Class used to control the learning steps of a TPGGraph within a given LearningEnvironment. More...

#include <learningAgent.h>

Inheritance diagram for Learn::LearningAgent:

Public Member Functions
	LearningAgent (LearningEnvironment &le, const Instructions::Set &iSet, const LearningParameters &p, const TPG::TPGFactory &factory=TPG::TPGFactory())
	Constructor for LearningAgent.

virtual	~LearningAgent ()=default
	Default destructor for polymorphism.

std::shared_ptr< TPG::TPGGraph >	getTPGGraph ()
	Getter for the TPGGraph built by the LearningAgent.

std::shared_ptr< Selector::Selector >	getSelector ()
	Getter for the Selector built by the LearningAgent.

const Archive &	getArchive () const
	Getter for the Archive filled by the LearningAgent.

const Environment &	getEnvironment () const
	Accessor to the Environment of the TPGGraph.

Mutator::RNG &	getRNG ()
	Getter for the RNG used by the LearningAgent.

void	addLogger (Log::LALogger &logger)
	Adds a LALogger to the loggers vector.

virtual std::shared_ptr< EvaluationResult >	evaluateJob (TPG::TPGExecutionEngine &tee, const Job &job, uint64_t generationNumber, LearningMode mode, LearningEnvironment &le) const
	Evaluates policy starting from the given root.

bool	isRootEvalSkipped (const TPG::TPGVertex &root, std::shared_ptr< Learn::EvaluationResult > &previousResult) const
	Method detecting whether a root should be evaluated again.

virtual std::multimap< std::shared_ptr< EvaluationResult >, const TPG::TPGVertex * >	evaluateAllRoots (uint64_t generationNumber, LearningMode mode)
	Evaluate all root TPGVertex of the TPGGraph.

virtual std::shared_ptr< EvaluationResult >	evaluateOneRoot (uint64_t generationNumber, LearningMode mode, const TPG::TPGVertex *root)
	Evaluate one root TPGVertex of the TPGGraph.

virtual void	trainOneGeneration (uint64_t generationNumber, bool doPopulate=true)
	Train the TPGGraph for one generation.

virtual uint64_t	train (volatile bool &altTraining, bool printProgressBar)
	Train the TPGGraph for a given number of generation.

virtual std::shared_ptr< Learn::Job >	makeJob (const TPG::TPGVertex vertex, Learn::LearningMode mode, int idx=0, TPG::TPGGraph tpgGraph=nullptr)
	Takes a given TPGVertex and creates a job containing it.

virtual std::queue< std::shared_ptr< Learn::Job > >	makeJobs (Learn::LearningMode mode, TPG::TPGGraph *tpgGraph=nullptr)
	Puts all roots into jobs to be able to use them in simulation later.

virtual void	init (uint64_t seed=0)
	Initialize the LearningAgent.

Protected Attributes
LearningEnvironment &	learningEnvironment
	LearningEnvironment with which the LearningAgent will interact.

Environment	env
	Environment for executing Program of the LearningAgent.

Archive	archive
	Archive used during the training process.

LearningParameters	params
	Parameters for the learning process.

std::shared_ptr< TPG::TPGGraph >	tpg
	TPGGraph built during the learning process.

std::shared_ptr< Selector::Selector >	selector
	Selector used for the selection process.

Mutator::RNG	rng
	Random Number Generator for this Learning Agent.

uint64_t	maxNbThreads = 1
	Control the maximum number of threads when running in parallel.

std::vector< std::reference_wrapper< Log::LALogger > >	loggers
	Set of LALogger called throughout the training process.

Detailed Description

Class used to control the learning steps of a TPGGraph within a given LearningEnvironment.

Constructor & Destructor Documentation

◆ LearningAgent()

Learn::LearningAgent::LearningAgent	(	LearningEnvironment &	le,
		const Instructions::Set &	iSet,
		const LearningParameters &	p,
		const TPG::TPGFactory &	factory = TPG::TPGFactory() )

inline

Constructor for LearningAgent.

Parameters

[in]	le	The LearningEnvironment for the TPG.
[in]	iSet	Set of Instruction used to compose Programs in the learning process.
[in]	p	The LearningParameters for the LearningAgent.
[in]	factory	The TPGFactory used to create the TPGGraph. A default TPGFactory is used if none is provided.

Member Function Documentation

◆ addLogger()

void Learn::LearningAgent::addLogger ( Log::LALogger & logger )

Adds a LALogger to the loggers vector.

Adds a logger to the loggers vector, so that it will be called in addition of the others at some determined moments. This enables to have several loggers that log different things on different outputs simultaneously.

Parameters

[in] logger The logger that will be added to the vector.

◆ evaluateAllRoots()

std::multimap< std::shared_ptr< Learn::EvaluationResult >, const TPG::TPGVertex * > Learn::LearningAgent::evaluateAllRoots	(	uint64_t	generationNumber,
		Learn::LearningMode	mode )

virtual

Evaluate all root TPGVertex of the TPGGraph.

