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.

通过在生成的课程上训练，我们教会了目标驱动型智能体在位置化环境中交互行为和语言表达。我们通过在大规模众包幻想文本冒险游戏（LIGHT）中学习，增强了 LIGHT 的功能，并学会了生成额外的小说文本世界和任务，以创建一个渐进式递增难度的课程，以训练智能体达到这样的目标。通过从分布的尾部学习，我们测量该课程的难度，根据最初的训练分布中任务的稀有程度评估其难度 —— 更容易的环境是更有可能在未增强数据集中找到的环境。消融研究表明，从分布的尾部学习的这种方法，可以在从未见过的任务上度量的零 - shot 性能方面，产生显着更高的泛化能力。