  In many settings, it is desirable to learn decision-making and control
policies through learning or bootstrapping from expert demonstrations. The most
common approaches under this Imitation Learning (IL) framework are Behavioural
Cloning (BC), and Inverse Reinforcement Learning (IRL). Recent methods for IRL
have demonstrated the capacity to learn effective policies with access to a
very limited set of demonstrations, a scenario in which BC methods often fail.
Unfortunately, due to multiple factors of variation, directly comparing these
methods does not provide adequate intuition for understanding this difference
in performance. In this work, we present a unified probabilistic perspective on
IL algorithms based on divergence minimization. We present $f$-MAX, an
$f$-divergence generalization of AIRL [Fu et al., 2018], a state-of-the-art IRL
method. $f$-MAX enables us to relate prior IRL methods such as GAIL [Ho &
Ermon, 2016] and AIRL [Fu et al., 2018], and understand their algorithmic
properties. Through the lens of divergence minimization we tease apart the
differences between BC and successful IRL approaches, and empirically evaluate
these nuances on simulated high-dimensional continuous control domains. Our
findings conclusively identify that IRL's state-marginal matching objective
contributes most to its superior performance. Lastly, we apply our new
understanding of IL methods to the problem of state-marginal matching, where we
demonstrate that in simulated arm pushing environments we can teach agents a
diverse range of behaviours using simply hand-specified state distributions and
no reward functions or expert demonstrations. For datasets and reproducing
results please refer to
https://github.com/KamyarGh/rl_swiss/blob/master/reproducing/fmax_paper.md .
