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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 - 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.
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Submitted on : Monday, July 20, 2015 - 10:10:21 AM
Last modification on : Thursday, January 20, 2022 - 4:16:50 PM
Long-term archiving on: : Wednesday, October 21, 2015 - 5:00:43 PM


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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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