InforLorV4, PascalFrancis, Curation, bibRecord, 000942

Classification structurée pour l'apprentissage par renforcement inverse

Identifieur interne : 000942 ( PascalFrancis/Curation ); précédent : 000941; suivant : 000943

Classification structurée pour l'apprentissage par renforcement inverse

Auteurs : Edouard Klein [France] ; Bilal Piot [France] ; Matthieu Geist [France] ; Olivier Pietquin [France]

Source :

Revue d'intelligence artificielle [ 0992-499X ] ; 2013.

RBID : Pascal:13-0216741

Descripteurs français

Pascal (Inist)
- Classification, Structure donnée, Apprentissage renforcé, Paramétrisation, Simulateur, Récompense, Politique, Automobile, Conduite véhicule, Structure interne, Algorithme apprentissage, Problème inverse, Problème direct, Méthode heuristique.
Wicri :
- topic : Classification, Politique, Automobile.

English descriptors

KwdEn :
- Classification, Data structure, Direct problem, Heuristic method, Internal structure, Inverse problem, Learning algorithm, Motor car, Parameterization, Policy, Reinforcement learning, Reward, Simulator, Vehicle driving.

Abstract

This paper adresses the inverse reinforcement learning (IRL) problem, that is inferring a reward for which a demonstrated expert behavior is optimal. We introduce a new algorithm, SCIRL, whose principle is to use the so-called feature expectation of the expert as the parameterization of the score function of a multiclasse classifier. This approach produces a reward function for which the expert policy is provably near-optimal. Contrary to most of existing IRL algorithms, SCIRL does not require solving the direct RL problem. Moreover, with an appropriate heuristic, it can succeed with only trajectories sampled according to the expert behavior. This is illustrated on a car driving simulator.

