  Interactive Fiction games are text-based simulations in which an agent
interacts with the world purely through natural language. They are ideal
environments for studying how to extend reinforcement learning agents to meet
the challenges of natural language understanding, partial observability, and
action generation in combinatorially-large text-based action spaces. We present
KG-A2C, an agent that builds a dynamic knowledge graph while exploring and
generates actions using a template-based action space. We contend that the dual
uses of the knowledge graph to reason about game state and to constrain natural
language generation are the keys to scalable exploration of combinatorially
large natural language actions. Results across a wide variety of IF games show
that KG-A2C outperforms current IF agents despite the exponential increase in
action space size.
