Situated Dialogue Learning through Procedural Environment Generation
Prithviraj Ammanabrolu, Renee Jia, Mark Riedl · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2022
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 zeroshot performance on never-before-seen quests.