Learning Distributed Control for an Object-Clustering Task

This paper reports on experiments involving simulated robot-like agents. Motivated by social insects, we investigated two approaches to learning distributed controllers for an object-clustering task: genetic algorithms and reinforcement learning. In the case of reinforcement learning, a new learning algorithm was developed in an attempt to remove the need for a global fitness observer. Results from both approaches are presented and compared.