This method calls the evaluateJob method for every root TPGVertex of the TPGGraph. The method returns a sorted map associating each root vertex to its average score, in ascending order or score.

Parameters

[in]	generationNumber	the integer number of the current generation.
[in]	mode	the LearningMode to use during the policy evaluation.

Reimplemented in Learn::ParallelLearningAgent.

◆ evaluateJob()

std::shared_ptr< Learn::EvaluationResult > Learn::LearningAgent::evaluateJob	(	TPG::TPGExecutionEngine &	tee,
		const Job &	job,
		uint64_t	generationNumber,
		Learn::LearningMode	mode,
		LearningEnvironment &	le ) const

virtual

Evaluates policy starting from the given root.

The policy, that is, the TPGGraph execution starting from the given TPGVertex is evaluated nbIteration times. The generationNumber is combined with the current iteration number to generate a set of seeds for evaluating the policy.

The method is const to enable potential parallel calls to it.

Parameters

[in]	tee	The TPGExecutionEngine to use.
[in]	job	The job containing the root and archiveSeed for the evaluation.
[in]	generationNumber	the integer number of the current generation.
[in]	mode	the LearningMode to use during the policy evaluation.
[in]	le	Reference to the LearningEnvironment to use during the policy evaluation (may be different from the attribute of the class in child LearningAgentClass).

Returns: a std::shared_ptr to the EvaluationResult for the root. If this root was already evaluated more times then the limit in params.maxNbEvaluationPerPolicy, then the EvaluationResult from the resultsPerRoot map is returned, else the EvaluationResult of the current generation is returned, already combined with the resultsPerRoot for this root (if any).

◆ evaluateOneRoot()

std::shared_ptr< Learn::EvaluationResult > Learn::LearningAgent::evaluateOneRoot	(	uint64_t	generationNumber,
		Learn::LearningMode	mode,
		const TPG::TPGVertex *	root )

virtual

Evaluate one root TPGVertex of the TPGGraph.

This method calls the evaluateJob method for a specified TPGVertex of the TPGGraph. The method returns the average score of this root. It is important to note that the specified TPGVertex may be an internal or even a leaf vertex of the graph (i.e. not a root).

Parameters

[in]	generationNumber	the integer number of the current generation.
[in]	mode	the LearningMode to use during the policy evaluation.
[in]	root	the evaluated TPGVertex of the TPGGraph.

Returns: the averaged EvaluationResult for the given TPGVertex.

Exceptions

an	exception in case the given root does not exist in the TPGGraph.

◆ getArchive()

const Archive & Learn::LearningAgent::getArchive ( ) const

Getter for the Archive filled by the LearningAgent.

Returns: a const reference to the Archive.

◆ getEnvironment()

const Environment & Learn::LearningAgent::getEnvironment ( ) const

Accessor to the Environment of the TPGGraph.

Returns: the const reference to the env attribute.

◆ getRNG()

Mutator::RNG & Learn::LearningAgent::getRNG ( )

Getter for the RNG used by the LearningAgent.

Returns: Get a reference to the RNG.

◆ getSelector()

std::shared_ptr< Selector::Selector > Learn::LearningAgent::getSelector ( )

Getter for the Selector built by the LearningAgent.

Returns: Get a shared_pointer to the Selector.

◆ getTPGGraph()

std::shared_ptr< TPG::TPGGraph > Learn::LearningAgent::getTPGGraph ( )

Getter for the TPGGraph built by the LearningAgent.

Returns: Get a shared_pointer to the TPGGraph.

Copyright or © or Copr. IETR/INSA - Rennes (2019 - 2025) :

Karol Desnos kdesn.nosp@m.os@i.nosp@m.nsa-r.nosp@m.enne.nosp@m.s.fr (2019 - 2022) Nicolas Sourbier nsour.nosp@m.bie@.nosp@m.insa-.nosp@m.renn.nosp@m.es.fr (2019 - 2020) Pierre-Yves Le Rolland-Raumer plero.nosp@m.lla@.nosp@m.insa-.nosp@m.renn.nosp@m.es.fr (2020) Quentin Vacher qvach.nosp@m.er@i.nosp@m.nsa-r.nosp@m.enne.nosp@m.s.fr (2023 - 2025)

GEGELATI is an open-source reinforcement learning framework for training artificial intelligence based on Tangled Program Graphs (TPGs).

This software is governed by the CeCILL-C license under French law and abiding by the rules of distribution of free software. You can use, modify and/ or redistribute the software under the terms of the CeCILL-C license as circulated by CEA, CNRS and INRIA at the following URL "http://www.cecill.info".

As a counterpart to the access to the source code and rights to copy, modify and redistribute granted by the license, users are provided only with a limited warranty and the software's author, the holder of the economic rights, and the successive licensors have only limited liability.

