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

2021/09/20 by Philip D. Osborne, Osborne, Philip, Heido Nõmm +3 · 1 citation
Computer Science · #Artificial Intelligence (cs.AI) #Artificial Intelligence in Games #FOS: Computer and information sciences #I.2.0 #I.2.1 #I.2.7

paper · pdf · doi:10.48550/arxiv.2109.09478

openalex publication_date 2021/09/20 · openalex created_date 2022/10/02 · openalex updated_date 2026/07/28

Abstract

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.

Cited by

• elsciRL: Integrating Language Solutions into Reinforcement Learning Problem Settings

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