  Taking into account background knowledge as the context has always been an
important part of solving tasks that involve natural language. One
representative example of such tasks is text-based games, where players need to
make decisions based on both description text previously shown in the game, and
their own background knowledge about the language and common sense. In this
work, we investigate not simply giving common sense, as can be seen in prior
research, but also its effective usage. We assume that a part of the
environment states different from common sense should constitute one of the
grounds for action selection. We propose a novel agent, DiffG-RL, which
constructs a Difference Graph that organizes the environment states and common
sense by means of interactive objects with a dedicated graph encoder. DiffG-RL
also contains a framework for extracting the appropriate amount and
representation of common sense from the source to support the construction of
the graph. We validate DiffG-RL in experiments with text-based games that
require common sense and show that it outperforms baselines by 17% of scores.
The code is available at https://github.com/ibm/diffg-rl
