Class ValueIteration<S extends State,A extends Action>

java.lang.Object

org.tweetyproject.machinelearning.rl.mdp.algorithms.OfflineAlgorithm<S,A>

org.tweetyproject.machinelearning.rl.mdp.algorithms.ValueIteration<S,A>

Type Parameters:

S - The type of states

A - The type of actions

public class ValueIteration<S extends State,A extends Action>
extends OfflineAlgorithm<S,A>

The value iteration algorithm for determining optimal policies

Author:

Matthias Thimm

Constructor Summary

Constructors

Constructor	Description
ValueIteration(long num_iterations)	Creates a new value iteration algorithm

Method Summary

Modifier and Type	Method	Description
Policy<S,A>	getPolicy(MarkovDecisionProcess<S,A> mdp, double gamma)	Determines the optimal policy for the given MDP.

Methods inherited from class org.tweetyproject.machinelearning.rl.mdp.algorithms.OfflineAlgorithm

getPolicy

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Details

ValueIteration

public ValueIteration(long num_iterations)

Creates a new value iteration algorithm

Parameters:

num_iterations - the given number of num_iterations

Method Details

getPolicy

public Policy<S,A> getPolicy(MarkovDecisionProcess<S,A> mdp,
 double gamma)

Description copied from class: OfflineAlgorithm

Determines the optimal policy for the given MDP.

Specified by:

getPolicy in class OfflineAlgorithm<S extends State,A extends Action>

Parameters:

mdp - some MDP

gamma - the used discount factor for utility determination

Returns:

the optimal policy