Self-Organization of Place Cells and Reward-Based Navigation for a Mobile Robot∗

Abstract

We investigate a method to navigate a mobile robot by using self-organizing map and reinforcement learning. Modeling hippocampal place cells, the map consists of units activated at specified locations in an environment. In order to adapt the map to a realworld environment, preferred locations of these units are self-organized by Kohonen's algorithm using the robot's actual position data. Then an actor-critic network is provided the position information from the selforganized map and trained to acquire goaldirected behavior of the robot. It is shown by simulation that the network successfully achieves the navigation avoiding obstacles. 1