  Adversarial learning methods have been proposed for a wide range of
applications, but the training of adversarial models can be notoriously
unstable. Effectively balancing the performance of the generator and
discriminator is critical, since a discriminator that achieves very high
accuracy will produce relatively uninformative gradients. In this work, we
propose a simple and general technique to constrain information flow in the
discriminator by means of an information bottleneck. By enforcing a constraint
on the mutual information between the observations and the discriminator's
internal representation, we can effectively modulate the discriminator's
accuracy and maintain useful and informative gradients. We demonstrate that our
proposed variational discriminator bottleneck (VDB) leads to significant
improvements across three distinct application areas for adversarial learning
algorithms. Our primary evaluation studies the applicability of the VDB to
imitation learning of dynamic continuous control skills, such as running. We
show that our method can learn such skills directly from \emph{raw} video
demonstrations, substantially outperforming prior adversarial imitation
learning methods. The VDB can also be combined with adversarial inverse
reinforcement learning to learn parsimonious reward functions that can be
transferred and re-optimized in new settings. Finally, we demonstrate that VDB
can train GANs more effectively for image generation, improving upon a number
of prior stabilization methods.
