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Authors: Yuya Kondo and Ahmed Moustafa

Affiliation: Nagoya Institute of Technology, Japan

Keyword(s): Reinforcement Learning, Offline Reinforcement Learning, Transfer Learning.

Abstract: Service-Oritented Architeture (SOA) is a style of system design in which the entire system is built from a combination of services, which are functional units of software. The performance of a system designed with SOA depends on the combination of services. In this research, we aim to use reinforcement learning for service selection in SOA. Service selection in SOA is characterized by its dynamic environment and inefficient collection of samples for training. We propose an offline reinforcement learning method in a dynamic environment to solve this problem. In the proposed method, transfer learning is performed by applying fine tuning and focused sampling. Experiments show that the proposed method can adapt to dynamic environments more efficiently than redoing online reinforcement learning every time the environment changes.

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Paper citation in several formats:
Kondo, Y. and Moustafa, A. (2022). Service Selection for Service-Oriented Architecture using Off-line Reinforcement Learning in Dynamic Environments. In Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART; ISBN 978-989-758-547-0; ISSN 2184-433X, SciTePress, pages 64-70. DOI: 10.5220/0010872400003116

@conference{icaart22,
author={Yuya Kondo. and Ahmed Moustafa.},
title={Service Selection for Service-Oriented Architecture using Off-line Reinforcement Learning in Dynamic Environments},
booktitle={Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART},
year={2022},
pages={64-70},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010872400003116},
isbn={978-989-758-547-0},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART
TI - Service Selection for Service-Oriented Architecture using Off-line Reinforcement Learning in Dynamic Environments
SN - 978-989-758-547-0
IS - 2184-433X
AU - Kondo, Y.
AU - Moustafa, A.
PY - 2022
SP - 64
EP - 70
DO - 10.5220/0010872400003116
PB - SciTePress

- Science and Technology Publications, Lda.
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