A Reinforcement Learning-Based Model for Mapping and Goal-Directed Navigation Using Multiscale Place Fields
This work addresses the problem of adaptive robot navigation in partially observable settings, representing an incremental improvement over existing bio-inspired models.
The paper tackled autonomous navigation in complex environments by introducing a multiscale place field model with replay-based reward and dynamic scale fusion, resulting in improved path efficiency and accelerated learning compared to single-scale baselines.
Autonomous navigation in complex and partially observable environments remains a central challenge in robotics. Several bio-inspired models of mapping and navigation based on place cells in the mammalian hippocampus have been proposed. This paper introduces a new robust model that employs parallel layers of place fields at multiple spatial scales, a replay-based reward mechanism, and dynamic scale fusion. Simulations show that the model improves path efficiency and accelerates learning compared to single-scale baselines, highlighting the value of multiscale spatial representations for adaptive robot navigation.