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

A Survey of Text Games for Reinforcement Learning informed by Natural Language