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A08	`01`	`1`	`FRE`	`@1 Classification structurée pour l'apprentissage par renforcement inverse`
A09	`01`	`1`	`FRE`	`@1 Apprentissage par renforcement et planification adaptative`
A11	`01`	`1`		`@1 KLEIN (Edouard)`
A11	`02`	`1`		`@1 PIOT (Bilal)`
A11	`03`	`1`		`@1 GEIST (Matthieu)`
A11	`04`	`1`		`@1 PIETQUIN (Olivier)`
A12	`01`	`1`		`@1 ZANUTTINI (Bruno) @9 ed.`
A12	`02`	`1`		`@1 LAURENT (Guillaume) @9 ed.`
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A15	`02`			`@1 Institut FEMTO-ST/ENSMM @2 Besançon @3 FRA @Z 2 aut.`
A15	`03`			`@1 LORIA/INRIA @2 Nancy @3 FRA @Z 3 aut.`
A20				`@2 151, 155-169 [16 p.]`
A21				`@1 2013`
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A24	`01`			`@0 eng`
A43	`01`			`@1 INIST @2 21320 @5 354000173351010010`
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C01	`01`		`ENG`	@0 This paper adresses the inverse reinforcement learning (IRL) problem, that is inferring a reward for which a demonstrated expert behavior is optimal. We introduce a new algorithm, SCIRL, whose principle is to use the so-called feature expectation of the expert as the parameterization of the score function of a multiclasse classifier. This approach produces a reward function for which the expert policy is provably near-optimal. Contrary to most of existing IRL algorithms, SCIRL does not require solving the direct RL problem. Moreover, with an appropriate heuristic, it can succeed with only trajectories sampled according to the expert behavior. This is illustrated on a car driving simulator.
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C03	`01`	`X`	`SPA`	`@0 Clasificación @5 06`
C03	`02`	`X`	`FRE`	`@0 Structure donnée @5 07`
C03	`02`	`X`	`ENG`	`@0 Data structure @5 07`
C03	`02`	`X`	`SPA`	`@0 Estructura datos @5 07`
C03	`03`	`X`	`FRE`	`@0 Apprentissage renforcé @5 08`
C03	`03`	`X`	`ENG`	`@0 Reinforcement learning @5 08`
C03	`03`	`X`	`SPA`	`@0 Aprendizaje reforzado @5 08`
C03	`04`	`X`	`FRE`	`@0 Paramétrisation @5 09`
C03	`04`	`X`	`ENG`	`@0 Parameterization @5 09`
C03	`04`	`X`	`SPA`	`@0 Parametrización @5 09`
C03	`05`	`X`	`FRE`	`@0 Simulateur @5 10`
C03	`05`	`X`	`ENG`	`@0 Simulator @5 10`
C03	`05`	`X`	`SPA`	`@0 Simulador @5 10`
C03	`06`	`X`	`FRE`	`@0 Récompense @5 18`
C03	`06`	`X`	`ENG`	`@0 Reward @5 18`
C03	`06`	`X`	`SPA`	`@0 Recompensa @5 18`
C03	`07`	`X`	`FRE`	`@0 Politique @5 19`
C03	`07`	`X`	`ENG`	`@0 Policy @5 19`
C03	`07`	`X`	`SPA`	`@0 Política @5 19`
C03	`08`	`X`	`FRE`	`@0 Automobile @5 20`
C03	`08`	`X`	`ENG`	`@0 Motor car @5 20`
C03	`08`	`X`	`SPA`	`@0 Automóvil @5 20`
C03	`09`	`X`	`FRE`	`@0 Conduite véhicule @5 21`
C03	`09`	`X`	`ENG`	`@0 Vehicle driving @5 21`
C03	`09`	`X`	`SPA`	`@0 Conducción vehículo @5 21`
C03	`10`	`X`	`FRE`	`@0 Structure interne @5 23`
C03	`10`	`X`	`ENG`	`@0 Internal structure @5 23`
C03	`10`	`X`	`SPA`	`@0 Estructura interna @5 23`
C03	`11`	`X`	`FRE`	`@0 Algorithme apprentissage @5 24`
C03	`11`	`X`	`ENG`	`@0 Learning algorithm @5 24`
C03	`11`	`X`	`SPA`	`@0 Algoritmo aprendizaje @5 24`
C03	`12`	`X`	`FRE`	`@0 Problème inverse @5 25`
C03	`12`	`X`	`ENG`	`@0 Inverse problem @5 25`
C03	`12`	`X`	`SPA`	`@0 Problema inverso @5 25`
C03	`13`	`X`	`FRE`	`@0 Problème direct @5 26`
C03	`13`	`X`	`ENG`	`@0 Direct problem @5 26`
C03	`13`	`X`	`SPA`	`@0 Problema directo @5 26`
C03	`14`	`X`	`FRE`	`@0 Méthode heuristique @5 27`
C03	`14`	`X`	`ENG`	`@0 Heuristic method @5 27`
C03	`14`	`X`	`SPA`	`@0 Método heurístico @5 27`
N21				`@1 203`
N44	`01`			`@1 OTO`
N82				`@1 OTO`

