Class List

Here is the list of all classes, struct and interfaces used in SDM'Studio:

• namespace sdm Namespace grouping all tools required for sequential decision making.
  • class ActionVFBase
  • class ActionVFInterface
  • class ActionVFMaxplan
  • class ActionVFMaxplanLP
  • class ActionVFMaxplanLPSerial
  • class ActionVFMaxplanSerial
  • class ActionVFMaxplanWCSP
  • class ActionVFSawtoothLP
  • class ActionVFSawtoothLPSerial
  • class ActionVFSawtoothWCSP
  • class ActionVFTabulaire
  • class Algorithm The public interface common to all algorithms in SDM'Studio .
  • class AlphaStar The class for the algorithm A* (opens new window) .
    • class AlphaStarItem The state in terms of A* algorithm.
  • class BackupBase
  • class BackupInterface
  • class BackupInterfaceForValueFunction
  • class BackwardInduction The algorithm Backward Induction (opens new window) .
  • class BaseAction A base class inheriting from the Action interface.
  • class BaseBeliefMDP This class provides a way to transform a POMDP into beliefMDP formalism.
  • struct BaseItem A base class inheriting from the Item interface.
  • class BaseLogger This class provide a common interface for all loggers.
  • class BaseObservation A base class inheriting from the Observation interface.
  • class BaseOccupancyMDP This class provides a way to transform a Dec-POMDP into an occupancy MDP formalism.
  • class BasePointSetValueFunction
  • class BaseRelaxedValueFunction
  • class BaseSerialInterface
  • class BaseState A base class inheriting from the State interface.
  • class BaseTabularValueFunction
  • class BaseValueFunction This class is the abstract class of all kind of value functions. All {state,action,q}-value function must derived this class.
  • class Belief
  • class Belief2OccupancyValueFunction
  • class BeliefDefault
  • class BeliefInterface A common interface for objects that represent a belief.
  • class BinaryFunction
  • class BlindInitializer This initializer calculates the initial lower bound ${V}\_0$ using the blind policy method [Hauskrecht, 1997]. Trey Smith and Reid Simmons used this initialization procedure in https://arxiv.org/pdf/1207.4166.pdf (opens new window) .
  • class BoostSerializable
  • class BoundInitializer This initializer initializes a value function to the estimation of the value if we get a constant reward at every timestep.
  • class CSVLogger The CSV logger will print logs/data in a file with csv format. This logger can be used to save training data.
  • class CompressibleOccupancyStateInterface
  • class DecentralizedLP
  • class DecisionProcessInterface The class for Discrete Markov Decision Processes.
  • class DecisionRule A public interface for decision rules. Contains all the methods that must be implemented to well define a decision rule in SDMS.
  • class DeterministicDecisionRule This class provide a way to manipulate data relative to a deterministic decision rule.
  • class DiscreteDistribution
  • class DiscreteSpace The discrete space class give a way to keep all possible values of a finite space.
  • class Distribution
  • class EpsGreedy
  • class ExperienceMemory
  • class ExperienceMemoryInterface
  • class Exploration
  • class FileLogger The file logger will print logs/data in a file.
  • class Function
  • class FunctionReward This class provides getters and setters for the reward model.
  • class FunctionSpace The class for function spaces. This is helpfull in case we need to enumerate all possible functions (only usable when input space and output space are DiscreteSpace ).
  • class Graph A structure to manipulate graphs.
  • class GraphNode Node of graphs.
  • class GymInterface
  • class HSVI Heuristic Search Value Iteration (HSVI) (opens new window) and its extensions (FB-HSVI, one-sidedHSVI ).
  • class HierarchicalMPOMDP The Hierarchical MPOMDP is a transformation of a standardMPOMDP assuming there exists a hierarchy among agents.
  • class HierarchicalOccupancyMDP
  • class HistoryInterface A common interface for objects that represent a history.
  • class HistoryTree History class that use a representation by tree.
  • class HyperplanValueFunction
  • class IncrementalValueFunction
  • class Initializer Abstract class for initializer.
  • class InitializerFactory The InitializerFactor class facilitates users to interact and instanciate value function initializers. Some of the available initializers are : MinInitializer ,MaxInitializer ,BlindInitializer ,ZeroInitializer . For a complete list of initializer, you can use : std::cout <<InitializerFactory::available() << std::endl;.
  • class InteractiveWorld
  • class Item A public interface for actions, states and observations.
  • class ItemPair
  • class Iterator Common interface to all SDMS Iterators.
  • class Joint This class is used for joint objects. It can be a JointHistoryTree , a JointObservation, a JointAction, etc.
  • class JointDeterministicDecisionRule This class provide a way to manipulate a function that maps joint histories to joint actions.
  • class JointHistoryInterface A common interface for objects that represent a joint history.
  • class JointHistoryTree Joint history class that use a representation by tree.
  • class LPBase
  • class LPInterface
  • class Logger The main logger. This logger will print logs with a given format on the output stream.
