TextWorld: A Learning Environment for Text-Based Games

Abstract

We introduce TextWorld, a sandbox learning environment for the training and evaluation of RL agents on text-based games. TextWorld is a Python library that handles interactive playthrough of text games, as well as backend functions like state tracking and reward assignment. It comes with a curated list of games whose features and challenges we have analyzed. More significantly, it enables users to handcraft or automatically generate new games. Its generative mechanisms give precise control over the difficulty, scope, and language of constructed games, and can be used to relax challenges inherent to commercial text games like partial observability and sparse rewards. By generating sets of varied but similar games, TextWorld can also be used to study generalization and transfer learning. We cast text-based games in the Reinforcement Learning formalism, use our framework to develop a set of benchmark games, and evaluate several baseline agents on this set and the curated list.

Deep Learning for Video Game Playing

“…A lot of research has focused on games that run on Infocom's Z-Machine game engine, an engine that can play a lot of the early, classic games. Recently, Microsoft has introduced the environment TextWorld to help create a standardized text adventure environment [25].…”

Interactive Fiction Games: A Colossal Adventure

“…The TextWorld framework (Côté et al 2018) supports procedural generation of parser-based IF games, allowing complexity and content of the generated games to be scaled to the difficulty needed for research. TextWorld domains have already proven suitable for reinforcement learning agents (Yuan et al 2018) which were shown to be capable of learning on a set of environments and then generalizing to unseen ones at test time.…”

"I’m Sorry Dave, I’m Afraid I Can’t Do That" Deep Q-Learning from Forbidden Actions

“…TextWorld (Côté et al, 2018) is a text-based game where the agent interacts with the environment using short sentences. We generated a game composed of 3 rooms, 7 objects, and quest length of size 4.…”