  Sequential decision-making agents struggle with long horizon tasks, since
solving them requires multi-step reasoning. Most reinforcement learning (RL)
algorithms address this challenge by improved credit assignment, introducing
memory capability, altering the agent's intrinsic motivation (i.e. exploration)
or its worldview (i.e. knowledge representation). Many of these components
could be learned from offline data. In this work, we follow the hypothesis that
exploration and representation learning can be improved by separately learning
two different models from a single offline dataset. We show that learning a
state representation using noise-contrastive estimation and a model of
auxiliary reward separately from a single collection of human demonstrations
can significantly improve the sample efficiency on the challenging NetHack
benchmark. We also ablate various components of our experimental setting and
highlight crucial insights.
