Comparison of Evolutionary Strategies for Reinforcement Learning in a Swarm Aggregation Behaviour

Abstract

This article studies the performance of different evolutionary strategies for deep reinforcement learning policy optimization. The policy will be centred in an important swarm robotic task: the aggregation of simple robots in the environment. The main inspiration for robotic swarm comes from the observation of social animals. Ants, bees, birds, and fish are some examples of how simple individuals can succeed when they gather in groups. In addition, is important to highlight that aggregation may be considered as a previous requirement to tackle another tasks.