Fast Task-Adaptation for Tasks Labeled Using Natural Language in
  Reinforcement Learning
  Over its lifetime, a reinforcement learning agent is often tasked with
different tasks. How to efficiently adapt a previously learned control policy
from one task to another, remains an open research question. In this paper, we
investigate how instructions formulated in natural language can enable faster
and more effective task adaptation. This can serve as the basis for developing
language instructed skills, which can be used in a lifelong learning setting.
Our method is capable of assessing, given a set of developed base control
policies, which policy will adapt best to a new unseen task.