  • class MDP The class for Discrete Markov Decision Processes.
  • class MDPInitializer The MDP initializer enables to initialize the upper bound inHSVI with the underlyingMDP optimal value function. This is a common usage inHSVI to use the solution of a relaxation of the problem in order to get a accurate upper bound (see also the classPOMDPInitializer ).
  • class MDPInterface The class for Discrete Markov Decision Processes.
  • class MMDP The class for Discrete Markov Decision Processes.
  • class MMDPInterface The class for Discrete Markov Decision Processes.
  • class MPOMDP The class for Discrete Partially Observable Markov Decision Processes.
  • class MPOMDPInterface The class for Discrete Markov Decision Processes.
  • class MappedMatrix Mapped matrices are matrices that use map to store values. Can be view as a sparse matrix with templated indexes.
  • class MappedVector Mapped vectors are vectors with specific type of indexes. They are represented by a map.
  • class MatrixInterface The Matrix interface. To be considered as a matrix in SDM'Studio, a class must implement all the following functions.
  • class MaxInitializer This initializer initializes a value function to the best value. This is an optimistic initialization.
  • class MaxPlanBackup
  • class MinInitializer This initializer initializes a value function to the worst value. This is a pessimistic initialization.
  • class MultiDiscreteSpace This class provide a way to instantiate multi discrete space (i.e. list of discrete spaces). Typically it is used to store a set of spaces, one by agent (i.e. action_spaces in POSG). This can be view as a set of discrete spaces or as a discrete space of joint items.
  • class MultiLogger The multi logger will print logs in several loggers.
  • class MultiSpace A multi-space is a set a spaces.
  • class NetworkedDistributedPOMDP
    • class Node
  • class NetworkedDistributedPOMDPInterface
  • class Observation A public interface for observations.
  • class ObservationDynamicsInterface This class provides a common interface for every models of observation dynamics.
  • class OccupancyState An occupancy state refers to the complete knowledge the central planner have access to take decisions.
  • class OccupancyStateInterface A common interface for objects that represent an occupancy state.
  • class POMDP The class for Discrete Partially Observable Markov Decision Processes.
  • class POMDPInitializer The POMDP initializer enables to initialize the upper bound inHSVI with the underlyingPOMDP optimal value function.
  • class POMDPInterface The class for Discrete Markov Decision Processes.
  • class PrivateHierarchicalOccupancyMDP
  • class PrivateHierarchicalOccupancyMDPWithHistory
  • class PrivateOccupancyState A private occupancy state is an occupancy state (i.e. $p(s\_t, \theta^{-i}\_{t} \mid \iota\_t, \theta\_t^i)$ ).
  • class QInitializer Abstract class for initializer.
  • class QLearning Q-Learning and its extensions (DQN, etc).
  • class QValueBackupInterface
  • class QValueFunction This class is the abstract class of value function. All value function must derived this class.
  • class QValueFunctionConditioning
  • class RecursiveMap The recursive map class (i.e. map<T0, map<T1, ..... , map<TN-1, TN>)
  • class RecursiveMap< T0, T1 > RecursiveMap specialization when it is simple map.
  • class RecursiveMap< T0, T1, T2, Ts... >
  • class RelaxedValueFunction
  • class ReplayMemory
  • class RewardInterface This class provides a common interface for every models of reward.
  • class SerialInterface A common interface for objects that are serialized.
  • class SerialMMDPInterface The class for Discrete Markov Decision Processes.
  • class SerialMPOMDPInterface The class for Discrete Markov Decision Processes.
  • class SerialOccupancyInterface
  • class SerialOccupancyMDP
  • class SerialOccupancyState
  • class SerialProblemInterface
  • class SerializedMMDP
  • class SerializedMPOMDP
  • class SerializedState
  • struct Set
  • class SolvableByDP Public interface that must be implemented by all transformed problems that can be solved using HSVI (i.e. beliefMDP, occupancyMDP, occupancyGame, etc).
  • class SolvableByHSVI Public interface for problems that can be solved using HSVI (i.e. beliefMDP, occupancyMDP, occupancyGame, etc).HSVI can be used to solve the problem that implement this interface.
  • class SolvableByMDP The class for Markov Decision Processes.
  • class Space This class is an abstract interface that all spaces should inherite.
  • class State A public interface for states.
  • class State2OccupancyValueFunction
  • class StateDynamicsInterface This class provides a common interface for every models of state dynamics.
  • class StateGraph A graph that keep all beliefs.
  • class StdLogger The standard logger will print logs on the standard output stream.
  • class StochasticDecisionRule The stochastic decision rule class. This class is a function that maps generic states distribution over generic actions.
  • class TabularBackup
  • class TabularObservationDynamics Tabular observation dynamics.
  • class TabularObservationDynamicsAS Tabular representation for the observation dynamics p(o' | a, s').
  • class TabularObservationDynamicsS Tabular representation for the observation dynamics p(o' | s').
