Title: Learning Abstract World Models with a Group-Structured Latent Space
Primary area: reinforcement learning
Keywords: representation learning, reinforcement learning, geometric prior, abstract world model, model-based reinforcement learning

Abstract: Learning meaningful abstract models of Markov Decision Processes (MDPs) is
crucial for improving generalization from limited data. In this work, we show how
geometric priors can be imposed on the low-dimensional representation manifold
of a learned transition model. We incorporate known symmetric structures via
appropriate choices of the latent space and the associated group actions, which
encode prior knowledge about invariances in the environment. In addition, our
framework allows the embedding of additional unstructured information alongside
these symmetries. We show experimentally that this leads to better predictions of
the latent transition model than fully unstructured approaches, as well as better
learning on downstream RL tasks, in environments with rotational and translational
features, including in first-person views of 3D environments. Additionally, our
experiments show that this leads to simpler and more disentangled representations.