Maximum Entropy Semi-Supervised Inverse Reinforcement Learning

Julien Audiffren 1 Michal Valko 2 Alessandro Lazaric 2 Mohammad Ghavamzadeh 2
2 SEQUEL - Sequential Learning
Inria Lille - Nord Europe, CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189
Abstract : A popular approach to apprenticeship learning (AL) is to formulate it as an inverse reinforcement learning (IRL) problem. The MaxEnt-IRL algorithm successfully integrates the maximum entropy principle into IRL and unlike its predecessors, it resolves the ambiguity arising from the fact that a possibly large number of policies could match the expert's behavior. In this paper, we study an AL setting in which in addition to the expert's trajectories, a number of unsupervised trajectories is available. We introduce MESSI, a novel algorithm that combines MaxEnt-IRL with principles coming from semi-supervised learning. In particular, MESSI integrates the unsupervised data into the MaxEnt-IRL framework using a pairwise penalty on trajectories. Empirical results in a highway driving and grid-world problems indicate that MESSI is able to take advantage of the unsupervised trajectories and improve the performance of MaxEnt-IRL.
Type de document :
Communication dans un congrès
International Joint Conference on Artificial Intelligence, Jul 2015, Bueons Aires, Argentina
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Contributeur : Alessandro Lazaric <>
Soumis le : lundi 20 juillet 2015 - 10:10:21
Dernière modification le : mardi 3 juillet 2018 - 11:40:41
Document(s) archivé(s) le : mercredi 21 octobre 2015 - 17:00:43


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  • HAL Id : hal-01146187, version 1


Julien Audiffren, Michal Valko, Alessandro Lazaric, Mohammad Ghavamzadeh. Maximum Entropy Semi-Supervised Inverse Reinforcement Learning. International Joint Conference on Artificial Intelligence, Jul 2015, Bueons Aires, Argentina. 〈hal-01146187〉



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