  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.
