Ammanabrolu, P; Urbanek, J; Li, M; Szlam, A; Rocktäschel, T; Weston, J; (2021) How to Motivate Your Dragon: Teaching Goal-Driven Agents to Speak and Act in Fantasy Worlds. In: Toutanova, K and Rumshisky, A and Zettlemoyer, L and Hakkani-Tür, D and Beltagy, I and Bethard, S and Cotterell, R and Chakraborty, T and Zhou, Y, (eds.) Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. (pp. pp. 807-833). Association for Computational Linguistics

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Abstract

We seek to create agents that both act and communicate with other agents in pursuit of a goal. Towards this end, we extend LIGHT (Urbanek et al. 2019)—a large-scale crowd-sourced fantasy text-game—with a dataset of quests. These contain natural language motivations paired with in-game goals and human demonstrations; completing a quest might require dialogue or actions (or both). We introduce a reinforcement learning system that (1) incorporates large-scale language modeling-based and commonsense reasoning-based pre-training to imbue the agent with relevant priors; and (2) leverages a factorized action space of action commands and dialogue, balancing between the two. We conduct zero-shot evaluations using held-out human expert demonstrations, showing that our agents are able to act consistently and talk naturally with respect to their motivations.

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