Classification structurée pour l'apprentissage par renforcement inverse
Identifieur interne : 000065 ( PascalFrancis/Corpus ); précédent : 000064; suivant : 000066Classification structurée pour l'apprentissage par renforcement inverse
Auteurs : Edouard Klein ; Bilal Piot ; Matthieu Geist ; Olivier PietquinSource :
- Revue d'intelligence artificielle [ 0992-499X ] ; 2013.
Descripteurs français
- Pascal (Inist)
English descriptors
- KwdEn :
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.
Notice en format standard (ISO 2709)
Pour connaître la documentation sur le format Inist Standard.
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Format Inist (serveur)
NO : | PASCAL 13-0216741 INIST |
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FT : | Classification structurée pour l'apprentissage par renforcement inverse |
ET : | (Structured classification for inverse reinforcement learning) |
AU : | KLEIN (Edouard); PIOT (Bilal); GEIST (Matthieu); PIETQUIN (Olivier); ZANUTTINI (Bruno); LAURENT (Guillaume); BUFFET (Olivier) |
AF : | LORIA - équipe ABC/Nancy/France (1 aut.); Supélec - Groupe de recherche IMS-MaLIS/Metz/France (1 aut., 2 aut., 3 aut., 4 aut.); UMI 2958 (GeorgiaTech-CNRS)/Metz/France (2 aut., 4 aut.); Greyc/UCBN/Caen/France (1 aut.); Institut FEMTO-ST/ENSMM/Besançon/France (2 aut.); LORIA/INRIA/Nancy/France (3 aut.) |
DT : | Publication en série; Niveau analytique |
SO : | Revue d'intelligence artificielle; ISSN 0992-499X; France; Da. 2013; Vol. 27; No. 2; 151, 155-169 [16 p.]; Abs. anglais; Bibl. 1 p. |
LA : | Français |
EA : | 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. |
CC : | 001D02C02; 001D15C |
FD : | Classification; Structure donnée; Apprentissage renforcé; Paramétrisation; Simulateur; Récompense; Politique; Automobile; Conduite véhicule; Structure interne; Algorithme apprentissage; Problème inverse; Problème direct; Méthode heuristique |
ED : | Classification; Data structure; Reinforcement learning; Parameterization; Simulator; Reward; Policy; Motor car; Vehicle driving; Internal structure; Learning algorithm; Inverse problem; Direct problem; Heuristic method |
SD : | Clasificación; Estructura datos; Aprendizaje reforzado; Parametrización; Simulador; Recompensa; Política; Automóvil; Conducción vehículo; Estructura interna; Algoritmo aprendizaje; Problema inverso; Problema directo; Método heurístico |
LO : | INIST-21320.354000173351010010 |
ID : | 13-0216741 |
Links to Exploration step
Pascal:13-0216741Le document en format XML
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<term>Inverse problem</term>
<term>Learning algorithm</term>
<term>Motor car</term>
<term>Parameterization</term>
<term>Policy</term>
<term>Reinforcement learning</term>
<term>Reward</term>
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<term>Récompense</term>
<term>Politique</term>
<term>Automobile</term>
<term>Conduite véhicule</term>
<term>Structure interne</term>
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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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<s5>06</s5>
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<s5>06</s5>
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<s5>09</s5>
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<s5>09</s5>
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<s5>09</s5>
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<s5>10</s5>
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<s5>10</s5>
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<s5>10</s5>
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<s5>19</s5>
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<s5>19</s5>
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<s5>20</s5>
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<s5>20</s5>
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<s5>21</s5>
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<s5>21</s5>
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<s5>23</s5>
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<s5>23</s5>
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<s5>23</s5>
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<s5>24</s5>
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<s5>24</s5>
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<s5>24</s5>
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<s5>25</s5>
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<s5>25</s5>
</fC03>
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<s5>25</s5>
</fC03>
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<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>
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<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>
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<server><NO>PASCAL 13-0216741 INIST</NO>
<FT>Classification structurée pour l'apprentissage par renforcement inverse</FT>
<ET>(Structured classification for inverse reinforcement learning)</ET>
<AU>KLEIN (Edouard); PIOT (Bilal); GEIST (Matthieu); PIETQUIN (Olivier); ZANUTTINI (Bruno); LAURENT (Guillaume); BUFFET (Olivier)</AU>
<AF>LORIA - équipe ABC/Nancy/France (1 aut.); Supélec - Groupe de recherche IMS-MaLIS/Metz/France (1 aut., 2 aut., 3 aut., 4 aut.); UMI 2958 (GeorgiaTech-CNRS)/Metz/France (2 aut., 4 aut.); Greyc/UCBN/Caen/France (1 aut.); Institut FEMTO-ST/ENSMM/Besançon/France (2 aut.); LORIA/INRIA/Nancy/France (3 aut.)</AF>
<DT>Publication en série; Niveau analytique</DT>
<SO>Revue d'intelligence artificielle; ISSN 0992-499X; France; Da. 2013; Vol. 27; No. 2; 151, 155-169 [16 p.]; Abs. anglais; Bibl. 1 p.</SO>
<LA>Français</LA>
<EA>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.</EA>
<CC>001D02C02; 001D15C</CC>
<FD>Classification; Structure donnée; Apprentissage renforcé; Paramétrisation; Simulateur; Récompense; Politique; Automobile; Conduite véhicule; Structure interne; Algorithme apprentissage; Problème inverse; Problème direct; Méthode heuristique</FD>
<ED>Classification; Data structure; Reinforcement learning; Parameterization; Simulator; Reward; Policy; Motor car; Vehicle driving; Internal structure; Learning algorithm; Inverse problem; Direct problem; Heuristic method</ED>
<SD>Clasificación; Estructura datos; Aprendizaje reforzado; Parametrización; Simulador; Recompensa; Política; Automóvil; Conducción vehículo; Estructura interna; Algoritmo aprendizaje; Problema inverso; Problema directo; Método heurístico</SD>
<LO>INIST-21320.354000173351010010</LO>
<ID>13-0216741</ID>
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