  Reinforcement learning (RL) algorithms typically start tabula rasa, without
any prior knowledge of the environment, and without any prior skills. This
however often leads to low sample efficiency, requiring a large amount of
interaction with the environment. This is especially true in a lifelong
learning setting, in which the agent needs to continually extend its
capabilities. In this paper, we examine how a pre-trained task-independent
language model can make a goal-conditional RL agent more sample efficient. We
do this by facilitating transfer learning between different related tasks. We
experimentally demonstrate our approach on a set of object navigation tasks.
