AIToolbox::MDP::OffPolicyEvaluation< Derived > Class Template Reference

This class is a general version of off-policy evaluation. More...

#include <AIToolbox/MDP/Algorithms/Utils/OffPolicyTemplate.hpp>

Inheritance diagram for AIToolbox::MDP::OffPolicyEvaluation< Derived >:

Public Types
using	Parent = OffPolicyBase
Public Types inherited from AIToolbox::MDP::OffPolicyBase
using	Trace = std::tuple< size_t, size_t, double >
using	Traces = std::vector< Trace >

Public Member Functions
	OffPolicyEvaluation (const PolicyInterface &target, double discount=1.0, double alpha=0.1, double tolerance=0.001)
	Basic constructor. More...
void	stepUpdateQ (const size_t s, const size_t a, const size_t s1, const double rew)
	This function updates the internal QFunction using the discount set during construction. More...
Public Member Functions inherited from AIToolbox::MDP::OffPolicyBase
	OffPolicyBase (size_t s, size_t a, double discount=1.0, double alpha=0.1, double tolerance=0.001)
	Basic construtor. More...
void	setLearningRate (double a)
	This function sets the learning rate parameter. More...
double	getLearningRate () const
	This function will return the current set learning rate parameter. More...
void	setDiscount (double d)
	This function sets the new discount parameter. More...
double	getDiscount () const
	This function returns the currently set discount parameter. More...
void	setTolerance (double t)
	This function sets the trace cutoff parameter. More...
double	getTolerance () const
	This function returns the currently set trace cutoff parameter. More...
void	clearTraces ()
	This function clears the already set traces. More...
const Traces &	getTraces () const
	This function returns the currently set traces. More...
void	setTraces (const Traces &t)
	This function sets the currently set traces. More...
size_t	getS () const
	This function returns the number of states on which QLearning is working. More...
size_t	getA () const
	This function returns the number of actions on which QLearning is working. More...
const QFunction &	getQFunction () const
	This function returns a reference to the internal QFunction. More...
void	setQFunction (const QFunction &qfun)
	This function allows to directly set the internal QFunction. More...

Protected Attributes
const PolicyInterface &	target_
Protected Attributes inherited from AIToolbox::MDP::OffPolicyBase
size_t	S
size_t	A
double	discount_
double	alpha_
double	tolerance_
QFunction	q_
Traces	traces_

Additional Inherited Members
Protected Member Functions inherited from AIToolbox::MDP::OffPolicyBase
void	updateTraces (size_t s, size_t a, double error, double traceDiscount)
	This function updates the traces using the input data. More...

Detailed Description

template<typename Derived>
class AIToolbox::MDP::OffPolicyEvaluation< Derived >

This class is a general version of off-policy evaluation.

This class is used to compute the QFunction of a given policy, when you are actually acting and gathering data following another policy (which is why it's called off-policy).

Keep in mind that these kind of methods are not very efficient when either the target or the behaviour policy are very deterministic. This is because greedy policies (at least with methods that use some kind of importance sampling) tend to cut traces short, which is basically equivalent to discarding data (this must be done to ensure correctness though).

Note that this class does not necessarily encompass all off-policy evaluation methods. It only does for the one that use eligibility traces in a certain form, such as ImportanceSampling, RetraceLambda, etc.

This class is supposed to be used as a CRTP parent. The child must derive it as:

class Child : public OffPolicyEvaluation<Child> {};

In addition, the child must define the function

double getTraceDiscount(size_t s, size_t a, size_t s1, double rew) const;

This will then be automatically called here to compute the amount to decrease the traces during the stepUpdateQ. For example, in ImportanceSampling the function would return:

return target_.getActionProbability(s, a) / behaviour_.getActionProbability(s, a);

Member Typedef Documentation

Parent

template<typename Derived >

using AIToolbox::MDP::OffPolicyEvaluation< Derived >::Parent = OffPolicyBase

Constructor & Destructor Documentation

OffPolicyEvaluation()

template<typename Derived >

AIToolbox::MDP::OffPolicyEvaluation< Derived >::OffPolicyEvaluation	(	const PolicyInterface &	target,
		double	discount = 1.0,
		double	alpha = 0.1,
		double	tolerance = 0.001
	)

Basic constructor.

Parameters

target	The policy to be evaluated.
discount	The discount of the environment.
alpha	The learning rate parameter.
tolerance	The trace cutoff parameter.

Member Function Documentation

stepUpdateQ()

template<typename Derived >

void AIToolbox::MDP::OffPolicyEvaluation< Derived >::stepUpdateQ	(	const size_t	s,
		const size_t	a,
		const size_t	s1,
		const double	rew
	)

This function updates the internal QFunction using the discount set during construction.

This function takes a single experience point and uses it to update the QFunction. This is a very efficient method to keep the QFunction up to date with the latest experience.

Parameters

s	The previous state.
a	The action performed.
s1	The new state.
rew	The reward obtained.

Member Data Documentation

target_

template<typename Derived >

const PolicyInterface& AIToolbox::MDP::OffPolicyEvaluation< Derived >::target_

protected

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The documentation for this class was generated from the following file:

• include/AIToolbox/MDP/Algorithms/Utils/OffPolicyTemplate.hpp