Evolution, Individual Learning, and Social Learning in a Swarm of Real Robots

Abstract

We investigate a novel adaptive system based on evolution, individual learning, and social learning in a swarm of physical Thymio II robots. The system is based on distinguishing inheritable and learnable features in the robots and defining appropriate operators for both categories. In this study we choose to make the sensory layout of the robots inheritable, thus evolvable, and the robot controllers learnable. We run tests with a basic system that employs only evolution and individual learning and compare this with an extended system where robots can disseminate their learned controllers. Results show that social learning increases the learning speed and leads to better controllers.