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Pascal:13-0216741

Le document en format XML

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<front><div type="abstract" xml:lang="en">This paper adresses the inverse reinforcement learning (IRL) problem, that is inferring a reward for which a demonstrated expert behavior is optimal. We introduce a new algorithm, SCIRL, whose principle is to use the so-called feature expectation of the expert as the parameterization of the score function of a multiclasse classifier. This approach produces a reward function for which the expert policy is provably near-optimal. Contrary to most of existing IRL algorithms, SCIRL does not require solving the direct RL problem. Moreover, with an appropriate heuristic, it can succeed with only trajectories sampled according to the expert behavior. This is illustrated on a car driving simulator.</div>
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<fC01 i1="01" l="ENG"><s0>This paper adresses the inverse reinforcement learning (IRL) problem, that is inferring a reward for which a demonstrated expert behavior is optimal. We introduce a new algorithm, SCIRL, whose principle is to use the so-called feature expectation of the expert as the parameterization of the score function of a multiclasse classifier. This approach produces a reward function for which the expert policy is provably near-optimal. Contrary to most of existing IRL algorithms, SCIRL does not require solving the direct RL problem. Moreover, with an appropriate heuristic, it can succeed with only trajectories sampled according to the expert behavior. This is illustrated on a car driving simulator.</s0>
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<s5>08</s5>
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<fC03 i1="04" i2="X" l="FRE"><s0>Paramétrisation</s0>
<s5>09</s5>
</fC03>
<fC03 i1="04" i2="X" l="ENG"><s0>Parameterization</s0>
<s5>09</s5>
</fC03>
<fC03 i1="04" i2="X" l="SPA"><s0>Parametrización</s0>
<s5>09</s5>
</fC03>
<fC03 i1="05" i2="X" l="FRE"><s0>Simulateur</s0>
<s5>10</s5>
</fC03>
<fC03 i1="05" i2="X" l="ENG"><s0>Simulator</s0>
<s5>10</s5>
</fC03>
<fC03 i1="05" i2="X" l="SPA"><s0>Simulador</s0>
<s5>10</s5>
</fC03>
<fC03 i1="06" i2="X" l="FRE"><s0>Récompense</s0>
<s5>18</s5>
</fC03>
<fC03 i1="06" i2="X" l="ENG"><s0>Reward</s0>
<s5>18</s5>
</fC03>
<fC03 i1="06" i2="X" l="SPA"><s0>Recompensa</s0>
<s5>18</s5>
</fC03>
<fC03 i1="07" i2="X" l="FRE"><s0>Politique</s0>
<s5>19</s5>
</fC03>
<fC03 i1="07" i2="X" l="ENG"><s0>Policy</s0>
<s5>19</s5>
</fC03>
<fC03 i1="07" i2="X" l="SPA"><s0>Política</s0>
<s5>19</s5>
</fC03>
<fC03 i1="08" i2="X" l="FRE"><s0>Automobile</s0>
<s5>20</s5>
</fC03>
<fC03 i1="08" i2="X" l="ENG"><s0>Motor car</s0>
<s5>20</s5>
</fC03>
<fC03 i1="08" i2="X" l="SPA"><s0>Automóvil</s0>
<s5>20</s5>
</fC03>
<fC03 i1="09" i2="X" l="FRE"><s0>Conduite véhicule</s0>
<s5>21</s5>
</fC03>
<fC03 i1="09" i2="X" l="ENG"><s0>Vehicle driving</s0>
<s5>21</s5>
</fC03>
<fC03 i1="09" i2="X" l="SPA"><s0>Conducción vehículo</s0>
<s5>21</s5>
</fC03>
<fC03 i1="10" i2="X" l="FRE"><s0>Structure interne</s0>
<s5>23</s5>
</fC03>
<fC03 i1="10" i2="X" l="ENG"><s0>Internal structure</s0>
<s5>23</s5>
</fC03>
<fC03 i1="10" i2="X" l="SPA"><s0>Estructura interna</s0>
<s5>23</s5>
</fC03>
<fC03 i1="11" i2="X" l="FRE"><s0>Algorithme apprentissage</s0>
<s5>24</s5>
</fC03>
<fC03 i1="11" i2="X" l="ENG"><s0>Learning algorithm</s0>
<s5>24</s5>
</fC03>
<fC03 i1="11" i2="X" l="SPA"><s0>Algoritmo aprendizaje</s0>
<s5>24</s5>
</fC03>
<fC03 i1="12" i2="X" l="FRE"><s0>Problème inverse</s0>
<s5>25</s5>
</fC03>
<fC03 i1="12" i2="X" l="ENG"><s0>Inverse problem</s0>
<s5>25</s5>
</fC03>
<fC03 i1="12" i2="X" l="SPA"><s0>Problema inverso</s0>
<s5>25</s5>
</fC03>
<fC03 i1="13" i2="X" l="FRE"><s0>Problème direct</s0>
<s5>26</s5>
</fC03>
<fC03 i1="13" i2="X" l="ENG"><s0>Direct problem</s0>
<s5>26</s5>
</fC03>
<fC03 i1="13" i2="X" l="SPA"><s0>Problema directo</s0>
<s5>26</s5>
</fC03>
<fC03 i1="14" i2="X" l="FRE"><s0>Méthode heuristique</s0>
<s5>27</s5>
</fC03>
<fC03 i1="14" i2="X" l="ENG"><s0>Heuristic method</s0>
<s5>27</s5>
</fC03>
<fC03 i1="14" i2="X" l="SPA"><s0>Método heurístico</s0>
<s5>27</s5>
</fC03>
<fN21><s1>203</s1>
</fN21>
<fN44 i1="01"><s1>OTO</s1>
</fN44>
<fN82><s1>OTO</s1>
</fN82>
</pA>
</standard>
</inist>
</record>

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   |area=    InforLorV4
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   |texte=   Classification structurée pour l'apprentissage par renforcement inverse
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Classification structurée pour l'apprentissage par renforcement inverse

Classification structurée pour l'apprentissage par renforcement inverse

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