R5: Rule Discovery with Reinforced and Recurrent Relational Reasoning

Shengyao Lu*, Bang Liu*, Keith G. Mills, Shangling Jui, Di Niu.

ICLR 2022 (Spotlight) [Paper] [Code] [Video]

We propose R5, a relational reasoning framework based on reinforcement learning that reasons over relational graph data and explicitly mines underlying compositional logical rules from observations. R5 has strong systematicity and being robust to noisy data. It consists of a policy value network equipped with Monte Carlo Tree Search to perform recurrent relational prediction and a backtrack rewriting mechanism for rule mining. By alternately applying the two components, R5 progressively learns a set of explicit rules from data and performs explainable and generalizable relation prediction.