Catalano.MachineLearning.Exploration

Class EpsilonGreedyExploration

java.lang.Object

Catalano.MachineLearning.Exploration.EpsilonGreedyExploration

All Implemented Interfaces:

IExplorationPolicy

public class EpsilonGreedyExploration
extends java.lang.Object
implements IExplorationPolicy

The class implements epsilon greedy exploration policy. Acording to the policy, the best action is chosen with probability 1-epsilon. Otherwise, with probability epsilon, any other action, except the best one, is chosen randomly. According to the policy, the epsilon value is known also as exploration rate.

Constructor Summary

Constructors

Constructor and Description
EpsilonGreedyExploration(double epsilon) Initializes a new instance of the EpsilonGreedyExploration class.

Method Summary

Modifier and Type	Method and Description
int	ChooseAction(double[] actionEstimates) The method chooses an action depending on the provided estimates.
double	getEpsilon() Get Episilon value.
void	setEpsilon(double epsilon) The value determines the amount of exploration driven by the policy.

Methods inherited from class java.lang.Object

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

Constructor Detail

EpsilonGreedyExploration

public EpsilonGreedyExploration(double epsilon)

Initializes a new instance of the EpsilonGreedyExploration class.

Parameters:

epsilon - Epsilon value (exploration rate).

Method Detail

getEpsilon

public double getEpsilon()

Get Episilon value.

Returns:

Return Epsilon value.

setEpsilon

public void setEpsilon(double epsilon)

The value determines the amount of exploration driven by the policy. If the value is high, then the policy drives more to exploration - choosing random action, which excludes the best one. If the value is low, then the policy is more greedy - choosing the beat so far action.

Parameters:

epsilon - Epsilon value (exploration rate), [0, 1].

ChooseAction

public int ChooseAction(double[] actionEstimates)

The method chooses an action depending on the provided estimates. The estimates can be any sort of estimate, which values usefulness of the action (expected summary reward, discounted reward, etc).

Specified by:

ChooseAction in interface IExplorationPolicy

Parameters:

actionEstimates - Action Estimates.

Returns:

Return Selected actions.