Drop-Bottleneck: Learning Discrete Compressed Representation for Noise-Robust Exploration

Understand

We propose a novel information bottleneck (IB) method named Drop-Bottleneck, which discretely drops features that are irrelevant to the target variable.

• Drop-Bottleneck not only enjoys a simple and tractable compression objective but also additionally provides a deterministic compressed representation of the input variable, which is useful for inference tasks that require consistent representation.
• Moreover, it can jointly learn a feature extractor and select features considering each feature dimension's relevance to the target task, which is unattainable by most neural network-based IB methods.
• We propose an exploration method based on Drop-Bottleneck for reinforcement learning tasks.