  While recent state-of-the-art results for adversarial imitation-learning
algorithms are encouraging, recent works exploring the imitation learning from
observation (ILO) setting, where trajectories \textit{only} contain expert
observations, have not been met with the same success. Inspired by recent
investigations of $f$-divergence manipulation for the standard imitation
learning setting(Ke et al., 2019; Ghasemipour et al., 2019), we here examine
the extent to which variations in the choice of probabilistic divergence may
yield more performant ILO algorithms. We unfortunately find that $f$-divergence
minimization through reinforcement learning is susceptible to numerical
instabilities. We contribute a reparameterization trick for adversarial
imitation learning to alleviate the optimization challenges of the promising
$f$-divergence minimization framework. Empirically, we demonstrate that our
design choices allow for ILO algorithms that outperform baseline approaches and
more closely match expert performance in low-dimensional continuous-control
tasks.
