  Learning world models from their sensory inputs enables agents to plan for
actions by imagining their future outcomes. World models have previously been
shown to improve sample-efficiency in simulated environments with few objects,
but have not yet been applied successfully to environments with many objects.
In environments with many objects, often only a small number of them are moving
or interacting at the same time. In this paper, we investigate integrating this
inductive bias of sparse interactions into the latent dynamics of world models
trained from pixels. First, we introduce Variational Sparse Gating (VSG), a
latent dynamics model that updates its feature dimensions sparsely through
stochastic binary gates. Moreover, we propose a simplified architecture Simple
Variational Sparse Gating (SVSG) that removes the deterministic pathway of
previous models, resulting in a fully stochastic transition function that
leverages the VSG mechanism. We evaluate the two model architectures in the
BringBackShapes (BBS) environment that features a large number of moving
objects and partial observability, demonstrating clear improvements over prior
models.
