  Given a dataset of expert agent interactions with an environment of interest,
a viable method to extract an effective agent policy is to estimate the maximum
likelihood policy indicated by this data. This approach is commonly referred to
as behavioral cloning (BC). In this work, we describe a key disadvantage of BC
that arises due to the maximum likelihood objective function; namely that BC is
mean-seeking with respect to the state-conditional expert action distribution
when the learner's policy is represented with a Gaussian. To address this
issue, we introduce a modified version of BC, Adversarial Behavioral Cloning
(ABC), that exhibits mode-seeking behavior by incorporating elements of GAN
(generative adversarial network) training. We evaluate ABC on toy domains and a
domain based on Hopper from the DeepMind Control suite, and show that it
outperforms standard BC by being mode-seeking in nature.
