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

Abstract: 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.

• The paper introduces the Variational Discriminator Bottleneck to mitigate adversarial instability by strategically constraining the discriminator's information flow.
• It demonstrates improved performance in imitation learning and inverse RL by effectively learning control tasks from raw video and dynamic simulations.
• The method enhances GAN training by achieving lower Fréchet Inception Distances and more stable image generation compared to traditional regularization techniques.

Variational Discriminator Bottleneck: Enhancing Adversarial Learning

This paper presents the concept of the Variational Discriminator Bottleneck (VDB), a novel approach aiming to improve the stability and performance of adversarial learning frameworks like Generative Adversarial Networks (GANs), Imitation Learning, and Inverse Reinforcement Learning (IRL). Recognizing the fundamental instability inherent in training adversarial models, the authors propose a method to constrain information flow within discriminators by applying an information bottleneck. This technique limits mutual information between observations and the discriminator's internal representation, thereby controlling the discriminator's accuracy and ensuring informative gradients are provided to the generator.

Key Contributions

1. Introduction of VDB: The central proposal of the paper is the introduction of an information bottleneck into the discriminator's architecture. By enforcing this bottleneck, the authors can control the discriminator's effectiveness, preventing it from overpowering the generator and thus addressing a common source of instability in adversarial training.
2. Application in Imitation Learning and IRL: The VDB outperforms existing adversarial imitation learning methods when applied to dynamic continuous control tasks. The study demonstrates that VDB can learn control tasks directly from raw video demonstrations significantly better than prior methods. In the context of adversarial IRL, VDB enables the derivation of parsimonious reward functions that can be efficiently transferred to and optimized in new environments.
3. Improvement in GANs: VDB contributes significantly to stabilizing GAN training procedures, showing measurable improvements over previous stabilization methods like gradient penalty and instance noise. The study reports better image generation quality in GANs by employing VDB.

Detailed Results

• Numerical Evaluations: The paper emphasizes quantitative improvements, particularly within GANs on CIFAR-10, where a combination of VDB and other regularization techniques achieved a Fréchet Inception Distance smaller than what existing methods provide.
• Wide-ranging Applicability: VDB demonstrates versatility across various domains: it augments learning of complex humanoid skills in simulation, enhances reward learning in maze environments for IRL, and produces high-quality images in GAN-based tasks.

Implications and Future Directions

The theoretical and empirical findings suggest that incorporating information-theoretic principles into adversarial learning frameworks can mitigate instability issues. The proposed method has promising implications for practical applications in robotics, where learning from video or sparse data is often required.

The VDB's ability to improve performance without significant domain-tuning could inspire further research in simplifying and enhancing existing architectures in deep learning. Moreover, the demonstration of VDB's efficacy in various setups points towards expanded applications in settings where data labeling is expensive, and robust imitation learning is necessary from minimal demonstrations.

In the future, this work could inspire further derivations of training stability theorems or frameworks that incorporate adaptive constraints similar to variational bottlenecks. There is also a compelling case for more elaborate empirical studies involving real-world videos rather than simulated examples, which could bring immediacy to the tool's applicability in consumer-grade technologies such as virtual assistants and autonomous craft.

Overall, this study contributes a robust toolset to the machine learning community for addressing adversarial learning challenges, equipping researchers and practitioners with a theoretically sound and empirically validated technique to enhance the stability and performance of their models.