  • class TabularObservationDynamicsSAS Tabular representation for the observation dynamics p(o' | s, a, s').
  • class TabularQValueBackup
  • class TabularQValueFunction
  • class TabularQValueFunctionConditioning
  • class TabularReward This class provide a way to represent the reward model with a tabular representation.
  • class TabularStateDynamics Tabular representation for the state dynamics.
  • class TemporalFunction
  • class TensorImpl The vector interface. To be considered as a vector in SDM'Studio, a class must implement all the following functions.
  • class TransformedMPOMDP
  • class Tree Generic Tree class.
  • class ValueFunction This class is the abstract class of value function. All value function must derived this class.
  • class ValueFunctionFactory The ValueFunctionFactory class facilitates users to interact and instanciate value functions.
  • class ValueInitializer This initializer initializes a value function to a constant value.
  • class ValueIteration Value Iteration (opens new window) forMDP .
  • class VarNaming
  • class Variations Iterator ofVariations .
  • class VectorInterface The vector interface. To be considered as a vector in SDM'Studio, a class must implement all the functions of the interface.
  • class World
  • class ZeroInitializer This initializer initializes a value function to zero.
  • namespace algo Namespace grouping functions to manipulate algorithms.
  • namespace ast Namespace that is used by the parser.
    • struct discrete_space_encoder encodes the input into a discrete space class
    • struct dpomdp_encoder
    • struct dpomdp_printer
    • struct dpomdp_t
    • struct identifier_t
    • struct item_encode encodes the input into a item index (string))
    • struct joint_item_encode encodes the input into a joint element (vector of number)
    • struct matrix_encoder encodes the input into a mapped matrix
    • struct matrix_t
    • struct multi_discrete_space_encoder encodes the input into a multi discrete space class
    • class obs_dynamics_encoder
    • struct observation_entry_1_t
    • struct observation_entry_2_t
    • struct observation_entry_3_t
    • struct observation_entry_t
    • struct observation_transition_encoder encodes the input into an observation dynamic class
    • struct real_vector_t
    • struct reward_entry_1_t
    • struct reward_entry_2_t
    • struct reward_entry_t
    • class state_dynamics_encoder encodes state transition dynamics (i.e. TabularStateDynamics class)
    • struct state_encoder encodes the input into a vector of number (vector of states) "*" -> [0,1,2,3,4,...,n] "s0" -> [0] 0 -> [0]
    • class state_transition_encoder encodes state transition dynamics (i.e. TabularStateDynamics class)
    • struct str_visitor
    • struct tabular_reward_encoder
    • struct tabular_rewards_encoder
    • struct transition_entry_1_t
    • struct transition_entry_2_t
    • struct transition_entry_3_t
    • struct transition_entry_t
    • struct value_t
    • struct values_t
    • struct vector_encoder encodes the input into a mapped vector
  • namespace common Namespace grouping all common functions to the whole project.
  • namespace config Namespace grouping a set of configurations.
  • struct equal_container
  • struct equal_from_ptr
  • namespace exception Namespace grouping all exceptions.
    • class Exception This class is the base class for SDMS exceptions.
    • class FileNotFoundException File not found exception.
    • class NotImplementedException Not implemented method exception.
    • class ParsingException Developpers use this class to raise a parsing exception.
  • struct hash_container
  • struct hash_from_ptr
  • namespace iterator Namespace grouping all SDMS iterators.
    • class CombinationIterator The combination iterator provides a way to go simultaneously over multiple iterators in order to generate all combinations of items.
    • class FunctionIterator The function iterator is an SDMS iterator generating functions from iterable possible inputs and outputs.
    • class SuperIterator A super iterator is an SDMS iterator that simply iterate over a standard STD iterator.
  • namespace nn Namespace grouping all neural networks definitions.
    • struct DQNImpl
  • namespace parser Namespace grouping all functions for parsing files.
    • struct error_handler_base
  • class sdmsMatrix
  • class sdmsVector Create a SDMS Vector. A SMDS Vector is used to optimize the calculation, however, you have to be careful when using it because it's not possible to add element after the initialization.
  • namespace tools Namespace grouping different kind of tools.
  • namespace world Namespace grouping functions to manipulate problems.
• namespace std
  • struct Compare
  • class MultipleInheritableEnableSharedFromThis
  • struct Performance
  • struct hash< sdm::Belief >
  • struct hash< sdm::DeterministicDecisionRule >
  • struct hash< sdm::JointDeterministicDecisionRule >
  • struct hash< sdm::MappedMatrix< TLig, TCol, TValue > >
  • struct hash< sdm::OccupancyState >
  • struct hash< sdm::Pair< T, U > >
  • struct hash< sdm::SerialOccupancyState >
  • struct hash< sdm::SerializedState >
  • struct hash< sdm::Tuple< TT... > >
  • struct hash< std::vector< T > >
  • class inheritable_enable_shared_from_this
  • struct is_any
• namespace @111