Learning and stabilization of altruistic behaviors in multi-agent systems

Optimization of performance in collective systems often requires altruism. Emergence and stabilization of altruistic behaviors are difficult because the agents incur a cost when behaving altruistically. In this paper we propose a biologically inspired strategy to learn stable altruistic behaviors in artificial multi-agent systems, namely reciprocal altruism. Our multi-agent system is made up of autonomous agents with a behavior-based architecture. Agents learn the most suitable cooperative strategy for different environments by means of a reinforcement learning algorithm. Each agent receives a reinforcement signal that only measures its individual performance. Simulation results show how the multi-agent system learns stable altruistic behaviors, so reaching optimal (or near-to-optimal) performances in unknown and changing environments