Active Predictive Coding: A Unified Neural Framework for Learning Hierarchical World Models for Perception and Planning

• URL: http://arxiv.org/abs/2210.13461v1
• Date: Sun, 23 Oct 2022 05:44:22 GMT
• Title: Active Predictive Coding: A Unified Neural Framework for Learning Hierarchical World Models for Perception and Planning
• Authors: Rajesh P. N. Rao, Dimitrios C. Gklezakos, Vishwas Sathish
• Abstract summary: We propose a new framework for predictive coding called active predictive coding. It can learn hierarchical world models and solve two radically different open problems in AI.
• Score: 1.3535770763481902
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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