| ▼NAIToolbox | |
| ►NBandit | |
| CEpsilonPolicy | |
| CESRLPolicy | This class implements the Exploring Selfish Reinforcement Learning algorithm |
| CExperience | This class computes averages and counts for a Bandit problem |
| CLRPPolicy | This class implements the Linear Reward Penalty algorithm |
| CModel | This class represent a multi-armed bandit |
| CPolicyInterface | Simple typedef for most of a normal Bandit's policy needs |
| CQGreedyPolicy | This class implements a simple greedy policy |
| CQGreedyPolicyWrapper | This class implements some basic greedy policy primitives |
| CQSoftmaxPolicy | This class implements a softmax policy through a QFunction |
| CQSoftmaxPolicyWrapper | This class implements some basic softmax policy primitives |
| CRandomPolicy | This class represents a random policy |
| CSuccessiveRejectsPolicy | This class implements the successive rejects algorithm |
| CT3CPolicy | This class implements the T3C sampling policy |
| CThompsonSamplingPolicy | This class implements a Thompson sampling policy |
| CTopTwoThompsonSamplingPolicy | This class implements the top-two Thompson sampling policy |
| ►NFactored | |
| ►NBandit | |
| CEpsilonPolicy | |
| CExperience | This class computes averages and counts for a multi-agent cooperative Bandit problem |
| CFlattenedModel | This class flattens a factored bandit model |
| CLLRPolicy | This class represents the Learning with Linear Rewards algorithm |
| CLocalSearch | This class approximately finds the best joint action using Local Search |
| CMakeGraph | This class is the public interface for initializing the graph in generic code that uses the maximizers |
| CMakeGraph< MaxPlus > | |
| CMakeGraph< ReusingIterativeLocalSearch > | |
| CMakeGraphImpl | This class clumps all implementations that create graphs for data for certain Maximizers |
| CMakeGraphImpl< LocalSearch, Iterable > | |
| CMakeGraphImpl< LocalSearch, QFunction > | |
| CMakeGraphImpl< VariableElimination, Data > | |
| CMAUCEPolicy | This class represents the Multi-Agent Upper Confidence Exploration algorithm |
| CMaxPlus | This class represents the Max-Plus optimization algorithm for loopy FactorGraphs |
| CMiningBandit | This class represents the mining bandit problem |
| CModel | This class represents a factored multi-armed bandit |
| CMOQFunctionRule | This struct represents a single action/values pair |
| ►CMultiObjectiveVariableElimination | This class represents the Multi Objective Variable Elimination process |
| CEntry | |
| CPolicyInterface | Simple typedef for most of a normal Bandit's policy needs |
| CQFunctionRule | This struct represents a single action/value pair |
| CQGreedyPolicy | This class implements a greedy policy through a QFunction |
| CRandomPolicy | This class represents a random policy |
| CReusingIterativeLocalSearch | This class approximately finds the best joint action with Reusing Iterative Local Search |
| CSingleActionPolicy | This class represents a policy always picking the same action |
| CThompsonSamplingPolicy | This class implements a Thompson sampling policy |
| ►CUCVE | This class represents the UCVE process |
| CEntry | |
| CUpdateGraph | This class is the public interface for updating the input graph with the input data in generic code that uses the maximizers |
| CUpdateGraph< MaxPlus > | |
| CUpdateGraph< ReusingIterativeLocalSearch > | |
| CUpdateGraphImpl | This class clumps all implementations that update graphs with data for certain Maximizers |
| CUpdateGraphImpl< LocalSearch, Iterable > | |
| CUpdateGraphImpl< LocalSearch, QFunction > | |
| CUpdateGraphImpl< VariableElimination, Iterable > | |
| CUpdateGraphImpl< VariableElimination, QFunction > | |
| CVariableElimination | This class represents the Variable Elimination algorithm |
| ►NMDP | |
| CBanditPolicyAdaptor | This class extends a Bandit policy so that it can be called from MDP code |
| CCooperativeExperience | This class keeps track of registered events and rewards |
| CCooperativeMaximumLikelihoodModel | This class models CooperativeExperience as a CooperativeModel using Maximum Likelihood |
