  Reinforcement Learning has shown success in a number of complex virtual
environments. However, many challenges still exist towards solving problems
with natural language as a core component. Interactive Fiction Games (or Text
Games) are one such problem type that offer a set of partially observable
environments where natural language is required as part of the reinforcement
learning solutions.
  Therefore, this survey's aim is to assist in the development of new Text Game
problem settings and solutions for Reinforcement Learning informed by natural
language. Specifically, this survey summarises: 1) the challenges introduced in
Text Game Reinforcement Learning problems, 2) the generation tools for
evaluating Text Games and the subsequent environments generated and, 3) the
agent architectures currently applied are compared to provide a systematic
review of benchmark methodologies and opportunities for future researchers.
