functionapproximation
-
- DifferentiableValueFunction - Interface in burlap.behavior.singleagent.learnfromdemo.mlirl.support
-
- DifferentiableVI - Class in burlap.behavior.singleagent.learnfromdemo.mlirl.differentiableplanners
-
Performs Differentiable Value Iteration using the Boltzmann backup operator and a
DifferentiableRF.
- DifferentiableVI(SADomain, DifferentiableRF, double, double, HashableStateFactory, double, int) - Constructor for class burlap.behavior.singleagent.learnfromdemo.mlirl.differentiableplanners.DifferentiableVI
-
Initializes the valueFunction.
- DifferentiableVIFactory(HashableStateFactory) - Constructor for class burlap.behavior.singleagent.learnfromdemo.mlirl.support.QGradientPlannerFactory.DifferentiableVIFactory
-
- DifferentiableVIFactory(HashableStateFactory, TerminalFunction, double, int) - Constructor for class burlap.behavior.singleagent.learnfromdemo.mlirl.support.QGradientPlannerFactory.DifferentiableVIFactory
-
Initializes.
- DifferentiableVInit - Interface in burlap.behavior.singleagent.learnfromdemo.mlirl.differentiableplanners.diffvinit
-
An interface for value function initialization that is differentiable with respect to some parameters.
- DiffStateNode(HashableState, int) - Constructor for class burlap.behavior.singleagent.learnfromdemo.mlirl.differentiableplanners.DifferentiableSparseSampling.DiffStateNode
-
- DiffVFRF - Class in burlap.behavior.singleagent.learnfromdemo.mlirl.differentiableplanners.diffvinit
-
A differentiable reward function wrapper for use with
MLIRL when
the reward function is known, but the value function initialization for leaf nodes is to be learned.
- DiffVFRF(RewardFunction, DifferentiableVInit) - Constructor for class burlap.behavior.singleagent.learnfromdemo.mlirl.differentiableplanners.diffvinit.DiffVFRF
-
- diffVInit - Variable in class burlap.behavior.singleagent.learnfromdemo.mlirl.differentiableplanners.diffvinit.DiffVFRF
-
- dim - Variable in class burlap.behavior.singleagent.learnfromdemo.mlirl.commonrfs.LinearStateActionDifferentiableRF
-
The dimension of this reward function
- dim - Variable in class burlap.behavior.singleagent.learnfromdemo.mlirl.commonrfs.LinearStateDifferentiableRF
-
The dimension of this reward function
- dim - Variable in class burlap.behavior.singleagent.learnfromdemo.mlirl.differentiableplanners.diffvinit.DiffVFRF
-
- dim - Variable in class burlap.behavior.singleagent.learnfromdemo.mlirl.differentiableplanners.diffvinit.LinearDiffRFVInit
-
- dim - Variable in class burlap.behavior.singleagent.learnfromdemo.mlirl.differentiableplanners.diffvinit.LinearStateDiffVF
-
- dimensionMask - Variable in class burlap.behavior.functionapproximation.sparse.tilecoding.Tiling
-
The dimensions on which this tiling are dependent
- dir - Variable in class burlap.domain.singleagent.blockdude.state.BlockDudeAgent
-
- dir(String) - Method in class burlap.domain.singleagent.mountaincar.MountainCar.MCModel
-
- directEpisodes - Variable in class burlap.behavior.singleagent.auxiliary.EpisodeSequenceVisualizer
-
- directGameEpisodes - Variable in class burlap.behavior.stochasticgames.auxiliary.GameSequenceVisualizer
-
- direction - Variable in class burlap.behavior.singleagent.auxiliary.valuefunctionvis.common.ArrowActionGlyph
-
The direction of the arrow.0: north; 1: south; 2: east; 3:west
- DiscConfig - Class in burlap.statehashing.discretized
-
- DiscConfig() - Constructor for class burlap.statehashing.discretized.DiscConfig
-
- DiscConfig(double) - Constructor for class burlap.statehashing.discretized.DiscConfig
-
- DiscConfig(Map<Object, Double>, double) - Constructor for class burlap.statehashing.discretized.DiscConfig
-
- DiscMaskedConfig - Class in burlap.statehashing.maskeddiscretized
-
- DiscMaskedConfig() - Constructor for class burlap.statehashing.maskeddiscretized.DiscMaskedConfig
-
- DiscMaskedConfig(double) - Constructor for class burlap.statehashing.maskeddiscretized.DiscMaskedConfig
-
- DiscMaskedConfig(Set<Object>, Set<String>, Map<Object, Double>, double) - Constructor for class burlap.statehashing.maskeddiscretized.DiscMaskedConfig
-
- discount - Variable in class burlap.behavior.singleagent.interfaces.rlglue.RLGlueAgent
-
The RLGlue specified discount factor
- discount - Variable in class burlap.behavior.singleagent.options.EnvironmentOptionOutcome
-
The discount factor to apply to the value of time steps immediately following the application of an
Option.
