Language understanding for textbased games using deep reinforcement learning

In this paper, we consider the task of learn-ing control policies for text-based games. In these games, all interactions in the vir-tual world are through text and the un-derlying state is not observed. The re-sulting language barrier makes such envi-ronments 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. This frame-work enables us to map text descriptions into vector representations that capture the semantics of the game states. We eval-uate our approach on two game worlds, comparing against baselines using bag-of-words and bag-of-bigrams for state rep-resentations. Our algorithm outperforms the baselines on both worlds demonstrat-ing the importance of learning expressive representations. 1