  We study the problem of binding actions to objects in object-factored world
models using action-attention mechanisms. We propose two attention mechanisms
for binding actions to objects, soft attention and hard attention, which we
evaluate in the context of structured world models for five environments. Our
experiments show that hard attention helps contrastively-trained structured
world models to learn to separate individual objects in an object-based
grid-world environment. Further, we show that soft attention increases
performance of factored world models trained on a robotic manipulation task.
The learned action attention weights can be used to interpret the factored
world model as the attention focuses on the manipulated object in the
environment.