- discount - Variable in class burlap.behavior.singleagent.options.model.BFSMarkovOptionModel
-
- discount - Variable in class burlap.behavior.singleagent.shaping.potential.PotentialShapedRF
-
The discount factor the MDP (required for this to shaping to preserve policy optimality)
- discount - Variable in class burlap.behavior.stochasticgames.agents.madp.MADPPlannerFactory.MAVIPlannerFactory
-
The discount factor in [0, 1]
- discount - Variable in class burlap.behavior.stochasticgames.agents.maql.MAQLFactory
-
- discount - Variable in class burlap.behavior.stochasticgames.agents.maql.MultiAgentQLearning
-
The discount factor
- discount - Variable in class burlap.behavior.stochasticgames.agents.naiveq.history.SGQWActionHistoryFactory
-
The discount rate the Q-learning algorithm will use
- discount - Variable in class burlap.behavior.stochasticgames.agents.naiveq.SGNaiveQFactory
-
The discount rate the Q-learning algorithm will use
- discount - Variable in class burlap.behavior.stochasticgames.agents.naiveq.SGNaiveQLAgent
-
The discount factor
- discount - Variable in class burlap.behavior.stochasticgames.madynamicprogramming.MADynamicProgramming
-
The discount factor in [0, 1]
- discount - Variable in class burlap.domain.singleagent.rlglue.RLGlueEnvironment
-
The discount factor of the task
- discountedReturn(double) - Method in class burlap.behavior.singleagent.Episode
-
Will return the discounted return received from the first state in the episode to the last state in the episode.
- DiscreteObservationFunction - Interface in burlap.mdp.singleagent.pomdp.observations
-
Defines additional methods for an
ObservationFunction for the case when the set of observations are
discrete and able to be enumerated.
- DiscretizingHashableStateFactory - Class in burlap.statehashing.discretized
-
A factory for producing
HashableState objects that computes hash codes
and test for state equality after discretizing any real values (Float or Double).
- DiscretizingHashableStateFactory(double) - Constructor for class burlap.statehashing.discretized.DiscretizingHashableStateFactory
-
Initializes with object identifier independence and no hash code caching.
- DiscretizingHashableStateFactory(boolean, double) - Constructor for class burlap.statehashing.discretized.DiscretizingHashableStateFactory
-
Initializes with non hash code caching
- DiscretizingMaskedHashableStateFactory - Class in burlap.statehashing.maskeddiscretized
-
A class for producing
HashableState objects that computes hash codes and tests
for
State equality by discretizing real-valued attributes and by masking (ignoring)
either state variables and/or
OOState clasees.
- DiscretizingMaskedHashableStateFactory(double) - Constructor for class burlap.statehashing.maskeddiscretized.DiscretizingMaskedHashableStateFactory
-
Initializes with object identifier independence, no hash code caching and object class or attribute masks.
- DiscretizingMaskedHashableStateFactory(boolean, double) - Constructor for class burlap.statehashing.maskeddiscretized.DiscretizingMaskedHashableStateFactory
-
Initializes with non hash code caching and object class or attribute masks
- DiscretizingMaskedHashableStateFactory(boolean, double, boolean, String...) - Constructor for class burlap.statehashing.maskeddiscretized.DiscretizingMaskedHashableStateFactory
-
Initializes with a specified attribute or object class mask.
