Unsupervised Skill Discovery with Bottleneck Option Learning
This addresses the problem of acquiring skills without external rewards for reinforcement learning agents, representing a novel method rather than an incremental improvement.
The paper tackles unsupervised skill discovery by proposing Information Bottleneck Option Learning (IBOL), which combines environment linearization with an information bottleneck framework to learn diverse and abstract skills. The method outperforms state-of-the-art approaches on information-theoretic evaluations and downstream tasks in MuJoCo environments like Ant and HalfCheetah.
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.