Variational Discriminator Bottleneck: Improving Imitation Learning, Inverse RL, and GANs by Constraining Information Flow

Understand

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.