  Reinforcement learning (RL) is a powerful approach for acquiring a
good-performing policy. However, learning diverse skills is challenging in RL
due to the commonly used Gaussian policy parameterization. We propose
\textbf{Di}verse \textbf{Skil}l \textbf{L}earning (Di-SkilL\footnote{Videos and
code are available on the project webpage:
\url{https://alrhub.github.io/di-skill-website/}}), an RL method for learning
diverse skills using Mixture of Experts, where each expert formalizes a skill
as a contextual motion primitive. Di-SkilL optimizes each expert and its
associate context distribution to a maximum entropy objective that incentivizes
learning diverse skills in similar contexts. The per-expert context
distribution enables automatic curricula learning, allowing each expert to
focus on its best-performing sub-region of the context space. To overcome hard
discontinuities and multi-modalities without any prior knowledge of the
environment's unknown context probability space, we leverage energy-based
models to represent the per-expert context distributions and demonstrate how we
can efficiently train them using the standard policy gradient objective. We
show on challenging robot simulation tasks that Di-SkilL can learn diverse and
performant skills.
