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

Packages that use Policy

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

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

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

Modifier and Type	Class	Description
class	FixedPolicy<S extends State,A extends Action>	A fixed policy for MDPs, i.e., a simple map from states to actions.

Methods in org.tweetyproject.machinelearning.rl.mdp with parameters of type Policy

Modifier and Type	Method	Description
double	MarkovDecisionProcess.expectedUtility(Policy<S,A> pi, int num_episodes, double gamma)	Approximates the expected utility of the given policy within this MPD using Monte Carlo search (which uses the given number of episodes)
Episode<S,A>	MarkovDecisionProcess.sample(S s, Policy<S,A> pi)	Samples an episode wrt.

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

Methods in org.tweetyproject.machinelearning.rl.mdp.algorithms that return Policy

Modifier and Type	Method	Description
Policy<S,A>	OfflineAlgorithm.getPolicy(Map<S,Double> utilities, MarkovDecisionProcess<S,A> mdp, double gamma)	Determines the best policy, given the utilities
abstract Policy<S,A>	OfflineAlgorithm.getPolicy(MarkovDecisionProcess<S,A> mdp, double gamma)	Determines the optimal policy for the given MDP.
Policy<S,A>	PolicyIteration.getPolicy(MarkovDecisionProcess<S,A> mdp, double gamma)
Policy<S,A>	ValueIteration.getPolicy(MarkovDecisionProcess<S,A> mdp, double gamma)

Methods in org.tweetyproject.machinelearning.rl.mdp.algorithms with parameters of type Policy

Modifier and Type	Method	Description
Map<S,Double>	IterativePolicyEvaluation.getUtilities(MarkovDecisionProcess<S,A> mdp, Policy<S,A> pi, double gamma)
Map<S,Double>	PolicyEvaluation.getUtilities(MarkovDecisionProcess<S,A> mdp, Policy<S,A> pi, double gamma)	Determines the utilities of the states in the MDP wrt.