Contrastive Learning of Structured World Models
A structured understanding of our world in terms of objects, relations, and hierarchies is an important component of human cognition.Learning such a structured world model from raw sensory data remains a challenge.As a step towards this goal, we introduce Contrastively-trained Structured World Models.C-SWMs utilize a contrastive approach for representation learning in environments with compositional structure.We structure each state embedding as a set of object representations and their relations, modeled by a graph neural network.This allows objects to be discovered from raw pixel observations without direct supervision as part of the learning process.We evaluate C-SWMs on compositional environments involving multiple interacting objects that can be manipulated independently by an agent, simple Atari games, and a multi-object physics simulation.Our experiments demonstrate that C-SWMs can overcome limitations of models based on pixel reconstruction and outperform typical representatives of this model class in highly structured environments, while learning interpretable object-based representations.
Contrastively-trained Structured World Models (C-SWMs) learn object-oriented state representations and a relational model of an environment from raw pixel input.The authors overcome the problem of using pixel-based losses in the construction and learning of structured world models by using a contrastive latent space.