Assembling Modular, Hierarchical Cognitive Map Learners with Hyperdimensional Computing

Cognitive map learners (CML) are a collection of separate yet collaboratively trained single-layer artificial neural networks (matrices), which navigate an abstract graph by learning internal representations of the node states, edge actions, and edge action availabilities. A consequence of this atypical segregation of information is that the CML performs near-optimal path planning between any two graph node states. However, the CML does not learn when or why to transition from one node to anothe...