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arXiv · 2110.02063

A Critique of Strictly Batch Imitation Learning

Gokul Swamy·Sanjiban Choudhury·J. Andrew Bagnell·Zhiwei Steven Wu
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Abstract

Recent work by Jarrett et al. attempts to frame the problem of offline imitation learning (IL) as one of learning a joint energy-based model, with the hope of out-performing standard behavioral cloning. We suggest that notational issues obscure how the psuedo-state visitation distribution the authors propose to optimize might be disconnected from the policy's $\textit{true}$ state visitation distribution. We further construct natural examples where the parameter coupling advocated by Jarrett et al. leads to inconsistent estimates of the expert's policy, unlike behavioral cloning.

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Gokul Swamy, Sanjiban Choudhury, J. Andrew Bagnell, Zhiwei Steven Wu. 2021-10-05. A Critique of Strictly Batch Imitation Learning. https://arxiv.org/abs/2110.02063

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