Reward-based epigenetic learning algorithm for a decentralised multi-agent system
International Journal of Intelligent Unmanned Systems
ISSN: 2049-6427
Article publication date: 24 April 2020
Issue publication date: 26 June 2020
Abstract
Purpose
This paper aims to propose a novel epigenetic learning (EpiLearn) algorithm, which is designed specifically for a decentralised multi-agent system such as swarm robotics.
Design/methodology/approach
First, this paper begins with overview of swarm robotics and the challenges in designing swarm behaviour automatically. This should indicate the direction of improvements required to enhance an automatic swarm design. Second, the evolutionary learning (EpiLearn) algorithm for a swarm system using an epigenetic layer is formulated and discussed. The algorithm is then tested through various test functions to investigate its performance. Finally, the results are discussed along with possible future research directions.
Findings
Through various test functions, the algorithm can solve non-local and many local minima problems. This article also shows that by using a reward system, the algorithm can handle the deceptive problem which often occurs in dynamic problems. Moreover, utilization of rewards from the environment in the form of a methylation process on the epigenetic layer improves the performance of traditional evolutionary algorithms applied to automatic swarm design. Finally, this article shows that a regeneration process that embeds an epigenetic layer in the inheritance process performs better than a traditional crossover operator in a swarm system.
Originality/value
This paper proposes a novel method for automatic swarm design by taking into account the importance of multi-agent settings and environmental characteristics surrounding the swarm. The novel evolutionary learning (EpiLearn) algorithm using an epigenetic layer gives the swarm the ability to perform co-evolution and co-learning.
Keywords
Citation
Mukhlish, F., Page, J. and Bain, M. (2020), "Reward-based epigenetic learning algorithm for a decentralised multi-agent system", International Journal of Intelligent Unmanned Systems, Vol. 8 No. 3, pp. 201-224. https://doi.org/10.1108/IJIUS-12-2018-0036
Publisher
:Emerald Publishing Limited
Copyright © 2020, Emerald Publishing Limited