Predicting the Future with Simple World Models
Tankred Saanum Peter Dayan Eric Schulz
World models can represent potentially high-dimensional pixel observations in
compact latent spaces, making it tractable to model the dynamics of the
environment. However, the latent dynamics inferred by these models may still be
highly complex. Abstracting the dynamics of the environment with simple models
can have several benefits. If the latent dynamics are simple, the model may
generalize better to novel transitions, and discover useful latent
representations of environment states. We propose a regularization scheme that
simplifies the world model's latent dynamics. Our model, the Parsimonious
Latent Space Model (PLSM), minimizes the mutual information between latent
states and the dynamics that arise between them. This makes the dynamics softly
state-invariant, and the effects of the agent's actions more predictable. We
combine the PLSM with three different model classes used for i) future latent
state prediction, ii) video prediction, and iii) planning. We find that our
regularization improves accuracy, generalization, and performance in downstream
tasks.