- 【Updated on May 12, 2025】 Integration of CiNii Dissertations and CiNii Books into CiNii Research
- Trial version of CiNii Research Knowledge Graph Search feature is available on CiNii Labs
- 【Updated on June 30, 2025】Suspension and deletion of data provided by Nikkei BP
- Regarding the recording of “Research Data” and “Evidence Data”
Data-Driven Deep Reinforcement Learning Framework for Large-Scale Service Composition
-
- KONDO Yuya
- Nagoya Institute of Technology
-
- MOUSTAFA Ahmed
- Nagoya Institute of Technology
Description
<p>In this research, reinforcement learning is used to select service component for SOA. QoS is the evaluation criterion of service component and it is used to represent payoff. Considering real application links to the problem that the number of interaction with environment is limited in real application. Offline RL, which learns their policy function from fixed interaction data, is one of method to solve this. There was little work to focus on application of RL to SOA in the offline setting. In this research, We focus on application RL to the setting where the part of service component is changed. Offline RL enables learning using a smaller number of data than conventional online methods, and that pre-learning of models can be performed even when the environment changes.</p>
Journal
-
- Proceedings of the Annual Conference of JSAI
-
Proceedings of the Annual Conference of JSAI JSAI2021 (0), 1N4IS1a05-1N4IS1a05, 2021
The Japanese Society for Artificial Intelligence
- Tweet
Keywords
Details 詳細情報について
-
- CRID
- 1390851320454018048
-
- NII Article ID
- 130008051642
-
- ISSN
- 27587347
-
- Text Lang
- ja
-
- Data Source
-
- JaLC
- CiNii Articles
-
- Abstract License Flag
- Disallowed