Concurrent Individual And Social Learning In Robot Teams

Abstract

This article discusses effective mechanisms that enable a group of robots to autonomously generate, adapt, and enhance team behaviors while improving their individual performance simultaneously. Two promising team learning concepts, namely, cooperative learning and advice‐sharing, are integrated to provide a platform that encompasses a comprehensive approach to team‐performance enhancement. These methods were examined in relation to the performance characteristics of standard single‐robot Q‐learning to ascertain whether they retain viable learning characteristics despite the integration of individual learning into team behaviors.