  Interactive fiction games have emerged as an important application to improve
the generalization capabilities of language-based reinforcement learning (RL)
agents. Existing environments for interactive fiction games are domain-specific
or time-consuming to generate and do not train the RL agents to master a
specific set of skills. In this work, we introduce an interactive environment
for self-supervised RL, STARLING, for text-based games that bootstraps the
text-based RL agents with automatically generated games (based on the seed set
of game ideas) to boost the performance and generalization capabilities to
reach a goal of the target environment. These games let the agent hone their
skills on a predefined set of tasks. We create and test an environment with 100
games, generated using this automated framework that uses large language models
(GPT-3) and an interactive fiction game engine (based on Inform7) to provide
the user with the ability to generate more games under minimal human
supervision. Experimental results based on both the human participants and
baseline text-based RL agents reveal that current state-of-the-art text-based
RL agents cannot use previously learned skills in new situations at the level
humans can. These results enforce STARLING's potential to serve as a sandbox
environment for further research in self-supervised text-based RL.
