  Text-based games are becoming commonly used in reinforcement learning as
real-world simulation environments. They are usually imperfect information
games, and their interactions are only in the textual modality. To challenge
these games, it is effective to complement the missing information by providing
knowledge outside the game, such as human common sense. However, such knowledge
has only been available from textual information in previous works. In this
paper, we investigate the advantage of employing commonsense reasoning obtained
from visual datasets such as scene graph datasets. In general, images convey
more comprehensive information compared with text for humans. This property
enables to extract commonsense relationship knowledge more useful for acting
effectively in a game. We compare the statistics of spatial relationships
available in Visual Genome (a scene graph dataset) and ConceptNet (a text-based
knowledge) to analyze the effectiveness of introducing scene graph datasets. We
also conducted experiments on a text-based game task that requires commonsense
reasoning. Our experimental results demonstrated that our proposed methods have
higher and competitive performance than existing state-of-the-art methods.
