Specialization in multi-agent systems through learning

Specialization is a common feature in animal societies that leads to an improvement in the fitness of the team members and to an increase in the resources obtained by the team. In this paper we propose a simple reinforcement learning approach to specialization in an artificial multi-agent system. The system is composed of homogeneous and non-communicating agents. Because there is no communication, the number of agents in the team can easily scale up. Agents have the same initial functionalities, but they learn to specialize and so cooperate to achieve a complex gathering task efficiently. Simulation experiments show how the multi-agent system specializes appropriately so as to reach optimal (or near-to-optimal) performance in unknown and changing environments.