  Text Worlds are virtual environments for embodied agents that, unlike 2D or
3D environments, are rendered exclusively using textual descriptions. These
environments offer an alternative to higher-fidelity 3D environments due to
their low barrier to entry, providing the ability to study semantics,
compositional inference, and other high-level tasks with rich high-level action
spaces while controlling for perceptual input. This systematic survey outlines
recent developments in tooling, environments, and agent modeling for Text
Worlds, while examining recent trends in knowledge graphs, common sense
reasoning, transfer learning of Text World performance to higher-fidelity
environments, as well as near-term development targets that, once achieved,
make Text Worlds an attractive general research paradigm for natural language
processing.