- displayPlots - Variable in class burlap.behavior.singleagent.auxiliary.performance.LearningAlgorithmExperimenter
-
Whether the performance should be visually plotted (by default they will)
- displayPlots - Variable in class burlap.behavior.stochasticgames.auxiliary.performance.MultiAgentExperimenter
-
Whether the performance should be visually plotted (by default they will)
- distance(double[], double[]) - Method in interface burlap.behavior.functionapproximation.dense.rbf.DistanceMetric
-
Returns the distance between state s0 and state s1.
- distance(double[], double[]) - Method in class burlap.behavior.functionapproximation.dense.rbf.metrics.EuclideanDistance
-
- DistanceMetric - Interface in burlap.behavior.functionapproximation.dense.rbf
-
An interface for defining the distance between two states that are represented with double arrays.
- domain - Variable in class burlap.behavior.singleagent.auxiliary.EpisodeSequenceVisualizer
-
- domain - Variable in class burlap.behavior.singleagent.auxiliary.StateEnumerator
-
The domain whose states will be enumerated
- domain - Variable in class burlap.behavior.singleagent.interfaces.rlglue.RLGlueAgent
-
The BURLAP
Domain specifying the RLGlue problem representation and action space.
- domain - Variable in class burlap.behavior.singleagent.learnfromdemo.IRLRequest
-
The domain in which IRL is to be performed
- domain - Variable in class burlap.behavior.singleagent.learnfromdemo.RewardValueProjection
-
- domain - Variable in class burlap.behavior.singleagent.learning.actorcritic.actor.BoltzmannActor
-
The domain in which this agent will act
- domain - Variable in class burlap.behavior.singleagent.MDPSolver
-
The domain to solve
- domain - Variable in class burlap.behavior.stochasticgames.agents.madp.MADPPlanAgentFactory
-
- domain - Variable in class burlap.behavior.stochasticgames.agents.madp.MADPPlannerFactory.MAVIPlannerFactory
-
The domain in which planning is to be performed
- domain - Variable in class burlap.behavior.stochasticgames.agents.maql.MAQLFactory
-
- domain - Variable in class burlap.behavior.stochasticgames.agents.naiveq.history.SGQWActionHistoryFactory
-
The stochastic games domain in which the agent will act
- domain - Variable in class burlap.behavior.stochasticgames.agents.naiveq.SGNaiveQFactory
-
The stochastic games domain in which the agent will act
- domain - Variable in class burlap.behavior.stochasticgames.agents.SetStrategySGAgent.SetStrategyAgentFactory
-
The domain in which the agent will play
- domain - Variable in class burlap.behavior.stochasticgames.agents.twoplayer.repeatedsinglestage.GrimTrigger.GrimTriggerAgentFactory
-
The domain in which the agent will play
- domain - Variable in class burlap.behavior.stochasticgames.agents.twoplayer.repeatedsinglestage.TitForTat.TitForTatAgentFactory
-
The domain in which the agent will play
- domain - Variable in class burlap.behavior.stochasticgames.auxiliary.GameSequenceVisualizer
-
- domain - Variable in class burlap.behavior.stochasticgames.madynamicprogramming.MADynamicProgramming
-
The domain in which planning is to be performed
- domain - Variable in class burlap.domain.singleagent.rlglue.RLGlueEnvironment
-
The BURLAP domain
- Domain - Interface in burlap.mdp.core
-
This is a marker interface for a problem domain.
- domain(Object) - Method in interface burlap.mdp.core.state.vardomain.StateDomain
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Returns the numeric domain of the variable for the given key.
- domain - Variable in class burlap.mdp.singleagent.common.VisualActionObserver
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The domain this visualizer is rendering
- domain - Variable in class burlap.mdp.singleagent.pomdp.beliefstate.TabularBeliefState
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The POMDP domain with which this belief state is associated.
