Language Understanding for Text-based Games Using Deep Reinforcement Learning

Understand

In this paper, we consider the task of learning control policies for text-based games.

• In these games, all interactions in the virtual world are through text and the underlying state is not observed.
• The resulting language barrier makes such environments challenging for automatic game players.
• We employ a deep reinforcement learning framework to jointly learn state representations and action policies using game rewards as feedback.