  Predictive coding has emerged as a prominent model of how the brain learns
through predictions, anticipating the importance accorded to predictive
learning in recent AI architectures such as transformers. Here we propose a new
framework for predictive coding called active predictive coding which can learn
hierarchical world models and solve two radically different open problems in
AI: (1) how do we learn compositional representations, e.g., part-whole
hierarchies, for equivariant vision? and (2) how do we solve large-scale
planning problems, which are hard for traditional reinforcement learning, by
composing complex action sequences from primitive policies? Our approach
exploits hypernetworks, self-supervised learning and reinforcement learning to
learn hierarchical world models that combine task-invariant state transition
networks and task-dependent policy networks at multiple abstraction levels. We
demonstrate the viability of our approach on a variety of vision datasets
(MNIST, FashionMNIST, Omniglot) as well as on a scalable hierarchical planning
problem. Our results represent, to our knowledge, the first demonstration of a
unified solution to the part-whole learning problem posed by Hinton, the nested
reference frames problem posed by Hawkins, and the integrated state-action
hierarchy learning problem in reinforcement learning.
