  Text-based games provide an interactive way to study natural language
processing. While deep reinforcement learning has shown effectiveness in
developing the game playing agent, the low sample efficiency and the large
action space remain to be the two major challenges that hinder the DRL from
being applied in the real world. In this paper, we address the challenges by
introducing world-perceiving modules, which automatically decompose tasks and
prune actions by answering questions about the environment. We then propose a
two-phase training framework to decouple language learning from reinforcement
learning, which further improves the sample efficiency. The experimental
results show that the proposed method significantly improves the performance
and sample efficiency. Besides, it shows robustness against compound error and
limited pre-training data.
