While current deep learning systems excel at tasks such as object
classification, language processing, and gameplay, few can construct or modify
a complex system such as a tower of blocks. We hypothesize that what these
systems lack is a "relational inductive bias": a capacity for reasoning about
inter-object relations and making choices over a structured description of a
scene. To test this hypothesis, we focus on a task that involves gluing pairs
of blocks together to stabilize a tower, and quantify how well humans perform.
We then introduce a deep reinforcement learning agent which uses object- and
relation-centric scene and policy representations and apply it to the task. Our
results show that these structured representations allow the agent to
outperform both humans and more naive approaches, suggesting that relational
inductive bias is an important component in solving structured reasoning
problems and for building more intelligent, flexible machines.

本研究探讨当下深度学习系统在处理物体分类、语言处理及游戏上表现出色，但却不能构建或修改建筑块等复杂系统，我们认为这是由于这些系统缺乏 “关系归纳偏置”，即一种可以推理物体间关系并根据场景结构作出决策的能力。本文研究了涉及胶合引起的稳定的建塔任务，通过引入一个使用物体及关系中心的视觉与策略表达方式的深度强化学习代理来验证该假设。结果表明，这些结构化的表达方式允许代理赢过人类及其他机器学习方法，这表明关系归纳偏置对于解决结构化推理问题及构建更智能、灵活的机器是重要组成部分。