We propose schemas as a model for abstractions that can be used for rapid
transfer learning, inference, and planning. Common structured representations
of concepts and behaviors -- schemas -- have been proposed as a powerful way to
encode abstractions. Latent graph learning is emerging as a new computational
model of the hippocampus to explain map learning and transitive inference. We
build on this work to show that learned latent graphs in these models have a
slot structure -- schemas -- that allow for quick knowledge transfer across
environments. In a new environment, an agent can rapidly learn new bindings
between the sensory stream to multiple latent schemas and select the best
fitting one to guide behavior. To evaluate these graph schemas, we use two
previously published challenging tasks: the memory & planning game and one-shot
StreetLearn, that are designed to test rapid task solving in novel
environments. Graph schemas can be learned in far fewer episodes than previous
baselines, and can model and plan in a few steps in novel variations of these
tasks. We further demonstrate learning, matching, and reusing graph schemas in
navigation tasks in more challenging environments with aliased observations and
size variations, and show how different schemas can be composed to model larger
2D and 3D environments.Comment: 12 pages, 5 figures in main paper, 12 pages and 8 figures in appendi