Taylor, Adam and Dusparic, Ivana and Galvan, Edgar and Clarke, Siobhan and Cahill, Vinny (2014) Accelerating Learning in multi-objective systems through Transfer Learning. 2014 International Joint Conference on Neural Networks (IJCNN). pp. 2298-2305. ISSN 2161-4407

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Abstract

Large-scale, multi-agent systems are too complex for optimal control strategies to be known at design time and as a result good strategies must be learned at runtime. Learning in such systems, particularly those with multiple objectives, takes a considerable amount of time because of the size of the environment and dependencies between goals. Transfer Learning (TL) has been shown to reduce learning time in single-agent, single-objective applications. It is the process of sharing knowledge between two learning tasks called the source and target. The source is required to have been completed prior to the target task. This work proposes extending TL to multi-agent, multi-objective applications. To achieve this, an on-line version of TL called Parallel Transfer Learning (PTL) is presented. The issues involved in extending this algorithm to a multi-objective form are discussed. The effectiveness of this approach is evaluated in a smart grid scenario. When using PTL in this scenario learning is significantly accelerated. PTL achieves comparable performance to the base line in one third of the time.

Item Type:	Article
Keywords:	Accelerating; Learning; multi-objective systems; Transfer Learning;
Academic Unit:	Faculty of Science and Engineering > Computer Science Faculty of Science and Engineering > Research Institutes > Hamilton Institute
Item ID:	15361
Identification Number:	https://doi.org/10.1109/IJCNN.2014.6889438
Depositing User:	Edgar Galvan
Date Deposited:	31 Jan 2022 14:24
Journal or Publication Title:	2014 International Joint Conference on Neural Networks (IJCNN)
Publisher:	IEEE
Refereed:	Yes
URI:	Publisher
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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