  Text adventure games, in which players must make sense of the world through
text descriptions and declare actions through text descriptions, provide a
stepping stone toward grounding action in language. Prior work has demonstrated
that using a knowledge graph as a state representation and question-answering
to pre-train a deep Q-network facilitates faster control policy transfer. In
this paper, we explore the use of knowledge graphs as a representation for
domain knowledge transfer for training text-adventure playing reinforcement
learning agents. Our methods are tested across multiple computer generated and
human authored games, varying in domain and complexity, and demonstrate that
our transfer learning methods let us learn a higher-quality control policy
faster.
