Name
Abstract | ||
|---|---|---|
Reinforcement learning has been used to adaptive service composition. However, traditional algorithms are not suitable for large-scale service composition. Based on Q-Learning algorithm, a multi-task oriented algorithm named multi-Q learning is proposed to realize subtask-assistance strategy for large-scale and adaptive service composition. Differ from previous studies that focus on one task, we take the relationship between multiple service composition tasks into account. We decompose complex service composition task into multiple subtasks according to the graph theory. Different tasks with the same subtasks can assist each other to improve their learning speed. The results of experiments show that our algorithm could obtain faster learning speed obviously than traditional Q-learning algorithm. Compared with multi-agent Q-learning, our algorithm also has faster convergence speed. Moreover, for all involved service composition tasks that have the same subtasks between each other, our algorithm can improve their speed of learning optimal policy simultaneously in real-time. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1587/transinf.2017EDP7131 | IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS |
Keywords | Field | DocType |
service composition, reinforcement learning, Q-learning, subtask-assistance | Computer vision,Computer science,Human–computer interaction,Artificial intelligence | Journal |
Volume | Issue | ISSN |
E101D | 5 | 1745-1361 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||