- domain - Variable in class burlap.mdp.singleagent.pomdp.beliefstate.TabularBeliefUpdate
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- domain - Variable in class burlap.mdp.stochasticgames.agent.SGAgentBase
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- domain - Variable in class burlap.mdp.stochasticgames.common.VisualWorldObserver
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The domain this visualizer is rendering
- domain - Variable in class burlap.mdp.stochasticgames.tournament.common.ConstantWorldGenerator
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- domain - Variable in class burlap.mdp.stochasticgames.world.World
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- domain - Variable in class burlap.shell.BurlapShell
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- domain - Variable in class burlap.shell.command.env.AddStateObjectCommand
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- domain - Variable in class burlap.shell.command.env.ExecuteActionCommand
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- domain - Variable in class burlap.shell.command.world.AddStateObjectSGCommand
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- domain - Variable in class burlap.shell.visual.SGVisualExplorer
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- domain - Variable in class burlap.shell.visual.VisualExplorer
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- DomainGenerator - Interface in burlap.mdp.auxiliary
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This class provides a simple interface for constructing domains, but it is not required to create domains.
- domains - Variable in class burlap.behavior.functionapproximation.dense.NormalizedVariableFeatures
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- domainSet - Variable in class burlap.behavior.singleagent.interfaces.rlglue.RLGlueAgent
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A variable for synchronized checking if the domain has been set.
- door - Variable in class burlap.domain.singleagent.pomdp.tiger.TigerState
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- DOOR_RESET - Static variable in class burlap.domain.singleagent.pomdp.tiger.TigerDomain
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The observation value for when reaching a new pair of doors (occurs after opening a door)
- dot(double[], double[]) - Static method in class burlap.behavior.stochasticgames.solvers.GeneralBimatrixSolverTools
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Returns the dot product of two vectors
- doubleEpislon - Static variable in class burlap.behavior.stochasticgames.agents.twoplayer.singlestage.equilibriumplayer.BimatrixEquilibriumSolver
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The epislon difference used to test for double equality.
- doubleEquality(double, double) - Static method in class burlap.behavior.stochasticgames.agents.twoplayer.singlestage.equilibriumplayer.BimatrixEquilibriumSolver
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- dp - Variable in class burlap.behavior.singleagent.planning.deterministic.DDPlannerPolicy
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- dp - Variable in class burlap.behavior.singleagent.planning.deterministic.SDPlannerPolicy
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- DPOperator - Interface in burlap.behavior.singleagent.planning.stochastic.dpoperator
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Defines a function for applying a dynamic programming operator (e.g., reducing the Q-values into a state value).
- DPPInit(SADomain, double, HashableStateFactory) - Method in class burlap.behavior.singleagent.learnfromdemo.mlirl.differentiableplanners.DifferentiableDP
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- DPPInit(SADomain, double, HashableStateFactory) - Method in class burlap.behavior.singleagent.planning.stochastic.DynamicProgramming
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- DPrint - Class in burlap.debugtools
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A class for managing debug print statements.
- dwidth - Variable in class burlap.domain.singleagent.gridworld.GridWorldVisualizer.CellPainter
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- dwidth - Variable in class burlap.domain.singleagent.gridworld.GridWorldVisualizer.LocationPainter
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- dwidth - Variable in class burlap.domain.singleagent.gridworld.GridWorldVisualizer.MapPainter
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- DynamicProgramming - Class in burlap.behavior.singleagent.planning.stochastic
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A class for performing dynamic programming operations: updating the value function using a Bellman backup.
- DynamicProgramming() - Constructor for class burlap.behavior.singleagent.planning.stochastic.DynamicProgramming
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- DynamicWeightedAStar - Class in burlap.behavior.singleagent.planning.deterministic.informed.astar
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Dynamic Weighted A* [1] uses a dynamic heuristic weight that is based on depth of the current search tree and based on an expected depth of the search.
- DynamicWeightedAStar(SADomain, StateConditionTest, HashableStateFactory, Heuristic, double, int) - Constructor for class burlap.behavior.singleagent.planning.deterministic.informed.astar.DynamicWeightedAStar
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Initializes