| CCooperativeModel | This class models a cooperative MDP |
| CCooperativePrioritizedSweeping | This class implements PrioritizedSweeping for cooperative environments |
| CCooperativeQLearning | This class represents the Cooperative QLearning algorithm |
| CCooperativeThompsonModel | This class models CooperativeExperience as a CooperativeModel using Thompson Sampling |
| CEpsilonPolicy | This class represents an epsilon-greedy policy for Factored MDPs |
| CFactoredLP | This class represents the Factored LP algorithm |
| CJointActionLearner | This class represents a single Joint Action Learner agent |
| CLinearProgramming | This class solves a factored MDP with Linear Programming |
| CMakeGraph | This class is the public interface for initializing the graph in generic code that uses the maximizers |
| CMakeGraph< Bandit::MaxPlus > | |
| CMakeGraph< Bandit::ReusingIterativeLocalSearch > | |
| CMakeGraphImpl | This class clumps all implementations that create graphs for data for certain Maximizers |
| CMakeGraphImpl< Bandit::LocalSearch, Iterable > | |
| CMakeGraphImpl< Bandit::LocalSearch, MDP::QFunction > | |
| CMakeGraphImpl< Bandit::VariableElimination, Data > | |
| CMOQFunctionRule | This struct represents a single state/action/values tuple |
| CQFunctionRule | This struct represents a single state/action/value tuple |
| CQGreedyPolicy | This class implements a greedy policy through a QFunction |
| CSparseCooperativeQLearning | This class represents the Sparse Cooperative QLearning algorithm |
| CTigerAntelope | This class represents a 2-agent tiger antelope environment |
| CUpdateGraph | This class is the public interface for updating the input graph with the input data in generic code that uses the maximizers |
| CUpdateGraph< Bandit::MaxPlus > | |
| CUpdateGraph< Bandit::ReusingIterativeLocalSearch > | |
| CUpdateGraphImpl | This class clumps all implementations that update graphs with data for certain Maximizers |
| CUpdateGraphImpl< Bandit::LocalSearch, Iterable > | |
| CUpdateGraphImpl< Bandit::LocalSearch, MDP::QFunction > | |
| CUpdateGraphImpl< Bandit::VariableElimination, Iterable > | |
| CUpdateGraphImpl< Bandit::VariableElimination, MDP::QFunction > | |
| CValueFunction | This struct represents a factored ValueFunction |
| CBasisFunction | This struct represents a basis function |
| CBasisMatrix | This struct represents a basis matrix |
| CCPSQueue | This class is used as the priority queue for CooperativePrioritizedSweeping |
| CDynamicDecisionNetwork | This class represents a Dynamic Decision Network with factored actions |
| ►CDynamicDecisionNetworkGraph | This class represents the structure of a dynamic decision network |
| CParentSet | This class contains the parent information for a single next-state feature |
| CFactoredMatrix2D | This class represents a factored 2D matrix |
| CFactoredVector | This class represents a factored vector |
| ►CFactorGraph | This class offers a minimal interface to manager a factor graph |
| CFactorNode | |
| CFasterTrie | This class is a generally faster implementation of a Trie |
| CFilterMap | This class is a container which uses PartialFactors as keys |
| CGenericVariableElimination | This class represents the Variable Elimination algorithm |
| CPartialFactorsEnumerator | This class enumerates all possible values for a PartialFactors |
| CPartialIndexEnumerator | This class enumerates the indeces of all combinations where a value is fixed |
| CTrie | This class organizes data ids as if in a trie |
| ►NImpl | |
| ►NPOMDP | |
| CBeliefNodeNoEntropyAddon | |
| CBeliefParticleEntropyAddon | |
| CEmptyStruct | |
| CGetFunctionArguments | This struct helps decompose a function into return value and arguments |
| CGetFunctionArguments< R(*)(Args...)> | |
| CGetFunctionArguments< R(C::*)(Args...) const > | |
| CGetFunctionArguments< R(C::*)(Args...)> | |
| CIdPack | This class is simply a template container for ids |
| Cis_compatible_f | This struct reports whether a given function is compatible with a given signature |
