One Shot Spatial Learning through Replay in a Hippocampus-Inspired Reinforcement Learning Model

Abstract

The neural basis of spatial cognition and learning in mammals has been studied extensively for several decades. Research has focused in particular on the place cells of the hippocampus and the grid cells found in the entorhinal cortex. In turn, these studies have inspired several models for robotic navigation. One interesting, experimentally observed, feature of spatial learning in rodents is the importance of replay, where animals replay sequences of spatial representations they have experienced in order to learn and make decisions. This feature too has been incorporated into some computational models. In this paper, we describe a new approach to learning navigation in mazes using replay of intrinsically generated sequences rather than relying only on experienced sequences. We show that this improves generalization, and leads to effective one-shot learning that is closer to what is observed in animals.