Text-based games (e.g. Zork, Colossal Cave) are complex, interactive simulations in which text describes the game state and players make progress by entering text commands. They are fertile ground for language-focused machine learning research. In addition to language understanding, successful play requires skills like long-term memory and planning, exploration (trial and error), and common sense.
This demonstration is about TextWorld, a Python-based learning environment for text-based games. TextWorld can be used to play existing games, as the ALE does for Atari games. However, the real novelty is that TextWorld can generate new text-based games with desired complexity. Its generative mechanisms give precise control over the difficulty, scope, and language of constructed games, and can therefore be used to study generalization and transfer learning.