Toward Compositional Generalization in Object‑Oriented World Modeling

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 approximate but more efficient compositional generalization.

Publication
In ICML 2022 (Long Presentation, 118/5630, 2.1%)
Linfeng Zhao
Linfeng Zhao
CS Ph.D. Student

I am a CS Ph.D. student at Khoury College of Computer Sciences of Northeastern University, advised by Prof. Lawson L.S. Wong. My research interests include reinforcement learning, artificial intelligence, and robotics.

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