How to Motivate Your Dragon: Teaching Goal-Driven Agents to Speak and Act in Fantasy Worlds

• URL: http://arxiv.org/abs/2010.00685v3
• Date: Tue, 25 May 2021 15:26:16 GMT
• Title: How to Motivate Your Dragon: Teaching Goal-Driven Agents to Speak and Act in Fantasy Worlds
• Authors: Prithviraj Ammanabrolu, Jack Urbanek, Margaret Li, Arthur Szlam, Tim Rockt\"aschel, Jason Weston
• Abstract summary: We seek to create agents that both act and communicate with other agents in pursuit of a goal. We introduce a reinforcement learning system that incorporates large-scale language modeling-based and commonsense reasoning-based pre-training. 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.
• Score: 47.7511759322784
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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