A Meta Reinforcement Learning-based Approach for Self-Adaptive System

Mingyue Zhang, Jialong Li, Haiyan Zhao, Kenji Tei, Shinichi Honiden, Zhi Jin*

*この研究の対応する著者

研究成果: Conference contribution

抄録

A self-learning adaptive system (SLAS) uses machine learning to enable and enhance its adaptability. Such systems are expected to perform well in dynamic environments. For learning high-performance adaptation policy, some assumptions must be made on the environment-system dynamics when information about the real situation is incomplete. However, these assumptions cannot be expected to be always correct, and yet it is difficult to enumerate all possible assumptions. This leads to the problem of incomplete-information learning. We consider this problem as multiple model problem in terms of finding the adaptation policy that can cope with multiple models of environment-system dynamics. This paper proposes a novel approach to engineering the online adaptation of SLAS. It separates three concerns that are related to the adaptation policy and presents the modeling and synthesis process, with the goal of achieving higher model construction efficiency. In addition, it designs a meta-reinforcement learning algorithm for learning the meta policy over the multiple models, so that the meta policy can quickly adapt to the real environment-system dynamics. At last, it reports the case study on a robotic system to evaluate the adaptability of the approach.

本文言語English
ホスト出版物のタイトルProceedings - 2021 IEEE International Conference on Autonomic Computing and Self-Organizing Systems, ACSOS 2021
編集者Esam El-Araby, Vana Kalogeraki, Danilo Pianini, Frederic Lassabe, Barry Porter, Sona Ghahremani, Ingrid Nunes, Mohamed Bakhouya, Sven Tomforde
出版社Institute of Electrical and Electronics Engineers Inc.
ページ1-10
ページ数10
ISBN(電子版)9781665412612
DOI
出版ステータスPublished - 2021
イベント2nd IEEE International Conference on Autonomic Computing and Self-Organizing Systems, ACSOS 2021 - Virtual, Online, United States
継続期間: 2021 9月 272021 10月 1

出版物シリーズ

名前Proceedings - 2021 IEEE International Conference on Autonomic Computing and Self-Organizing Systems, ACSOS 2021

Conference

Conference2nd IEEE International Conference on Autonomic Computing and Self-Organizing Systems, ACSOS 2021
国/地域United States
CityVirtual, Online
Period21/9/2721/10/1

ASJC Scopus subject areas

  • 情報システムおよび情報管理
  • 制御と最適化
  • 人工知能
  • コンピュータ ネットワークおよび通信
  • ハードウェアとアーキテクチャ

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