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arXiv · 2204.13661

Toward Compositional Generalization in Object-Oriented World Modeling

Linfeng Zhao·Lingzhi Kong·Robin Walters·Lawson L. S. Wong
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Abstract

Compositional generalization is a critical ability in learning and decision-making. We focus on the setting of reinforcement learning in object-oriented environments to study compositional generalization in world modeling. We (1) formalize the compositional generalization problem with an algebraic approach and (2) study how a world model can achieve that. We introduce a conceptual environment, Object Library, and two instances, and deploy a principled pipeline to measure the generalization ability. Motivated by the formulation, we analyze several methods with exact or no compositional generalization ability using our framework, and design a differentiable approach, Homomorphic Object-oriented World Model (HOWM), that achieves soft but more efficient compositional generalization.

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Linfeng Zhao, Lingzhi Kong, Robin Walters, Lawson L. S. Wong. 2022-04-28. Toward Compositional Generalization in Object-Oriented World Modeling. https://arxiv.org/abs/2204.13661

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