  Pre-training Reinforcement Learning agents in a task-agnostic manner has
shown promising results. However, previous works still struggle in learning and
discovering meaningful skills in high-dimensional state-spaces, such as
pixel-spaces. We approach the problem by leveraging unsupervised skill
discovery and self-supervised learning of state representations. In our work,
we learn a compact latent representation by making use of variational and
contrastive techniques. We demonstrate that both enable RL agents to learn a
set of basic navigation skills by maximizing an information theoretic
objective. We assess our method in Minecraft 3D pixel maps with different
complexities. Our results show that representations and conditioned policies
learned from pixels are enough for toy examples, but do not scale to realistic
and complex maps. To overcome these limitations, we explore alternative input
observations such as the relative position of the agent along with the raw
pixels.
