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.

本文介绍了一种使用无监督技术和自监督学习的状态表征方法，可以帮助强化学习智能体快速学习到基本导航技能。在使用像素地图进行评估时，我们发现像素表示和条件政策学习适用于玩具例子，但不适用于逼真和复杂的地图。因此我们还研究了相对位移等替代观测输入。