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

Philip Osborne, Heido Nomm, Andre Freitas


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 safe, partially observable environments where natural language is required as part of the Reinforcement Learning solution. 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: 1) introduces the challenges in Text Game Reinforcement Learning problems, 2) outlines the generation tools for rendering Text Games and the subsequent environments generated and, 3) compares the agent architectures currently applied to provide a systematic review of benchmark methodologies and opportunities for future researchers.


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