Uses of Interface
org.tweetyproject.machinelearning.rl.mdp.State

Packages that use State

Package	Description
org.tweetyproject.machinelearning.rl.mdp
org.tweetyproject.machinelearning.rl.mdp.algorithms

Uses of State in org.tweetyproject.machinelearning.rl.mdp

Classes in org.tweetyproject.machinelearning.rl.mdp with type parameters of type State

Modifier and Type	Class	Description
class	Episode<S extends State,A extends Action>	This class models an episode in MPDs, i.e.
class	FixedPolicy<S extends State,A extends Action>	A fixed policy for MDPs, i.e., a simple map from states to actions.
class	MarkovDecisionProcess<S extends State,A extends Action>	This class models a Markov Decision Process (MDP, for fixed starting and terminal states), which can be used to represent reinforcement learning scenarios.
interface	Policy<S extends State,A extends Action>	A policy (map from states to actions) for Markov Decision Process

Classes in org.tweetyproject.machinelearning.rl.mdp that implement State

Modifier and Type	Class	Description
class	NamedState	A state in a Markov Decision Process that is solely characterized through its name.

Uses of State in org.tweetyproject.machinelearning.rl.mdp.algorithms

Classes in org.tweetyproject.machinelearning.rl.mdp.algorithms with type parameters of type State

Modifier and Type	Class	Description
class	IterativePolicyEvaluation<S extends State,A extends Action>	Determines utilities iteratively.
class	OfflineAlgorithm<S extends State,A extends Action>	A general interface for algorithms to determine optimal policies directly from an MDP
interface	PolicyEvaluation<S extends State,A extends Action>	The `PolicyEvaluation` interface provides methods to evaluate the utility of states in a Markov Decision Process (MDP) with respect to a given policy.
class	PolicyIteration<S extends State,A extends Action>	The policy iteration algorithm for determining optimal policies
class	ValueIteration<S extends State,A extends Action>	The value iteration algorithm for determining optimal policies