  Having the ability to acquire inherent skills from environments without any
external rewards or supervision like humans is an important problem. We propose
a novel unsupervised skill discovery method named Information Bottleneck Option
Learning (IBOL). On top of the linearization of environments that promotes more
various and distant state transitions, IBOL enables the discovery of diverse
skills. It provides the abstraction of the skills learned with the information
bottleneck framework for the options with improved stability and encouraged
disentanglement. We empirically demonstrate that IBOL outperforms multiple
state-of-the-art unsupervised skill discovery methods on the
information-theoretic evaluations and downstream tasks in MuJoCo environments,
including Ant, HalfCheetah, Hopper and D'Kitty.