| Cis_compatible_f< R(Args...), R2(Args2...)> | |
| Cis_compatible_f< R(C::*)(Args...) const, R2(Args2...)> | |
| Cis_compatible_f< R(C::*)(Args...), R2(Args2...)> | |
| CMatcher | This struct allows to match between two tuples types |
| CMatcher< N, std::tuple< F, A... >, std::tuple< F, B... >, IDs... > | |
| CMatcher< N, std::tuple< FA, A... >, std::tuple< FB, B... >, IDs... > | |
| CMatcher< N, std::tuple<>, std::tuple< B... >, IDs... > | |
| ►NMDP | |
| CBanditPolicyAdaptor | This class extends a Bandit policy so that it can be called from MDP code |
| CDoubleQLearning | This class represents the double QLearning algorithm |
| CDyna2 | This class represents the Dyna2 algorithm |
| CDynaQ | This class represents the DynaQ algorithm |
| CEpsilonPolicy | |
| CExpectedSARSA | This class represents the ExpectedSARSA algorithm |
| CExperience | This class keeps track of registered events and rewards |
| CGenerativeModelPython | This class allows to import generative models from Python |
| ►CGridWorld | This class represents a simple rectangular gridworld |
| CState | |
| CHystereticQLearning | This class represents the Hysteretic QLearning algorithm |
| CImportanceSampling | This class implements off-policy control via importance sampling |
| CImportanceSamplingEvaluation | This class implements off-policy evaluation via importance sampling |
| CLinearProgramming | This class solves an MDP using Linear Programming |
| CMaximumLikelihoodModel | This class models Experience as a Markov Decision Process using Maximum Likelihood |
| ►CMCTS | This class represents the MCTS online planner using UCB1 |
| CActionNode | |
| CStateNode | |
| CModel | This class represents a Markov Decision Process |
| COffPolicyBase | This class contains all the boilerplates for off-policy methods |
| COffPolicyControl | This class is a general version of off-policy control |
| COffPolicyEvaluation | This class is a general version of off-policy evaluation |
| CPGAAPPPolicy | This class implements the PGA-APP learning algorithm |
| CPolicy | This class represents an MDP Policy |
| CPolicyEvaluation | This class applies the policy evaluation algorithm on a policy |
| CPolicyInterface | Simple typedef for most of MDP's policy needs |
| CPolicyIteration | This class represents the Policy Iteration algorithm |
| CPolicyWrapper | This class provides an MDP Policy interface around a Matrix2D |
| CPrioritizedSweeping | This class represents the PrioritizedSweeping algorithm |
| CQGreedyPolicy | This class implements a greedy policy through a QFunction |
| CQL | This class implements off-policy control via Q(lambda) |
| CQLearning | This class represents the QLearning algorithm |
| CQLEvaluation | This class implements off-policy evaluation via Q(lambda) |
| CQPolicyInterface | This class is an interface to specify a policy through a QFunction |
| CQSoftmaxPolicy | This class implements a softmax policy through a QFunction |
| CRetraceL | This class implements off-policy control via Retrace(lambda) |
| CRetraceLEvaluation | This class implements off-policy evaluation via Retrace(lambda) |
| CRLearning | This class represents the RLearning algorithm |
| CSARSA | This class represents the SARSA algorithm |
| CSARSAL | This class represents the SARSAL algorithm |
| CSparseExperience | This class keeps track of registered events and rewards |
| CSparseMaximumLikelihoodModel | This class models Experience as a Markov Decision Process using Maximum Likelihood |
| CSparseModel | This class represents a Markov Decision Process |
| CThompsonModel | This class models Experience as a Markov Decision Process using Thompson Sampling |
| CTreeBackupL | This class implements off-policy control via Tree Backup(lambda) |
| CTreeBackupLEvaluation | This class implements off-policy evaluation via Tree Backup(lambda) |
| CValueFunction | |
| CValueIteration | This class applies the value iteration algorithm on a Model |
| CWoLFPolicy | This class implements the WoLF learning algorithm |
| ►NPOMDP | |
| CActionNode | |
