ScriptWorld: Text Based Environment For Learning Procedural Knowledge
Abhinav Joshi Areeb Ahmad Umang Pandey Ashutosh Modi
Text-based games provide a framework for developing natural language
understanding and commonsense knowledge about the world in reinforcement
learning based agents. Existing text-based environments often rely on fictional
situations and characters to create a gaming framework and are far from
real-world scenarios. In this paper, we introduce ScriptWorld: a text-based
environment for teaching agents about real-world daily chores and hence
imparting commonsense knowledge. To the best of our knowledge, it is the first
interactive text-based gaming framework that consists of daily real-world human
activities designed using scripts dataset. We provide gaming environments for
10 daily activities and perform a detailed analysis of the proposed
environment. We develop RL-based baseline models/agents to play the games in
Scriptworld. To understand the role of language models in such environments, we
leverage features obtained from pre-trained language models in the RL agents.
Our experiments show that prior knowledge obtained from a pre-trained language
model helps to solve real-world text-based gaming environments. We release the
environment via Github: https://github.com/Exploration-Lab/ScriptWorld