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MESSI: Maximum Entropy Semi-Supervised Inverse Reinforcement Learning
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.
Cited literature [9 references]
https://hal.inria.fr/hal-01177446
Contributor : Michal Valko <>
Submitted on : Thursday, July 16, 2015 - 11:19:52 PM
Last modification on : Thursday, July 2, 2020 - 5:17:17 PM
Document(s) archivé(s) le : Wednesday, April 26, 2017 - 6:53:34 AM
Contributor : Michal Valko <>
Submitted on : Thursday, July 16, 2015 - 11:19:52 PM
Last modification on : Thursday, July 2, 2020 - 5:17:17 PM
Document(s) archivé(s) le : Wednesday, April 26, 2017 - 6:53:34 AM
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audiffren2014messi.pdf
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