| CAMDP | This class implements the Augmented MDP algorithm |
| CBeliefGenerator | This class generates reachable beliefs from a given Model |
| CBeliefNode | This is a belief node of the rPOMCP tree |
| CBeliefParticle | |
| CBlindStrategies | This class implements the blind strategies lower bound |
| CFastInformedBound | This class implements the Fast Informed Bound algorithm |
| CGapMin | This class implements the GapMin algorithm |
| CHeadBeliefNode | This class is the root node of the rPOMCP graph |
| CIncrementalPruning | This class implements the Incremental Pruning algorithm |
| Cis_witness_lp | This check the interface for a WitnessLP |
| CLinearSupport | This class represents the LinearSupport algorithm |
| CModel | This class represents a Partially Observable Markov Decision Process |
| CPBVI | This class implements the Point Based Value Iteration algorithm |
| CPERSEUS | This class implements the PERSEUS algorithm |
| CPolicy | This class represents a POMDP Policy |
| ►CPOMCP | This class represents the POMCP online planner using UCB1 |
| CActionNode | |
| CBeliefNode | |
| CProjecter | This class offers projecting facilities for Models |
| CQMDP | This class implements the QMDP algorithm |
| CrPOMCP | This class represents the rPOMCP online planner |
| CRTBSS | This class represents the RTBSS online planner |
| CSARSOP | This class implements the SARSOP algorithm |
| CSparseModel | This class represents a Partially Observable Markov Decision Process |
| CVEntry | |
| CWitness | This class implements the Witness algorithm |
| CAdam | This class implements the ADAM gradient descent algorithm |
| CCassandraParser | This class can parse files containing MDPs and POMDPs in the Cassandra file format |
| Ccopy_const | This struct is used to copy constness from one type to another |
| CEpsilonPolicyInterface | This class is a policy wrapper for epsilon action choice |
| CEpsilonPolicyInterface< void, void, Action > | This class represents the base interface for epsilon policies in games and bandits |
| CIndexMap | This class is an iterable construct on a list of ids on a given container |
| CIndexMapIterator | This class is a simple iterator to iterate over a container with the specified ids |
| CIndexSkipMap | This class is an iterable construct on a list of ids on a given container |
| CIndexSkipMapIterator | This class is a simple iterator to iterate over a container without the specified ids |
| CLP | This class presents a common interface for solving Linear Programming problems |
| CNoCheck | This is used to tag functions that avoid runtime checks |
| CPolicyInterface | This class represents the base interface for policies |
| CPolicyInterface< void, void, Action > | This class represents the base interface for policies in games and bandits |
| CPruner | This class offers pruning facilities for non-parsimonious ValueFunction sets |
| CSeeder | This class is an internal class used to seed all random engines in the library |
| CStatistics | This class registers sets of data and computes statistics about it |
| CStorageMatrix2D | This class provides an Eigen-compatible automatically resized Matrix2D |
| CStorageVector | This class provides an Eigen-compatible automatically resized Vector |
| CSubsetEnumerator | This class enumerates all possible vectors of finite subsets over N elements |
| CVoseAliasSampler | This class represents the Alias sampling method |
| CWitnessLP | This class implements an easy interface to do Witness discovery through linear programming |
| CEigenVectorFromPython | |
| COldMDPModel | This class represents a Markov Decision Process |
| COldPOMDPModel | This class represents a Partially Observable Markov Decision Process |
| CPairFromPython | |
| CPairToPython | |
| ▼CSeedPrinter | |
| CAllPassVisitor | |
| ▼CTupleFromPython | |
| CExtractPythonTuple | |
| CExtractPythonTuple< 0, dummyForSpecialization > | |
| ▼CTupleToPython | |
| Cgenerator | |
| Cgenerator< 0, S... > | |
| Csequence | |
| CVector2DFromPython | |
| CVector3DFromPython | |
| CVectorFromPython | |