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Acquiring abilities in the absence of a task-oriented reward function is atthe frontier of reinforcement learning research. This problem has been studiedthrough the lens of empowerment, which draws a connection between optiondiscovery and information theory. Information-theoretic skill discovery methodshave garnered much interest from the community, but little research has beenconducted in understanding their limitations. Through theoretical analysis andempirical evidence, we show that existing algorithms suffer from a commonlimitation -- they discover options that provide a poor coverage of the statespace. In light of this, we propose 'Explore, Discover and Learn' (EDL), analternative approach to information-theoretic skill discovery. Crucially, EDLoptimizes the same information-theoretic objective derived from the empowermentliterature, but addresses the optimization problem using different machinery.We perform an extensive evaluation of skill discovery methods on controlledenvironments and show that EDL offers significant advantages, such asovercoming the coverage problem, reducing the dependence of learned skills onthe initial state, and allowing the user to define a prior over which behaviorsshould be learned. Code is publicly available athttps://github.com/victorcampos7/edl.

Explore, Discover and Learn: Unsupervised Discovery of State-Covering Skills