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Conference papers

Batch, Off-policy and Model-Free Apprenticeship Learning

Edouard Klein 1, 2 Matthieu Geist 1 Olivier Pietquin 1, 3
2 ABC - Machine Learning and Computational Biology
LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : This paper addresses the problem of apprenticeship learning, that is learning control policies from demonstration by an expert. An efficient framework for it is inverse reinforcement learning (IRL). Based on the assumption that the expert maximizes a utility function, IRL aims at learning the underlying reward from example trajectories. Many IRL algorithms assume that the reward function is linearly parameterized and rely on the computation of some associated feature expectations, which is done through Monte Carlo simulation. However, this assumes to have full trajectories for the expert policy as well as at least a generative model for intermediate policies. In this paper, we introduce a temporal difference method, namely LSTD-mu, to compute these feature expectations. This allows extending apprenticeship learning to a batch and offpolicy setting.
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Contributor : Sébastien van Luchene Connect in order to contact the contributor
Submitted on : Friday, May 27, 2011 - 10:58:47 AM
Last modification on : Wednesday, November 3, 2021 - 8:36:08 AM


  • HAL Id : hal-00596370, version 1


Edouard Klein, Matthieu Geist, Olivier Pietquin. Batch, Off-policy and Model-Free Apprenticeship Learning. IJCAI Workshop on Agents Learning Interactively from Human Teachers (ALIHT 2011), Jun 2011, Barcelona, Spain. 6 p. ⟨hal-00596370⟩



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