  World modelling, i.e. building a representation of the rules that govern the
world so as to predict its evolution, is an essential ability for any agent
interacting with the physical world. Recent applications of the Transformer
architecture to the problem of world modelling from video input show notable
improvements in sample efficiency. However, existing approaches tend to work
only at the image level thus disregarding that the environment is composed of
objects interacting with each other. In this paper, we propose an architecture
combining Transformers for world modelling with the slot-attention paradigm, an
approach for learning representations of objects appearing in a scene. We
describe the resulting neural architecture and report experimental results
showing an improvement over the existing solutions in terms of sample
efficiency and a reduction of the variation of the performance over the
training examples. The code for our architecture and experiments is available
at https://github.com/torchipeppo/transformers-and-slot-encoding-for-wm
