Learning sparse relational transition models

Victoria Xia, Zi Wang, Kelsey Allen, Tom Silver, Leslie Pack Kaelbling

Abstract: We present a representation for describing transition models in complex uncertain domains using relational rules. For any action, a rule selects a set of relevant objects and computes a distribution over properties of just those objects in the resulting state given their properties in the previous state. An iterative greedy algorithm is used to construct a set of deictic references that determine which objects are relevant in any given state. Feed-forward neural networks are used to learn the transition distribution on the relevant objects' properties. This strategy is demonstrated to be both more versatile and more sample efficient than learning a monolithic transition model in a simulated domain in which a robot pushes stacks of objects on a cluttered table.

Keywords: Deictic reference, relational model, rule-based transition model

TL;DR: A new approach that learns a representation for describing transition models in complex uncertaindomains using relational rules.