We tackle embodied visual navigation in a task-agnostic set-up by putting the focus on the unsupervised discovery of skills (or options) that provide a good coverage of states. Our approach intersects with empowerment: we address the reward-free skill discovery and learning tasks to discover “what” can be done in an environment and “how”. For this reason, we adopt the existing Explore, Discover and Learn (EDL) paradigm, tested only in toy example mazes, and extend it to pixel-based state representations available for embodied AI agents.