An Investigation of Environmental Influence on the Benefits of Adaptation Mechanisms in Evolutionary Swarm Robotics

TL;DR

This paper investigates how different environmental conditions influence the effectiveness of evolutionary and individual learning mechanisms in swarm robotics, providing insights into optimal adaptation strategies in dynamic environments.

Contribution

It analyzes the impact of environmental variability on the performance of various learning mechanisms in swarm robotics, highlighting how environment shapes the choice of adaptation method.

Findings

Environmental characteristics determine the most effective learning approach.

Evolutionary algorithms perform better in stable environments.

Individual learning mechanisms excel in highly dynamic settings.

Abstract

A robotic swarm that is required to operate for long periods in a potentially unknown environment can use both evolution and individual learning methods in order to adapt. However, the role played by the environment in influencing the effectiveness of each type of learning is not well understood. In this paper, we address this question by analysing the performance of a swarm in a range of simulated, dynamic environments where a distributed evolutionary algorithm for evolving a controller is augmented with a number of different individual learning mechanisms. The learning mechanisms themselves are defined by parameters which can be either fixed or inherited. We conduct experiments in a range of dynamic environments whose characteristics are varied so as to present different opportunities for learning. Results enable us to map environmental characteristics to the most effective learning algorithm.