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arXiv:1806.10166cs.LG

Modular meta-learning

Ferran Alet, Tomás Lozano-Pérez, Leslie P. Kaelbling

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Abstract

Many prediction problems, such as those that arise in the context of robotics, have a simplifying underlying structure that, if known, could accelerate learning. In this paper, we present a strategy for learning a set of neural network modules that can be combined in different ways. We train different modular structures on a set of related tasks and generalize to new tasks by composing the learned modules in new ways. By reusing modules to generalize we achieve combinatorial generalization, akin to the "infinite use of finite means" displayed in language. Finally, we show this improves performance in two robotics-related problems.

arXiv comments
Presented at CoRL 2018
Journal reference
DOI
Subjects
cs.LGMachine Learning cs.RORobotics stat.MLMachine Learning (Statistics)

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  1. Ask HN: Are modular neural networks an interesting avenue for further research? 1 comment

    • Learning Quickly to Plan Quickly Using Modular Meta-Learning https://arxiv.org/abs/1809.07878 Automatically Composing Representation Transformations as a Means for Generalization https://arxiv.org/abs/1807.04640 Modular meta-learning https://arxiv.org/abs/1806.10166 Omega: An…

      MichaelanjelloView on HNParent context

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