کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
13436914 | 1843056 | 2019 | 13 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Large-scale and adaptive service composition based on deep reinforcement learning
دانلود مقاله + سفارش ترجمه
دانلود مقاله ISI انگلیسی
رایگان برای ایرانیان
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
چشم انداز کامپیوتر و تشخیص الگو
پیش نمایش صفحه اول مقاله
![عکس صفحه اول مقاله: Large-scale and adaptive service composition based on deep reinforcement learning Large-scale and adaptive service composition based on deep reinforcement learning](/preview/png/13436914.png)
چکیده انگلیسی
Service composition is a research hotspot with practical value. With the development of Web service, many Web services with the same functional attributes emerge. However, service composition optimization is still a big challenge since the complex and unstable composition environment. To solve this problem, we propose an adaptive service composition based on deep reinforcement learning, where recurrent neural network (RNN) is utilized for predicting the objective function, improving its expression and generalization ability, and effectively solving the shortcomings of traditional reinforcement learning in the face of large-scale or continuous state space problems. We leverage heuristic behavior selection strategy to divide the state set into hidden state and fully visible state. Effective simulation of hidden state space and fully visible state of the evaluation function can further improve the accuracy and efficiency of the combined results. We conduct comprehensive experiment and experimental results have shown the effectiveness of our method.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Visual Communication and Image Representation - Volume 65, December 2019, 102687
Journal: Journal of Visual Communication and Image Representation - Volume 65, December 2019, 102687
نویسندگان
Jiang-Wen Liu, Li-Qiang Hu, Zhao-Quan Cai, Li-Ning Xing, Xu Tan,