Text-based games have emerged as an important test-bed for Reinforcement
Learning (RL) research, requiring RL agents to combine grounded language
understanding with sequential decision making. In this paper, we examine the
problem of infusing RL agents with commonsense knowledge. Such knowledge would
allow agents to efficiently act in the world by pruning out implausible
actions, and to perform look-ahead planning to determine how current actions
might affect future world states. We design a new text-based gaming environment
called TextWorld Commonsense (TWC) for training and evaluating RL agents with a
specific kind of commonsense knowledge about objects, their attributes, and
affordances. We also introduce several baseline RL agents which track the
sequential context and dynamically retrieve the relevant commonsense knowledge
from ConceptNet. We show that agents which incorporate commonsense knowledge in
TWC perform better, while acting more efficiently. We conduct user-studies to
estimate human performance on TWC and show that there is ample room for future
improvement.

本文研究了 RL 智能体如何在具备通识知识的情况下进行决策。通过设计新的基于文本的游戏环境，实现了一种感知建模的系统，同时，也为该环境提出了多个基线 RL 智能体，这些智能体能通过动态检索 ConceptNet 中相关的通识知识，实现了更好的表现。