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Proc. International Conference on Neural Information Processing(ICONIP2001), paper ID #251, 2001
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 real-world 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 self-organized map and trained to acquire goal-directed behavior of the robot. It is shown by simulation that the network successfully achieves the navigation avoiding obstacles.
