• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Journal of visual communication & image representation >Large-scale and adaptive service composition based on deep reinforcement learning
【24h】

Large-scale and adaptive service composition based on deep reinforcement learning

机译：基于深度强化学习的大规模自适应服务组合

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

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. (C) 2019 Published by Elsevier Inc.
机译：服务组合是具有实用价值的研究热点。随着Web服务的发展，出现了许多具有相同功能属性的Web服务。然而，由于复杂而不稳定的组合环境，服务组合优化仍然是一个巨大的挑战。为了解决这个问题，我们提出了一种基于深度强化学习的自适应服务组合，其中利用递归神经网络（RNN）预测目标函数，提高其表达和泛化能力，并有效地解决了传统强化学习在目标学习中的不足。面对大规模或连续状态空间问题。我们利用启发式行为选择策略将状态集分为隐藏状态和完全可见状态。有效评估隐藏状态空间和评估功能的完全可见状态可以进一步提高合并结果的准确性和效率。我们进行了全面的实验，实验结果表明了该方法的有效性。 （C）2019由Elsevier Inc.发布

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号