Evolutionary training and abstraction yields algorithmic generalization of neural computers

Daniel Tanneberg, Elmar Rueckert, Jan Peters

Abstract: A hallmark of intelligent behaviour is the ability to learn abstract strategies that can be transferred across different tasks, but it has been challenging to incorporate this ability in artificial systems. The authors present a modular architecture for the learning of algorithmic solutions, and demonstrate generalization and scaling on 11 diverse algorithms.