  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.

Machine Learning