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

ICLR 2019 Xue Bin Peng • Angjoo Kanazawa • Sam Toyer • Pieter Abbeel • Sergey Levine

Adversarial learning methods have been proposed for a wide range of applications, but the training of adversarial models can be notoriously unstable. 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.

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