论文标题
如何激励您的龙：教目标驱动的特工在幻想世界中说话和行动
How to Motivate Your Dragon: Teaching Goal-Driven Agents to Speak and Act in Fantasy Worlds
论文作者
论文摘要
我们试图创建与其他代理商一起采取行动和沟通以追求目标的代理商。为此，我们扩展了光线（Urbanek等人，2019年），这是一个大规模的众包幻想文字游戏 - 带有一个任务数据集。这些包含自然语言动机与游戏中的目标和人类示威相结合；完成任务可能需要对话或行动（或两者）。我们介绍了一种强化学习系统，（1）结合了基于大规模的语言建模和基于常识性推理的预训练，以使代理商与相关的先验浸入； （2）利用一个分解的动作空间命令和对话，在两者之间保持平衡。我们使用持有的人类专家示范进行零射门评估，表明我们的代理人能够始终如一地行动并就其动机自然进行交谈。
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.