In this respect, the user's attention is drawn to the risks associated with loading, using, modifying and/or developing or reproducing the software by the user in light of its specific status of free software, that may mean that it is complicated to manipulate, and that also therefore means that it is reserved for developers and experienced professionals having in-depth computer knowledge. Users are therefore encouraged to load and test the software's suitability as regards their requirements in conditions enabling the security of their systems and/or data to be ensured and, more generally, to use and operate it in the same conditions as regards security.

The fact that you are presently reading this means that you have had knowledge of the CeCILL-C license and that you accept its terms.

◆ init()

void Learn::LearningAgent::init ( uint64_t seed = 0 )

virtual

Initialize the LearningAgent.

Calls the TPGMutator::initRandomTPG function. Initialize the Mutator::RNG with the given seed. Clears the Archive.

Parameters

[in] seed the seed given to the TPGMutator.

◆ isRootEvalSkipped()

bool Learn::LearningAgent::isRootEvalSkipped	(	const TPG::TPGVertex &	root,
		std::shared_ptr< Learn::EvaluationResult > &	previousResult ) const

Method detecting whether a root should be evaluated again.

Using the resultsPerRoot map and the params.maxNbEvaluationPerPolicy, this method checks whether a root should be evaluated again, or if sufficient evaluations were already performed.

Parameters

[in]	root	The root TPGVertex whose number of evaluation is checked.
[out]	previousResult	the std::shared_ptr to the EvaluationResult of the root from the resultsPerRoot if any.

Returns: true if the root has been evaluated enough times, false otherwise.

◆ makeJob()

std::shared_ptr< Learn::Job > Learn::LearningAgent::makeJob	(	const TPG::TPGVertex *	vertex,
		Learn::LearningMode	mode,
		int	idx = 0,
		TPG::TPGGraph *	tpgGraph = nullptr )

virtual

Takes a given TPGVertex and creates a job containing it.

Parameters

[in]	vertex	the TPGVertex stemming a TPGGraph to be evaluated.
[in]	mode	the mode of the training, determining for example if we generate values that we only need for training.
[in]	idx	The index of the job, can be used to organize a map for example.
[in]	tpgGraph	The TPG graph from which we will take the root.

Returns: A job representing the root.

◆ makeJobs()

std::queue< std::shared_ptr< Learn::Job > > Learn::LearningAgent::makeJobs	(	Learn::LearningMode	mode,
		TPG::TPGGraph *	tpgGraph = nullptr )

virtual

Puts all roots into jobs to be able to use them in simulation later.

Parameters

[in]	mode	the mode of the training, determining for example if we generate values that we only need for training.
[in]	tpgGraph	The TPG graph from which we will take the roots.

Returns: A queue containing pointers of the newly created jobs.

◆ train()

uint64_t Learn::LearningAgent::train	(	volatile bool &	altTraining,
		bool	printProgressBar )

virtual

Train the TPGGraph for a given number of generation.

The method trains the TPGGraph for a given number of generation, unless the referenced boolean value becomes false (evaluated at each generation). Optionally, a simple progress bar can be printed within the terminal. The TPGGraph is NOT (re)initialized before starting the training.

Parameters

[in]	altTraining	a reference to a boolean value that can be used to halt the training process before its completion.
[in]	printProgressBar	select whether a progress bar will be printed in the console.

Returns: the number of completed generations.

◆ trainOneGeneration()

void Learn::LearningAgent::trainOneGeneration	(	uint64_t	generationNumber,
		bool	doPopulate = true )

virtual

Train the TPGGraph for one generation.

Training for one generation includes:

Populating the TPGGraph according to given MutationParameters.
Evaluating all roots of the TPGGraph. (call to evaluateAllRoots)
Removing from the TPGGraph the worst performing root TPGVertex.

Parameters

[in]	generationNumber	the integer number of the current generation.
[in]	doPopulate	boolean to indicate if the populateTPG method should be called. This parameter is used to avoid populating at the last generation of a training.

Member Data Documentation

◆ loggers

std::vector<std::reference_wrapper<Log::LALogger> > Learn::LearningAgent::loggers

protected

Set of LALogger called throughout the training process.

Each LALogger of this set will be invoked at pre-defined steps of the training process. Dedicated method in the LALogger are used for each step.

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

D:/a/gegelati/gegelati/gegelatilib/include/learn/learningAgent.h
D:/a/gegelati/gegelati/gegelatilib/src/learn/learningAgent.cpp

Public Member Functions

Protected Attributes

Detailed Description

Constructor & Destructor Documentation

◆ LearningAgent()

Member Function Documentation

◆ addLogger()

◆ evaluateAllRoots()

◆ evaluateJob()

◆ evaluateOneRoot()

◆ getArchive()

◆ getEnvironment()

◆ getRNG()

◆ getSelector()

◆ getTPGGraph()

◆ init()

◆ isRootEvalSkipped()

◆ makeJob()

◆ makeJobs()

◆ train()

◆ trainOneGeneration()

Member Data Documentation

◆ loggers