RouletteWheelExploration Class

Roulette wheel exploration policy.

Inheritance Hierarchy

SystemObject
Accord.MachineLearningRouletteWheelExploration

Namespace: Accord.MachineLearning
Assembly: Accord.MachineLearning (in Accord.MachineLearning.dll) Version: 3.8.0

Syntax

public class RouletteWheelExploration : IExplorationPolicy

Public Class RouletteWheelExploration
	Implements IExplorationPolicy

The RouletteWheelExploration type exposes the following members.

Constructors

	Name	Description
	RouletteWheelExploration	Initializes a new instance of the RouletteWheelExploration class.

Methods

	Name	Description
	ChooseAction	Choose an action.
	Equals	Determines whether the specified object is equal to the current object. (Inherited from Object.)
	Finalize	Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection. (Inherited from Object.)
	GetHashCode	Serves as the default hash function. (Inherited from Object.)
	GetType	Gets the Type of the current instance. (Inherited from Object.)
	MemberwiseClone	Creates a shallow copy of the current Object. (Inherited from Object.)
	ToString	Returns a string that represents the current object. (Inherited from Object.)

Extension Methods

	Name	Description
	HasMethod	Checks whether an object implements a method with the given name. (Defined by ExtensionMethods.)
	IsEqual	Compares two objects for equality, performing an elementwise comparison if the elements are vectors or matrices. (Defined by Matrix.)
	To(Type)	Overloaded. Converts an object into another type, irrespective of whether the conversion can be done at compile time or not. This can be used to convert generic types to numeric types during runtime. (Defined by ExtensionMethods.)
	ToT	Overloaded. Converts an object into another type, irrespective of whether the conversion can be done at compile time or not. This can be used to convert generic types to numeric types during runtime. (Defined by ExtensionMethods.)

Remarks

The class implements roulette whell exploration policy. Acording to the policy, action a at state s is selected with the next probability:

                  Q( s, a )
p( s, a ) = ------------------
             SUM( Q( s, b ) )
              b

where Q(s, a) is action's a estimation (usefulness) at state s.

Note
The exploration policy may be applied only in cases, when action estimates (usefulness) are represented with positive value greater then 0.