  We teach goal-driven agents to interactively act and speak in situated
environments by training on generated curriculums. Our agents operate in LIGHT
(Urbanek et al. 2019) -- a large-scale crowd-sourced fantasy text adventure
game wherein an agent perceives and interacts with the world through textual
natural language. Goals in this environment take the form of character-based
quests, consisting of personas and motivations. We augment LIGHT by learning to
procedurally generate additional novel textual worlds and quests to create a
curriculum of steadily increasing difficulty for training agents to achieve
such goals. In particular, we measure curriculum difficulty in terms of the
rarity of the quest in the original training distribution -- an easier
environment is one that is more likely to have been found in the unaugmented
dataset. An ablation study shows that this method of learning from the tail of
a distribution results in significantly higher generalization abilities as
measured by zero-shot performance on never-before-seen quests.
