A Survey of Text Games for Reinforcement Learning informed by Natural Language
It provides a systematic review for researchers working on text-based reinforcement learning, but is incremental as it synthesizes existing work.
This survey addresses the challenges of using natural language in reinforcement learning for interactive fiction games, summarizing key problems, evaluation tools, and agent architectures to guide future research.
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.