Web: http://arxiv.org/abs/2209.10968

Sept. 23, 2022, 1:11 a.m. | Luca Viano, Angeliki Kamoutsi, Gergely Neu, Igor Krawczuk, Volkan Cevher

cs.LG updates on arXiv.org arxiv.org

This work develops new algorithms with rigorous efficiency guarantees for
infinite horizon imitation learning (IL) with linear function approximation
without restrictive coherence assumptions. We begin with the minimax
formulation of the problem and then outline how to leverage classical tools
from optimization, in particular, the proximal-point method (PPM) and dual
smoothing, for online and offline IL, respectively. Thanks to PPM, we avoid
nested policy evaluation and cost updates for online IL appearing in the prior
literature. In particular, we do …

arxiv imitation learning

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